esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Carbon nanomaterials, such as graphene and carbon nanotubes (CNT), are taken as the most promising candidates for next-generation materials for integrated electronic device application because of their astonishing physical properties [1; 2; 3; 4]. However, the zero-band gap in graphene limits its application for digital electronics. Meanwhile, the single-wall carbon nanotube (SWCNT) has been investigated widely for nano integrated circuit application based on the purification technology of CNTs [3; 4; 5; 6]. The single semiconducting SWCNT(s-SWCNT)-based devices have been especially developed for nano electronic and photonic devices [4; 6], although the device manufacturing based on a single SWCNT is technically complex and not compatible with the traditional Si process, hindering their wide applications. Hence, it is important to utilize a suitable approach to maximize the advantage of carbon nanomaterials for large-scale application. Meanwhile, hybrid graphene/CNT materials show intriguing potential for widespread application [7; 8; 9], which offers new opportunities for optoelectronics applications. It is particularly significant for the practical preparation and development of the carbon-based optoelectronic devices, which has attracted increasing attention [9; 10; 11]. In a variety of devices, the metal/semiconductor Schottky photodiode--the basic key component in integrated circuitsand the semiconductor industry--reveals multiple advantages, such as simple device architecture, high response speed, and high photoconductive gain [12; 13; 14; 15; 16; 17]. Meanwhile, with the development of multielement detectors, high-density photodetector arrays are highly demanded. Until now, great efforts have been made to produce carbon-based photodiode, and scalable production of carbon-based Schottky photodiode arrays with well controlled architectures is still a great challenge. Recently, the high-purity SWCNT thin-film transistors have been successful fabricated [5; 18] and the growth and transfer of graphene is becoming increasingly sophisticated [19; 20; 21], which make it feasible to construct high-quality and large-area all-carbon optoelectronic devices.  
Here, we present a controllable approach for fabricating graphene/s-SWCNT film Schottky junctions and the cross-shaped film photodiode array structures based on the as-fabricated s-SWCNT/graphene Schottky junctions in a large scale. The cross-shaped structure consists of multielement graphene/s-SWCNT Schottky photodiode, which present a rich collection of electronics and photonics. The as-fabricated all-carbon Schottky photodiode was demonstrated with an ultra-broadband photodetection characteristic from ultraviolet to near infrared range with high responsivity and a fast response rise time. The gate-tunable and wavelength-dependent photoelectric characteristics of our device were also demonstrated. By virtue of the simple device architecture and the Si compatible fabrication process, the all-carbon graphene/s-SWCNT photodiode array can provide low-cost and high throughput solutions for future nano multielement optoelectronic devices.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
###### Abstract  
The number of buildings with passive control systems is steadily growing worldwide. For this reason, this study focuses on the reliability analysis of these systems employing fragility curves. The structural performance evaluation is obtained for a 10-story steel building with two different sections (trimmed and conventional). The trimmed section of the building was evaluated with hysteresis and oil dampers, while the conventional section of the building was evaluated without damper. The fragility curves were obtained from the incremental dynamic analysis using 20 ground motion records. Spectral acceleration response at the fundamental period of the building was considered and used as the intensity measure for the ground motion records. The maximum inter-story drift ratio of the building was employed as the damage measure. In addition, the seismic energy absorption rate was compared between hysteresis and oil dampers. As a result, hysteresis dampers were found to be more effective for high ground motion intensities. On the other hand, the oil damper dissipates energy immediately, even for low ground motion intensities. Furthermore, the combination of different types of dampers improved the seismic performance of the trimmed section of the building to almost the same level as the conventional section of the building. Eventually, a combination of hysteresis and oil dampers in a building is suggested to improve structural performance.  
