esa-sceva/satcom-chunk-collection huggingface dataset:
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 3 Experiments
### Object Tracking and Region Extraction  
To assess the efficacy of region extraction in mitigating computational demands, we executed a series of experiments on an edge computing framework. These experiments were designed to facilitate a comparative analysis by performing inference on identical segments of video surveillance footage, thereby evaluating the inference latency across varying window dimensions. Our edge computing platform is RK3399 Pro, which integrates an NPU dedicated to neural network computing. Our investigation delineates the computational process into two primary segments: neural network inference conducted by the NPU and post-processing. Furthermore, we conducted evaluations to ascertain the impact of varying pruning rates on model performance using DepGraph [23].  
Table 1 demonstrates that the utilization of ROI extraction techniques can effectively reduce the inference time of the model. A reduction in the detection window size leads to decreased inference time. Furthermore, there is an increase in post-processing time when using a detection window, which can be attributed to the need to manage a larger number of duplicate targets with high confidence levels, particularly when these targets are nearby. Additionally, a reduction in the size of the detection window is associated with a decrease in detection accuracy, which in turn results in the loss of targets.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pruning Ratio** & **Window Size** & **Average Inference Time** & **Average Post-Processing Time** & **Number of Target Losses** \\ \hline  & - & 327.43 & 4.54 & 0 \\
0\% & 128 & 162.76 & 8.50 & 18 \\
0\% & 96 & 135.22 & 5.94 & 17 \\  & 64 & 111.32 & 3.58 & 20 \\ \hline  & - & 247.35 & 4.90 & 0 \\
50\% & 128 & 128.77 & 7.18 & 9 \\  & 96 & 104.66 & 5.36 & 6 \\  & 64 & 83.06 & 3.02 & 7 \\ \hline  & - & 242.01 & 4.87 & 6 \\
62.5\% & 128 & 118.63 & 6.70 & 4 \\  & 96 & 107.70 & 5.62 & 34 \\  & 64 & 80.66 & 2.83 & 7 \\ \hline  & - & 220.15 & 4.70 & 0 \\
75\% & 128 & 122.24 & 5.50 & 28 \\  & 96 & 93.36 & 3.36 & 15 \\  & 64 & 58.20 & 0.91 & 48 \\ \hline \end{tabular}
\end{table}
Table 1: Detection time consumption under different window sizes.  
Figure 13: Loss components and performance metrics.
## 3 Methodology  
In this study, we proposed a predictive optimal control mechanism of indoor temperature for energy sustainability, indoor comfort, and operational efficiency. For this purpose, two primary AI models have been utilized. The first one is an LSTM-based model designed to predict future temperature changes using historical sensor data. LSTM (Long Short-Term Memory), a specialized type of RNN, effectively addresses the long-term dependency issues of conventional RNNs, accurately capturing essential temporal patterns even from extended time-series data. The second model employs DQN (Deep Q-Network), a reinforcement learning algorithm, to determine optimal HVAC control parameters. The DQN model learns state-action value functions, deriving optimal control conditions to minimize energy consumption while maintaining user comfort. Sensor data collected in real time were stored in InfluxDB, a time-series database. Additionally, an external dataset consisting of 8761 hourly average temperature measurements from the experimental area in Cheonan, provided by the Korea Meteorological Administration for January to December 2023, was analyzed. The sensors used in the system exhibit error margins of \(\pm\)0.2 \({}^{\circ}\)C for temperature, \(\pm\)3% RH for humidity, and \(\pm\)1% for voltage and current measurements.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis
#### 4.5.1 Feature Arrangement Sensitivity Test  
CNNs applied to non-natural image data may exhibit spatial sensitivity depending on the arrangement of features. To investigate this, we conducted an ablation experiment comparing three arrangement strategies in a fixed order (baseline):  
* Features are mapped to the 5 \(\times\) 5 matrix using a consistent row-wise order, based on their sequence in Table 2.
* Random Order: For each training epoch, the feature-to-pixel mapping is randomly permuted.
* Correlation-Based Order: Features are ordered via hierarchical clustering based on Pearson correlation, grouping similar features spatially.  
Each experiment used the same architecture and identical training settings. The results are summarized in Table 4.  
These results suggest that the model is indeed sensitive to the spatial layout of input features. The performance degradation under random arrangements validates the necessity of a consistent feature mapping strategy. Moreover, correlation-based arrangements yield results comparable to the baseline.
