esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 5 Performance Analysis
### Link Margin  
The link budget is typically calculated as follows [42; 43]:  
\[SNR_{received}(dB)=EIRP-(PL_{FSL}+PL_{A}+PL_{SM}+PL_{SL})+\frac{G}{T}-k-B, \tag{15}\]  
where \(SNR_{received}\) denotes the received SNR in dB for a receiver, \(EIRP\) is the effective isotropic radiated power, \(\frac{G}{T}\) is the antenna-gain-to-noise-temperature, and \(k\) is the Boltz  
Figure 10: Proposed NB-IoT resource grid. (**a**) Two \(DMRS\) symbols per slot, (**b**) Three \(DMRS\) symbols per slot.  
mann constant, which is \(-228.6\). \(B\) is the bandwidth. Units are dBW for \(EIRP\), dB/K for \(\overleftarrow{\text{T}}\), dBW/K/Hz for \(k\), and dBHz for \(B\). Therefore, the link margin is calculated as follows:  
\[LM_{i}=SNR_{received}-SNR_{required}. \tag{16}\]  
This study calculates \(LM_{i}\) every \(i\)-th sample time of 30 s through satellite orbit simulation for 24 h. Let us define an indicator function \(\mathbf{1}_{\{LM_{i}<0\}}=1\) if \(LM_{i}<0\) and zero otherwise. We assume a communication outage for the \(i\)-th sample time of 30 s corresponding to \(LM_{i}<0\). We define the per-day outage time ratio as follows:  
\[P_{\text{OTR}}(\%)=\frac{30\ sec\times\Sigma_{i}\mathbf{1}_{\{LM_{i}<0\}}}{24 \times 3600\ sec}\times 100. \tag{17}\]  
Next, we define the per-day total service time in h with an indicator function \(\mathbf{1}_{\{LM_{i}\geq 0\}}=1\) if \(LM_{i}\geq 0\), and zero otherwise as follows:  
\[T_{S}=\frac{30\ sec\times\Sigma_{i}\mathbf{1}_{\{LM_{i}\geq 0\}}}{3600}. \tag{18}\]  
We define the maximum persistent service time (_MPST_) in h as follows:  
\[MPST=\frac{30}{3600}\times max(i_{2}-i_{1})\ such\ that\ LM_{i}\geq 0\ for\ all\ i\in[i_{1},i_{2}], \tag{19}\]  
where, \(i\), \(i_{1}\), and \(i_{2}\) are the time indices of sampling the link margin data. With a similar expression, we define the maximum persistent outage time (_MPOT_) in h as follows:  
\[MPOT=\frac{30}{3600}\times max(i_{2}-i_{1})\ such\ that\ LM_{i}<0\ for\ all\ i\in[i_{1},i_{2}]. \tag{20}\]  
The metrics defined above are essential for IoT NTN. Furthermore, it is crucial to estimate the minimum number of satellites needed to meet the target value of the metrics, which are provided in the following section.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 1 Introduction  
Cyber threat intelligence (CTI) sharing is essential for proactive defense against evolving cyber threats. However, the existing systems face challenges in ensuring trust, security, privacy, automation, and regulatory compliance. This paper addresses these challenges by proposing TrustShare, a decentralized trust-aware infrastructure that enables secure and efficient threat intelligence sharing across diverse stakeholders, as shown in Figure 1.  
We begin by analyzing the limitations of the current solutions through a systematic review of the literature, expert interviews, and industry feedback. These investigations revealed critical gaps in data integrity, trust evaluation, and compliance with frameworks like the General Data Protection Regulation (GDPR)--particularly the _right to be forgotten (RtbF)_.  
To address these gaps, we designed a framework that integrates the following technologies and standards: _Hyperledger Fabric_ for permissioned blockchain support, _Structured Threat Information eXpression (STIX)_ and _Trusted Automated Exchange of Intelligence Information (TAXII)_ for standardized threat exchange, _Ciphertext-Policy Attribute-Based Encryption (CP-ABE)_ for access control, and the _Interplanetary File System (IPFS)_ for scalable off-chaindata storage. The framework is further aligned with the _MITRE ATT&CK_ matrix for comprehensive threat modeling.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 3 Flow of DTS Prediction Based on the ISSA-DELM  
In this study, the ISSA optimizes the input weights of DELM to compensate for the influence of random input weights on the network and improves stability and accuracy. A DTS prediction model based on the ISSA-DELM is constructed. The model contains three hidden layers, each with a different number of nodes: the first hidden layer has 200 nodes, the second has 100 nodes, and the third has 50 nodes. Figure 3 displays the structure and diagram of the prediction model.  
