esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: Open Questions  
The first planar diodes for THz applications were made in the 1990s [4]. However, some questions as to the overall efficiency still remain open. At present, the overall efficiency is well below the theoretical predictions of around 0.001%, preventing the commercial development of this technology [7]. This value can improve if only some conditions are met. While there are currently fewer impediments in the fabrication process of the antenna, at optical and IR spectra, unfortunately, it is not the same for diodes, which have to meet many requirements as the operating frequency increases. Nanoscale antennas and related structures exhibit potential benefits in terms of production, fine-tuning, and high confinement of the electric field (\(E\)) intensity in the gap [30; 31; 32; 33]. However, losses in noble metals negatively affect the antenna efficiency and consequently the overall efficiency [34; 35]. Therefore, a careful choice of materials is required. Moreover, complete matching between the antenna and the rectifier to transfer maximum power to the load is mandatory. In addition, the open circuit voltage and the available power obtained by the rectenna are approximately a few tens of microvolt and a few picowatts, respectively. It is clear that an arrangement in an array is necessary. The practical realization of this array would require further studies to avoid parasitic interactions among them. Regarding the rectifier diode, the main issues not yet resolved at these frequencies concern the design and model, fabrication, characterization, and integration to the antenna. The requirement for highly precise fabrication processes, such as deposition of smooth metal electrodes, ultrathin oxide layer, and patterning nanoscale devices, are some other open questions. Finally, the reverse-bias leakage, lower RC time constant, and coupling efficiency problems limit its applicability. The single parameters that influence the behavior of the diode are shown in Table 1 and are explained in detail. Asymmetry is one of the parameters that can be engineered. A high difference in the work function (\(\Delta\varphi\)) between the metal electrodes involves high asymmetry and nonlinearity;
## 3 Connectivity as a Design Function
In the model of Figure 3, the function of providing Connectivity considers an input from an edge device (sensor or control), and includes actions such as preprocessing of the data as they are transferred to the Cloud or another device. Data can flow in either direction, and security protocols must be followed. The actual action of preprocessing and conditioning of the data is done according to specific protocols. Energy is required in this process. The implementation of Connectivity using this idea is discussed in the sections that follow.
# Can Biochar Improve the Sustainability of Animal Production?
## 3 Animal Feed
### Gut Health  
The health of an animal's gastrointestinal tract is an important factor in overall health and performance (weight gain, milk/meat/egg quality and yield, etc.). Optimal stomach microbial activity improves the overall nutrition and feed digestibility, reduces pathogen inundation, and strengthens the immune system [74].  
[PERSON] et al. [75] studied the effect of biochar feed addition on the gut pH, short chain fatty acid (SCFA) profile, and bacterial communities. This 42 day, live-bird trial used wood chip biochar added at 30 g kg\({}^{-1}\) feed to a typical meat chicken diet. There was no change in the digestive organ pH throughout the trial compared to the control. SCFA profiles play an important role in overall animal physiology and metabolism, especially acetate.  
While the overall microbial populations were similar between the control and biochar treatment, the biochar did shift communities resulting in increased production of acetic (\(p=0.01\)) and caproic (\(p=0.003\)) acid. Acetic acid in the gut is also an energy source for the muscles as well as toxic to some in vitro pathogenic bacteria [76]. Adsorption capacity of the biochar, while important, cannot answer all the phenomena observed when biochar is added to animal feed; therefore, biochar mechanisms in the gut remain an area for future research.  
[PERSON] et al. [77] compared the use of wood/greenwaste biochar with bentonite and zeolite (commercial adsorbents) to suppress pathogen loads in laying chicken intestine while mantaining microbial richness. The additive supplementation rate for each treatment was 4%. All three treatments showed potential for the reduction of major poultry zoonotic pathogens without reducing microbiota diversity. Biochar reduced the reactive abundance of proteobacteria, gammaproteobacteria, and campylobacter classes of bacteria in the chicken cloaca by about half. These bacteria are associated with foodborne illness and can contaminate egg production. Supplementation with biochar could reduce the need for subtherapeutic antibiotic use in the poultry industry [78].
## 4 Results and Discussion
### RQ9 and RQ10: What Is the Research Focus and Limitations According to the PS?
#### 4.9.7 Deep Learning-Based Feature Extraction  
Ref. [34] represented a static analysis method for detecting malicious JS code by using a Stacked Denoising Auto-Encoder for feature extraction and logistic regression for classification. However, the limitations of the study include the need for minification and obfuscation of JS code and the long time it takes for training the proposed model.  
Ref. [58] used n-gram and Stacked Auto-Encoder for feature construction and extraction, as well as Isolation Forest for classification to build an anomaly web attacks detection method.  
