esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Face Identification  
The accuracy of the one-shot identification at rank 1 and rank 5 was evaluated using the minimum loss models in Section 4.3. Models trained with the SOTA learning method, the triplet loss learning method and the ArcFace learning method included the L2-norm layer at the end, and models trained with the proposed learning method did not. From the 580 testing set for 100 dogs, one image was randomly selected for each object to configure 100 sub-training sets, and the remaining 480 images were configured as sub-testing sets. Rank 1 and rank 5 were extracted by comparing the distance between the embedding vectors of one sub-testing data and all sub-training data. The accuracy of the extracted rank 1 and rank 5 was calculated and compared. The experiment was repeated 1000 times. Table 4 represents the experiment results. As a result, the mean of the rank 1 and rank 5 of the model trained with the SOTA learning method was 10.96% and 32.58%, respectively. The mean of the rank 1 and rank 5 of the model trained with the triplet loss learning method was 37.52% and 65.84%, respectively, and this accuracy was improved by 2.22% and 2.96% to 39.74% and 68.80%, respectively, with the model trained using the proposed learning method combined with triplet loss. The model trained with the proposed learning method combined with triplet loss obtained the highest mean, maximum, and minimum accuracy for both rank 1 and rank 5.
## 4 Organizational Experimental Setup
### Training  
For training the model, we used a system configuration with the following parameters:System Specification:  
Processor: AMD Ryzen 7 5800X (AMD, Santa Clara, CA, USA).  
GPU: Nvidia RTX 3070 (NVIDIA, Sunnyvale, CA, USA).  
RAM: 16.00 GB.  
Operating System: 64-bit Windows 11.  
All the implementations in this work were performed in Python 3 and MATLAB R2023a for the analysis of the results.  
Training is conducted using a \"minibatch\" size of 100 entries. TensorFlow's Adam optimizer is employed, utilizing its default learning rate, typically 0.001. We do not set a specific learning rate or control its reduction over time, allowing [PERSON], [PERSON], and [PERSON] to adapt to changes in each other's parameters and hopefully achieve a robust solution.  
Adam, which stands for \"Adaptive Moment Estimation\", combines the ideas of two other optimization algorithms: Momentum and RMSprop [18]. It maintains a moving average of both the gradients (to get the momentum-like effect) and the squared gradients (to get the RMSprop-like effect) [19] as shown in Equation (6).  
Given  
\[\begin{gathered} g_{t}=\text{gradient at timestep}\\ m^{t}=\text{moving average of the gradient}\\ v_{t}=\text{moving average of the squared gradient}\\ \beta_{1},\ \beta_{2}=\text{decay rates}\ (typically\ \text{close to}\ 1)\\ \alpha=\text{learning rate}\\ \in=\text{small constant to avoid division by zero}\end{gathered} \tag{6}\]  
The updates are computed as  
1. \(m_{t}=\beta_{1}\cdot m_{t-1}+(1-\beta_{1})\cdot g_{t}\to\text{Moving average of the gradient}\).
2. \(v_{t}=\beta_{2}\cdot v_{t-1}+(1-\beta_{2})\cdot g_{t}^{2}\to\text{Moving average of the squared gradient}\).
3. \(\hat{m_{t}}=\frac{m_{t}}{1-\beta_{1}^{2}}\to\text{Bias-corrected moving average of the gradient}\).
4. \(\hat{v_{t}}=\frac{\hat{v_{t}}}{1-\beta_{1}^{2}}\to\text{corrected moving average of the squared gradient}\).
5. Weight update: \(\theta_{t+1}=\theta_{t}-\alpha\cdot\frac{\hat{m_{t}}}{\sqrt{\hat{v_{t}}+\hat{c}}}\).  
The training process updates both [PERSON] and [PERSON]'s models and [PERSON]'s model once per training step. Unlike other approaches, this lack of a specified ratio between the training of [PERSON] and [PERSON] does not afford any computational advantage to [PERSON].  
The different experimental framework is an exploration into the promising realm of adversarial neural cryptography. Unlike traditional cryptographic algorithms that rely on mathematical theorems and principles [20], this setup explores the ability of neural networks to evolve and form secure communication channels on their own.