The specific flow is as follows:  
(1) Eliminate and normalize the outliers from the logging data and divide them into training sets and test sets.  
(2) Initialize ISSA parameters.  
(3) Determine the network structure of DELM and set relevant parameters.  
(4) The objective function trained by DELM is set as the fitness value of ISSA. The root mean square error (RMSE) is applied as the objective function and defined as Equation (9):  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{k}-\hat{y}_{k})^{2}} \tag{9}\]  
(5) Update the fitness value according to the search direction and movement speed, and check whether the current weights are optimum, otherwise, perform iteration.  
(6) Send the optimum weight values into DELM as the weight values of the input layer.  
(7) Send the test data into the ISSA-DELM model for prediction and verify the validity of the model.  
Figure 3: The structure and diagram of the prediction model based on the ISSA-DELM.
## 1 Introduction
### Related Work
As observed in this work, the approach can maximise the total throughput but at the cost of a less fair throughput distribution. Hence, investigating throughput maximisation while ensuring a fair data rate in a 5G network is important. In this work, we propose a hybrid scheme (ILPSO) for user pairing and power allocation based on integer linear programming (ILP) and particle swarm optimisation (PSO), respectively, to achieve trade-offs between throughput maximisation and fairness. The optimisation problem is formulated by converting multi-objective functions into a single function using the scalarisation of multi-objective optimisation problems (SMOO). ILP is the method proposed to perform the user pairing scheme to reduce the complexity of the resource allocation problem [18]. The optimisation techniques used in PSO are inspired by the behaviours of natural organisms, such as birds flocking and fish schooling [18,19]. Cooperation amongst individual animals assists the groups to which they belong in achieving common objectives, such as sourcing food within an efficient time. The rest of the paper is organised as follows. In Section 2, the NOMA system model is described, and the problem formulation for the objective function is proposed. In Section 3, the user pairing scheme is suggested, and the PSO algorithm used for power allocation is presented. Section 4 describes the simulations for the proposed algorithms. Finally, Section 5 summarises the results.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
###### Abstract  
The quad flat no-lead package (QFN) is widely used in integrated circuits due to its advantages in performance and cost. With the increasing power of electronic products, effective heat dissipation from QFN packages has become crucial to prevent product damage. The focus of this study is to investigate the thermal performance of QFN packages soldered onto printed circuit boards (PCB) by finite element analysis (FEA). Conventional QFN, dual-row QFN, and high-lead QFN packages were modeled and compared by ANSYSY software. The effect of thermal via design (the distance, number, distribution, diameter, and thickness of thermal was) on the QFN package was investigated. The study revealed that the high-lead QFN package consistently demonstrated superior heat dissipation performance than the other two under different conditions. Placing thermal vias closer to the heat source enhances heat dissipation efficiency. Thermal vias positioned beneath the thermal pad were particularly effective. Increasing thermal vias quantity and diameter improved heat dissipation, with square distribution layouts showing advantages. However, excessive copper plating thickness can increase thermal resistance and hinder heat dissipation.  
