esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Chronic respiratory diseases are a leading cause of lung-related death worldwide. The vital factors causing lung pathogenesis include consistent exposure to tobacco smoke, air pollution, and occupational risks. Regarding the significant morbidity and mortality linked to lung pathogenesis, there are neither conclusive treatments nor wholly preventive strategies. In the present study, the protective mechanism of Ajwa date extract (ADE), on Benzopyrene [B(a)P]-induced lung injury in animal models was investigated using antioxidant, lipid peroxidation, anti-inflammatory activities, angiogenesis, histopathological studies, and apoptosis assays. B(a)P treatment significantly decreased the level of antioxidant enzymes such as catalase (Cat) (13.4 vs. 24.7 U/mg protein), Superoxide dismutase (SOD) (38.5 vs. 65.7 U/mg protein), Glutathione peroxidase (_GPx_) (42.4 vs. 57.3 U/mg protein) and total antioxidant capacity (TAC) (49.8 vs. 98.7 nM) as compared to the treatment group (_p_ < 0.05). B(a)P treatment led to increased expression of pro-inflammatory markers such as TNF-\(\alpha\) (88.5 vs. 72.6 pg/mL), IFN-\(\gamma\) (4.86 vs. 3.56 pg/mL), interleukin-6 (IL-6) (109.6 vs. 85.4 pg/mL) and CRP (1.84 vs. 0.94 ng/mL) as compared to the treatment group (_p_ < 0.05). The data shows a significant increase in lipid peroxidation and angiogenesis factors such as vascular endothelial growth factor (VEGF) by B(a)P treatment (_p_ < 0.05). However, ADE treatment showed an improvement of these factors. In addition, ADE treatment significantly ameliorated histopathological changes, collagen fiber deposition, and expression pattern of VEGF and Bax proteins. Furthermore, the flow cytometry data demonstrated that B(a)P intoxication enhanced the apoptosis ratio, which was significantly improved with ADE treatment. Finally, we may infer that Phyto-constituents of ADE have the potential to protect against B(a)P-induced lung pathogenesis. Therefore, Ajwa dates might be used to develop a possible potent alternative therapy for lung pathogenesis.  
Ajwa dates; lung injury; oxidative stress; inflammation; apoptosis Article
## 3 Method  
### Feature Extraction  
Our methodology employs the Momentum Contrast (MoCo) framework to pre-train the encoder using a substantial corpus of unlabeled data, based on contrastive learning principles. MoCo is notable for its dynamic dictionary mechanism, which significantly improves the efficiency of contrastive learning across large datasets. Essentially, MoCo treats contrastive learning as a dictionary lookup task, where an encoded query is compared against a dictionary of encoded keys to identify a similar key, ensuring it remains distinct from others [33], as illustrated in Figure 1. This architecture facilitates the development of large, coherent dictionaries that facilitate unsupervised representation learning via contrastive loss.  
MoCo begins this process by creating various perspectives of images via stochastic data augmentation. These perspectives are fed into a neural network that produces L2-normalized query representations, referred to as \(q\). Simultaneously, a momentum-enhanced version of the network processes another set of perspectives to update a dictionary of key representations, known as \(k\)[33]. This dictionary is initially populated randomly and is maintained independently of the mini-batch size using a queuing mechanism where new entries are added and the oldest removed with each training iteration. The stability of the dictionary is ensured by encoding the keys utilizing a momentum-driven moving average of the query encoder, which promotes the generation of consistent representations across different views of the same image.  
Figure 1: Momentum contrast.  
The primary learning objective of MoCo centers on minimizing the InfoNCE loss function [48]:  
\[\mathcal{L}_{q,k+,\{k^{-}\}}=-log\frac{\exp\left(q\cdot k^{+}/\tau\right)}{\exp \left(q\cdot k^{+}/\tau\right)\ +\sum_{k^{-}}\exp\left(q\cdot k^{-}/\tau\right)} \tag{1}\]  
where \(q\) denotes the query representation, \(k^{+}\)  represents a related key, and \(\{k^{-}\}\) denotes the dissimilar keys, with \(\tau\)  being a temperature hyper-parameter. The loss function in question encourages the model to exhibit a significant level of resemblance between the depictions of positive pairs, while simultaneously maintaining a greater degree of dissimilarity between those of negative pairs [33]. Negative keys are queued in MoCo, whereas queries and positive keys are re-encoded in each training batch. A momentum encoder improves representation coherence between current and prior keys, allowing MoCo to divorce the learning process from batch size and negative sample number.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 5 Discussions
### Broader Impacts of the Multi-Layer Encoding Framework on Privacy-Sensitive Domains  
The proposed multi-layer encoding framework will enhance cloud data security, and it will offer broader applicability across domains that require strong privacy measures. Its layered structure will enable fragmentation and encryption of data to provide protection for storage and transmission after extending its usefulness to privacy-centric information systems.  