Ref. [80] used word-level embedding to convert URLs to numerical matrices. Then, a Convolutional-Pooling layer is used to extract features from the embedded matrices. Afterward, the extracted features are merged with statistical features before they are fed into an SVM classifier. They built a function that takes as input the output of the max-pooling layer and outputs the corresponding words in the input URL, which may help researchers validate the trained model and understand which words contribute to a specific URL classification. Moreover, they showed the impact of testing a model on a dataset containing duplicated data: the proposed model accuracy goes from 99.33 to 100% when tested on the CSIC 2010 dataset--which contains a repetition rate of 81%--without de-duplication.  
Ref. [47] used genetic algorithms to distinguish normal network traffic from abnormal network traffic. Then, they used a shallow Neural Network to classify attack types: either web attacks, infiltration attacks, PortScan, BruteForce, DoS, or Botnet attacks. Finally, they showed that the proposed model is better than other Machine Learning techniques in terms of accuracy, detection rate, recall, and precision.  
Ref. [49] exploited different Deep Learning models, namely Stacked Auto-Encoder and Deep Belief Network, for feature extraction and used one-class learning algorithms, namely Isolation Forest, one-class SVM, and Elliptic Envelop, for web requests classification.
# A Review of Milk Frauds and Adulterations from a Technological Perspective
## 3 Results and Discussion
### Methods for Detecting Adulterants in Milk
#### 3.2.1 Physical Methods (Quantitative)  
The most commonly applied methods based on the physical properties of milk are, among others, the density, the freezing point, and the refractive index. These techniques are easy to perform and relatively cheap and convenient; however, due to natural variations in milk composition, they may not be highly accurate. In addition, the physical methods have limitations (i.e., seasonal changes, composition of milk, regional factors) due to natural variations, such as lower sensitivity in comparison to chemical and instrumental ones [12; 35; 46].
## 6 Conclusions and Future Research Directions  
In this paper, we considered the use of UAVs for autonomous goods delivery to multiple destinations. The scenario studied corresponded to remote areas, where internet connectivity is not available. We proposed an approach for recording and securing the transaction logs of a multihop delivery process. The proposed approach relies on a combination of cipher block chaining and physical layer security, in addition to symmetric and asymmetric cryptography. It represents a framework that can be implemented with existing encryption algorithms. It was shown to provide unparalleled levels of security in harsh conditions that usually make the UAV vulnerable to attack. The features of the proposed approach were discussed and analyzed in detail, showcasing the security levels provided. The proposed approach was shown to be robust to brute-force attacks. Moreover, it could record events related to unauthorized access to the drone or data (thus providing forensic evidence). Furthermore, in the extreme case of perpetrated physical damage on the drone, it could allow for measures to recover the data and guide forensic investigators toward the area where the incident happened.  
Future work could consist of adapting the proposed framework dynamically to the drone's capabilities. In fact, the proposed approach is a framework that works with many algorithms with different levels of complexity and computational requirements. The symmetric and asymmetric encryption algorithms used with the proposed approach could be selected dynamically in an optimized way, depending on the available energy, carried load, and remaining destinations for the UAV. Another direction for future research would be to consider scenarios with intermittent connectivity: In the absence of connectivity, the proposed approach is implemented. When connectivity returns, the UAV can use traditional techniques to save power and memory space. Last but not least, an important area for future work is to implement the proposed approach in actual deployments and perform practical case studies with drone-based delivery to multiple destinations in the absence of connectivity.  
Conceptualization, E.Y., K.A. and M.M.; methodology, E.Y. and M.M.; software, E.Y.; validation, E.Y., K.A. and M.M.; investigation, E.Y. and K.A.; writing--original draft preparation, E.Y.; writing--review and editing, E.Y., K.A. and M.M.; visualization, E.Y. and K.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication was supported by Qatar University grant no. QUCD-IRCC-CENG-24-349. The findings achieved herein are solely the responsibility of the authors.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** All data are contained within the article. The original contributions presented in the study are included in the article--Appendix A. Further inquiries can be directed to the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Background
### Socio-Economic Impacts of Power-to-Gas
Under the sustainability paradigm, the impacts of innovative technologies have to consider the environmental, economic, and social dimensions, which comprise the triple bottom line of an organization [66]. Thus, further research is required to enhance the knowledge about the future socio-economic impacts of PtG, particularly at a large scale. Thus, the following research question is proposed:  
**RQ 2:**: _What are the main socio-economics impacts of the investments in PtG in a specific territory?_
## 5 The Modelling of GNSS Spoofing  
The problem of modelling GNSS spoofing is of independent importance. Here we restrict ourselves to considering one of the most advanced simulation systems, namely, SimSAFE (Figure 7) as the comprehensive simulation to protect against GNSS spoofing. SimSAFE represents an innovative way to simulate complex spoofing attacks, proving detection and mitigation measures [9].  
