esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Distributed Mapping Algorithms
```
0:\(\mathcal{U}\), \(\mathcal{V}\), \(\mathcal{F}\), \(\mathbb{P}\)
0:A, \(Q\)
1:for\(n=1\) to \(N\)do
2: Read the Video Request Table \(\mathbb{P}\)
3: Select the video \(m\) requested by the user \(n\)
4: Read the segments Allocation Map \(\mathbf{A}^{0}\)
5:for all\(p_{m}\in\mathbf{A}^{0}\)do
6:for all\(k\in\mathcal{F}\)do
7: Calculate \(\delta(n,k,p_{m})=\frac{d_{pn}^{t}}{VBR(q)}-t^{k,n}(p_{m})\)
8:while\(\delta(n,k,p_{m})<0\) and \(A(k,p_{m})\
eq 1\)do
9:\(A(k,p_{m})=A(k,p_{m})-1\)
10: Update \(\delta(n,k,p_{m})=\frac{d_{pn}^{t}}{VBR(q)}-t^{k,n}(p_{m})\)
11:endwhile
12:endfor
13:endfor
14:endfor
15:for\(p_{m}\in\mathbf{A}^{0}\)do
16: Find \(\bar{k}=\arg\max\delta(n,k,p_{m})\)
17:\(A(\bar{k},p_{m})=q\)
18:endfor
```  
**Algorithm 2** Quality downgrade. In addition, a playout rate-based buffering technique, while taking advantage of the position of the segments and the length of the video, works in parallel with any of the previous algorithms by adapting the buffering on the playout rate so that the segments downloaded first will belong to the first portion of the video. In this way, the video can be loaded faster and the rules can be relaxed for the remaining segments of the video. This is favourable in slow networks in which the method allows to stream the first part of the videos as fast as it can to permit the users to start watching and then it downloads the rest while the user is occupied with watching, but it can be detrimental if the users want to skip the first part, as that will lead to a waste of bandwidth. The two previously introduced algorithms have been compared with two other baseline algorithms in order to better understand their advantages and possible issues. In the first one, named _fixed allocation_, the segments of the videos on the fog nodes are considered to be fixed throughout the whole evaluation. Taking into account (1), the segments are distributed over the allocation matrix to \(K\) nodes handling \(P\) segments distributed for \(M\) videos. Each node is able to handle a maximum number of segments, leaving some segments not available and having to request to the cloud for downloading the required segment. This is the simplest algorithm, and it can be executed without much additional cost, but it does leave room for improvement. It is a general-purpose algorithm that can be most beneficial for fast networks. The second benchmark is instead named _optimal buffering_. In this case, instead, the goal was to reallocate the segments originally placed so as to respect both (5) and the requested quality. However, in order to avoid having unfeasible solutions while minimizing \(\mathbf{P}\mathbf{I}\), we consider that the quality should always follow the one requested.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 2 Materials and Methods
### Aptamer Binding to Acetamiprid  
Aptamers are known to be very stable at RT [34]. Therefore, the aptasensing platform was incubated in 50 \(\mu\)L of acetamiprid solution with different concentrations ranging from 50 to 450 fM for 30 min which is enough time scale to achieve aptamer binding with the target [34]. The electrode was then washed with buffer solution before performing measurements to remove non-attached molecules.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.2 Multi-Field Coupling Modeling  
Multi-field coupling models, particularly those integrating thermal-hydraulic-mechanical-chemical (THMC) interactions, are critical for grasping the difficulties of open caisson shaft excavations. These models can aid in forecasting soil-structure behavior under a variety of environmental and loading circumstances. Recent advances in THMC modeling are aimed at dealing with the complicated interactions between the thermal, hydraulic, mechanical, and chemical fields. To accurately simulate ground behavior during excavations, several physical processes must be integrated, according to the studies referenced. In frozen soils, [PERSON] et al. [184] carried out studies that showed that temperature gradients on the surface can influence hydraulic conductivity and pore pressure, changing soil stability and deformation. The results of these investigations have led to an improved numerical model that can replicate related processes in various circumstances, such as seasonal shifts.  
