esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SET Operation
During the cooling stage, the crystallization proceeds as the temperature drops from melting temperature (Tm) to crystallization temperature (Tc), shown as the dotted line in Figure 9. Compared with the final phase distribution in Figure 7b, only the GST material at the center of the GST-BE interface has a long crystallization time, leading to complete crystallization together with grain growth. For the annular-like temperature distribution in the GST layer, there is an annular crystalline region formed in the PCM cell. The inner part has insufficient time for grain growth, and the GST material in the outer part does not crystallize due to insufficient heating. Hence, the phase change dynamics in the case of the SET operation are more rigorous to the temperature than that of the RESET operation; the GST material near the GST-BE interface requires a high enough temperature and a proper cooling rate for complete crystallization. Figure 8: (**a**) Variations of the temperature and (**b**) heating/cooling rate at two different positions in the GST layer. A at the position of the hot spot in the GST layer and B at the center of the GST–BE interface which marked in Figure 7a, respectively. Figure 9: Temperature variation between the A and B points in the cooling stage, the arrow indicates the five points from A to B in Figure 7a. The impact of thermoelectric effects on the SET operation is now discussed in this paper. Figure 10a shows the temperature profiles of the cells at the end of the SET pulse with and without thermoelectric effects. Similarly, under the positive current polarity, the thermoelectric effects improve the heating efficiency to bring the GST materials up to a higher temperature with the same current pulse. Because of releasing thermal energy at the GST-BE interface, the Peltier effect expands the temperature distribution vertically, which results in a vertically broader high-conductive current path formation and then contacts the top and bottom electrodes more easily, as shown in Figure 10b. Therefore, the contribution of Peltier heat to the programming current reduction (17%) is larger than that of Thomson heat (11%) with the same pulse width of 150 ns.
## 2 Materials and Methods
### Ethographic Evaluation  
All patients underwent standardized bedside ultrasound assessment using Lumify(Philips Iberica, Madrid, Espana) and Omega(Esaote Espana S.A.U., Barcelona, Spain) devices, equipped with both phased-array and convex probes. Two trained physicians performed image acquisition after a calibration phase using ten representative cases, achieving interobserver agreement with a kappa coefficient of 0.80 (see Supplementary Materials).  
LVEF was obtained from a standard transthoracic echocardiogram performed either during admission or within the previous 12 months. Patients were categorized as having preserved LVEF (HFpEF, \(\geq\)50%) or reduced LVEF (HFrEF, \(<\)50%).  
Lung ultrasound was performed to assess B-lines in eight thoracic quadrants. Pleural effusion and ascites were evaluated via abdominal ultrasound. [IVC diameter was measured approximately 2 cm from its junction with the right atrium, and inspiratory collapsibility was recorded. A collapsibility \(>\) 50% was considered normal.].  
The VExUS protocol was applied in patients with an IVC diameter \(\geq\) 2 cm. It included a Doppler analysis of the hepatic, portal, and intrarenal veins:  
* In the hepatic veins, waveforms were classified as normal, mildly, or severely altered based on the S/D wave ratio and flow direction.
* Portal vein pulsatility was categorized as \(<\)30%, 30-50%, or \(>\)50%, with higher pulsatility indicating greater congestion.
* Intrarenal venous flow was categorized as continuous, biphasic, or monophasic, with monophasic indicating severe congestion.  