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 5 Conclusions  
In this paper, we propose a method for using UAVs to perform beamforming for secure communication and target sensing in the presence of multiple eavesdroppers and multiple communication users. Through the joint optimization of the UAV transmit beamforming and position, this approach minimizes the maximum eavesdropping SINR while simultaneously ensuring the required minimum SINR for user communication and the necessary beam pattern gain for target sensing. In a multi-user scenario, a SDR-based algorithm is employed to address the non-convex optimization challenge associated with transmit beamforming. The method involves using slack variables to solve the MM problem and employing the Dinkelbach algorithm to address the fractional problem in the communication SINR. For the UAV positioning problem, a SCA algorithm that utilizes a first-order Taylor expansion is employed to convert the non-convex problem into a convex one. Simulation results confirm that the proposed algorithm facilitates a versatile trade-off among communication quality, target sensing quality, and system security performance. These results also demonstrate that UAV multi-antenna beamforming significantly enhances the PLS of ISAC systems. How to extend the results to other scenarios (e.g., 1. increasing the number of users; 2. incorporating mobile users and eavesdroppers; 3. multiple UAV collaboration; and 4. complex channel models) is interesting and worth pursuing in future work.  
Conceptualization: C.C. and Q.Z.; methodology: C.C.; software: C.C.; validation: C.C. and Q.Z.; formal analysis: C.C.; investigation: C.C.; resources: Q.Z.; data curation: C.C.; writing: C.C.; visualization: C.C.; supervision: Q.Z.; project administration: Q.Z.; funding acquisition: Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Jiangsu Provincial Key Research and Development Program (BE2022068-2), the National Natural Science Foundation of China (92367302).  
Not applicable.  
Data Availability Statement: Data are contained within the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Materials  
For the experiments, disks of pure magnesium were prepared from magnesium rod (99.99% purity) purchased from Oxymet, Krakow, Poland. The discs had a diameter of 1.8 cm and thickness of 4 mm. Before the experiments, all disks were purified using an ultrasonic bath and polished using sandpaper. All of the reagents, including Na\({}_{2}\)SiO\({}_{3}\), KOH, NaOH, and NH\({}_{4}\)F, were purchased from Warchem, Zakret, Poland. Lysine and L-aspartic acid were purchased from PolAura, Dywity, Poland. Simulated Body Fluid (SBF) in an amount of 1 L was prepared by dissolving 8.035 g of NaCl, 0.355 g of NaHCO\({}_{3}\), 0.225 g of KCl, 0.231 g of KH\({}_{2}\)PO\({}_{4}\), 0.311 g of MgCl\({}_{2}\):6H\({}_{2}\)O, 0.292 g of CaCl\({}_{2}\):2H\({}_{2}\)O, and 0.072 g of Na\({}_{2}\)SO\({}_{4}\) in approximately 900 mL of distilled water. To stabilize the solution and maintain physiological conditions, 6.118 g of Tris(hydroxymethyl)aminomethane (TRIS) buffer was added, and the pH was adjusted to 7.4 using 1 M HCl. The solution was then diluted to a final volume of 1 L with distilled water and filtered through a 0.22 \(\upmu\)m membrane to ensure sterility. A cell culture medium was prepared using Dulbecco's Modified Eagle Medium (DMEM) with high glucose content and phenol red. To prevent microbial contamination, antibiotic/antimycotin solution was added (1% concentration in the final volume), as well as fetal bovine serum (FBS), to provide nutrients (10% final concentration). All reagents, together with DMEM without phenol red, XTT assay (Roche), phosphate buffer solution (PBS), and trypsin/EDTA solution, were purchased from SigmaAldrich, Lublin, Poland. The cytotoxicity study was carried out on an MG-63 osteosarcoma cell line purchased from SigmaAldrich, Lublin, Poland, dedicated to scientific research only, and obtained from the European Collection of Authenticated Cell Cultures (ECACC).
