esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Power Adaption for Full-Duplex Relay-Aided Device-to-Device Communication
## 4 Simulation Results
The simulation result also shows that the performance of a D2D achievable rate is significantly improved when \(\gamma_{L}<15\) dB and it also has a faster increase tendency than the benchmark of HD mode, even in the presence of \(\gamma_{L}=15\) dB. The reason for this is that the FD relaying-based D2D scheme could transmit and receive signal simultaneously rather than occupy one of two orthogonal time slots for transmitting and receiving signal respectively. For this reason, the FD relaying based D2D communication scheme inherently has a much higher spectrum efficiency in comparison to HD mode. Figure 4: Optimal transmit power of relay. Figure 3: System performance with different power splitting factor. In contrast to Figure 6, Figure 7 reveals the energy efficiency of our proposed scheme when compared to the traditional HD mode. We can observe that the proposed scheme improves the energy efficiency of HD mode greatly. The reason for this is that the relay node of the HD mode forwards the cellular signal with maximum power usage for D2D relay, namely, \(p_{R}^{*}=1\). However, the optimal  
Figure 5: Optimal power splitting factor of relay. Figure 6: Achievable rate for D2D communication. Figure 7: Energy efficient of D2D communication. power for the FD mode is always less than the maximum power and decreases as the channel gain for the loopback link increases. It also can be seen that the performance of energy efficiency does not improve with the mitigation of the self-interference of the relay. The reason for this result is that, upon efficient mitigation of self-interference of relay, the optimal power at D2D relay is increasing, and as we can see from Figure 4, this ultimately decreases the energy efficiency.
## 3 Results and Discussions
### Quantitative Analysis  
Figure 7 shows the graphical representation of the biofilm concentration between sides A and B (parallel and perpendicular electrodes) after a statistical t-distribution analysis. For each electric potential, three different samples were prepared for the quantitiative analysis, the results representing an average of the measurements carried out on these samples. As can be observed, the overall trend of the graphic suggests the formation of a thicker biofilm on the side of the channel accommodating the perpendicular electrodes, confirming the previously stated positive dielectric properties of the bacterial cells. However, there is a slight variation at the 40 V mark, where the concentration of the biofilm is greater on the B side of the channel. This behaviour needs further investigation and tests.  
Another noticeable aspect is that above the electric potential of 40 V, the \(p\)-value is less than 0.05, indicating a statistically significant difference between the concentrations of biofilms along the two sides of the channel. This is a good indication of the fact that up to that electric potential value, the dielectrophoretic forces' effect on the cells, however existent, is not as impactful as those above this value. Above 40 V, the effect of the dielectrophoretic forces increases, creating a greater difference in biofilm concentration between the two sides. For the side of the channel accommodating the parallel electrode above 50 V, the concentration of the biofilm seems to remain stationary, while the values of the concentration for the B side seem to decrease. This could be an indication of the decreasing distance between the high electric gradient regions along the main channel, as seen in the simulations.  
Certainly, this work has its limitation, one of which is the small size of the samples, with only five samples used for the statistical analysis. Another limitation is given by the timeframe used for incubation. As the amount of biofilm was determined only after the 120 h mark, future research should focus on the influence of the cultivation period on the quantity of biofilm formed inside the microfluidic devices.  
Several limitations are given by the scale of the device and the placement of the electrodes along the central channel. Future work should explore the effects of increasing  
Figure 7: Graphical representation of the biofilm concentration inside the A and B parts of the channel as a function of the applied electric potential difference.  
the length of the microfluidic device, as well as the different possibilities of positioning the parallel electrodes along the main channel in order to alter the distribution of the electric field.  
Another direction for future research could include analysis of the effect of the different electric potentials (in magnitude and uniformity) on the biofilm attachment and formation for several other bacterial types inside 3D printed microfluidic devices.