The SimSAFE software emulates signal spoofing attacks and test receiver mitigation techniques. SimSAFE allows flexibility in the attack scenario definition and test of interference detection techniques. The tool can be integrated into existing radio navigation testing laboratories, in order to leverage existing hardware such as signal simulators, interference generators, and hardware GNSS receivers.  
Figure 6: The basic GNSS spoofing scheme of USVs: at some point in time, the spoofer determines its position \((x_{s},\ y_{s})\), introducing some delays into each of the satellite channels, introducing some other signal distortion, and transmitting the false position \((x_{s},\ y_{s})\) on the same frequency, which aims to deceive GNSS receivers and create a false PVT solution. All USVs in the spoofer activation zone are positioned as \(\text{USV}_{i,\ false}=(x_{s}+\Delta x_{s},\ y_{s}+\Delta y_{s})\).  
Figure 7: The basic GNSS meaconing scheme of USVs: at some point in time, the meaconer determines its position \((x_{s},\ y_{s})\), introducing some delays into each of the satellite channels, and transmitting the false position \((x_{s},\ y_{s})\) on the same frequency.  
This technology aims to investigate the effectiveness of GNSS spoofing protection [10; 11; 12; 13; 14; 15].  
The complexity of the anti-spoofing solution problem cannot be overemphasized. See, for example, [16; 17; 18]. Probably no one has offered anything better than GNSS/INS [19; 20]. However, in this case, there is a problem of data integration from multiple sensors [21; 22].
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation
### Dataset  
We evaluate ErrorExplainer on the SC-Bench dataset [5], a corpus of real-world Solidity smart contracts drawn from blockchain explorers: Etherscan (Ethereum) and Polygonscan (Polygon). The benchmark covers the most popular token standards (ERC-20, ERC-721, and ERC-1155) and spans a wide range of contracts that capture the structural complexity found in production code. The dataset is designed to analyze the impact of error-injected smart contracts on real-world applications. Thus, it provides each contract in two versions: (1) the original source code and (2) its _mutated_ version in which randomly selected statements have been altered or removed. Accompanying metadata specify exactly which statements were deleted or mutated.  
This configuration is suitable for our evaluation because real-world smart contracts rarely document the total number of transaction-reverting statements. If ErrorExplainer counts all transaction-reverting statements in both the original and mutated versions correctly, the difference between the two counts should be equal to the number of deletions recorded in the metadata, providing reliable ground-truth for RQ1.  
We therefore filter SC-Bench for our evaluation as follows:  
1. Keep only contract pairs whose mutation log explicitly includes the _removal_ of at least one transaction-reverting statement.
2. Discard any pair whose mutated contract no longer compiles.  
The resulting evaluation dataset contains 335 original-mutated pairs, i.e., 670 individual smart contracts.  
**Runtime Cost.** We measured the computational overhead of ErrorExplainer's static analysis pipeline. On an Apple silicon M1 (10-core CPU, 32 GB RAM), the end-to-end analysis of a single contract takes, on average, 0.64 s with a standard deviation of 0.55 s across the entire evaluation set. Because blockchain networks typically operate with block times on the order of seconds, this latency is negligible for practical front-end integration andreal-time error reporting. Furthermore, this analysis is required only once per contract, and the results can be cached and reused, as the smart contracts remain immutable for their lifetime.
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 3 Notebook Federator
### Challenges and Assumptions  
Implementing an FL ecosystem in a real-world medical environment comes with a myriad of challenges related to its practical application. First and foremost, not all medical institutions can count on the necessary hardware resources to train AI models. When they do, these might vary across sites, as do operating systems and network conditions. Moreover, different collaborators might store their raw data in varying formats and structures, thus limiting the automation of the data-loading process. Therefore, to enable the correct practical implementation of the FL setup, we proceed on the basis that the following assumptions are valid:  
* All the participating institutions are already registered as part of the federation;
* Local data cannot be shipped outside the institution;
* Local data are stored on each client machine following the same--previously defined--nomenclature for labels and metadata across institutions. As some institutions may have their own tools or methods to process raw data, we assume the data have passed the quality check;
* Each medical collaborator either has local machines (e.g., local computers) to run the code or an agreement with a research group that acts as a local infrastructure (e.g., high-performance servers, or computer clusters) and is thus able to train models;
* Each participating entity has a Docker cluster or is able to build Docker images to run the corresponding code.  
To reproduce the FL process, it requires configuring the corresponding software dependencies, input and/or output parameters, and services. In a traditional way, a user has to manually install all necessary software, enable services, and feed with suitable inputs, a process that is burdensome and time-consuming.