The coupled THMC processes in geological media are important for a variety of subsurface engineering applications [185; 186; 187]. Recent advances in modeling THMC interactions included the creation of fully coupled frameworks that combined various codes to simulate complicated subsurface systems. Experiments have also been carried out to investigate THMC coupling in frozen vertical shafts, revealing the spatiotemporal evolution of temper ature, moisture, and stress fields [188]. The use of coupled THMC modeling in open caisson shaft design was still unexplored. Open caissions, prone to complex subsurface interactions such as heat effects, groundwater movement, mechanical loads, and chemical reactions, could benefit from THMC modeling to enhance stress distribution, soil deformation, and ecological impact.  
Geothermal variations and groundwater flow might impact the rigidity of soil and pressure within pores, whereas mechanical stress and chemical responses can change soil properties gradually. The incorporation of chemical remedies such as grouting can improve the stability of soil through minimizing permeability and enhancing strength. However, chemical processes could cause dissolution and precipitation of minerals, which can change soil structure over time, influencing both mechanical and hydraulic performance. Establishing THMC models for open caissions could improve construction accuracy, safety, and long-term viability, especially in difficult ground conditions. Additional research and simulations are required to bridge this gap.
## 3 Materials and Methods  
### System Overview  
The proposed system includes one passive subsystem, a validation system, two active systems in which manual arm control and auto-scanning modes are toggled, and a mobile app for user feedback. In the idle state, the system is in manual control mode, where the linear actuator and the arm gripper are controllable by the user once an item is picked up. The auto scanning mode is enabled manually by the user; it locates the item in front of the scanner base, starts the barcode detection and scanning process, then drops the item in a non-congested section in the cart. Items scanned are registered in the system and validated,and the selected item and quantity are presented in the app. The system overview is shown in Figure 1.  
The scanner base is vertically rotatable through a stepper motor installed on the cart edge and connected to the base with a coupler. The base includes a camera used for the deep learning-based barcode detection system. The base rotation allows the detection of barcodes from the lower side of items when the actuator is still unpacked.  
If no barcodes are detected until the actuator is packed, auto scanning starts by placing the item near the scanner base, not too close so that the entire item is in the camera's field of view, and not too far so that the camera can detect the barcode. The gripper starts rotating while a YOLO detection pipeline is running, and the center of the ROI with the highest confidence score is used to align the barcode with the scanner in a feedback PID control loop. The visual difference, in pixels, between the detected centroid and the scanner sensor, mapped to the frame in pixels, is used in the control loop to determine the following motion input of the gripper in the \(x\)-axis and the \(y\)-axis. Scanning the item and registering its barcode ends the loop and allows it to be placed in the cart. Manual scanning is possible if the timeout duration is reached without barcode registration.  
Accordingly, the visual difference, in pixels, between the detected centroid and the scanner sensor, mapped to the frame in pixels, is used in the control loop to determine the following motion input of the gripper in the \(x\)-axis and the \(y\)-axis. Scanning the item and registering its barcode ends the loop and allows it to be placed in the cart. Manual scanning is possible if the timeout duration is reached without barcode registration.  
Finally, the validation system is used to check if the item inserted was scanned or not. The system uses a camera with a view of the items inside the cart and load cells mounted at the base to detect the insertion of new items. The items are further checked to be the same as those scanned, using matched SURF features and weight comparisons. The actual weights and features are stored in the database and accessed by the system.  
Figure 1: System overview.