response controlled systems; dampers' combination; fragility curves; incremental dynamic analysis +
Footnote †: journal: Applied  
doi:10.3390/app12157717  
Academic Editor [PERSON]
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Static Traffic Patterns
#### 6.2.1 Without Hidden Node
In addition, for low traffic load \(T=0.095\) Mbps, up to \(1.25\%\pm 0.13\%\) packets are lost for TAROA, and there is no packet loss for EDCA/DCF. Packet loss caused by collisions for EDCA/DCF increases as the number of stations increases for traffic load \(T=0.150\) Mbps and \(T=0.110\) Mbps. In a network with 2048 stations, it goes up to \(40.07\%\pm 0.54\%\) and \(18.62\%\pm 9.85\%\), respectively, while no packets are lost due to collision when using TAROA. For the LT scenario, EDCA/DCF always results in better latency, due to the slotted nature of RAW and the large quantities of packet loss of EDCA/DCF in very dense networks. Figure 6: Performance comparison between TAROA and EDCA/DCF for both the HT and LT scenarios without hidden nodes for different traffic loads and number of stations. (**a**) throughput, HT; (**b**) packet loss, HT; (**c**) latency, HT; (**d**) throughput, LT; (**e**) packet loss, LT; (**f**) latency, LT. Figure 7 zooms in on the performance (throughput, latency and packet loss) in the HT scenario with a fixed number of RAW groups (\(R=32\), 128) under traffic load of 0.85 Mbps, each RAW group has the same number of stations. The results suggest that the performance of a fixed number of RAW groups varies as the number of stations changes. This conclusion is further supported by Table 5. As such, there is no one optimal fixed RAW group configuration for all different network topologies. This proves the further need for a dynamic RAW configuration algorithm, such as TAROA, which achieves good performance regardless of the network topology.
## 4 Evaluation  
We evaluate our approach as follows. Initially, we implemented and deployed the architecture introduced in Section 3.2 in a Colombian Coffee Smart Farm scenario. Then, we collected temperature datasets at the Colombian Coffee Smart Farm. After, we run the FDM and FRM at the Fog Tier of the mentioned architecture. Finally, we tested the proposed mechanisms' effectiveness to deliver reliable data regarding Accuracy, Recall, Precision, FAR, F-Score, and RMSE.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 3 Proposed Method
### Utilized Features
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Type** & **Description** \\ \hline Latitude & Spatial & Latitude (WG84) of air quality monitoring devices \\ Longitude & Spatial & Longitude (WG84) of air quality monitoring devices \\ Altitude & Spatial & Altitude of air quality monitoring devices (unit: meter) \\ Observed Time & Temporal & Observed time of monitored data \\ Temperature & Temporal & Temperature data (unit: Celsius) \\ Humidity & Temporal & Relative humidity data (unit: \%) \\ Wind Speed & Temporal & Wind speed data (unit: m/s) \\ Wind Direction & Temporal & Wind direction data (unit: bearing angle) \\ Pressure & Temporal & Pressure data (unit: hPa) \\ Rainfall & Temporal & Daily rainfall (unit: mm) \\ PM2.5 & Temporal & PM2.5 value (unit: ug/m\({}^{3}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of all features. Obtaining a similar time series of the PM2.5 forecast for adjacent devices was expected; however, an accurate forecast time series as a standard was difficult to discern. We used the median of the estimated PM2.5 value in the temporal part for each hour as a more accurate time series, rather than selecting a forecast time series that may be accurate. The time series of adjacent devices within a specific range were smoothed closer. The temporal and spatial methods will be discussed, respectively, in the following sections. Figure 4: PM2.5 time series of adjacent devices within 5 km. Figure 3: Relationship between (**a**) temperature, (**b**) humidity, (**c**) pressure, (**d**) wind direction, (**e**) wind speed, (**f**) rainfall, (**g**) time, (**h**) distance, and (**i**) weather difference and PM2.5 value error.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 5 Discussion
### Scalability  
One critical aspect of adopting DTs in agriculture, especially in areas like irrigation, hydroponics, aquaponics, and vertical farming, is their scalability and the ease with which these systems can grow from small experimental setups to larger farms. For instance, smaller farms often face a significant digital divide, where limited financial resources and technical know-how make adopting advanced DT systems challenging, while platforms using open and flexible standards (such as FIWARE-based frameworks and OPC UA communication protocols) are designed explicitly for scalability.  
