esa-sceva/satcom-chunk-collection huggingface dataset:
# A 4.1 GHz-9.2 GHz Programmable Frequency Divider for Ka Band PLL Frequency Synthesizer
## 2 Materials and Methods  
### Analysis of Dividers in PLL  
In a Ka band phase-locked-loop system shown in Figure 1, two types of dividers (prescalers and MMD) are used in the systems as illustrated in part one. The division ratio of a MMD is restricted by two limited condition,  
\[\text{mmd\_ratio}_{\text{min}}<\frac{\text{f}_{\text{pll\_out\_min}}}{2^{\text{N }}\cdot\text{f}_{\text{ref}}} \tag{1}\]  
\[\text{mmd\_ratio}_{\text{max}}>\frac{\text{f}_{\text{pll\_out\_max}}}{2^{\text{N }}\cdot\text{f}_{\text{ref}}} \tag{2}\]  
where mmd_ratio\({}_{\text{min}}\) and mmd_ratio\({}_{\text{max}}\) stand for the minimum and maximum division ratio of MMD, \(\text{f}_{\text{pll\_out\_min}}\) and \(\text{f}_{\text{pll\_out\_max}}\) are the minimum and maximum frequency of the PLL output, N is the amount of the prescaler in the PLL system and the prescaler has a constant division ratio.  
In the design of a PLL, the division ratio range can be determined due to the output frequency range and reference frequency. The typical type of prescaler used in Ka band PLL is current-mode logic (CML) latch [14] and injection-locked frequency divider (ILFD) [15], as shown in Figure 2a,b. Both CML latch and ILFD occupy a large area due to the load inductors and resisters [16]. And the static power they consume is large, which will influence the performance of the PLL [17]. If the division ratio of the MMD increases by one time, the prescaler used in the system can take one off. Therefore, the area of the whole system and the power consume of the whole system can be reduced [18].  
A 2/3 divider-chain is a common structure of MMD [19; 20; 21; 22; 23; 24; 25]. The division ratio of such MMD ranges from 2\({}^{\text{n}}\) to 2\({}^{\text{n+1}}\) - 1, where n is the amount of the 2/3 divider cell. This structure can obtain a high division ratio with a simple circuit design. It can also obtain a high operating frequency  
Figure 1: Block diagram of a Ka band PLL.  
Figure 2: (**a**) A topology of CML divider; (**b**) A topology of ILFD.  
compared with other MMD structures, but the power efficiency of such a structure is quite low. It is not practical in most case as the division ratio is restricted to a certain range, though it can be overcome by alter the structure of divider chain [9; 20], which will increase the power consumption of the MMD. Pulse swallow divider can overcome this problem [11]. It can alter the division ratio arbitrary, while it is not easy to achieve a high operating frequency, which restrict the usage of such structure in the mmW PLL design.  
In order to obtain a high operating frequency pulse swallow MMD, the component of MMD should be specially designed. The demand of the operating frequency increases by one times as the division ratio multiplies by two if the reference frequency is certain. In the meanwhile, counter bit size of counter P and counter S increase, and the structure of the circuit is more complex, which is more difficult to obtain a high operating frequency.
## 4 Discussion  
### Sand Sedimentation Mechanism in the Railway Culvert  
The systematic analysis of the flow field, sand volume fraction, and the influence of the inflow velocity on the sand sedimentation characteristics in the railway culvert indicate extensive sand deposition. The reasons were the deflection by the wing walls, the blocking effect of the subgrade, and a low inflow velocity.  
First, the airflow in front of the culvert was the highest at the culvert inlet because of the angle of the wing walls. The airflow cross-section narrowed as the airflow moved from the near field to the inlet of the culvert, and the speed and pressure of the airflow crossing the culvert increased, resulting in an acceleration of the airflow. Numerous sand particles were carried by the airflow from the end faces to the wing walls and accumulated at the culvert inlet due to the deflection by the wing walls. [PERSON] and [PERSON] et al. proposed that the culvert inlet and outlet are low-velocity sand accumulation areas or wind-drift areas to sand deposition [21, 22]. The main reason is that the sudden decrease of the airflow velocity at the entrance of the culvert leads to sand accumulation, but the transport effect of the wing walls and the end faces on the wind-blown sand flow were not considered.  