In text retrieval services, existing methods like dummy query generation and keyword confusion aim to mask user interests. The proposed framework complements such approaches by encoding user queries and access patterns. Further, it protects against profiling and data leaks. For mobile reading platforms and personalized search engines, encoding metadata and user preferences across layers will significantly reduce the risks of unauthorized profiling. Hence, it enhances user anonymity.  
The proposed framework is also aligned with location privacy techniques, where dummy queries obscure real movements in the cloud-IoT environment. Here, multi-layer encoding will mask the real access points and introduce uncertainty in data tracing. In health information services, layered encoding will ensure that sensitive user queries remain confidential, and it will help in meeting the legal privacy standards.  
In IoT and edge-computing environments, the encoding layers are distributed across nodes to ensure that no single device holds complete data. This decentralization technique will increase resistance in localized breaches. Similarly, cloud data retrieval systems benefit from encoding to support role-based access. Further, it reveals data based on user privileges and maintains tight control over information exposure.
## 2 Related Work
### Path-Based Methods  
Path-based methods [8; 24; 25; 26; 27] explicitly explore multi-hop semantic paths connecting users and items to uncover latent interests. For example, RippleNet [7] propagates user preferences along KG paths rooted at items the user has interacted with, while PGPR [28] formulates path selection as a reinforcement learning problem.  
These methods offer strong interpretability, as they reveal why a user might be linked to a given item. However, their effectiveness is hindered by the following:  
* The need for manually crafted meta-paths or domain-specific rules, which limit generalizability across domains.
* High computational overhead, especially as the number of hops increases exponentially.
* A loose coupling between path selection and recommendation objectives, which may lead to suboptimal performance.
## 2 Background Material--Introduction to FL
### Integrating Security and Storage  
Here, we clarify which parts of the process described above deal with security and storage considerations, which are discussed later in the paper. FL prioritizes local data storage on devices (e.g., cellphones, IoT devices) to safeguard privacy and reduce the reliance on centralized servers. This local storage solution is critical in complying with data privacy rules (such as GDPR) and minimizing the risk of exposing sensitive data. However, it poses new issues in controlling storage restrictions on edge devices, which we will describe later. During the local training phase, the FL process uses local storage to compute updates, rather than sending raw data. Security is a critical component during the sharing of model updates. When devices send their locally trained updates back to the central server, privacy and security risks emerge. Malicious entities could intercept the updates or manipulate them to corrupt the global model (via model poisoning or adversarial attacks). Therefore, secure aggregation techniques and differential privacy mechanisms must be integrated into the FL process to ensure that updates cannot be reverse-engineered to expose sensitive data or be compromised by attackers. This security challenge relates directly to the sharing and aggregation stages of the process. In the aggregation phase, the central server aggregates the updates from multiple devices to update the global model. To secure this process, homomorphic encryption and secure multi-party computation (SMPC) are commonly used to ensure that the updates are encrypted and privacy is preserved during aggregation. These measures address both security (protecting the updates from interception) and storage efficiency (since raw data are never transferred, and only model updates are sent, minimizing the storage load on the server).