## 3 Wettability Alteration
### Injection of Carbonated Water  
Other studies focus on the role of CO2 in altering rock wettability. Research indicates that the introduction of CO2 into water can reduce oil wettability and increase oil recovery in carbonate reservoirs [65; 66; 67]. [PERSON] et al. demonstrated that the addition of CO2 to water reduces the contact angle in limestone, shifting wettability from oil-wet to water-wet, which positively impacts oil recovery (see Figure 18) [65].  
When carbonated water is injected into carbonate reservoir rocks, it can alter the wettability of the rock surface, shifting it toward a more water-wet state. This mechanism of wettability alteration has been shown to enhance oil recovery by increasing oil permeability [65].  
Figure 17: The effect of concentrations of ionic liquid [C12 mim]+[Cl]– on the wettability angle of the oil–rock–seawater system at 25 \({}^{\circ}\)C for (a) brine-saturated and (b) oil-saturated rocks [62].  
Studies on surface complexation modeling have shown that during the injection of carbonated water, wettability shifts toward more water-wet conditions, ultimately affecting the overall wettability of the system [68]. Understanding and managing wettability changes resulting from carbonated water injection can significantly impact oil recovery processes in carbonate reservoirs.  
[PERSON] et al. investigated the alteration of sandstone wettability using supercritical CO\({}_{2}\) silicification and demonstrated its ability to modify the wettability of the pore space in reservoir formations [69]. Studies show that silicification using scCO\({}_{2}\) can provide similar or more effective chemical surface coatings compared to traditional silicification methods, due to the enhanced mass transfer and diffusion rates characteristic of this approach. This improved surface coating leads to a more significant wettability alteration of the rock, which is assessed through contact angle measurements [69].  
[PERSON] et al. investigated the mechanisms of wettability alteration in carbonate reservoirs using contact angle measurements and imbibition experiments [70]. They demonstrated the effectiveness of carbonated water in enhancing oil recovery through wettability alteration. They also emphasized the importance of ion management in smart water injection for controlling the mechanisms of wettability change in carbonate rocks.
## 3 Challenges in Terahertz-Based FSO Communication
### Manufacturing Challenges in THz Communication
#### 3.6.2 THz Transmitter and Receiver Limitations  
Efficiently generating and detecting THz signals remains one of the biggest hurdles in THz communication. Current THz sources, such as photonic-based quantum cascade lasers (QCLs) and Schottky diode-based mixers, exhibit low power conversion efficiency and require cryogenic cooling to achieve optimal performance. Similarly, THz detectors, including bolometers and high-electron-mobility transistors (HEMTs), lack the sensitivity and dynamic range needed for high-speed communication. Achieving room-temperature operation with high efficiency remains a key manufacturing challenge, requiring innovations in semiconductor processing and metamaterial engineering [40].
## 4 Discussion
### Analysis Regarding Devices Working with a Fixed Input Power to ARCM
#### 4.1.2 Local 10g-SAR  
The obtained 10g-SAR values displayed much smaller differences than the antenna gain, as shown in Figure 6. Up to 15% and 35% differences in the 10g-SAR values (normalized to the APD exposure scenario with the anatomical [A] chest case) within all exposure scenarios were found for the APD and CWED exposure scenarios, respectively.  
The obtained 10g-SAR value in the simplified [S] model of the chest in CWED (ARCM 2 mm away from the body) exposure scenario was 5.70 W/kg, which corresponds to the value reported by [PERSON] et al., who obtained values of 5.41 and 5.07 W/kg for exposure scenarios with antennas located 1 mm and 3 mm from the body, respectively [7]. Similarly, the 10g-SAR value in the case of the CWED scenario with the simplified [S] model of an arm (5.35 W/kg) also corresponds to values reported by [PERSON] et al. (5.51 W/kg) [11]. In addition, the observed distribution of 10g-SAR values in exposure scenarios with the forearm, arm, and chest models is consistent with the results reported by [PERSON] et al. (the absolute values from their studies cannot be directly compared with our results, due to different distances between the antenna and the human body: i.e., 0 mm or 2 mm in our study, vs. 0 mm or 5 mm) [10].  
The differences in the 10g-SAR values obtained for exposure scenarios using various ARCM locations on (APD) or near (CWED) the body were found to be within the standard uncertainty of their determination (\(\pm\)(20-25)%). This suggests that compliance with the SAR limits obtained for the ARCM at any considered locations may also be considered representative for all of the locations covered by this study.