Based on these findings, a global VExUS score was assigned, classifying patients into four grades: grade 0 (no congestion), grade 1 (mild), grade 2 (moderate), and grade 3 (severe). For statistical analysis, this classification was dichotomized into low (grades 0 and 1) versus high (grades 2 and 3) systemic venous congestion.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 5 New Materials and Techniques
Given the potential of artificial neural networks to save time and reduce costs in solving various engineering problems, a study was carried out to estimate the abrasive wear of concrete with varying cement contents and hematite aggregate replacements under different applied loads. The study found that artificial neural networks (ANNs) provide better predictions of concrete's abrasive wear than general linear models (GLMs), with hematite content and applied load being the most influential factors. The ANN model showed higher accuracy in predicting wear, outperforming the GLM in key statistical measures, with the authors suggesting that it can be used to estimate concrete's abrasive wear [82]. A similar study was conducted to explore the use of artificial neural networks (ANNs) and random forests (RFs) to predict the abrasion resistance of cementitious mortars modified with granite powder and fly ash. By feeding mortar ingredients into a neural network, the outcome was predicted prior to mixing. The model performed well in predictions and has accuracy comparable to other models in the field. Fly ash and granite powder do not degrade the material's abrasion resistance, which is advantageous for creating environmentally friendly cementitious composites [83]. [PERSON] et al. [81] carried out a study to investigate the abrasion resistance of self-consolidating concrete (SCC) by comparing the performance of artificial neural networks (ANNs) against traditional regression models in estimating abrasion depth. The research involved creating twelve SCC mixtures with varying proportions and measuring their abrasion depths over time. The ANN models generally outperformed the regression models, particularly when testing samples fell within the mixture proportions and abrasion depths used for training. Another study was carried out to explore the use of multi-output machine learning models to predict the abrasion resistance of construction materials. The study focused on simultaneously predicting Los Angeles (LA) and Micro-Deval Abrasion (MDA) values using linear regression (LR), Huber, RANSAC, and other techniques. The models were trained and validated using a dataset of 229 data points from rock samples. The findings showed that the LR model achieved the highest predictive accuracy. According to the study, when simple models like linear regression are matched with the properties of the data, their performance is on par with or better than more sophisticated models [84]. Similar results were obtained in another study [85] carried out to develop a predictive model for the depth of wear in cementitious composites modified with high calcium fly ash, where the ANN model performed with a high degree of accuracy when compared to the experimental results from similar studies.
## 2 Background  
The EAFZ constitutes a significant tectonic feature that delineates the boundary between the Anatolian and Arabian tectonic plates (see Figure 1a). It is a 700 km-long left-lateral strike-slip fault system that starts in the northeastern Karlova triple junction and joins the NAFZ [28]. It continues southwestward to the Mediterranean Sea, ultimately connecting with the Dead Sea Fault [4, 29, 30]. [PERSON] et al. used seismic databases from 2006 to 2016 to examine the stress tensor within the Marmara area surrounding the NAFZ in Turkey [31]. The findings showed that the region's tectonic system was defined by normal and strike-slip faults, with tensile stress present throughout [31]. Using seismic databases from 1976 to 2024, [PERSON] et al. investigated the stress tensor in the area around the EAFZ in Turkey and demonstrated that strike-slip and reversal faults are prevalent in this area [32]. Despite its historically lower seismicity relative to the NAFZ, the EAFZ possesses the capacity to generate significant earthquakes [33, 34, 35]. The Arabian Plate's northward movement toward the Eurasian Plate affects the EAFZ's overall tectonic structure [30, 36]. The Anatolian microplate is encouraged to extrude westward by the compressional pressures created by this tectonic action [28, 37]. Consequently, the tectonic dynamics associated with the EAFZ render it a significant subject of investigation within the fields of seismology and geophysics [38, 39, 40, 41, 42].  
Eight earthquakes (\(\geq\)6 \(M\)w) have occurred in the vicinity of the EAFZ in the last 40 years (Figure 1b). The southern portion of the EAFZ experienced a 7.7 \(M\)w earthquake on 6 February 2023, at 04:17 local time [5]. Then, at 13:24 local time, another 7.6 \(M\)w earthquake took place, with the distance of the two epicenters approximately 100 km apart [5]. The first large earthquake ruptured from its epicenter in a northeast-to-southwest direction, while the second ruptured in an east-west direction. The earthquake rupture zone crossed the border between Turkiye and Syria, causing devastating damage in both countries [6]. This devastating double quake also triggered two major aftershocks of 6.6 \(M\)w and 6.4 \(M\)w. Four earthquakes occurring in such proximity produced more catastrophic damage than the previous four earthquakes greater than 6 \(M\)w.
## 3 SNR Evaluation Based on Proposed TP-CNN
### Proposed TP-CNN Model  
In the proposed model, the original signal data are fed into two separate convolution modules with different structures and parameters, and the two modules are parallel and uncorrelated. After extracting the features using different convolution kernels, the features are fused. The fused feature vectors are obtained by adding the feature vectors from the two neural networks using the concat method. Then, all the features are concatenated and fed into the fully connected layer for linear transformation. Based on the investigating of baseline methods, the performance of the single-way convolution neural network with a convolution kernel size of 7 \(\times\) 7 is worse than that with a convolution kernel size of 3 \(\times\) 3, the results are still acceptable and the F1 values are better than those with a convolution kernel size of \(5\times 5\). Therefore, the multi-features can be extracted as an aid to the original model to obtain better results. The above analysis shows the structure of the proposed Two-Pathway CNN(TP-CNN) in Figure 3. The pseudo code of the proposed algorithm is shown in Algorithm 1.  
