esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Deployment Logistics  
The B-LiFE was deployed in the forest area of Guinea Conakry, close to the city of N'Zerekore, Guinea, from December 2014 to March 2015 to support the local ETC (Figure 1). This healthcare facility was managed by Alliance for International Medical Action, a French non-governmental organisation. The laboratory team consisted of four laboratory experts and six non-laboratory members involved mainly in organisational aspects such as internet connectivity, communication, logistics, and decontamination (Figure 1a). The laboratory staff was rotated every four weeks, with staff overlapping for four days to ensure efficient handover of the laboratory and communication of all relevant operational information.  
Figure 1: Organisation of the B-LiFE base of operation during the Ebola mission in Guinea. There were several modules consisting of (**a**) fully equipped inflatable laboratory tent (**1**), two containers for laboratory supplies and the command centre with a satellite internet network (**2**), and the Ebola treatment centre located inside the red zone and apart from the B-LiFE (**3**). (**b**) The laboratory tent, supported by a cement platform, is surrounded by two fences, one of which protects the overall perimeter of the laboratory and its auxiliary structures (including the power supply and air conditioning), and the other encloses the sensitive operational area (i.e., the laboratory tent and two Algeco containers). The tent and the two containers delineate the clean zone. The red arrows indicate the path followed by ECT personnel when bringing in samples, starting at a decontamination station (foot and hand washing) and ending at a “Samples/Wastes” window in the inner fence, opposite the front of the laboratory. Samples are passed to the laboratory staff through this window. The laboratory staff follows the same path to dispose of the waste (the laboratory wastes are systematically stored next to the window). The entrance and exit to the laboratory are located at the front of the tent. The green arrows show the path that the B-LiFE members must take to enter the tent or reach the clean zone. The blue and brown circles within the inner fence’s right and left borders indicate the location of the poles that support the outdoor Wi-Fi access points (router). The three black circles on the front of the tent represent water taps (chlorinated water). The uninterrupted power supply (UPS) is represented by the black rectangle to the right of the Algeco container in the top left corner. (**c**) Inside the laboratory tent, different gloveboxes were used for virus inactivation (**4**), biochemical testing (**c**) (**5**)—note that the large plexiglass glovebox was not in place at the time of the photo. (**d**) The extraction (**6**) and PCR (**7**) workstations were located at the entrance of the laboratory.
## 6 Experimental Study
### Performance of DRL Method to Network
#### 6.3.1 Convergence Analysis  
The dataset [20, 10] is used to validate the convergence of the network. The parameter combination for the reward function is \((\alpha,\beta)\) = (1, 0.5). The learning processes of four training methods are compared: Exponential, Critic, and Rollout baselines under the Actor-Critic method, as well as the Rollout Baseline under the PPO method. The changes in weighted reward during the training process are shown in Figure 6.  
It can be observed that among the Actor-Critic methods, the Exponential Baseline and Critic Baseline perform poorly, while the Rollout Baseline achieves the best results. All three methods converge after epoch 80. It can be observed that using an actual policy network as the Baseline yields better results compared to using the Exponential method or a Critic network for prediction. However, changes in the Baseline do not significantly affect the convergence.  
When using the Actor-Critic methods, significant fluctuations are observed throughout the training process due to the lack of gradient clipping or simple value clipping. In contrast, although PPO-Rollout does not show a clear advantage over Actor-Critic Rollout in terms of final reward, training based on the PPO method achieves similar results to Actor-Critic Rollout after only 60 epochs and exhibits smaller fluctuations during training. This indicates the stability of the PPO algorithm during training.
## 3 Results
### Effects of FF on Body Weight and Food Intake  
In this study, we investigated the effect of dietary fat content on body weight gain and dietary efficiency in mice over an eight-week period. Body weight changes and food intake were measured to assess these effects (Table 2). Our results indicate that, after eight weeks, mice on the HFD exhibited significant weight gain compared to those on the NFD. A daily weight gain of 1.99 \(\pm\) 0.15 g/day was observed in the NFD group, whereas the HFD group showed a significant increase of 4.02 \(\pm\) 0.18 g/day (\(p\) < 0.05). Additionally, administration with FF led to a dose-dependent reduction in body weight gain, particularly in the FF 250 group, which showed a significant decrease to 2.89 \(\pm\) 0.24 g/day. Evaluation of the food efficiency ratio (FER) revealed a significant increase in HFD-consumption groups compared to the NFD group. Significantly, administration with FF 250 resulted in a significant decrease in FER compared to the HFD group. These findings indicate the potential of FF to reduce obesity by reducing weight gain compared to dietary fat intake alone [42].  