###### Abstract  
Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in order to recommend textbook content based on exercises. Such methods are typically based on semantic textual similarity (STS) and the use of embeddings for text representation. However, it remains unclear what types of embeddings should be used for this task. In this study, we carry out an extensive empirical evaluation of embeddings derived from three different types of models: (i) static embeddings trained using a concept-based knowledge graph, (ii) contextual embeddings from a pre-trained language model, and (iii) contextual embeddings from a large language model (LLM). In addition to evaluating the models individually, various ensembles are explored based on different strategies for combining two models in an early vs. late fusion fashion. The evaluation is carried out using digital textbooks in Swedish for three different subjects and two types of exercises. The results show that using contextual embeddings from an LLM leads to superior performance compared to the other models, and that there is no significant improvement when combining these with static embeddings trained using a knowledge graph. When using embeddings derived from a smaller language model, however, it helps to combine them with knowledge graph embeddings. The performance of the best-performing model is high for both types of exercises, resulting in a mean Recall@3 of 0.96 and 0.95 and a mean MRR of 0.87 and 0.86 for quizzes and study questions, respectively, demonstrating the feasibility of using STS based on text embeddings for educational content recommendation. The ability to link digital learning materials in an unsupervised manner--relying only on readily available pre-trained models--facilitates the development of AI-enhanced learning.  
educational content recommendation; AI-enhanced learning; pre-trained language models; ensemble embeddings; knowledge graph embeddings; text similarity; textual semantic search; natural language processing +
Footnote †: journal: _Future Internet_
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.8 ESL (Ephemeral Secret Leakage) Attack Resistance  
Assuming that the secret value \(u\), \(v\), \(or\)\(e_{i}\) is obtained by the adversary \(A\), \(A\) can calculate \(M_{2}\) and \(M_{6}\). In order to obtain \(SK\), \(A\) must be able to calculate \(\{E_{i},E_{i}^{new}\}\) and the real \(ID_{i}\). However, \(A\) cannot calculate the correct \(\{E_{i},E_{i}^{new}\}\) that needs to be calculated using the long-term secret value \(s\) in polynomial time. Therefore, the scheme can resist the ESL attack.
## 3 Network Model  
Figure 1 illustrates the common architecture of BSNs. A BSN comprises multiple sensor nodes and a coordinator worn on a human body. Sensor nodes measure the physiological information of the BSN user (e.g., heart rate, blood pressure, body and skin temperature, oxygen saturation, respiration rate). The sensory data is delivered to the coordinator on the body (e.g., a personal digital assistant, a smart phone, or a micro-controller board), which then in turn displays the corresponding information on a user interface or transmits the aggregated vital signs to a remote server through a network interface for further processing [32, 33]. As in typical BSN application areas, e.g., inpatient department or emergency department in a hospital, wireless local area network (WLAN) access points (APs) are commonly deployed for public Internet services, a BSN always connects to an applicable WLAN AP (as the network interface) when multiple interfaces, e.g., cellular network and WLAN, may be available.  
The signal attenuates as it propagates over space in BSNs. The attenuation may either be due to propagation losses caused by the natural expansion of the radio wave in the environment, referred to as path-loss, or multi-path propagation, referred to as multi-path induced fading, or due to shadowing from obstacles affecting the wave propagation, sometimes referred to as shadow fading [34, 35]. In this study, we consider the path-loss and shadow fading for simplicity. The path-loss function is given by \(l(d)=G\cdot d^{-\alpha}(d>d_{0})\), where \(d\) is the distance between the transmitter and receiver, \(G\) is a constant accounting for system loss, \(\alpha\) is the path-loss exponent with \(\alpha>2\), and \(d_{0}\) is the reference distance. Denote the shadow fading factor as \(\gamma\). At a given time \(t\), the received signal strength is expressed as  
\[\Omega\left(d\right)=\Omega_{0}\cdot\gamma\cdot l\left(d\right) \tag{1}\]  
where \(\Omega_{0}\) is the transmission power.  
In BSNs, there are two different channel models, _i.e._, the wireless channel between the sensor nodes and the coordinator in a BSN (referred to as on-body channel model) and that between two BSNs  
Figure 1: The common architecture of body sensor networks (BSNs).  