```
0: UAV remote control signal.
0: Classification result \(\hat{y}\).
1: Obtain frequency hopping signal based on UAV frequency hopping communication system.
2: Transform the signal dimension from one-dimension to two-dimension based on signal time domain diagram method.
3: Pre-process the two-dimension data and obtain the dataset \(D\).
4: Train the image dataset at batch-size \(B\) based on proposed TP-CNN model.
5: Calculate the MAE and MSE error according to predicted value \(\hat{y}\) and true value \(y\).
```  
**Algorithm 1** Proposed SNR evaluation algorithm.  
Figure 3: Two-path convolution neural network signal-to-noise ratio evaluation model.  
Figure 2: Two methods for feature fusion.  
Figure 4 shows the work process. Based on the location in the schematic, the model is divided into two layers, both of which use the same input and 128 \(\times\) 128 data size, and use the maximum pooling method with a convolution kernel shift step of 1. The upper layer consists of a convolution layer and a pooling layer, with the convolution kernel size set to 7 \(\times\) 7, which extracts larger features. The lower layer consists of a convolution layer and a pooling layer, with the convolution kernel size set to 3 \(\times\) 3, which is mainly used for extracting smaller and more subtle features. The networks with different convolution kernels are fused by padding the edges of the image to ensure that the feature maps are of the same size.  
Figure 5 shows an example of a 12 \(\times\) 12 input size with edge fill. The blank in the center indicates the input size. In order to fuse the features, the input data needs to be filled. The blue part of the figure indicates the fill. In our experiments, we have chosen to fill with 0.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Simulation
#### 4.1.2 Performance with Respect to Noise on Point Cloud  
The errors of depth data during LiDAR measurement and corner points during camera measurement are primary error sources in the extrinsic calibration process. In this experiment, we explore the calibration result with respect to noise on point cloud. Zero mean Gaussian noise is added to the direction vector of LiDAR point so as to simulate real measurement. The standard deviation \(\sigma\) of noise is changed from 0.01 m to 0.1 m. For each noise level, 300 independent tests are carried out. The absolute errors of rotation \(e(\textbf{R})\) and translation \(e(T)\) are demonstrated in Figure 4. In the boxplot, the horizontal boundaries of the box represent the first and third quartiles, separated by the red middle line, which is the data's median. And red points represent average absolute errors of each noise level. From Figure 4, we can observe that noise increases almost linearly with the increasing of noise level. Hence, reducing noise error of LiDAR point is an essential way to obtain an accurate calibration result. When \(\sigma=25\) mm, which is the noise level of a normal flash LiDAR, the average error of rotation is about 0.5\({}^{\circ}\) and the average error of translation is about 7.4 mm.
# A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
## 2 Related Work  
### Unsupervised Domain Adaptation  
The source domain is labeled and the target domain is not labeled. The UDA methods focus on solving the target domain without any labels [29; 30; 31; 32]. In order to learn the discriminative features in the target domain, early-stage methods focus on the feature/sample mapping between the source domain and target domain. Some existing solutions [16; 33; 34; 35] reduce the deviation between the source domain and target domain at the image level. The image-to-image conversion from the source domain to the target domain is first performed [22; 36], and then the converted images are used to train the model. In addition, some methods [14; 37; 38; 39] associate two domains with common auxiliary tasks. [PERSON] et al. [14] proposed an attribute consistent framework to achieve UDA on unlabeled target domains by learning the semantics of pedestrian attributes. [PERSON] et al. [37] performed human body segmentation and posed estimation on two domains at the same time to align local features and improve the generalization performance of the corresponding model. Some recently published solutions [9; 23; 24; 25] recognize the importance of mining discriminative features in the target domain. [PERSON] et al. [9] first used the features of source domain as references to measure whether the image features of the target domain are similar. Then, soft labels were assigned to unlabeled pedestrian identities to explore potential pairwise relationships. Finally, contrast loss was used to reinforce the relationships. [PERSON] et al. [23] introduced the concept of memory to store the intermediate features of the target data during the training process. Three invariance constraints (named as sample invariance, camera invariance and neighborhood invariance) were imposed on target domain samples to reduce the influence of changes in the corresponding domain. [PERSON] et al. [25] proposed an adaptive exploration (AE) method. In the target domain, the distance between all pedestrians is maximized, and the distance between similar pedestrians is minimized. [PERSON] et al. [24] proposed a patch-based unsupervised framework to learn discriminative features from image patches instead of the entire image. At present, many methods [40; 41; 42] first adopt pseudo-label estimation schemes, then use some clustering algorithms to label target samples, and finally train the corresponding model accordingly. The above operations are repeated until the trained model converges, which results in high computation costs.