Figure 1: Effects of Fig and fermented fig (_Ficus carica_ L., FF) on the antioxidants (**A**) DPPH; (**B**) ABTS; (**C**) Total polyphenol; (**D**) Total flavonoids. Fig and FF extracts were treated at concentration of 1–20 mg/mL. The results were expressed as mean \(\pm\) SEM (\(n\) = 8). **\(p\)** < 0.01 compared with Fig and FF. ***\(p\) < 0.
## 3 Results
#### 3.2.1 Cases Overview  
On 27 January 2023, during roadway excavation along the No.3 coal seam floor at the ZF1212 heading face of Jushan coalmine (Shanxi Lanhua Group, Jincheng City), unidentified water accumulation from overlying abandoned workings breached the coal face due to unmitigated hazards. Approximately 7100 m\({}^{3}\) of water inundated the excavation zone, resulting in four fatalities and direct economic losses of CNY 8.7612 million. This accident is categorized as a structurally uncontrolled instantaneous water inrush, occurring in a drilling-blasting excavation roadway.  
The accident occurred at the heading face of ZF1212 roadway in Panel 1 of the No.3 coal seam, located 117 m from the portal. Geometrical configurations include a coal seam strike angle of +11\({}^{\circ}\), rectangular cross-section (width 3.4 m; height 2.5 m), and daily advance rate of 3 m. Prior to the accident, the primary mining scope encompassed the northern wing of Panel 1 in the No.3 coal seam, exclusively involving secondary mining operations with localized abandoned goat areas remaining from historical No.3 seam extraction. The ZF1212 excavation roadway was situated within this northern wing, where the No.3 seam averaged 4.2-6.12 m thickness. Numerous goat areas had formed due to room-and-pillar mining conducted along the roof. Excavation at 90 m encountered a steep strata dip rise that exposed sandy mudstone flooring. This resulted in persistent half-coal-rock conditions  
Figure 14: \(T_{JS}\) curve of excavation progress from October 24 to November 2 of roadway No.9103.  
up to the 117 m incident location. The operational design required anomaly detection ahead via transient electromagnetic (TEM) prospecting and underground borehole probing (long-range/short-range). Ultimately, both methods failed to identify the water hazard ahead.  
In the TEM survey on 2 January at 70 m, 100 m coverage failed to consider significant strata attitude changes near the synclinal axis. No design adjustment corrected the undetected transition from bedding-parallel to floor-perpendicular detection. This caused critical omission at 117 m.  
For drilling operations after reaching 112 m, the final long-range probing on 19 January executed only four boreholes during 8:00/16:00 shifts versus the eight designed. All holes measured <20 m against the required >30 m depth. Furthermore, short-range probing was omitted for six consecutive shifts pre-accident.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 5 Experimental Results, Analysis, and Discussion  
This section of results and discussions presents the performance evaluation and comparison between our proposed LEACH-EE protocol with LEACH and LEACH-C protocols. We use some essential metrics for the evaluation process, such as the cluster headcount and the building time for a cluster, energy usage, network lifetime, and the probability of success. In this experimentation, we use MATLAB as a simulation environment. Table 4 shows other parameter settings for the simulation process.