QFN; finite element simulation; thermal vias; thermal management Article
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 3 Anti-Biofilm Agents to Tackle _P. aeruginosa_
### Analogs of Native Autoinducers
Pharmacomodulations led to the discovery of the derivative **D88 (ABA-34)** based on an \(N\),\(N\)-biaryl malonamide scaffold, which presents very potent anti-PqsR activity (IC\({}_{50}\) = 1.31 uM) [82]. **D88** inhibits 50% of biofilm formation in the PA14 strain at 10 uM, reduces pyocyanin secretion with an IC\({}_{50}\) of 0.53 uM and possesses a more favorable ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profile than its analog **M64** (aqueous solubility of 490 uM in PBS, no cytotoxicity on several eukaryotic cell lines). Its in vivo activity was assessed in monotherapy on a murine burn PA14 infection model. Interestingly, **D88** could inhibit the bacterial dissemination towards intestinal tissues. It seems that **D88** can diminish the morphological alteration of intestinal cells mediated by PA14 infection. This finding represents a significant improvement in the search for treatments against hard-to-treat _P. aeruginosa_ infections associated with gut-deprived sepsis. Therefore, **D88** exhibits anti-QS and anti-virulence properties as potent as **M64**. A molecular docking study indicated a similar binding of **D88** in the autoinduction site of PqsR as **M64**. Notably, **D88** establishes hydrophobic interactions with Leu189 and Ile236 as well as a \(\pi\)-stacking with Tyrz58. QSIs are highly promising ABAs. Moreover, they often possess broad anti-virulence properties including an inhibition of pyocyanin, rhamnoidpid or elastase production, or a reduction in bacterial motility. At first, most research works focused on the development of _pan_-QSI targeting LasR. Hence, some non-analogs of odDHL such as **ABA-21** and **ABA-28** appear very potent, with 50% inhibition of biofilm formation from 10 uM and at 1.6 uM, respectively. Yet, the _pqs_ system has been more and more studied due to its pseudomonal specificity, whereas the _las_ and _rhl_ circuits are AHL-dependent communication networks that can be found in many bacterial species. Among the most promising ABAs, one of the first relevant PqsR inhibitors **M64 (ABA-33)** and its recent derivative **D88 (AAB-34)** showed powerful anti-QS and anti-virulence efficacy thanks to a competitive inhibition of their target. The improvement of **D88**'s biopharmaceutical profile makes it an even more interesting QSI. The AQ analogs **ABA-12** and **ABA-14**, bearing a pyridinone or pyridine core, also display highly promising anti-biofilm efficacies with IC\({}_{50}\) equal to 4.5 and below 0.5 \(\upmu\)M, respectively. Therefore, the understanding of key structural features for ligand-target interaction as well as the development of new in silico tools for ADMET predictions enable the rational design of novel potent molecules. A wise use of such knowledge may still pave the way for further promising discoveries.
## 4 Experiments
### Ablation Experiments
#### 4.3.2 Performance Comparison with Different RL Algorithms  
To verify the effectiveness of PPO used in this experiment, we replaced PPO with other RL algorithms. In this experiment, we did not use the intrinsic reward and surrogate model, and performed comparisons on nine tabular datasets by replacing PPO with advantage actor-critic (A2C), deep deterministic policy gradient (DDPG) [36], soft actor-critic (SAC) [37], and twin delayed DDPG (TD3) [38]. For A2C, DDPG and SAC, we set the hidden size to a range of \(\{32,64,128\}\). For TD3, we set the exploration noise scale to a range of \(\{0.5,1\}\) and the delay update frequency to a range of \(\{3,4,5\}\). The hyperparameters of these RL algorithms were determined by the grid search within their respective ranges, ensuring the final model performance. Table 6 shows the accuracy and search time of the five sets of experiments. We ran each method five times, and trained 200 episodes each time.  
As can be seen from Table 6, the highest accuracy is achieved on most of the datasets using the PPO algorithm, while other algorithms can only achieve the highest accuracy on one data set. DDPG has the lowest accuracy on all datasets. However, in terms of search time, PPO is slower than other algorithms. A2C has a relatively fast search time on small datasets, DDPG and TD3 have a relatively fast search time on large datasets, but none of  
Figure 8: The performance of curiosity-driven method on dataset balance_scale. (**a**) Reward returns for each episode. The green line is the reward return using only the accuracy of the validation set, and the coral line is the reward return using both the intrinsic reward and the accuracy of the validation set. (**b**) The accuracy of the validation set corresponding to the best hyperparameter configuration found by the current episode.  
the corresponding accuracies is the highest. Therefore, for the task of HPO, we believe that PPO is more effective than other RL algorithms.
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 5 Energy-Transfer Characterization of the Pulse (Unstable Oscillatory Regime)
### Stabilization of the Oscillatory Amplitude of the Unstable Regime  
As mentioned before, the amplitude of the \(U_{h_{T}}\) oscillations shows evidence of the presence of a second dissipative mechanism that acts in parallel with the one associated with (the now amplifying) \(\lambda_{1}\). This second dissipative mechanism also seems to be aeroelastic in nature, and it starts acting when the amplitude reaches a certain threshold that depends exclusively on the value of the uniform-stream wind speed, regardless of the nature of the pulse that triggered the oscillations in the first place.  