Second, the subgrade acted as a wind barrier in the flow field. The wind speed decelerated on the windward side of the subgrade, resulting in more sand deposition. The culvert's acceleration effect caused the sand particles to be transported to the inlet and inside the culvert. As the wind speed decreased, a large amount of sand was deposited in the culvert.  
Third, the airflow characteristics along railway were determined by geographic locations, unique environment and other factors. The inflow velocity is a critical factor affecting sand deposition, as confirmed by our results. Figure 10 shows the influence of the inflow velocity on the sand volume fraction of the culvert. It was found that more sand was deposited at \(v\leq 15\) m/s than at \(v\geq 20\) m/s, indicating that a low inflow velocity results in more sand sedimentation in the culvert.  
The wing walls, the subgrade, and a low inflow velocity affected sand sedimentation in the culvert. However, the subgrade ensures the safe operation of the railway and can't just be changed to mitigate sand sedimentation in the culvert. In addition, there are many railways in desert areas. The environmental conditions are changeable, the landforms are complex, and the flow field cannot be changed. Therefore, we propose an optimization of the culvert structure to minimize the deflection by the wing walls and decrease sand sedimentation in the railway culvert while extending the maintenance cycle and reducing costs.
###### Abstract  
Low-Power and Lossy Networks (LLNs) have grown rapidly in recent years owing to the increased adoption of Internet of Things (IoT) and Machine-to-Machine (M2M) applications across various industries, including smart homes, industrial automation, healthcare, and smart cities. Owing to the characteristics of LLNs, such as Lossy channels and limited power, generic routing solutions designed for non-LLNs may not be adequate in terms of delivery reliability and routing efficiency. Consequently, a routing protocol for LLNs (RPL) was designed. Several RPL objective functions have been proposed to enhance the routing reliability in LLNs. This paper analyses these solutions against performance and security requirements to identify their limitations. Firstly, it discusses the characteristics and security issues of LLN and their impact on packet delivery reliability and routing efficiency. Secondly, it provides a comprehensive analysis of routing solutions and identifies existing limitations. Thirdly, based on these limitations, this paper highlights the need for a reliable and efficient path-finding solution for LLNs.  
Low-Power and Lossy Networks (LLNs); objective functions; Packet Dropping Attacks (PDAs); routing protocol for Low-Power and Lossy Networks (RPL); security +
Footnote †: journal: Network  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture
### GPRS/3G/4G Technology  
General Packet Radio Service (GPRS) is a packet data service for GSM-based cellular phones. GPRS frequently experiences variable delays and throughputs, and such technology depends on the volume of consumers that share the same communication channels and resources. [PERSON] et al. [40] used the GPRS module and WSN to develop an automatic crop irrigation system based on the information collected by temperature and soil moisture sensors installed at the root zone of plants,and considered this system a cost-effective and practical solution for improving water quality in PA. A drip irrigation process was evaluated in [41] by measuring soil moisture. A prototype system was also developed based on a data management server and a WSN-GPRS gateway. WSN-GPRS gateway acts as a bridge between WSN and GPRS where the data from the WSN are transferred to a data management center. [PERSON] et al. [42] equipped various wireless nodes with GPRS to measure and transmit soil, plant, and atmosphere information. The wireless nodes have unlimited autonomy because of their independent nature and use of solar energy. Different sensors may transmit information to a remote location via a GPRS network for further analysis by using tablets, mobile phones, or computers. All agriculture sensors are interfaced to the sensor board to obtain agricultural information. Such information is transmitted to the remote server for further analysis through the GPRS-board, which depends on a GSM/GPRS mobile network.
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
###### Abstract  
We report on a laser photo-ionization study of silver in relation to the Selective Production of Exotic Species (SPES) project at INFN-LNL in the off-line laser laboratory. In this study, two dye lasers and an ablation laser operating at 10 Hz are used alongside a time-of-flight mass spectrometer (TOF-MS). Isotopic separation of the natural, stable isotopes \({}^{107}\)Ag and \({}^{109}\)Ag was clearly observed in the TOF signal. Resonant photo-ionization of silver was achieved with the use of the scheme 4d\({}^{105}\)s \({}^{2}\)S\({}_{1/2}\)\(\rightarrow\) 4d\({}^{105}\)p \({}^{2}\)S\({}_{3/2}\)\(\rightarrow\) 4d\({}^{10}\)6d \({}^{2}\)D\({}_{3/2}\) with transition wavelengths of 328.163 nm and 421.402 nm, respectively. Doppler-suppressed spectroscopy of these transition lines was performed in an ablation plume. Doppler broadening with collinear injection of excitation lasers and the effect of the linewidths of the excitation lasers were investigated. The fine-structure splitting of the level 4d\({}^{10}\)6d \({}^{2}\)D (J = 5/2 and J = 3/2) was confirmed to be 186 \(\pm\) 2 pm, corresponding to 314 \(\pm\) 3 GHz.  