In aquaponics, DT systems built around IoT technologies are indeed scalable because they simplify operations and reduce the need for highly specialized skills. Yet, even with these advantages, managing biological systems where fish and plants closely interact remains complex. This complexity requires significant technical knowledge, which can slow down or limit widespread adoption. Similarly, DT frameworks proposed for vertical and smart farming aim to help farmers better manage resources without needing constant on-site presence or specialized training.  
Finally, IoT-based DT frameworks intended for irrigation management are described as highly adaptable across different scales, from small-scale farms to larger agricultural operations. This adaptability is especially important in arid regions, where scalable, cost-effective solutions are crucial to addressing limited water resources and dense agricultural practices.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Types of Blood Vessels  
In normal tissues, arterial blood flows through arterioles, capillaries, post-capillary venules, and veins, except in certain tissues (e.g., skin), where arteriovenous shunts bypass capillaries. Cancerous tissues follow the same route. Each type of vessel has a characteristic structure and function. Arteries, large blood vessels lined by endothelium, are surrounded by elastic tissue and multiple layers of smooth muscle cells. Arterioles (-10-20 um in diameter) transition to capillaries, the main sites of nutrient and waste exchange due to their thin walls and small diameter (4-9 um). Capillaries have endothelial junctions spaced 100-200 nm apart, facilitating molecular exchange via paracellular and transcellular pathways, while larger proteins are transported via vesicles. Their continuous endothelial lining is supported by a basement membrane and pericytes. In specialized tissues, such as the kidney, the endothelium shows fenestrations, i.e., zones of the extreme thinning of endothelial cells (50-150 nm in diameter). Venules, larger than capillaries (approximately 20 um in diameter), are lined by cuboidal monolayer endothelium, basement membrane,and pericytes, but lack smooth muscle cells [75]. Another region considered favorable for metastasis is these post-capillary venules [76].
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 2 Traditional Method of In-Motion Coarse Alignment and Related Work  
In this section, the traditional method of in-motion coarse alignment is briefly described. Then, the limits and practical issues of constructing a vector using velocity measurement are introduced.
# Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis
## 3 Methodology and Framework
### Data Set  
The data set consists of 1200 portable executable (PE) malware and 400 benign samples. The malicious files were collected from two sources. The first source is the VirusTotal repository [30], where 900 malicious files were downloaded, which were captured between 2017 and 2019. The additional 300 malware samples captured between 2015 and 2017 were downloaded from Das Malwerk [31]. The malware data set contained an equal quantity of the following malware families: Adware, Ransomware, Keylogger, Downloader and Backdoor. The purpose of collecting malware from two sources in two different periods is to have a comprehensive and reliable data set as malware are changing their tactics and behaviors [21]. On the other hand, benign files were collected from Windows 7 operating system (Win 7 32-bit) files.
# A Decoding Algorithm for Convolutional Codes
## 1 Introduction  
Many aspects of daily life require the transmission of large amounts of digital data in a timely and reliable manner. Among the different methods to detect and correct errors occurring during the transmission, convolutional codes are a standard tool for dealing with this issue. While the encoding algorithms present no difficulty at all, decoding algorithms are more involved and have been studied for decades. Depending on the signal, they can be classified into _hard_ and _soft_ decoding algorithms (e.g., [1], Ch. 14, 15). When it comes to decoding, maximum likelihood algorithms (e.g., such as Viterbi) often require a large amount of memory and their complexity depends exponentially on the degree [2, 3]. Sequential decoding algorithms use a metric and a threshold in order to maintain memory and complexity under certain limits. Other decoding methods include list decoding and iterative decoding (see [4], Ch. 4-7).  
An extremely active area of research is the pursuit of decoding algorithms of convolutional codes that balance the use of memory and computational complexity. For decades, the Viterbi algorithm, along with its variants and improvements, has been regarded as one of the most widely used maximum likelihood decoding algorithms.  