# Hydrodynamic Modelling in a Mediterranean Coastal Lagoon--The Case of the Stagnone Lagoon, Marsala
## 3 Results and Discussion
### Calibration with 2006 Data
The Val_06 station velocities, referring to the area in the northern part of the lagoon, appears to be the worst described by the model, probably due to the presence of damping effects caused by leafy vegetation. The lowest NSE values are recorded by the northernmost station, where the ratio between vegetation height and water depth is lower than the southern area. The damping \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NSE** & **h—Depth** & **Vy Velocity** & **Vx Velocity** \\ \hline Vec\_06 & 0.77 & 0.13 & \(-\)2.63 \\ \hline Adv\_06 & & 0.67 & \(-\)18.37 \\ \hline Val\_06 & 0.50 & \(-\)0.62 & \(-\)3.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the NSE value for the simulation with Ks = 20 m\({}^{1/3}\)/s, reproducing the 2006 campaign. Figure 12: NSE values obtained by the comparison of Vec_06 (**a**), Adv_06 (**b**), Val_06 (**c**), and simulated ones, depending on bed roughness. All the Ks values are in m\({}^{1/3}\)/s. effect of the vegetation in hydrodynamics is well known [26]. The flow over a vegetated bed is characterized by friction velocities depending mainly on the Reynolds number, canopy density, and canopy height [27]. From the comparison of the NSE values for level and velocity, it was possible to figure out the behaviour of the model in response to changes in the bottom roughness. In fact, the simulation with the smallest Gauckler-Strickler coefficients returns, generally, the highest values of NSE. Considering the magnitude of the variation in terms of NSE, a constant Ks equal to 20 \(\frac{\text{m}^{\frac{1}{3}}}{\text{s}}\) appears to represent the behaviour of the lagoon. To estimate the influence of wind forces, considering a homogeneous Ks = 20 \(\frac{\text{m}^{\frac{1}{3}}}{\text{s}}\), several simulations with different values of the wind friction factor \(f\) have been run. It is possible to observe how the wind is important in the simulations. For example, when \(f\) = 0, the NSE's values for Vy (Figure 13b) remain around zero, showing a poor reliability of the model if wind is neglected. A wind friction coefficient close to \(f\) = 0.001-0.002 returns modelled values closer to the recorded ones, as shown on Figure 13.  
Different eddy viscosity values were tested, and the Smagorinsky formulation was also tested. But no significant effects are reported in the simulation values due to the changes in the eddy viscosity parameter, showing the negligible effect that this parameter plays in this context, probably because the velocity divergence between mesh element is irrelevant. In lagoon areas such as the Stagnone, more attention must be given to parameters such as the wind friction factors and the bed roughness.
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 4 Covariance in Meta-Analysis
### Fixed-Effect Meta-Analysis for Correlation Coefficient \(r\)  
To conduct a meta-analysis for correlation coefficients, [PERSON]'s \(z\)-transformation is first applied, expressed as  
\[z_{i}=0.5\times ln(\frac{1+r_{i}}{1-r_{i}}) \tag{15}\]  
The variance of \(z_{i}\) is calculated as  
\[v_{i}=\frac{1}{n_{i}-3} \tag{16}\]  
where \(n_{i}\) represents the valid sample size for each cohort.  
The meta-analysis for \(z_{i}\) follows the same procedure as for other statistics, such as \(\beta_{i}\). Once the overall \(z\) value is obtained, it is transformed back to \(r\) using the equation  
\[r=\frac{e^{2z}-1}{e^{2z}+1} \tag{17}\]  
For the fixed-effect meta-analysis of \(z_{i}\), the weight is given by  
\[w_{i}=\frac{1}{v_{i}} \tag{18}\]  
where \(v_{i}\) is variance of \(z_{i}\), which is \(\frac{1}{n_{i}-3}\).  
The overall Fisher \(Z\) is calculated as  
\[Z_{overall}=\frac{\sum_{i=1}^{k}w_{i}z_{i}}{\sum_{i=1}^{k}w_{i}} \tag{19}\]  
where \(k\) is the total number of studies included in the meta-analysis.  
All above calculations are for fixed-effect meta-analysis. Then, we can transform \(Z_{overall}\) back to \(r_{overall}\) using Equation (17).