Doppler-suppressed profiles; laser spectroscopy; photo-ionization; silver +
Footnote †: journal:  
0000-0  
In this article, the off-line laser spectroscopy of silver (Ag, Z = 47) is reported. The laser photo-ionization of Ag was successfully achieved during this study. Doppler-suppressed resonance profiles for these transitions are presented. The influence of linewidth of the exciting laser light in a Doppler broadening scenario was also demonstrated. Moreover, the fine structure of the 4d\({}^{10}6d\)\({}^{2}\)D levels was clearly resolved.
###### Abstract  
In the context of the relentless pursuit of precision, intelligence, and personalization within the realm of medical technology, the real-time monitoring of human physiological signals has assumed heightened significance. Implantable wireless sensor devices have exhibited extraordinary capabilities in tracking internal physiological parameters, including intraocular pressure, blood glucose levels, electrocardiographic activity, and arterial blood pressure. These devices are characterized by elevated temporal continuity and exceptional measurement accuracy. This paper undertakes an in-depth investigation into the key technologies underlying biodegradable implantable sensing devices. Initially, it expounds on diverse sensing mechanisms employed in implantable devices. Additionally, it presents common data transmission and power supply strategies for wireless sensing systems. Finally, it introduces biodegradable materials suitable for human implantation and their respective application domains and enumerates several implantable devices that are either under development or have already been commercialized. Through an in-depth and comprehensive discourse on the current state of development and extant challenges in this domain, the development trajectory of biodegradable devices is put forward. Moreover, this paper also serves as a valuable reference for the design and selection of implantable medical devices.  
biomedical sensors; implantable sensors; biodegradable sensors; biocompatibility; wireless transmission 2020 May 2025  
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## 3 Results and Discussion
### Building a Formulation as RM for the Winey-Vinegary Defect
AV8 was characterized with artificial sensory notes, although to a lower degree than in the previous formulation. However, assessors reported that the vinegary aroma in this formulation was more evident than in AV7, and, in consequence, AV8 was considered as the best formulation to be proposed as a RM of winey-vinegary sensory descriptor. When the headspace of this formulation was analyzed by SPME-GC-FID the relative chromatographic area of acetic acid was 9.700, close to the value for the IOC RM (8.688, Table 2). However, in the case of ethanol, its relative chromatographic area (75.81) was much higher than the IOC RM (0.581), although closer to the maximum values found in the winey-vinegary oils (19.039) and in oils with other defects (53.688) in the data set studied (Table 2).
# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 4 Methodology
### Modification Process  
Figure 1 illustrates the input modification process defined using the ABAA model. This process is divided into three main steps: (1) input representation, (2) term filtering, and (3) input modification to generate adversarial examples. The steps involved are described as follows:  
* Input representation. For a given opinion \(x\) and its associated aspects \(A\), for each aspect \(a_{i}\), we define the set of terms included in the mentioned aspect \(a_{terms}\) and the set of opinion terms \(x_{terms}\), which includes all terms in the opinion excluding those in \(a_{terms}\). Opinion terms and aspect terms are represented by word embeddings. (The experiments were carried out using pre-trained embeddings on Wikipedia 300 d and Twitter 200 d. Twitter GloVe embeddings were chosen due to their orientation toward language and text size in social networks; Wikipedia GloVe was used to extend the vocabulary coverage. Nonetheless, alternative options could be considered.)For each aspect \(a\in A\), we define a unique vector \(\overrightarrow{emb}(a_{i})\) to represent it (Equation (3)). This unique vector is computed as the average of the embedding terms in \(a_{terms}\): \[\overrightarrow{emb}(a_{i})=\frac{1}{|a_{i}|}\sum_{\forall t\in a_{i}}emb(t)\] (3) The goal of aspect-based adversarial examples is to generate an adversarial example for each \(a\in A\), by modifying the corresponding terms in \(x_{terms}\), causing misclassification.