A main goal of this paper is to offer a hard decoding algorithm (see Section 3.2), which is a combination of an algorithm proposed by [PERSON] [5] with another one given by [PERSON] [6] in terms of _error-correcting pairs_. The resulting algorithm is of the same type as [PERSON]'s; namely, it is an _iterative algebraic decoding algorithm_. A second main result consists of lower and upper bounds for the number of arithmetical operations required to decode a codeword (see Section 3.4), which we call the _computational cost per codeword_. Note that the complexity is directly proportional to the computational cost per codeword. It is worth noting that, unlike the Viterbi algorithm, the bounds for our algorithm depend polynomially on the code parameters.  
A case study is presented in Section 4. Indeed, we apply our results to the family of convolutional codes considered in ([7], SS IV; [8], SS 6.3). We analyze how the parameters should be chosen in order for the assumptions to hold true. Finally, we are able to write simple expressions for the bounds of the computational cost per codeword.  
The authors would like to thank the referees for their comments and suggestions that have helped to improve the paper.
## 4 Friction Coefficient Compensation Control Strategy
### Compensation Control Principle  
The optimal shifting force trajectory \(F^{*}\) was obtained by the optimal control method. Through the force controller, the voltage input to the electromagnetic linear actuator at different synchronization times was obtained, and the actuator output different shifting forces to the synchronizer according to different voltage values. During the synchronization process of the synchronizer, due to the difference in the rotation speed between the synchronizing ring and the target ring gear, the shifting force acted on the friction pair to generate the friction torque. The friction torque caused the temperature to rise during the synchronous speed difference, and the temperature accumulation caused the friction coefficient between the friction pairs to decrease. The real-time shifting force, the speed difference, and the temperature during the synchronization process were transmitted to the controller through the sensor [31; 32; 33]. The controller obtained the real-time friction coefficient \(\mu\) by querying the relevant friction coefficient MAP. The real-time friction coefficient was compared with the expected friction coefficient \(\mu^{*}\) to obtain the difference. The compensation controller calculated the compensation shifting force \(F\) that needed to be output according to the difference. The compensation control force corrected the optimal control force in real time, and changed the force signal input to the force converter to maintain the friction coefficient of the friction pair of the synchronizer near the expected value. The feedback compensation control schematic is shown in Figure 8.
## 4 Experiment
### Data Sources and Preprocessing  
#### 4.2.1 Remote Sensing Data  
This study used high-resolution data from the multispectral imaging satellite Sentinel-2 and SAR satellite Sentinel-1. Comprising Sentinel-2A and Sentinel-2B, the Sentinel-2 system operates in a collaborative fashion, offering a 5-day revisit period. For the Yellow River Basin in 2021, surface reflectivity products (L2A) were sourced from the European Space Agency's (ESA) Copernicus Data Center (available at [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), (accessed on 12 November 2023) Date: from January to December 2021). The dataset, delivered in 12 monthly segments, encompasses a total of 1768 image captures. These images incorporate 12 spectral bands, featuring spatial resolutions of 10 m, 20 m, and 60 m. For compatibility with deep learning methodologies, bands of lower resolution were  
Figure 9: Geographical location of the study area.  
resampled to a uniform 10 m, culminating in the integration of bands to produce optical image data with consistent resolution.  
Sentinel-1, part of ESA's Copernicus Earth Observation Project, is a high-resolution SAR satellite featuring a six-day revisit period. It offers Level 1 Ground Range Detected (GRD) products across four modes: SM, IW, EW, and WV. This research focuses on high-resolution GRD data from the IW mode, boasting a 10-m resolution and supporting dual polarizations: vertical (VV) and cross-polarization (VH). Data was obtained through Google Earth Engine and synthesized via averaging techniques into monthly installments, which amounted to 12 editions for the year, readily usable for deep learning endeavors.  
Furthermore, this study integrates auxiliary data for a comprehensive analysis. This includes ESA's 10 m surface coverage product [43] and the Joint Research Centre's (JRC) global water body data [44]. ESA product is instrumental in creating sample data, thereby refining the deep learning model's accuracy and reliability. Concurrently, JRC and ESA datasets facilitate the evaluation of the model's accuracy and a comparative study of the results.