## 2 Materials and Methods
### Analysis of cotA and mhQ Gene Expression in B. safensis CN12 in the Presence of NP
#### 2.9.2 qPCR Primer Design and qPCR Test  
The primers for real-time quantitative PCR (qPCR) reactions to analyze the expression of the _cotA_, _mlnqO_, and _16s rRNA_ genes of _B. safensis_ CN12 were designed in silico using the program Primer3 (primer3 [[https://primer3.ut.ee/](https://primer3.ut.ee/)]([https://primer3.ut.ee/](https://primer3.ut.ee/)) (accessed on 27 May 2025)). During the design of qPCR primers, several strategies were implemented to ensure optimal amplification efficiency: (1) amplicon lengths were restricted to under 200 bp to enhance reaction performance; (2) the potential for primer self-dimers, cross-dimers, and hairpin structures was assessed using the UNAFold Web Server ([[http://www.idtdna.com/UNAFold](http://www.idtdna.com/UNAFold)]([http://www.idtdna.com/UNAFold](http://www.idtdna.com/UNAFold)) (accessed on 27 May 2025), and (3) to prevent non-specific amplification, the NCBI Primer-BLAST tool ([[https://www.ncbi.nlm.nih.gov/tools/primer-blast/](https://www.ncbi.nlm.nih.gov/tools/primer-blast/)]([https://www.ncbi.nlm.nih.gov/tools/primer-blast/](https://www.ncbi.nlm.nih.gov/tools/primer-blast/)) (accessed on 27 May 2025) was employed using the _B. safensis_ CN12 genome as a reference to verify primer specificity. The primers selected for qPCR analysis included _cotA_-f: CAATCCAACCAGAGGTACACAC, _cotA_-r: CGATCCGAAATGACTTAC, _mlnqO_-f: AGCAAACGATGGGACTTG, and _mlnqO_-r: CGTGAAACAGGCGTAAATG. The primers for qPCR amplification of the _16s rRNA_ gene were _16s_-f: CTTGACATCCTCTGAAACC and _16s_-r: CTCGTTGGCAGCTTAAC.  
_cotA_ and _mlnqO_ expression were quantified by qPCR with cDNA synthesized immediately after total RNA extraction and using a Relative Quantitation of Gene Expression method. qPCR reactions were carried out using NZY qPCR Green Master Mix (2\(\times\)) (NZYTech, Portugal). Each 10 \(\upmu\)L reaction mixture contained 5 \(\upmu\)L of the master mix, 0.4 \(\upmu\)L each of the forward and reverse primers (10 \(\upmu\)M), 4.0 \(\upmu\)L of cDNA (previously normalized to the desired concentration), and 0.2 \(\upmu\)L of Milli-Q water. Amplification was performed on a CFX Connect real-time PCR thermocycler (Bio-Rad, Hercules, CA, USA) using the following cycling conditions: initial denaturation at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 15 s and annealing/extension at 60 \({}^{\circ}\)C for 1 min. The data were normalized with the 16S rRNA gene as an internal control gene because it is one of the most tested and validated genes in qPCR studies [45]. This study was conducted with the samples obtained from the biodegradation test in MSM + 1 g L\({}^{-1}\) of glucose spiked with 10 mg L\({}^{-1}\) of 4-n-NP and control samples in the absence of the contaminant (as a calibrator and a reference sample). Two biological replicates (2 different cultures for each time: 1, 3, 5, and 7 days) were used, and three technical replicates were performed (three independent qPCR reactions for each biological replicate).
# Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays
###### Abstract  
In contemporary wireless communication systems, the multiple-input and multiple-output systems are extensively utilized due to their enhanced spectral efficiency and diversity. Densely packed antenna arrays play an important role in such systems to enhance their spatial diversity, array gain, and beam scanning capabilities. In this article, a slotted meta-material decoupling slab (S-MTM-DS) with dual reflexes slotted E-shapes and an inductive stub is proposed. Its function was validated when located between two microstrip patch antenna elements to reduce the inter-element spacing, the mutual coupling, the return losses, and manufacturing costs due to size reduction. A prototype is simply fabricated in a volume of 67.41 \(\times\) 33.49 \(\times\) 1.6 mm\({}^{3}\) and frequency-span measured from 8.4:11 GHz. At 9.4 GHz frequency, the spaces between the transmitting elements are decreased to 0.57 of the free space wavelength. When the proposed isolation S-MTM-DS is applied, the average isolation among them is measured to be \(-\)36 dB, the operational bandwidth is enhanced to be 1.512 GHz, the fractional bandwidth improved to be 16.04%, and the return losses are decreased to be \(-\)26.5 dB at 9.4 GHz center frequency. Consequently, the proposed design has the potential to be implemented simply in wireless contemporary communication schemes.  
antenna arrays; microstrip antennas; broadband antennas; frequency selective surfaces 26 June 202122 July 20212 July 20212 July 20212 July 20212 July 2021  
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# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 4 Discussion  
The 4 DT-Wx prototype system developed in this study presents an effective integration method for weather information for TBO. It differentiates itself from existing services through two main features: multi-model-based trajectory services and buffer zone information provision. These features can contribute significantly to the implementation of a high-performance ATM system, as recommended by ICAO's GSNP.  