## 3 Framework
### Bi-Level Optimization Model
The inner model is evaluated using the states \(V\) and \(\theta\) of the two buses connecting line \(km\) at previous time \(t^{-}\). \(p^{pert,loss}_{km}\) and \(Q^{pert,loss}_{km}\) are losses in the line evaluated given the states at time \(t^{-}\) and the current status of system database at time \(t\). In (34)-(37), only one estimated measurement of each type at time \(t^{-}\) is required. The other two can be free to be obtained by the chosen optimization solver. From the previous bi-level model, line parameters can be obtained independently from each other. This allows the system operators to take advantage of parallel computation. In addition, the inner optimization problem is linear in its decision variables. The constraint (33) ensures the optimal solution of parameter values are unique and correspond to the correct physical solution. Hence, any off-the-shelf solver can be used to seek solution. The flowchart of the presented framework is shown in \[g_{km}=g_{km}^{pert}-\Delta g_{km},\quad\forall km\in\mathcal{L}\]2  
As illustrated in Figure 1, from the prospective of SE, the process starts by uploading data of measurements and system model parameters. The SE routine is executed by system operator every often to monitor the grid. The framework presented in Section 3.3 is initialized with a true sample that is free from measurement and parameter errors. This sample is labeled as \(t^{-}\). Then, for a sample \(t\), SE routine is performed. On such, the bi-level model is executed. If SE detects an error, then the presented inner (lower) level model in Section 3.3 is performed to check if the error source is due to measurement model parameters. To do so, current status of the measurement model parameters are sent to the presented model to be executed. After execution, if errors in line parameters are above certain threshold, defined considering a level of confidence, then the corresponding line is updated to the solution obtained by the model in Section 3.3 and SE is executed again. Otherwise, no parameter error is detected [19]. If error is detected after updating measurement model parameters, then the source of this error is due to errors in measurement. In such case, [30] is run. After correcting errors from data at sample \(t\), the base data in the presented model can be updated if the sample \(t\) is trusted by system operator. Considering Figure 1, the contribution of this work towards the WLS SE state-of-the-art process is highlighted with the boxes colored in green.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## Appendix A  
```
% Genetic Algorithm-based PID Parameter Optimization (Minimize Speed Error) % Objective: Optimize PID parameters Kp, Ki, Kd % Clear workspace clear; clc; % Set Genetic Algorithm parameters pop_size = 50; % Population size max_gen = 5; % Maximum generations pc = 0.7; % Crossover probability pm = 0.01; % Mutation probability bounds = [1 200; % Range of Kp 0 10; % Range of Ki 0 10]; % Range of Kd % Initialize population pop = init_population(pop_size, bounds); % Set target speed (can be a constant value or based on model settings) target_speed = 30; % Assume target speed is 30 m/s % Main Genetic Algorithm loop for gen = 1:max_gen % Calculate fitness fitness = zeros(pop_size, 1); for i = 1:pop_size params = pop(i,.); fitness(i) = fitness_function(params, target_speed); % Evaluate individual end % Select individuals selected_pop = selection(pop, fitness); % Crossover operation new_pop = crossover(selected_pop, pc, bounds); % Mutation operation new_pop = mutation(new_pop, pm, bounds); % Update population pop = new_pop; % Output best fitness of current generation [best_fitness, best_index] = min(fitness); best_params = pop(best_index,.); fprintf('Generation: %d, Best Fitness: %.4f, Best Parameters: [Kp = %.4f, Ki = %.4f, Kd = %.4f]\
',...  gen, best_fitness, best_params (1), best_params (2), best_params (3)); end
```  
**Algorithm 1** PID Parameter Optimization Based on Genetic Algorithm
```
% Output final result fprintf('Optimization complete, Best PID Parameters: Kp = %.4f, Ki = %.4f, Kd = %.4f\(\backslash\)n\({}^{\prime}\),...  best_params (1), best_params (2), best_params (3)); %% Initialize Population Function function pop = init_population(pop_size, bounds) num_params = size(bounds, 1); pop = zeros(pop_size, num_params); for i = 1:pop_size for j = 1:num_params  pop(i, j) = bounds(j, 1) + rand \(\times\) (bounds(j, 2) - bounds(j, 1));  end end
```  