## 5 Swarm-Based Solution
### Routing Problem
#### 5.2.1 Pheromone Calculation  
Path quality and latency are used to determine the update pheromone. This improves network reliability and latency. Transmission, propagation, queuing, and processing are end-to-end delays. The processing delay may be removed owing to the sensor nodes' speed [8].  
Pheromone density increase along path \(p\) is defined as:  
\[\Delta\tau^{2}=\left(PRR_{p}\right)+\left(\overset{\text{IDL}}{\sum}\Big{/}delay _{p}\right) \tag{67}\]  
The forward ant travels to the mobile sink, where the pheromone update value is calculated and sent back to the source node as a backward ant. Whenever a node \(x\) receives a backward ant from a node \(y\), it modifies the concentration of pheromones in the following way:  
\[\tau_{xy}^{2}(t)=(1-\rho_{3})\tau_{xy}^{2}(t-1)+\rho_{3}\Delta\tau^{2} \tag{68}\]  
where \(\rho_{3}\in(0,1)\)\(\rho\in\) (0,1) tells the pheromone evaporation rate, which is the evaporation constant that [5].
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Trickle timer-Based Solutions
#### 4.1.3 Mobility Enhanced RPL  
Recall, that RPL faces many mobility issues such as lack of identification of mobile nodes, packet loss due to the selection of mobile node as a preferred parent, and mobile node getting detached from the DODAG due to Trickle timer interval issue. To cope with this, the authors [44] proposed Mobility Enhanced RPL to resolve the above-mentioned issues. For identifying mobile IoT nodes in the network, a node configured as mobile adds its mobility information in the DIO base object, e.g., the field mobility status is set to 1 in the DIO message if the node is configured as mobile otherwise it is set to 0. The second issue is resolved by not selecting the mobile nodes as Preferred Parents (PP) causing packet losses by checking the node mobility information in the DIO messages. Fixed nodes having lower ranks are considered as Preferred Parents. The authors consider the PP change as an inconsistency parameter for detecting the mobility of the nodes. In the proposed approach, the DIS messages are not only used to allow nodes to join the DODAG but also to update the routing information of a node experiencing more inconsistencies.  
Subsequently, the Inter-DIS interval is computed based on the number of PP changes in the previous inter-DIS time period. If the number of PP changes are high, then it means that the nodes are in an unstable environment and likely to remain the same in the next time period. The next inter-DIS period is decremented so that nodes can refresh their routing information by sending it in a DIO message. On the other hand, if the PP remains unchanged for several inter-DIS periods, then the next inter-DIS period is incremented. The simulation results show the packet loss rate of ME-RPL is lower as compared to standard RPL. Route stability is defined as a valid routes percentage for effectively routing data information in the network, furthermore, ME-RPL shows higher stable routes as compared to basic RPL.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 5 Simulation Results
### Impact of \(d\) on BECPB  
In this part, we use equation Equations (19) and (22) to compute BECPB when \(d\) increases from 20 m to 95 m with \(\gamma=0.015\) and \(B=10\) kHz for the two types of batteries. The results are shown in Figure 4. From the results, we can see the impact of \(d\) on the BECPB, and compare the BECPB for different batteries. Moreover, we can justify if the theoretical analysis agrees with the evaluated results. The following points can be observed:  
* When \(d\) is fixed, the BECPB for the battery with IDC-based BUF is higher than that with MDC-based BUF. As \(d\) increases, BECPBs for both types and their gap increase. When \(d=90\) m, the BECPB for the battery with IDC-based BUF is about 30% higher than that with the MDC-based BUF.
* Clearly, the BECPB for the two batteries is larger than that for the ideal battery. With the increase of \(d\), the BECPB gap between each battery and the ideal one gradually expands. When \(d=90\) m, the BECPB for the battery with IDC-based BUF and MDC-based BUF is about 60% and 25% higher than the ideal battery, respectively. The above observations support the theoretical analysis made in Section 4.2.  
Figure 3: BECPB for two types of batteries over \(\gamma\).