## 5 The INS-Aided Acquisition Experiments with Different Grade INS
### Simulation Scenario Design  
In order to confirm the performance of the INS-aided acquisition under highly dynamic conditions, a highly dynamic simulation scenario is designed. The trajectory includes acceleration, uniform motion, turning and climbing. The trajectory parameters, which include the acceleration and angular rate in the body frame, are listed in Table 2. The initial latitude, longitude and altitude of the trajectory is [39.977886\({}^{\circ}\), 116.343400\({}^{\circ}\), 10,000 m], and the initial velocity is [0 m/s, 200 m/s, 0 m/s]. The highly dynamic trajectory according to the designed scenario is depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Motion State**} & \multirow{2}{*}{**Time (s)**} & \multicolumn{4}{c}{**Angular Rate (\({}^{\circ}\)/s)**} & \multicolumn{4}{c}{**Acceleration (m/s\({}^{2}\))**} \\ \cline{3-8}  & & \(x\) **Axis** & \(y\) **Axis** & \(z\) **Axis** & \(x\) **Axis** & \(y\) **Axis** & \(z\) **Axis** \\ \hline Acceleration & 10 & 0 & 0 & 0 & 0 & 100 & 0 \\ Uniform motion & 30 & 0 & 0 & 0 & 0 & 0 & 0 \\ Turning right & 10 & 0 & 0 & 9 & \(-1.57\) & 0 & 0 \\ Uniform motion & 20 & 0 & 0 & 0 & 0 & 0 & 0 \\ Turning left & 10 & 0 & 0 & \(-9\) & 1.57 & 0 & 0 \\ Uniform motion & 30 & 0 & 0 & 0 & 0 & 0 & 0 \\ Climbing & 10 & 10 & 0 & 0 & 0 & 0 & 1.74 \\ Uniform motion & 20 & 0 & 0 & 0 & 0 & 0 & 0 \\ Yielding & 10 & \(-10\) & 0 & 0 & 0 & 0 & \(-1.74\) \\ Uniform motion & 30 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The trajectory parameters of simulation in high dynamic applications.  
Figure 8: The three-dimensional high dynamic trajectory in the geodetic frame.  
Figure 7: The RF frontend designed based on a MAX2769.
## 2 Materials and Methods
### Implementation of Intervention and CEPs  
Each CEP was coded with a number and assigned for a condition. Seventeen different programs were designed for thoracic kyphosis (1), lumbar lordosis (2), right thoracic scoliosis (3), left thoracic scoliosis (4), right lumbar scoliosis (5), left lumbar scoliosis (6), anterior pelvic tilt (7), posterior pelvic tilt (8), hamstring stretching (9), hip adductor muscle length--stretching (10), iiiopsoas stretching (11), rectus femoris stretching (12), tensor fasciae lateate stretching (13), hip external rotator stretching (14), hip internal rotator stretching (15), soleus stretching (16) and gastrocnemius stretching (17) (see Supplementary Materials). In addition, for convenience, the program code was referred to the participants by the researchers during the programs' demonstration. Each participant was also coded, and instead of their names, their code was used in each program. Each CEP targeted one condition, assigned a frequency of twice per week, with an illustration of each exercise and the assigned sets and repetitions. In stretching exercises, holding time was assigned in seconds (see below). Prior to the demonstration, the researcher verbally explained the general instructions, all of which also could be found printed behind each program. In scoliosis programs some exercises should be performed unilaterally. Therefore, the letter R was used for right and the L for left side.  
For maximizing muscle flexibility, the static stretching technique was used in the stretching CEPs [30]. The stretching programs were performed at the end of the regular daily training session for improving joint flexibility and range of motion (ROM) [30]. The duration of a static stretch should be less than 60 s to avoid it negatively influencing performance [31]. The static stretching protocol was designed based on [PERSON] (2016) and [PERSON] and [PERSON] (2020) [32; 33]. The start-of-hold duration was 5 s for the first two weeks and progressively increased with 10 s during the third to fifth week and an additional 10 s during the sixth to eighth week. Each player alongside his CEPs also received written relevant general instructions. All the programs were designed to target the muscles influenced by each asymmetry, either with stretching or strengthening exercises or both [25]. The illustrations used for each program were derived from the Exercise Pro Version 5.0 developed by BioEx Systems, Inc.\({}^{\copyright}\), Smithville, TX, USA [34].