## 2 Numerical Mean Echo Power Model for SAR Altimetry
### Backscattered Waveform Power Model  
Following [6; 16; 24], the average backscattered power from a flat surface can be obtained by an integral of the echo signal power over all the scatterers on the surface. For a rough surface with roughness characterized by a height probability density \(p(z)\), the average power in the delay-Doppler bin \((t_{r},k)\) is  
\[P_{I}(t_{r},k)=\iiint_{\mathbb{R}^{3}}p(z)\frac{\lambda^{2}G^{2}(\theta)\sigma _{0}}{(4\pi)^{3}R_{0}^{4}L_{p}}PTR\big{(}t_{r}-2(R_{0}-z)/c,k-Nf_{d}T_{r} \big{)}\,\mathrm{d}x\mathrm{d}y\mathrm{d}z \tag{12}\]  
when \(z\ll h\) for \(k=-N/2,\ldots,N/2-1\) and \(0\leq t_{r}\leq T_{r}\). Here, \(\lambda\) denotes the wavelength, \(\sigma_{o}\) is the backscattering cross-section per unit scattering area, and \(L_{p}\) is the two-way propagation loss. The point target response is  
\[PTR(t_{r},k)=|W_{r}(t_{r})W_{a}(k)|^{2}.\]  
In (12), \(G(\theta)\), \(R_{0}\), and \(f_{d}\) all depend on \(x\) and \(y\), while \(\sigma_{0}\) is assumed to be independent of \(x\) and \(y\) for simplify.  
Generally, it is not possible to provide a closed form result for the integration in (12). Nevertheless, a semi-analytic expression can be provided via the convolution model proposed in [16], as shown in the next section.
# Changes in Quality Features of Pork Burgers Prepared with Chokeberry Pomace During Storage
###### Abstract  
This study aimed to evaluate the effect of adding shredded black chokeberry (_Arontia melanocarpa_) pomace on the quality of heat-treated and vacuum-packed pork burgers stored for 14 days at +4 \({}^{\circ}\)C. Four burger treatments--Control (BC) and products with 2%, 3.5%, and 5% chokeberry pomace (B2, B3.5, and B5, respectively)--were analyzed for physicochemical properties (thermal loss, shrinkage, content of selected chemical components, pH, color parameters, and shear force) and microbial quality (aerobic mesophilic microorganisms, psychrotrrophic bacteria, lactic acid bacteria, _Enterobacteriaceae, Pseudomonas_ spp., _Broductivity thermoplastica_, and yeasts and molds). The addition of chokeberry pomace increased (\(p<0.05\)) the thermal loss of pork burgers from 23.5% (BC) to 30.8% (B5) and decreased (\(p<0.05\)) the pH from 6.93 (BC, day 1) to 6.74 (B5, day 14). The introduction of pomace into the pork burgers also significantly (\(p<0.05\)) affected the content of chemical components. However, the nutritional value of pork burgers remained high, with a protein content not lower than 26.68% (BC) and a fat content not exceeding 13.96% (B5). The most affected quality feature of the pork burgers was color. Products B2, B3.5, and B5 exhibited lower L* and b* parameters (\(p<0.05\)) while showing higher a* values. The b* parameter had negative values for products B3.5 and B5 on days 7 and 14. The use of chokeberry pomace did not deteriorate the microbial quality of pork burgers, as indicated by the maximum total count of aerobic mesophilic microorganisms, which reached 4.4 \(\times\) 10\({}^{3}\) cfu/g (B3.5). Moreover, on the final day of storage, moderate antimicrobial properties of chokeberry pomace were observed, with a lower (\(p<0.05\)) number of lactic acid bacteria and _Pseudomonas_ spp. in products B2-B5 compared to BC. The results indicate that incorporating shredded chokeberry pomace into burger-type ground pork products does not present major technological difficulties. However, raw shredded fruit pomace is a perishable microbiological material and requires rapid processing. Further research on the use of chokeberry pomace in burger-type meat products is recommended due to its nutritional value and health-promoting properties. However, this research should include a comprehensive sensory evaluation of the finished product.  