end %% Fitness Function (Objective Function - Minimize Speed Error) function fitness = fitness_function(params, target_speed) Kp = params (1); Ki = params (2); Kd = params (3); % Set PID controller parameters assignin('base', 'Kp', Kp); assignin('base', 'Ki', Ki); assignin('base', 'Kd', Kd); % Run Simulink model and get simulation results simOut = sim('vehicl_model_0910_2019b',... 'SimulationMode', 'normal',... 'SaveOutput', 'on',... 'SrcWorkspace', 'current'); % Extract vehicle speed and time data from simOut speed = simOut.vehicle_speed; % Extract vehicle speed time = simOut.tout; % Extract time data % Calculate speed error speed_error = target_speed - speed; % Calculate Integral of Absolute Error (IAE) as fitness function IAE = sum(abs(speed_error).\(\times\) diff([0; time])); % Fitness function (smaller IAE is better) fitness = IAE; end %% Selection Function (Roulette Wheel Selection) function selected_pop = selection(pop, fitness) pop_size = size(pop, 1); fitness_inv = 1./fitness; % Inverse fitness prob = fitness_inv/sum(fitness_inv); % Selection probability cum_prob = cumsum(prob); % Cumulative probability
## 2 Materials and Methods
### EMF Exposure Assessment  
The EMF exposure assessment for Wi-Fi 6 in this study was conducted for both single-user and multi-user scenarios, utilizing the same measurement equipment as described for the DC evaluations. The parameters used for SA measurements in the frequency-domain mode are listed in Table 4. The accuracy of SA measurements is influenced by parameter settings. Notably, because Wi-Fi 6 employs OFDMA with a symbol duration of 12.8 us and a minimum guard interval (GI) of 0.8 us, the SA's SWT was set to (12.8 + 0.8 us) \(\times\) 455 = 6.2 ms, following recommendations by [8], to ensure no underestimation of exposure levels.  
For single-user scenarios, the layout of the UD and AP remained consistent with the configuration detailed in Section 2.2.2 (as shown in Figure 1). The three-axis antenna was placed at three locations:  
1. At the AP: The three-axis antenna's center was placed 35 cm from the AP antennas (as illustrated in Figure 1b);
2. At the UD: The three-axis antenna's center was placed 30 cm from the UD's screen or antennas (as illustrated in Figure 1a);
3. At the center: The three-axis antenna was positioned equidistantly between the AP and UD in the LOS path, as shown in Figure 1c.  
Exposure levels during file transfers at AP bandwidths of 20 MHz, 40 MHz, 80 MHz, and 160 MHz were measured sequentially at each measurement position, as [9] reported that maximum exposure occurs during file transfers. Then we investigated the exposure levels for different UDs, for which the bandwidth of the AP was fixed at 80 MHz. The original UD (laptop) was replaced with a mobile phone, tablet, and UWiA respectively, with the distance between the center of the three-axis antenna and the screens or antennas of these devices remaining at 30 cm, the exposure levels of these UDs were evaluated.  
For the multi-user scenario, Figure 4 shows the layout of each UD and the AP in the laboratory, with the AP set to channel 58 with a bandwidth of 80 MHz. The exposure levels at all UDs, at the center marked by the black squares in Figure 4, and at the AP were assessed. The network application used during the measurements was still file transfer.
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 4 Mobile Element Scheduling Scheme  
After the optimization problem formulation that provides the paths for the \(M\) MoEls, an optimal solution results in feasible paths under the imposed constraints. In order to obtain an iterative extraction of these paths for the \(M\) mobile nodes, it is important to create an MES. Such a scheme accounts for the extraction of consecutive \(M\) paths while satisfying the constraints of the aforementioned optimization problem. In particular, the consecutive visits' interval limit (or expiration time) \(e_{j}\) for each static node-\(j\) is monitored, since this denotes either the criticality of a sensor's stored data or its storage limitation and imposes a priority in visiting for each static node \(j\). The adopted MES scheme [32] was adapted to expand the case of a static WSN for data harvesting purposes by \(M\) MoEls. The structure of the MES scheme is presented in (a) Algorithm 1, in which the problem variables are initialized, and (b) Algorithm 2, which explains the main steps of the overall procedure. Furthermore, Algorithms 3 and 4 are devoted to the calculation of expiration time \(e_{j}\), definition of priority set \(P_{r}\), and the corresponding re-calculation of each static node's profit based on its priority. The last two algorithmic processes take place after every iteration and solution of the optimization problem, and the resulting values are imported as inputs towards the extraction of the next \(M\) MoEl paths. The main Algorithm 2 of the scheme also accounts for the case that the optimization problem cannot find any feasible solution. In this case, lines 15-31 of this algorithm are executed recursively until the optimization problem finds a solution for a subset of nodes that belong to the priority set \(P_{r}\). When a solution is extracted, the initially prioritized nodes, which are not included in the extracted \(M\) paths at the end, are stored as data loss.