## 4 Markov Models for Adequacy Evaluation
### Markov Model of the Island's Load  
Considering an island \(j\) with \(L_{j}\) LPs, the model of an equivalent load is obtained by combining, and then quantizing, the historical data for power demand at each LP. To this end, the sampling start time of the historical data is indicated as \(T_{0}\), and the sampling period as \(\Delta\)\(T\). The equivalent load before quantization, \(D_{j}^{E,BQ}(p)\), is the overall power demand in the island \(j\) at the time slot \(p\), and is calculated as:  
\[D_{j}^{E,BQ}(p)=\sum_{i=1}^{L_{j}}D_{i}(p), \tag{4}\]  
where \(D_{i}(p)\) is the power absorbed at the LP \(i\) at the generic sampling instant \(T_{0}+p\,\Delta T\) (that is, at slot \(p\)). Such an approach enables one to account for correlations (e.g., hourly, seasonal, and so on) among power demands.  
Since the equivalent load exhibits continuous values, it is discretized through a uniform quantization, as shown in Figure 3. Let \(D_{j}^{E}(p)\) be the quantized process of the power demand in the island. By indicating the quantization interval for the island \(j\) as \(\Delta L_{j}\), the number of quantization levels used for the generic island \(j\) as \(N_{D,j}\), and the quantized values as \(d_{j}^{l}\), with \(l=1,\,2,\,...,\,N_{D,j}\), the sampled power demand assumes the value \(d_{j}^{l}\) if:  
\[D_{j}^{E,BQ}(p)\in\left[d_{j}^{l}-\frac{\Delta L_{j}}{2},d_{j}^{l}+\frac{ \Delta L_{j}}{2}\right[. \tag{5}\]  
Figure 3: Quantization of the power demand.  
In order to determine the model of the equivalent load, it is necessary to obtain the \(N_{D,j}\times N_{D,j}\) transition probabilities matrix \(P^{(D_{j})}\). Its generic element is defined as the probability that \(D_{j}^{E}(p)\) moves from the level \(\alpha\) to the level \(\beta\) in the transition from the time slot \(p\) to the time slot \(p+\ 1\), that is:  
\[P_{\left[\alpha,\ \beta\right]}^{(D_{j})}=\mathrm{Prob}\left\{\left.D_{j}^{E}(p+1) =d_{j}^{\beta}\right|D_{j}^{E}(p)=d_{j}^{\alpha}\right\}. \tag{6}\]  
From the historical data, the probability in Equation (6) can be derived as follows:  
\[P_{\left[\alpha,\beta\right]}^{(D_{j})}=\frac{v_{\alpha->\beta}^{(D_{j})}}{ \sum\limits_{Y=1}^{N_{D,j}}v_{\alpha->\gamma}^{(D_{j})}}. \tag{7}\]  
where \(\
u_{\alpha-\beta}^{(D_{j})}\) is the number of transitions from the level \(\alpha\) to the level \(\beta\) in the time period used to estimate the above probability.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Ablation Experiments
#### 4.4.2 The Impact of Selecting the Regional Range in EGA  
In order to verify the effects of different region selection ratios in EGA on the segmentation effect, we conducted validation experiments, as shown in Table 6, which compares the segmentation performance metrics under five different selected ratios, including DSC and HD95. DSC and HD95 generally improved when the ratio was increased from 0.0 to 0.4 on the meningioma dataset, indicating that appropriate region selection ratios can improve segmentation performance. At a ratio of 0.4, the DSC reaches an optimal value of 89.22 and HD95 reaches an optimal value of 2.88, indicating that the EGA module performs best at this ratio. As the ratio further increases to 0.8, the segmentation performance starts to decrease, the DSC decreases, and HD95 tends to increase, indicating that choosing too high of a ratio may lead to a decrease in segmentation effectiveness. In the BraTS2021 dataset, similar to the meningioma dataset, the DSC reaches its highest value at a ratio of 0.4, and HD95 reaches its minimum value. This indicates that the EGA module is able to better balance the DSC and HD95 metrics at this ratio. The segmentation performance decreases at either too low or too high a ratio.