(referred to inter-body channel model). Due to the blockage and disruption of the radio signal by the human body, the on-body channel model experiences more severe attenuation (with a path-loss exponent \(\alpha_{O}\)) than the inter-body channel model (with a lower path-loss exponent \(\alpha_{I}\) and \(2<\alpha_{I}<\alpha_{O}\)) [36, 37].  
The transmission status of a BSN could be transmitting or not transmitting. The transmission status is 1 when there is any node, either sensor node or coordinator node, transmitting. Then transmission status is 0 when none of the node transmits, it is inactive period in a superframe.
## 2 Materials and Methods
### Pollen Identification  
To ensure that _P. lactiflora_ plants were pollinated by _A. mellifera_ (bee pollen, BP) and _P. lactiflora_ flowers (flower pollen, FP) were collected and identified. Pollen samples from the orbiculae of _A. mellifera_ adults were randomly collected when they flew back to the hive. Pollen DNA was extracted using the QIAamp DNA Mini Kit following the manufacturer's instructions (QIAGEN, Hilden, Germany). The DNA samples were checked using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA).  
The _rbcl_ gene was used as a DNA barcode for the identification of _P. lactiflora_ pollen. Primers of this gene, namely 1F-5'-ATGTCACACAAAACAGAAC-3' and 742R-5'-TGCACTGTACGTGCAGTAGC-3', were applied in the PCR, with the following steps: initial denaturation at 95 \({}^{\circ}\)C for 2 min, 35 cycles of denaturation at 94 \({}^{\circ}\)C for 1 min, annealing at 55 \({}^{\circ}\)C for 30 s, extension at 72 \({}^{\circ}\)C for 1 min, and a final extension step at 72 \({}^{\circ}\)C for 10 min [24]. Subsequently, we purified the PCR products using the TaKaRa MiniBESTAgarose Gel DNA Extraction kit Ver.4.0 (Takara Biotech, Tokyo, Japan), attached them to a pMD18-T vector (Takara Biotech, Tokyo, Japan), and inserted them into _Escherichia coli_ for amplification. The _rbcl_ genes from BP and FP were sequenced by Sanger sequencing at Tsingke Biological Technology, Beijing, China. Last, the sequences of _rbcl_ genes from three FP and 15 BP were blasted and analyzed through DNAMAN 7 (Lynnon Biosoft, San Ramon, CA, USA).
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Recommendation ITU-R P.845-3
#### 3.1.2 HF Field-Strength Measurement Technique
Using either analogue or digital signal processing, average voltage amplitudes over a period of 4 s within each 12 s transmission cycle should first be determined. A minimum of 12 such samples within a given hour on a given day should be taken, uniformly distributed over the transmitting period.
## 3 State of the Art
### WuR Implementations  
Numerous implementations have been proposed in the literature, spanning from specifically designed Complementary Metal-Oxide Semiconductor (CMOS) circuits to discrete circuits. The performance of the latter is evaluated by integrating them into conventional IoT nodes. Their performance is estimated considering their power consumption in WuRx mode, their wake-up range, and their carrier frequency. A sample of the WuR-enabled IoT nodes mostly used for experimentation in the scientific community is presented in Table 1. The table details the performance of the WuR systems in terms of range and receiver energy consumption with regard to frequency and implementation technology.  
Whereas some of these nodes operate on unlicensed frequencies bands [22] used by low-power wide area networks (LPWANs) such as LoRa [9; 23; 24; 25; 26], others use frequencies shared with a power transmitter [9], with Wi-Fi [27] or with a dedicated WuR IC [28]. Using LoRa or unlicensed WLAN frequencies enables building upon a well-established protocol and public communication infrastructure. Additionally, the application's power transmitter can merge data and power transmission in a single wired device. Using less common frequencies can prevent interference with other wireless protocols in the area.  