# Cyclotomic Trace Codes
## 4 Support Designs and Generalized Block Graphs  
In this section, we describe support designs and generalized block graphs obtained from the codes described in Section 3. In Section 3 we presented SRGs constructed from two-weight codes but in this section we construct combinatorial structures from codes with more weights too. The support of a nonzero vector \(x=(x_{1},...,x_{n})\in F_{q}^{n}\) is the set of indices of its nonzero coordinates, that is, \(\mathrm{supp}(x)=\{i|x_{i}\
eq 0\}\). The support design of a code of length \(n\) for a given nonzero weight \(w\) is the design with points the \(n\) coordinate indices and blocks the supports of all codewords of weight \(w\). From the supports of all codewords of minimum weight of the one-weight codes we obtain the family of symmetric designs (see Reference [28] for more information), known as point-hyperplane designs. Since all linear 1-weight codes are characterized (see References [10; 11] we did not include that family in Table 9, so in Table 9 we give designs obtained as support designs of the codes with more than one weight. **Remark 9**. : _The designs 2-(31,7,7), \(b=155\) and 2-(127,31,155), \(b=2667\) from Table 9 are quasi-symmetric designs with intersection numbers \(\{1,3\}\) and \(\{7,15\}\), respectively (see Reference [29])._  
The following construction of graphs from support designs has been described in Reference [14]. Let \(S=\{|x\cap y|\ x,y\in\mathcal{B}\}\), where \(\mathcal{B}\) is the block set of the support design and let \(A\subset S\). One can define a graph whose vertices are the elements of the block set \(\mathcal{B}\) of the support design and two vertices are adjacent if the size of the intersection of the corresponding blocks is an element of \(A\). This construction is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\boldsymbol{p,q,m}\) & \(\boldsymbol{s}\) & **Code** & **Weight Dist.** & **Corr. Proj. Code** & **SRG** \\ \hline
8,4096, 4 & 3 & \([1365,4,1176]_{8}\) & \(<0,1>,<1176,2730>,<1232,1365>\) & \([195,4,168],d^{\perp}=3\) & (4096,1365,440,462) \\
8,4096, 4 & 5 & \([819,4,672]_{8}\) & \(<0,1>,<672,819>,<728,3276>\) & \([117,4,96],d^{\perp}=3\) & (4096,819,194,156) \\
8,4096, 4 & 9 & \([455,4,392]_{8}\) & \(<0,1>,<392,3640>,<448,455>\) & \([65,4,56]^{*},d^{\perp}=4\) & (4096,455,6,56) \\
8,4096, 4 & 13 & \([315,4,224]_{8}\) & \(<0,1>,<224,315>,<280,3780>\) & \([45,4,32],d^{\perp}=3\) & (4096,315,74,20) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Two-weight trace codes over \(GF(8)\).
## 3 Methodology
### Long Short-Term Memory (LSTM)  
Recurrent Neural Network (RNN) is widely adopted in predicting sequence data. However, problems such as vanishing/exploding gradients may occur when RNN models are used to model long-time dependencies in time-series data [41]. Derivatives of the neural network will be multiplied together during layers. If the derivatives are small, then the gradient will decrease exponentially; if the derivatives are large, then the gradient will increase exponentially. These two cases will cause the vanishing/exploding gradient problem. As an extension to the basic RNN, LSTM captures long-range dependencies and can well solve vanishing/exploding gradient problems [42]. The most important modification is that LSTM imports one cell state and adds three gates at each recurrent unit. These structures help the model capture long-term relationships.  
Figure 1 shows the updated cell (\(c_{t}\)) in the LSTM model. The notations in Figure 1 are explained by following equations.  
\[f_{t}=\sigma\left(W_{f}[h_{t-1},x_{t}]+b_{f}\right) \tag{1}\]  
\[u_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{3}\]  
\[\widetilde{c}_{t}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{4}\]  
\[c_{t}=f_{t}\odot c_{t-1}+u_{t}\odot\widetilde{c}_{t} \tag{5}\]  
\[h_{t}=o_{t}\odot tanh(c_{t}) \tag{6}\]  
where \(h_{t-1}\) represents the previous hidden state and \(x_{t}\) represents the current time step input. \(f_{t},u_{t},o_{t}\) refer to the forget gate, update gate, and output gate, respectively. \(\widetilde{c}_{t}\) represents the candidate cell state used to update the cell state. \(c_{t}\) is the current cell state, \(c_{t-1}\) represents the previous cell state, and \(h_{t}\) represents the current hidden state. \(W_{f},W_{i},W_{o},W_{c}\) are weighted matrices while \(b_{f},b_{i},b_{o},b_{c}\) are bias terms. All these parameters are trainable. \(\odot\) represents the Hadamard product. \(\sigma\) is the sigmoid function, and \(tanh\) is the TanHyperbolic function. The two non-linear functions are defined in the following equations (more details related to LSTM can be found in [42]).  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{7}\]  
\[tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{8}\]