## 2 Data  
### Satellite Data  
#### 2.1.1 Sentinel-3 Operational Products  
The Level-2 Near Real Time (NRT) products (i.e., surface reflectance, vegetation indices, land surface temperature) acquired by the Ocean and Land Colour Imager (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) mounted onboard Sentinel-3 satellites are freely available from the Copernicus Open Access Hub ([[https://schub.copernicus.eu/accessed](https://schub.copernicus.eu/accessed)]([https://schub.copernicus.eu/accessed](https://schub.copernicus.eu/accessed)) on 20 November 2021) with a delay up to 3 h after satellite acquisition. The following daily products from the Sentinel-3A and Sentinel-3B satellites for 2018-2020 were used in this study:  
1. Land Full Resolution (LFR) product derived from OLCI imagery at a 300-m resolution consisting of Global Vegetation Index (OGVI) and Terrestrial Chlorophyll Index (OTCI) indices accompanied with rectified reflectances at 681 nm (RED) and 865 nm (NIR) channels used in this study to calculate Normalized Difference Vegetation Index (NDVI) using formula: \[\mathrm{NDVI}=(\mathrm{NIR-RED})/(\mathrm{RED+NIR});\] (1)
2. Land Surface Temperature (LST) from the SLSTR sensor at a 1-km resolution.  
The acquired products were further mosaicked, cloud masked using associated quality flags and reprojected to the Poland CS92 coordinate system (EPSG: 2180) using ESA SNAP software.  
These satellite data provide information related to vegetation vigour and biomass (from NDVI) and vegetation transpiration (from LST derivatives). The NDVI and LST indicators provide complementary information on the vegetation physical status and access to water in the root zone (through evapotranspiration related to LST). These particular Sentinel-3 products were selected in line with the overall system design to be based on Copernicus' ready-to-use products, i.e., not requiring pre-processing of raw data. This ensures that the system will use the data generated by the latest processing methods provided by Copernicus (i.e., even if they will be updated).
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### Kurtosis (\(K_{SQI}\)) Analysis  
Table 2 summarizes the descriptive statistical information from the \(K_{SQI}\) analysis. In the dataset, the average kurtosis is high in the very low CF range (0-0.2), indicating a tendency towards leptokurtic distributions (kurtosis value greater than 3) characterized by sharp peaks and heavy tails. Such distributions suggest that the signals frequently exhibit more extreme values than expected in a normal distribution, evidenced by the wide range between the minimum (\(-\)1.17) and maximum (105.39) kurtosis values, pointing to inconsistent signal quality. As the CF increases to intermediate ranges (0.2-0.4 and 0.4-0.6), the kurtosis values decrease, with the 0.4 to 0.6 range showing a mean of 0.29, close to the ideal of 0. This suggests a more normal distribution, which is preferred for consistent and reliable PPG signal analysis due to the reduced presence of extreme values and artifacts. However, the kurtosis increases significantly in the highest CF ranges (0.6-0.8 and 0.8-1.0), indicating signal distortion from excessive pressure. This distortion is particularly pronounced in the 0.6 to 0.8 range, where the excessive pressure leads to sharp distribution peaks and heavy tails, rendering the measurements unreliable and reflecting the variations due to inconsistent contact between the sensor and the skin.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 3 Reviewing Spatial Coverage Quality
### Barrier Coverage  
Differently from target and area coverage, spatial coverage quality for barrier coverage applications cannot be described by coverage rate, since the barrier must be entirely covered, otherwise the monitoring application fails. For these applications, the quality of a WVSN can be stated in terms of the _Breadth Belt-Barrier_, which is called \(\beta\)-Quality of Monitor (\(\beta\)-QoM). This breadth is the area extension of each camera in a continuous barrier belt. In this case the thinner breadth of the barrier must have at least \(\beta\) units of distance [53; 54]. In other words, this means that the visual sensor nodes must have some intersecting area with neighbors nodes, and all extension along this redundant area must present the minimum \(\beta\) breadth.  
A slight variation of \(\beta\)-QoM is the Quality of Sensing [55]. In addition to the breadth of the barrier, it is also required a minimum length of coverage provided by each camera in the barrier. That way, in case of barrier violation, the entire violating object can be viewed by a single camera, avoiding the necessity of data fusion techniques to compose an single image from different cameras.  
Quality can also be measured by the barrier weight of a node, which is an indirect measure related to the lifetime that a barrier can provide the coverage service and the number of sensor nodes required for constructing a strong barrier coverage. Barrier weight is the ratio between energy expenditure on its location and orientation adjustment and the resulted barrier gain (increasing of barrier length). Large weight corresponds to low residual energy to be used to provide coverage service. Nodes are positioned to provide barrier formation with efficient energy management, thereby they might not contribute their full sensing range to the barrier [56; 57].