Energy consumption widely varies depending on a large number of factors, such as range, frequency, and implementation technology. Long-range solutions [25; 27] are the most energy-hungry implementations due to the power needed to amplify weak long-range signals. In particular, the implementation technology used strongly influences power consumption. For example, the AS3930-based solution [28], which depends on a nonoptimized demonstration board and the BEH solution [9], uses the analog-to-digital converter (ADC) of a microcontroller to measure the received radiofrequency (RF) energy instead of a dedicated WuRx circuit, which consumes far more than a dedicated WuRx circuit. This is a more recent and optimized solution.  
The technological solutions based on custom CMOS circuits enable a considerable reduction in energy consumption, with values of less than 100 nW, but they are generally not yet ready to be applied in industrial projects. So, we do not provide furether detail here. Nonetheless, this field of research is active, producing full AI-ready SoC with WuR capabilities, such as SamurAI [31], which consumes less than 40 nW in an idle mode.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Analysis of Haze and Rain Models for Creating Synthesized Training Dataset
#### 2.1.3 Heavy Rain Model  
In this section, the final model of the combined haze model and rain model is written as:  
\[O(x)=I(x)+S(x)-I(x)\circ S(x), \tag{5}\]  
where \(I\) means the haze image as expressed in (1). \(S\) is the rain streak image as described in (3). Through the above analysis, model rain streaks and haze on images can be expressed mathematically. Therefore, training dataset created on the blending model presents more realistic haze and rain phenomena. The following subsection describes how to decompose the degraded images into frequency domain which helps the network optimize more quickly and accurately.
###### Abstract  
This paper provides a novel application of the multi-criteria decision-making (MCDM) method to the multi-objective optimization problem (MOOP) of creating a two-stage helical gearbox (TSHG) with second-stage double gear sets (SDGSs). The aim of the study is to determine the optimum major design components for enhancing the gearbox efficiency while reducing the gearbox volume. In this work, three primary design parameters are chosen to accomplish this: the gear ratio of the first stage and the coefficients of the wheel face width (CWFW) of the first and second stages. Additionally, the study is conducted with two distinct objectives in mind: the lowest gearbox volume and the maximum gearbox efficiency. Moreover, phase 1 and phase 2, respectively, are the two stages of the MOOP. Phase 2 handles the MOOP to identify the ideal primary design factors as well as the single-objective optimization problem to minimize the difference between the variable levels. Additionally, the Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) approach is selected to deal with the MOOP. The results of the study are utilized to determine the ideal values for three crucial design parameters in order to create a TSHG with SDGSs.  
gearbox; helical gearbox; gear ratio; multi-objective optimization; MAIRCA method +
Footnote †: journal: Applied  
Article Solving a Multi-Objective Optimization Problem of a Two-Stage Helical Gearbox with Second-Stage Double Gear Sets Using the MAIRCA Method
## 3 Efficient Energy Aware Routing Protocols: R-EERP and S-EERP
### Forming Clusters and Cluster Head Selection  
We propose two routing protocols: R-EERP and S-EERP. In our R-EERP protocol (Randomly clustered Energy Efficient Routing Protocol) the nodes are distributed randomly and uniformly in a target area. Hence the clusters are also distributed uniformly over the area. The cluster formation and cluster head selection are very similar to the ones in the LEACH protocol (Figure 1).  
In our S-EARP protocol (Sequentially clustered Energy Efficient Routing Protocol), the clusters are organized sequentially along the floors of a building. Hence, it has the following assumptions: (i) the environment where the sensor network is deployed is a multi-story building (a multi-floor apartment building or an office building) with a number of rooms in each floor; (ii) the nodes are deployed in each room manually; (iii) the sensor nodes in a room or in a floor constitute a cluster spanning that room or floor; and (iv) the nodes forming a cluster (which is determined at cluster setup phase) do not change while the network is operating (see Figure 2), but the node having the cluster-head role in a cluster may change at each round of reconfiguration.  
Figure 1: Nodes are distributed in a field within \(200\times 200\).  
Figure 2: The deployment of the nodes on floors and in rooms on each of the floors.