## 4 Results
### Site Accuracy Validation  
Figure 9 presents a comparative analysis of the surface temperature data from microwave, thermal infrared, and FMLST images for the years 2017 to 2019. These datasets were validated against site-measured data, revealing their respective performance in surface temperature estimation.  
The scatter plots comparing the microwave data with site data show relatively low R\({}^{2}\) values and higher MAE and RMSE values across the 2017 to 2019 validations, consistent with the lower original resolution of the microwave data. While thermal infrared data was affected by cloud cover, it exhibited higher accuracy in cloud-free conditions, with generally higher R\({}^{2}\) values, indicating its direct effectiveness in surface temperature estimation.  
Building on this, the fused FMLST data, combined through stacked machine learning techniques, demonstrated the best accuracy performance across all three years of validation. Taking the 2019 data as an example, the R\({}^{2}\) value for FMLST reached 0.85, with MAE reduced to 4.71 K and RMSE reduced to 6.51 K. The dense clustering of data points in the density scatter plot highlights the significant effect of FMLST in filling cloud-covered areas and improving surface temperature estimation accuracy.  
Overall, the three years of validation results indicate that the FMLST model can effectively improve surface temperature prediction accuracy, particularly under complex cloud cover conditions. This finding underscores the importance of integrating different remote sensing data sources and provides empirical support for future applications in related fields.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 4 Prototype System
### Battery Sizing  
As the objective was to implement a mobile anti-UAV system, it is necessary to perform the sizing of the battery to be adopted.  
Considering that the adopted Raspberry Pi 3, Arduino Uno and BladeRF work with a voltage of about 5 V, the power supply module was designed so as to supply the same voltage to all these boards. The BladeRF, which was used for generating the jamming and spoofing signals, according to Nuand's official specifications, possesses a maximum consumption of 3644 W. To increase the transmitted power, we also adopted a Nuand XB-300 board. The Nuand XB-300 is an amplifier expansion card that greatly increases the range of the BladeRF. On the receive side, the XB-300 featured a low-noise amplifier (LNA) and a combiner for antenna diversity. The transmit side featured a power amplifier (PA), TRX switch, and a highly accurate ADC for measuring output power of the PA. The PA exhibited a typical gain of 20 dB at 2.45 GHz with a bandwidth of 100 MHz (2.4-2.5 GHz). The BladeRF, combined with the XB-300 module with Tx ON and Rx OFF, exhibited a consumption of 8584 W; consumptions available from the official Nuand specifications.  
As there is no reference amplifier for jammer signal spoofing and GPS amplification, an average value of 1 A was used as a reference. Total instantaneous current consumption was 3.74 A. In Table 2, we summarize the hardware consumption.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Ampere** \\ \hline BladeRF x40 & 0.73 \\ BladeRF x40 + xb300 & 1.72 \\ Arduino Uno & 0.05 \\ Raspberry Pi 3 & 0.24 \\ Power Amplifier (SPF5189Z) (\(\approx\)1.5 GHz) & 1 \\ \hline Total & 3.74 \\ \hline \end{tabular}
\end{table}
Table 2: Hardware current consumption.  
Figure 10: Power amplifier SPF5189Z.  
If we assume a hypothetical military scenario where a soldier works an 8 h shift and the battery is intended to be able to power the system about 20% of the shift time (1.6 h), then we can roughly estimate the required capacity as:  
\[Capacity=autonomy\ (hours)\times consumption\approx\ 6\ \mathrm{Ah} \tag{5}\]
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### Test Model Modeling  
In fault diagnosis, considering the influences of the interferences in the working environment of the sensor, it is difficult to judge the fault class precisely from a single measurement. When multiple measurements on the equipment in the same time interval \(\Delta t^{\prime}\) are obtained, the Gaussian type membership function can be obtained using Equations (15)-(17), which is denoted as:  
\[\mu_{T}\left(x\right):X\rightarrow\left[0,1\right],x\in X. \tag{18}\]  
\(\mu_{T}\) is taken as a test model. Compared with the single measurement and mean value of multiple measurements, it is more comprehensive and objective to reflect the change of the value of equipment fault characteristic parameters.