* Filtering terms. To select the terms related to the evaluated aspect, for each term in \(x_{terms}\), we calculate the semantic proximity (denominated as SP) between the embedding term under evaluation and \(\overrightarrow{emb}(a_{i})\) (Equation (4)). The SP measurement is conducted for each term within the opinion, \(t\in x_{terms}\). Specifically, the embedding corresponding to each term, \(emb(t_{i})\), is utilized to calculate its SP with \(\overrightarrow{emb}(a_{i})\): \[SP(a_{i},t_{i})=\cos(emb(t_{i}),\overrightarrow{emb}(a_{i}))\] (4) To define the terms associated with the aspect under evaluation, terms whose semantic proximity SP is equal to or above \(\beta\) (Equation (5)) will be modified. We empirically defined \(\beta\) considering that terms with semantic proximity close to 1 have the same direction as the vector of the aspect terms and therefore are strongly associated with the evaluated aspect. We consider \(\beta=\{0.2,0.3,0.4,0.5,0.6\}\). \[SP(a_{i},t_{i})\geq\beta\] (5)
* Input modification. Filtered terms are modified through word-level synonym substitution. To achieve this, a list of synonyms for each filtered term is generated and their semantic similarity is evaluated (using Equation (4)). The synonym selected to replace the term in the opinion is the one with the highest semantic proximity, determined by Equation (5).
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Adopted Notations and Representation for Work Plans  
The main indexes used to identify the different elements of the sets of interest are as follows:  
* \(i\): index of jobs, \(i\in\{1,\ldots,n\}\) with \(n=|J|\), where \(J\) is the set of jobs to be processed.
* \(k\), \(h\): index of operations, \(k\), \(h\in\{1,\ldots,S_{i}\}\) with \(S_{i}=|O_{i}|\), where \(O_{i}=\left\{O_{i1},\ldots,O_{S_{i}}\right\}\) is the set of operations of job \(i\).
* \(l\): index of machines, \(l\in\{1,\ldots,m\}\) with \(m=|M|\), where \(M\) is the set of machines.  
To each job \(i\), a directed a-cyclical graph (DAG) is attached, which represents the precedence and succession constraints between its operations. The operations of each job are performed to start and finish with dummy operations of zero duration on the same dummy machines. The operations of a job \(i\) are ordered by their increasing rank in \(G_{i}\). Let \(\Gamma_{ik}^{-1}\) and \(\Gamma_{ik}\) be, respectively, the set of predecessor and successor operations of \(O_{ik}\). The depth of the DAG associated with job \(i\) has a depth \(dr_{i}\), where rank \(1\) corresponds to the dummy starting operation and rank \(dr_{i}\) corresponds to the dummy closing operation. \(GL_{ir}\) is the subset of operations of job \(i\) at rank \(r\) and \(O_{i}=\oplus_{r=1}^{r=dr_{i}}GL_{ir}\). Let \(rt_{i}\) and \(dt_{i}\) be, respectively, the release time and the due time of job \(i\), \(i=1\) to \(n\), where these due times are supposed to be inferior or equal to \(T\). \(M_{ik}\) is the subset of machines that can perform operation \(O_{ik}\), \(\delta_{ikl}\) is the processing time of \(O_{ik}\) with its \(l\)th machine, and \(\tau_{ill^{\prime}}\) is the transfer time between machine \(l\) and machine \(l^{\prime}\) for job \(i\). The nominal transfer time is, in general, the transportation time of the product from one machine to the next, but it can also include other side operations (drying delays, set-up times, and inspection delays). Let \(pe_{ikl}\) be the energy necessary for machine \(l\), \(l\subset M_{ik}\), to perform \(O_{ik}\), and let \(te_{ill^{\prime}}\) be the energy necessary to transfer product \(i\) from machine \(l\) to machine \(l^{\prime}\) and other necessary inputs to perform \(O_{ik}\) on machine \(l^{\prime}\).  
considerations mean that this choice is limited and multiplies with the number of operations comprising the jobs and with the presence of several machines capable of carrying out the same operations. Here, the assignment of machines to the operations of each job is identified by a decision variable, leading to a two-level formulation of the considered scheduling problem. To accomplish this, additional notations must be introduced.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Nomadic Mobility Model (NCM)
#### 6.3.5 Average Power Consumption  
Figure 16 depicts the APC in a mobile environment utilizing the NCM model, with and without the Hello Flood attack. Mobile nodes denote scenarios that are free from attack, whereas attack nodes represent its presence within the same model. The percentage of mobile nodes ranges from 10% to 40%, and the node count varies from 10 to 60. Interestingly, APC remains relatively stable in the absence of attack, despite fluctuations in node count and mobile node percentage. However, when the network faces the Hello Flood attack, APC notably escalates, nearly doubling compared to non-attack scenarios. This substantial increase underscores the significant impact of the Hello Flood attack on energy consumption within the network.  