The multi-model approach combines the strengths of various weather models, providing more accurate and reliable weather information. This is crucial for improving the accuracy of aircraft trajectory predictions in a TBO environment. The provision of buffer zone information allows for flexible responses to unexpected situations, which is a crucial element in real-time decision making and strategic flight planning. This aspect has not been sufficiently addressed in previous studies.  
The performance evaluation results showed excellent scalability and efficiency, particularly in large-scale data processing. This suggests that the system can meet the real-time processing capabilities required in a TBO environment.  
Figure 8: Comparison of parallelization models ([PERSON] et al.’s model, [PERSON]’s law, and [PERSON]’s law) across different numbers of API requests (BOff).  
The example in Figure 9 shows a visualization of the actual weather information extracted in this study, demonstrating the system's practical applicability. The figure shows the temperature variations along the aircraft trajectories of KE725 (ICN-KIX) and KE1019 (GMP-CJU). KE1019, a domestic flight, uses only LDAPS data, while KE725 uses both LDAPS for high-resolution takeoff data and GDAPS for lower-resolution data outside the domestic region. The figure clearly illustrates how the presence of buffers enables the comprehensive monitoring of the surrounding conditions.  
This visualization provides valuable information for pilots and air traffic controllers in real-time flight planning and decision making. The inclusion of buffer zone information enhances the ability to identify potential hazards and optimal route changes, significantly improving safety and efficiency.  
Figure 9: Visualization of weather information extracted from the 4 DT-Wx prototype system for KE725 (ICN-KIX) and KE1019 (GMP-CJU).  
Future research can focus on improving and optimizing the integration of various weather models, long-term performance evaluations, and the validation of buffer zone usage in actual TBO environments and on developing customized weather information provision methods for different flight phases. In conclusion, the 4 DT-Wx prototype system developed in this study offers a new paradigm for weather information integration in TBO environments, contributing to enhanced aviation safety and operational efficiency while providing a robust foundation for addressing increasingly complex weather conditions and flight scenarios.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 3 Joint Cell Anti-Jamming Map Based on Dual Connection
### Interference Coordination Algorithm Based on Interference Graph  
In the interference coordination algorithm based on the interference graph, we use \(X\) to represent the sequence number of the conflicting clique, and then the effective communication rate of user \(I\) in clique \(X\) is as follows.  
\[R_{i}^{x}=\sum_{l=1}^{t}p_{i}^{x}(l)r_{i}^{x}(l) \tag{6}\]  
where \(t\) is the feasible scheduling permutation and combination in clique xand represents the communication rate that user \(i\) can achieve when scheduling in clique \(x\) with combination \(I\).  
In the interference coordination algorithm, for any user, the actual communication rate is composed of three parts. The first part is the rate achieved by the scheduling conflict clique when it uses the anti-jamming mechanism to schedule users. The second part is the rate achieved when the conflict clique does not use the interference coordination mechanism. The third part is the communication rate provided by macro base station. Therefore, for user \(i\), the actual statistical rate is as follows.  
\[R_{i}=\sum_{x=1}^{o}p_{x}R_{i}^{x}+\sum_{x=1}^{X-o}p_{x}p_{i}^{pico}t_{i}^{pico }+p_{i}^{macro}r_{i}^{macro} \tag{7}\]  
where \(X\) represents the set of all maximal cliques in the complement graph of scheduling conflict graph and \(o\) represents the set of maximal cliques containing user \(x\) in \(X\), which is a subset of \(X\). Representing the scheduling ratio corresponding as clique\(X\) and the statistical communication rate of user \(i\) can be obtained when all maximal cliques in \(o\) are not scheduled.  
In (10), Equation (6) is used to expand in (8) to solve the scheduling combination in each maximal clique.  