In the same manner, there is another threshold in the amplitude value at which the rate of energy dissipation associated with the second aeroelastic mechanism equates the rate of energy influx from the first physical mechanism associated with \(\lambda_{1}\) in such a manner that the oscillation amplitude levels out into a constant value. This stabilization threshold also depends exclusively on the wind speed.  
Figure 20 shows an example of the oscillatory signal at 18 m/s, indicating the threshold at which the enveloping curve starts departing from the exponential behavior, \(Dep\_U_{h}\), and  
Figure 19: Comparative plots of \(\lambda_{1}\) versus the mean wind speed for the complete range of wind speeds in the unstable oscillatory regime, which was obtained through a direct fitting on the exponential expansion stage of \(U_{hx}\) from the semi-log plots and from the energy-transfer relation in Equation (11).  
Figure 18: Logarithm of the kinetic energy ratio for different pulses occurring at the same mean wind speed versus the pulse time delay for wind speeds covering the entire unstable regime.  
the threshold at which the amplitude levels out into a stable regime, \(SS\_U_{h}\). It also shows the values of these two thresholds for the whole range of wind speeds corresponding to the unstable regime.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
###### Abstract  
The computational performance requirements of space payloads are constantly increasing, and the redevelopment of space-grade processors requires a significant amount of time and is costly. This study investigates performance evaluation benchmarks for processors designed for various application scenarios. It also constructs benchmark modules and typical space application benchmarks specifically tailored for the space domain. Furthermore, the study systematically evaluates and analyzes the performance of NVIDIA Jetson AGX Xavier platform and Loongson platforms to identify processors that are suitable for space missions. The experimental results of the evaluation demonstrate that Jetson AGX Xavier performs exceptionally well and consumes less power during dense computations. The Loongson platform can achieve 80% of Xavier's performance in certain parallel optimized computations, surpassing Xavier's performance at the expense of higher power consumption.  
embedded GPU; loongson platform; high-performance computing; performance evaluation 202424145[[https://doi.org/10.3390/s24010145](https://doi.org/10.3390/s24010145)]([https://doi.org/10.3390/s24010145](https://doi.org/10.3390/s24010145))
# Solution of Orifice Hollow Cathode Plasma Model Equations by Means of Particle Swarm Optimization
## 2 Plasma Model of Hollow Cathode
### Plasma Model Equations
#### 2.3.1 Keeper  
In the keeper region, the unknowns are the plasma number density (\(n_{e,k}\)), electron temperature (\(T_{e,k}\)) and neutral density (\(n_{n,k}\)), and the equations are the current density balance, Equation (1), the balance of ion flux, Equation (2), and the equation of state, Equation (3).  
\[n_{e,k}=n_{e,o}\frac{A_{o}}{A_{k}}\sqrt{\frac{T_{e,o}}{T_{e,k}}} \tag{1}\] \[q\tau t_{k}^{2}L_{k}n_{n,k}n_{e,k}K_{i}=j_{i,k}(2\pi\tau_{k}L_{ k})+2j_{th,k}\pi t_{k}^{2}\] (2) \[\Big{(}n_{e,k}+n_{n,k}\Big{)}k_{B}T_{w,k}\Big{(}1+\frac{\alpha T _{e,k}}{T_{w,k}}\Big{)}=\frac{\dot{m}}{\pi r_{k}^{2}}\sqrt{\frac{R_{S}T_{w,k} }{\gamma}\Big{(}1+\frac{\alpha T_{e,k}}{T_{w,k}}\Big{)}} \tag{3}\]
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 4 Calculation of TDMP Normalization Factor Based on Density Evolution  
When calculating the check node information, the TDMP algorithm uses the same nonlinear function as BP, which makes the hardware implementation difficult. The simplified algorithm in Reference [31] simplifies the nonlinear function and provides a possibility for hardware implementation. On the basis of the previous analysis, this paper presents the calculation process of the normalized factor of the simplified TDMP algorithm in Reference [31] based on the density evolution theory. The detailed process is as follows.