## 4 Simulation Results and Analysis
### Packets Delivery Ratio  
The Figure 10 indicates the analysis of PDR of the proposed algorithms with the CoDBR and DBR algorithms. The PDR of the CoDMR is highest for significant number of rounds. The reason of ensuring the maximum PDR of CoDMR protocol is tha the cooperation of a single relay node and placement of the minor sino nodes in the network. In the CoDMR scheme each node easily transmits the information to the desire minor sink node and maximum number of packets are reached successfully. Which ensures maximum information received by the major sink node and enhances the PDR. Nodes close to the minor sink node in each region send the information packets directly to it. While the farther nodes send the information via the cooperation of a single relay node. By using this mechanism, the ratio of packets dropped reduces as compared to the counterpart protocols and secures the highest PDR. The PDR of the DMR scheme is also better than the DBR because the DMR algorithm confronts the lowest depth node burden problem faced by the DBR scheme. Initially, the PDR value for CoDBR and CoDMR schemes starts from 1 due to cooperation, while DBR and DMR protocol starts from 0.5 due to non-cooperation, as cooperation brings reliability in information delivery. The CoDBR protocol has greatest PDR value than the other protocols till round 100. The reason behind this achievement is that the CoDBR protocol forwards data packet in cooperative manner and two relay nodes are contribute with the destination node. At the beginning, there is less chance of packet dropped. However, after round 100, the contribution of two relays consume maximum energy and nodes die swiftly as compared to CoDMR protocol. In CoDMR protocol, after round 100, the accomplishment of the reliable PDR is due to the fact of energy balancing technique. Which make nodes alive for long span of time and hold a reliable PDR till the last round. While the reason for enhanced PDR in the DMR scheme compared to DBR scheme is due to the avoidance of long transmission path.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 2 Materials and Methods
### Instrumentation
#### 2.2.5 Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS)  
During the cleaning, the ablated paint material was collected using a system that combined a micro-aspirator and quartz tissue CELSUS(r). Under pyrolysis conditions, the quartz fabric is inert and does not release any organic contaminants. Therefore, Py-GC/MS analyses were performed directly on the fluffy tissue with ablated material trapped on it, and paint pyrolysis markers were detected and identified.  
A micro-furnace Multi-Shot Pyrolyzer EGA/Py-3030D (Frontier Lab, Koriyama, Japan) coupled to a GC/MS system was used. Samples were placed into a stainless-steel cup inserted into the micro-furnace. The pyrolysis temperature was set at 500 \({}^{\circ}\)C for 12 s, the interface temperature was 320 \({}^{\circ}\)C, and the temperature of the GC injector was maintained at 250 \({}^{\circ}\)C. The GC was an 8860 GC System (Agilent Technologies, Sta. Clara, CA, USA) gas chromatograph with a methylphenyl-polysiloxane cross-linked 5% phenyl methyl silicone (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) capillary column. Helium was used as the carrier gas (1.0 mL/min), and the split ratio was 1/20 of the total flow. The mass spectrometer coupled to the GC apparatus was a 5977B Mass Selective Detector (Agilent Technologies, Sta. Clara, CA, USA). The mass spectra were recorded under electron impact at 70 eV, and a scan range of 45-800 m/z. The following temperature program was used for the gas chromatographic separation: isotherm of 2 min at 50 \({}^{\circ}\)C and ramp of 10 \({}^{\circ}\)C/min up to 300 \({}^{\circ}\)C. All instruments were controlled by Agilent MassHunter Workstation (ver. 10.1.49) software. The mass spectra assignment was performed by mass library searches and by comparison with the literature data.
# Ensemble-Based Correction for Anomalous Diffusion Exponent Estimation in Single-Particle Tracking
## 2 Materials and Methods
### TEA-MSD for Correction of TA-MSD Estimates  
Studies [10; 13; 14; 15] have shown that for short trajectories, estimates of the anomalous exponent \(\alpha\) obtained by the TA-MSD method for fBm trajectories exhibit systematic bias. These works demonstrate that for small trajectory lengths, the estimate \(\hat{\alpha}\) can be systematically overestimated or underestimated depending on the true \(\alpha\) and method parameters. The bias arises from the limited number of points available for averaging, correlations in fBm, and the sensitivity of logarithmic regression to statistical fluctuations.  