For scheduling combinations in any maximal clique, we use the same method to solve the priority iteration formula as the following section, and the corresponding priority scheduling mechanism can be obtained as  
\[\rho_{i}^{x}=\sum_{t\in x}\frac{R_{i}^{x}(k)}{R_{i}(k-1)} \tag{11}\]  
\[\rho_{i}^{macro}=\frac{r_{i}^{macro}(k)}{R_{i}(k-1)} \tag{12}\]
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 2 Filtering Antenna Design and Analysis
### Filtenna Design  
The antenna design obtained in the previous section is further optimized to obtain the filtering characteristics. Following the antenna obtained previously with miniaturized dimensions of 23 mm \(\times\) 30 mm, an additional rectangular-shaped stub is loaded between feedline and antenna. This additional stub filters out the upper band resonances at 5.2 GHz and 9 GHz and results in an antenna operating over the 2.45 GHz band. The resultant antenna offers harmonic suppression from 3 GHz to 11 GHz in order to reduce interference and an operational band at 2.45 GHz. The design steps are depicted in Figure 4, and the corresponding S-parameter results are demonstrated in Figure 5.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Simple Trajectory Gesture Recognition
#### 3.1.2 Median Filtering of Gesture Trajectory  
After obtaining the motion trajectory of gestures, due to the interference and noise present in the target detection, there are false alarms in the detection results which affect subsequent recognition. Therefore, median filtering can be used to filter out discrete points and smooth the gesture trajectory. Median filtering is a nonlinear signal processing technique based on statistical sorting theory, which effectively suppresses noise and is commonly used in noise reduction processing. The basic principle involves replacing the value at a certain point in a digital sequence with the median value of the data adjacent to that point. This approach not only brings the value closer to the true data value but also smooths the sequence data by eliminating isolated noise points.  
The measurement values of \(n\) points are \(h_{1},h_{2},h_{3^{\prime}},\cdots\cdots h_{n}\). To detect whether \(h_{i}\) is an outlier, the filter window length is set to \(2m+1\) (\(2m+1<n\)). This means that \(2m+1\) measurement values \(h_{i-m}\cdots h_{i}\cdots h_{i+m}\) are sequentially taken from the \(n\) measurement sequence and sorted in descending order. The median value after sorting is taken as the filtering output, denoted as \(median(h_{j})\). The pseudocode for median filtering is presented in Algorithm 1.  
```
MEDIAN (points, m) \(N\leftarrow\mathit{LENGTH}(points)\) \(\mathit{nevoPoints}\leftarrow\mathit{points}\) foreach point pi for \(i\geq 1\) in points do \(j\gets i+k\) if\(j\leq N\)then \(\mathit{tempWindow}\leftarrow\mathit{points}[j-k,j+k]\) \(\mathit{SORT}(\mathit{tempWindow})\) \(\mathit{nevoPoints}[i]\leftarrow\mathit{tempWindow}[k]\) return\(\mathit{nevoPoints}\)
```  
**Algorithm 1** Median filtering  
To verify the effectiveness of median filtering, a set of data for a counterclockwise circular hand gesture in the \(\mathit{xoy}\) plane were collected for testing. Median filtering was applied to process the gesture trajectory, and the filtered results of the \(x\), \(y\), and \(z\) three-dimensional coordinates are shown in Figure 11. Based on the filtered results, a relatively smooth gesture trajectory can be obtained through median filtering in three dimensions: range, azimuth, and elevation. To assess the results of median filtering, we employed the mean-squared error (MSE) to calculate the difference between the curves before and after median filtering. In this example, the MSE values for X, Y, and Z are 0.0012 m, 0.0016 m, and 0.0013 m, respectively, which illustrates that the median filtering maintains the curve information effectively.
# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 4 Evaluation  
In this section, we discuss the validation of the security of the federated-learning system trained for the proposed crowd-sensing application through experiments. For this purpose, we constructed a federated-learning system and simulated a crowd-sensing environment for validation experiments. We selected open-source large-scale datasets for actual neural network training tasks in the experiments. The experiments consisted of three parts: system accuracy experiments; comparative experiments on data transmission encryption in federated learning; and experiments on the system's resistance to common attack threats, including data poisoning, model attacks, and inference attacks. In the data transmission security experiments, we conducted comparative experiments using additive homomorphic encryption algorithms, multiplicative homomorphic encryption algorithms, and differential privacy. The experimental results and analysis are provided in the aforementioned section. The results of the experiments that assessed the system's resistance to attacks verified the attack resistance of the secure federated-learning framework based on the user-contribution-level-based malicious-user-screening mechanisms from the perspectives of the impact on model accuracy when malicious nodes were present in the user base and when poisoning, model attacks, and inference attacks were launched.