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Performance Comparison
#### 5.4.3 UAV Throughput  
In this section, we compare the performances of different algorithms in terms of UAV throughput by changing the number of CH nodes and CM nodes in the network. The experimental results are shown in Figure 9. In Figure 9a, we observe that as the number of nodes increases, all algorithms experience a decrease in UAV throughput. However, D-JERDG consistently achieves the highest throughput among the algorithms. Compared with MODDPG and random, the average growth rate of D-JEGDG in terms of UAV throughput  
Figure 8: Experimental results of node data overflow rate.  
is about 79.3% and 43%, respectively. In D-JERDG, the use of a K-means-based clustering algorithm for CH node selection introduces randomness, allowing the UAV to quickly learn the positions of all nodes by obtaining different node coordinates in each training round. This enables D-JERDG to achieve a higher node access count within the same flight time. At 250 nodes, D-JERDG reaches a node access count of 50, indicating that it completes two rounds of CH node traversal. Beyond 300 nodes, D-JERDG throughput reaches a plateau and remains competitive with MODDPG. This can be attributed to the increased number of CH nodes requiring the UAV to spend more time hovering for data collection, leading to longer flight times and a decrease in node access count. In Figure (b)b, we analyze the impact of different UAV flight speeds on throughput with a fixed number of 200 nodes. Overall, compared with MODDPG and random, D-JERDG outperforms MODDPG and random algorithms, and the throughput significantly increases as the flight speed rises. The average growth rate of D-JEGDG in terms of UAV throughput is about 55.3% and 42.4%, respectively.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Overview of Next Generation Networks (5G)
#### 2.4.1 PHY/MAC Layer Design Considerations for 5G
Low-density parity-check (LDPC) codes, turbo codes and polar codes are considered as the most-promising ECC schemes for higher reliability and spectral efficiency enhancement in the upcoming xG networks [66; 67; 68; 69; 70; 71].
## 3 LSTM Model and Forecasting  
Standard RNNs [56] often struggle with long-term dependencies due to issues such as vanishing gradients. LSTM networks, an advanced RNN variant, address these challenges by incorporating memory cells and gating mechanisms that regulate the flow of information over time [33]. Details about the LSTM architecture can be found in the literature [57]. Wechose the LSTM model as the coding example to assess OpenAI frameworks on complex coding tasks.  
The data preprocessing pipeline involves normalizing the input data to a specific range. A sliding window approach is employed to transform raw time series data into fixed-length sequences suitable for input into the LSTM [58]. The model is trained using the backpropagation through time (BPTT) algorithm, optimizing the mean squared error loss function. The trained model is subsequently evaluated on an unseen test set with performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Bias (MB), Mean Absolute Percentage Error (MAPE), Mean Fractional Error (MFE), Mean Fractional Bias (MFB), R\({}^{2}\) score, and Pearson correlation coefficient \(r\)[59; 60; 61].  
The authors developed an LSTM model (LSTM-H) and tested it to identify the optimal settings for forecasting meteorological variables. These settings are presented in Table 1. GPT, OAI1, and OAI3 were instructed to construct an LSTM model based on these settings.  
It is essential to note that the results presented correspond to the final 10% of the meteorological data set, equating to 4821 ten-minute intervals (approximately 33.3 days). Predictions are denormalized to the original scale for interpretability. For all LSTM models utilized, predictions are visualized alongside observations to evaluate performance.
# The Effects of _Anthyllis vulneraria_ Hydroalcoholic Leaf Extract as an Adjuvant in Wound Healing
###### Abstract  
_Anthyllis vulneraria_ is a traditional medicinal plant with confirmed anti-inflammatory properties, attributed to its high polyphenolic content. This study aimed to evaluate the wound-healing potential of _A. vulneraria_ leaf extract in a rat burn model. Four groups of eight Wistar rats each received the following daily topical applications for 14 days: vehicle cream (negative control); silver sulfadiazine (positive control); or plant-based creams containing either 1 mg/cm\({}^{2}\) or 2 mg/cm\({}^{2}\) of polyphenols (experimental groups 1 and 2, respectively). On days 7 and 14, four animals per group were euthanized for histological and oxidative stress evaluations. LC-MS/MS analysis of the leaf extract identified hyperoside, ferulic acid, and \(p\)-cumaric acid as major constituents. Experimental group 1 showed significantly enhanced wound closure on days 5 and 7, while group 2 exhibited a significant effect on day 5. All oxidative stress markers, except catalase activity, differed significantly among the groups, with the most favorable results observed in group 2. IL-8 levels decreased after the extract treatment, while no significant microscopic changes were observed. These results indicate that _A. vulneraria_ leaf extract may serve as a valuable adjuvant in burn wound healing.  
Author's 2020 15 8388 Article