Figure 15: ETX without and with attack using NCM.  
The NCM model, when coupled with the Hello Flood attack, contributes to an increase in Average Power Consumption within the network. This escalation in power consumption can be attributed to several interconnected factors. Firstly, the structured movement patterns inherent in the NCM model may cause congestion points within the network as nodes collectively navigate between different locations. This congestion results in more packet collisions and transmission delays, which demand higher energy consumption. Secondly, the Hello Flood attack inundates the network with excessive traffic, saturating network channels and exacerbating interference. This flood of malicious traffic intensifies the workload on network components, necessitating more energy for processing and transmission tasks. Additionally, the increased activity caused by the attack strains network resources, such as bandwidth and processing power, leading to higher energy consumption. Furthermore, the disruption caused by the attack may introduce additional processing overhead, further escalating energy usage. Overall, the combination of the NCM mobility model and the Hello Flood attack amplifies the network's energy demands, resulting in increased Average Power Consumption.
## 4 Results
### Validation of Hindcasting Wave  
As mentioned above, the WW3 performs well for large-scale oceans (i.e., the Western Pacific Ocean [51] and the Arctic Ocean [52; 53]), and the SWAN is typically employed for wave simulations in coastal waters [54]. Therefore, the waves were simulated by using the WW3 model for the North Pacific Ocean in order to confirm the applicability of the fusion wind. Simultaneously, the waves in the Gulf of Mexico were simulated using the SWAN model. As examples, Figure 8a,b depict the corresponding SWH maps, i.e., the simulations of the WW3 model for the East Pacific Ocean for 07:00 UTC on 7 September 2020 and those of the SWAN model for the Gulf of Mexico for 06:00 UTC on 3 September 2020. Similarly, the ERA-5 wave maps are shown in Figure 9. Judging from a qualitative perspective, the patterns of the modeled waves were consistent with those of the ERA-5 waves, and in particular, the hindcasting waves had more details.  
Figure 10a,b provide comparisons of the WW3-simulated SWHs with the observations of the NDBC buoys and the ERA-5 waves for June-September 2020, respectively, and the comparisons of the SWAN-simulated SWHs are exhibited in Figure 11. The statistical analysis yielded a < 0.5 m RMSE with a > 0.8 COR for SWHs up to 9 m. The accuracy levels of the simulations by using the WW3 and SWAN models were comparable with those forced by the numeric atmospheric models [32], i.e., the ECMWF and CCMP models. As reported in [32], the RMSE of SWH between the SWAN-simulated and the Jason-2 altimeter is about 0.7 m, which is worse than that in this study. However, the RMSE of SWH between the WW3-simulated and the Jason-2 altimeter is about 0.2 m. It is supposed that this is due to the lack of matchups at extreme sea states (SWHs > 6 m) [32]. Therefore, we think the fusion wind speed from radiometers and scatterometers is suitable to predict the waves from numeric wave models in near real time, which does not rely on the results from meteorological models.  
Figure 8: (**a**) The SWH map for 07:00 UTC on 7 September 2020 that was simulated by WAVEWATCH-III (WW3) for the East Pacific Ocean. (**b**) The SWH map for 06:00 UTC on 3 September 2020 that was simulated by Simulation Wave Nearshore (SWAN) for the Gulf of Mexico.  
Figure 10: The comparison of the WW3-simulated SWHs for June\(-\)September in 2020 with **(a)** the observations of the NDBC buoys and **(b)** the ERA-5 waves.  
Figure 9: (**a**) The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) SWH map taken at 07:00 UTC on 7 September 2020 in the East Pacific Ocean. (**b**) The ERA-5 SWH map taken at 06:00 UTC on 3 September 2020 in the Gulf of Mexico.