**Keywords:** chokeberry pomace; pork burgers; vacuum packaging; refrigerated storage; meat product quality
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
###### Abstract  
Partial shading conditions reduce the efficiency of photovoltaic (PV) systems by introducing multiple local maxima in the power-voltage curve, complicating Maximum Power Point Tracking (MPPT). Traditional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IC), frequently converge to local maxima, leading to suboptimal power extraction. This study proposes a hybrid reinforcement learning-based MPPT approach that combines fuzzy techniques with Deep Deterministic Policy Gradient (DDPG) to enhance tracking accuracy under partial shading. The method integrates fuzzy membership functions into the actor-critic structure, improving state representation and convergence speed. The proposed algorithm is evaluated in a simulated PV environment under various shading scenarios and benchmarked against conventional Perturb and Observe P&O and IC methods. Experimental results demonstrate that the Fuzzy-DDPG approach outperforms these classical techniques by achieving a higher tracking efficiency of 95%, compared to 85% for P&O and 88% for IC in average, while also minimizing steady-state oscillations. Additionally, the proposed method reduces tracking errors by up to 7.9% compared to conventional MPPT algorithms. These findings indicate that the combination of fuzzy logic and deep reinforcement learning provides a more adaptive and efficient MPPT solution, ensuring improved energy harvesting in dynamically changing conditions.  
maximum power point tracking (MPPT); partial shading; reinforcement learning; deep deterministic policy gradient (DDPG); fuzzy logic; photovoltaic systems; hybrid control; machine learning; energy optimization Article
## 2 Relevant Theoretical Models
### Transformer Feature Fusion  
#### 2.3.1 Attention Mechanism  
The self-attention mechanism is one of the most common attention mechanisms. It correlates the features at different positions in the sequence signal to capture the internal correlations of the data features. The self-attention feature calculates the attention through three parameters (\(Q\), \(K\), \(V\)), where \(Q\) represents the element being queried, \(K\) is the position encoding representing the feature, and \(V\) is each element's value. The specific calculation process of this mechanism is shown in Equation (14):  
\[Self-Attention(Q,K,V)=Softmax(\frac{xW_{Q}(xW_{K})^{T}}{\sqrt{d_{k}}})xW_{V} \tag{14}\]  
where \(x\) represents the input feature and \(d_{k}\) is the input layer dimension.  
The self-attention mechanism's capacity to collect effective characteristics is inferior to the CNN's because it operates on the concept of filtering out significant information and excluding uninteresting information. Only on the basis of extensive datasets can the self-attention mechanism successfully create precise global links. When the dataset is small, its effectiveness is not as good as that of the CNN.  
The multi-head attention mechanism achieves different linear transformations of the same input feature by stacking encoding layers. In particular, \(Q\), \(K\) and \(V\) undergo h linear mappings, each of which takes place simultaneously and produces a distinct output head. Ultimately, to create multi-head attention, all of the output heads are linked and projected. The specific process is shown in Equation (15):  
\[\left\{\begin{aligned} Multihead(Q,K,V)=Concat(head_{1},\dots, head_{h})W^{O}\\ head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V})\end{aligned}\right. \tag{15}\]  
The cross-attention mechanism combines these two features with the same input dimension and divides them into two parts. One part serves as the query matrices \(Q\), whose function is to weight the key values of the other input feature to determine how to aggregate the values of this input feature. The attention weights are computed using the key values, and the values are weighted and summed based on these weights to obtain the final output. Equation (16) illustrates the cross-attention mechanism's computation process:  
\[Cross-Attention(X_{1},X_{2})=(\frac{X_{1}W_{1}^{Q}(X_{2}W_{2}^{K})^{T}}{\sqrt{ d_{k}}}X_{2}W_{2}^{V}) \tag{16}\]
## 3 Results
### Comparison: Averaging vs. Dense Fusion Methods  
For the original O-SMA dataset data, the validation AUC curve (Figures 6 and 7) shows that the averaging fusion had smaller standard deviations compared to the dense fusion.  
For the darkened DO-SMA dataset data, the \"equalize\" SMA technique performed better using the averaging fusion method and demonstrated higher AUC (Figure 10) and accuracy (Figure 11) values in comparison to the AUC (Figure 7) and accuracy (Figure 8) values for the dense fusion method.  
For the other SMA techniques, the differences in the metric values between the fusion methods are in the limits of the standard deviations.  