For an ensemble of trajectories with short lengths, averaging the per-trajectory estimates \(\hat{\alpha}\) over the ensemble and comparing this result with the true value \(\alpha\) used in generation reveals systematic underestimation, especially for large values of \(\alpha\).  
To address this problem, we calculate the diffusion exponent estimate for the entire ensemble by using the TEA-MSD method [29], which includes averaging over both ensemble and time, providing a single estimate for the entire ensemble. The time-ensemble averaged mean square displacement TEAMSD(\(\tau\)) is defined as  
\[TEAMSD(\tau)=\frac{1}{M}\cdot\sum_{i=1}^{M}TAMSD_{i}(\tau), \tag{16}\]  
where M is the number of trajectories in the ensemble. Estimation of the anomalous diffusion exponent is achieved by creating a log-log plot of TEAMSD(\(\tau\)) versus time lag \(\tau\). The slope of this line estimates the anomalous diffusion exponent, \(\hat{\alpha}_{TEAMSD}\).  
Studies [13; 15] have shown that TEA-MSD, with a sufficient number of trajectories, provides significantly less bias than TA-MSD, because ensemble averaging reduces the influence of fluctuations. Thus, the estimate obtained using (16) corresponds quite closely to the true value of \(\alpha\), even for short trajectories.  
In this case, we can use the obtained ensemble estimate \(\hat{\alpha}_{TEAMSD}\) to refine the per-trajectory estimates \(\hat{\alpha}_{i}\), obtaining the following modified formula:  
\[\hat{\alpha}_{i}^{corrected}=\hat{\alpha}_{TEAMSD}+(\hat{\alpha}_{i}-\overline{ \mu}_{a})\sqrt{\frac{\partial_{total}^{2}-\sigma_{TAMSD}^{2}}{\partial_{total }^{2}}} \tag{17}\]  
This approach maintains simplicity, eliminates the need for additional model training, and significantly improves the reliability of the TA-MSD method used for analyzing ensembles of short particle trajectories in complex systems.
# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 3 Machine-Learning-Based Methodology for AE Testing
### Clustering Techniques
The authors used wavelet transforms as a descriptor and fuzzy C-means technology to distinguish sample damage mechanisms. Three damages were detected: matrix break, fiber-matrix detachment, and fiber break. [PERSON] et al. [43] detected interlaminar and intralaminar damage induced by dentation in laminated carbon-epoxy composites with AE and six different clustering methods including fuzzy C-means. [PERSON] et al. [44] estimated the leakage rate of a valve in a pipeline using various clustering techniques including fuzzy C-means using AE signals. [PERSON] et al. [45] detected damage in fiber-reinforced polymer (FRP) bars by applying a fuzzy c-means clustering algorithm to AE signals. [PERSON] et al. [46] identified the damage in the mixed-mode delamination of laminated composites using fuzzy clustering and the acoustic emission technique. [PERSON] et al. [47] investigated damage mechanisms in an open-hole carbon-epoxy laminate composite using wavelet packet transform and fuzzy C-means methods. [PERSON] et al. [48] detected failure of carbon-glass epoxy hybrid braided composites under tensile load based on acoustic emission signals and a fuzzy c-means algorithm. [PERSON] et al. [49] have adopted fuzzy c-means to detect damage in fiber-resin composite structures that are closely related to fiber weaving methods (FWMs). [PERSON] et al. [50] identified the progressive tensile damage of carbon fiber composites reinforced with multiwalled carbon nanotubes using AE and fuzzy c-means algorithms. [PERSON] et al. [51] adopted an unsupervised approach for detecting plastic deformation, crack initiation, and corrosion cracking. The authors proposed a simple but effective non-iterative clustering algorithm (adaptive sequential k-means), oriented to acoustic emissions (AEs). The number of clusters is not specified a priori but deduced from the data, whereas the properties of the background noise control the creation of new clusters. The approach adopted has proved effective in grouping the AE signals associated with different emission sources (performance indexes = 0.776, 0.751). In Table 1, the essential characteristics of the methodology based on clustering are summarized. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Input** & **Type** & **Accuracy (\%)** \\ \hline [PERSON] et al. [38] & AE from pulse, pencil, and spark & Fuzzy c-means & 80–93 \\ [PERSON] et al. [39] & AE from & Fuzzy c-means & 90–94 \\ Mş