Regarding the number of model training parameters, the dense fusion model has slightly more parameters (923,862) and a slightly greater size (3.52 MByte) compared to the number of parameters (845,022) and size (3.22 MByte) of the averaging fusion model.
# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 3 Materials and Methods
### Automatic Determination of the Type of Missing Data
This threshold was selected as missingness in MCAR data is entirely random and does not depend on any observed or unobserved data, meaning that the correlation between missing values and other variables would be very low or non-existent, which is reflected by a nullity correlation threshold of 0.1, which would indicate that any observed correlation is likely due to random chance. Figure 4: Nullity correlation scores for each of the three types of missing data as the percentage of missing data increases. Figure 3: Correlation heatmaps of the (**a**) MCAR data, (**b**) MAR data, and (**c**) MNAR data. Each heatmap is a visualization of the correlation at 90% missing data. If the average correlation score is between 0.1 and 0.6, then the missing data is classified as MAR. This is because the missingness is related to observed data but not to the missing data itself, resulting in moderate nullity correlation scores because the missingness can be predicted to some extent using the observed variables. If the average correlation score is above 0.6, then the missing data is classified as MNAR. This threshold indicates a strong correlation, capturing the relationship between the data missingness and the values themselves, which is indicative of MNAR data. We evaluated this method for detecting each of the three types of missing data for missing data values between 1 and 99%. The results of this experiment are shown in Table 3.
# Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques
## 5 Results
### Anti-Evasion Results Based on Individual Malware Samples  
Table 1 shows each of the 40 evasive malware samples, highlighting the anti-evasive methods that caused the malware to be triggered. Where different samples from the same malware family respond under differing conditions (i.e., different samples of the Tiny Banker, also known as the Tinba malware family), we append the last four digits of the MD5 hash to identify the sample. 8 of the 10 malware classifications tested are listed in the evasive malware results. The remaining two classification (Disk Wiper and RAT) were deemed as \"active\" when \"no changes\" to the environment were deployed. Given the nature of a disk wiper attack, the attack is essentially to cause disruption rather than to evade detection, and so it stands to reason that this would execute as intended under any environment configuration. Looking at the results, 12 of the samples triggered with GAU, foue triggered with TJ, 18 triggered with RTT, eight triggered with RegEdit, and two triggered with CAC. While samples may be triggered with further anti-evasive methods used, we show here the minimal required set of methods. Furthermore, there are four samples (Pushdo, Lokibot, IcedID and Adposhel) that required at least two evasive methods to be deployed in order for the malware to be triggered. This illustrates the level of sophistication increasing, since no response was observed when only a single method was utilised. Further research will seek to identify samples that require a combination of multiple anti-evasive checks. Three samples (Neutrino_4929, Gozi_3130 and Strictor_5e9c) were found to trigger under different \"single\" checks: specifically, they were triggered either by GAU or by RegEdit. Given that GAU performs a RegEdit as part of the removal process, we consider the RegEdit aspect to be what provokes the behaviour. Further findings reveal that different malware samples that are part of the same malware family may respond under different anti-evasion methods. For example, the Tinba malware family shows that different samples of the same malware family are conducting different evasion checks. All of the Tinba samples tested were attempting to contact multiple FQDNs (Fully Qualified Domain Names), with each matching the same pattern of 12 seemingly-random alphabetic characters with one of three domain names: 216.218.185.162 (registered to Hurricane Electric), 92.242.132.24 (error handling for Barefruit) and 81.99.162.48 (Virgin Media IP Blocking). It should be noted that two of these cases were the result of malicious traffic being redirected or blocked. These variations of Tinba illustrate the evolution of malware capabilities to naturally extend upon existing examples and require more sophisticated evasion checks.
## 3 Results
### FEM Results  
Detailed numerical analyses were performed for the model proposed in Section 2.1 and static analysis was performed. Here the material data for the mild steel S355J2+N structural steel were taken from the tension test, while the data for CFRP overlays (I700/QY9511) are taken from the literature [46]. In order to compare the effectiveness of different adhesives, the set of simulations was performed for selected adhesives listed in Tables 1 and 2. In this set, the thickness of the adhesive layer was set to \(t\) = 0.6 mm. The symmetric half of the investigated sample was subjected to static tension force \(Fmax\) = 47,500 N. The results of such studies are shown in Figure 15. There, the dependency between maximal stresses in the adhesive layer and the tensile modulus of the adhesive \(E_{all}\) are illustrated. Here, the growth of the maximal stress both in the notch area and in the overlay end is observed. In the case of the notch area, the growth of the tensile stress \(\sigma_{x}\) is the biggest, while in the case of the overlay end, the peeling stress \(\sigma_{z}\) grows the most when \(E_{all}\) increases. Keeping  
Figure 14: Adhesive failure mechanisms for sample with HY 4080 GY (\(N_{f}\)= 322,686 cycles) at interface with (**a**) steel (left part) with damaged adhesive layer, (**b**) magnified zone A, (**c**) steel (right part) with damaged adhesive layer, (**d**) CFRP close to notch and (**e**) magnified zone B. Blue line (marked as C) shows location of the same breakthrough in drawings (**a,c,d**).  
the level of stresses at an acceptable low level demands the application of relatively soft adhesives with a small Young's modulus.  
Following that observation, the detailed results of the numerical analysis are shown in Figures 16-18. Here, the results are given for the DP6310 NS adhesive. Figure 16a shows the overall \(u_{x}\) displacement of the symmetric half of the investigated sample. The illustration of the predominant effects of stress concentration appearing in the notch area is shown in Figure 16b. In this area also, certain plastic deformations are reported. The distribution of them is shown in Figure 17a. In Figure 17b, the tension stresses in the CFRP overlay are presented, and their maximal values are observed in the vicinity of the notch area. Figure 18a,b show the distribution of the peeling stresses \(\sigma_{z}\) in the adhesive and main shearing stresses \(\tau_{xz}\), which are of the main importance in the adhesive static and fatigue destruction process. Their maximal values are close to the admissible ones given in Table 1.  
Figure 16: FE results for specimen with DP 6310 NS: (**a**) distribution of displacements \(u_{x}\) (mm) in tension direction; (**b**) distribution of equivalent von Mises stresses (MPa) in steel core.  
Figure 15: Maximal stresses in adhesive interface in function of tensile modulus (**a**) in surroundings of notch and (**b**) at ends of overlays.
## 4 Results and Analysis
### Performance of the Composites for Reducing Cloud Contamination
#### 4.1.2 Visual Assessment of 8-Day, 16-Day and Monthly Composites  
Figure 5 illustrates the true color browse images that employed red (as red color), green (as green color) and blue (as blue color) band for the 23 scenes of 16-day HJ-1A/B composites. The browse images for 8-day composites and monthly composites are listed in Figures 6 and S1, respectively.  
Obviously, the 16-day composites had few clouds. Land covers such as open water, swamps, peat land, and meadow can be accurately retrieved as their outlines were clearly seen in composites for most dates. Also, the spatial distribution and growth process of vegetation can be evidently elucidated from the composites. The green color in the true color composite image normally represents land surface covered by vegetation. As shown in Figure 5, seasonal variations in the greenness of vegetation were evident, with differences in the timing of growth among swamp meadow, meadow and grassland. Besides, the variation trends of wetland inundation, particularly in and around the peat land or open water area were also apparent, where the changes of surface water level were responsible for the combined reflectance effects of mixed pixels. The seasonal variation for vegetation and wetland inundation can be clearly seen in both of the 16-day and 8-day time series composites.  
The 8-day composites had relatively more residual clouds than the 16-day composites due to the fewer acquisitions for compositing. Nevertheless, it still showed a good performance in reducing the cloud contamination over the study area.  
Figure 4: Comparison of cloud cover percentage before and after the temporal composition. The grey blocks with the grey bar were the average cloud cover percentage and its maximum and minimum value in the 8-day compositing period. The blue rounds, red diamonds and green triangle points were the residual cloud cover percentage of the 8-day, 16-day and monthly composites, respectively.