## 4 Discussion
### Potential Application for Postural Control Training and Rehabilitation
Clinically, this threshold for postural control breakdown could be tested using balance assessments with varying instability levels, allowing practitioners to identify specific deficits in sensory or motor function and design personalized rehabilitation programs that progressively challenge and improve postural stability.
## 3 Method
### Problem Definition  
In the actual educational data mining, we often encounter the situation of small data scale and lack of data features and need to manually mark them. However, due to the data privacy protection, in many cases, the data owners can not carry out collaborative training. To solve this problem, we simulated a federated training scenario between Party A and Party B.  
Consider two data owners, a source domain \(D_{A}=\{(x_{i}^{A},y_{i}^{A},z^{A})_{i=1}^{n}\}\) where \(x_{i}^{A}\in\mathbb{R}^{n}\), \(y_{i}^{A}\in\{0,1\}\) is the \(i\)th label and \(z^{A}=1\) is the domain label, and a target domain \(D_{B}=\{(x_{i}^{B},z^{B})_{i=1}^{n^{\prime}}\}\) where \(x_{i}^{B}\in\mathbb{R}^{n^{\prime}}\), \(z^{B}=0\) is the domain label. \(n,n^{\prime}\) represents the number of samples of the source domain and target domain, respectively. The total number of samples is \(N=n+n^{\prime}\).  
In our definition, Party A and Party B represent two platforms with educational data. Among them, Party A has complete data, label information, and class labels. Party A is the leading party in federated learning and the owner of the source domain data in federated transfer learning. Party B only has some data and class labels and has no data labels. Play the role of the customer in federal migration learning. Party A and Party B use the knowledge gained from federal migration learning to build a model and predict their users.  
Our goal is to establish a transfer learning model, accurately predict the category of students of Party B, and improve the classification effect of Party A. At the same time, their data will not be exposed. In this paper, domain adaptation is introduced into federated migration learning, which minimizes and maximizes the classification errors of the source domain, and then exchanges knowledge between the source domain and the target domain. Then, this federated learning problem can be formulated as follows:  
\[\underset{\mathbf{w}\in\mathbb{R}^{N}}{\text{minimize}}\mathcal{L}_{1}(\mathbf{w},D)= \frac{1}{N}\sum_{i=1}^{N}f(\mathbf{w};x_{i},y_{i}) \tag{1}\]  
Figure 1: Structure diagram of a neural network in the experiment.  
\[\underset{\mathbf{w}\in\mathbb{R}^{N}}{\text{maximize}}\mathcal{L}_{2}(\mathbf{w},D)= \sum_{i=1}^{N}\gamma(\mathbf{w};x_{i},z_{i}) \tag{2}\]  
where \(\mathbf{w}\) is the parameter of the model, \(f\) represents the loss function of the label classifier, and \(\gamma\) represents the loss function of the domain classifier.  
Our goal is to exchange only intermediate parameters to achieve the following goals on privacy protection:  
\[\underset{\mathbf{w}\in\mathbb{R}^{N}}{\text{minimize}}\mathcal{L}=\mathcal{L}_{1 }(\mathbf{w},D)-\mathcal{L}_{2}(\mathbf{w},D) \tag{3}\]
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion  
The following sections discuss the experimental results obtained from this study. First, a brief description of the independent variables and an overview of the participants are provided. Next, an objective system performance analysis is given. In the last section, the subjective results are discussed in terms of the perception of the network, the completion time, the subjective perception, and the occurrence of cybersickness symptoms.
# Safe Three-Dimensional Assembly Line Design for Robots Based on Combined Multiobjective Approach
## 2 Modeling of Assembly Line Layouts Based on the Safety Indicator
### Modeling of Safe Layout
#### 2.2.4 Objective Functions  
A multiobjective layout model is established with the optimization goals of minimizing the logistical cost LC and the actual floor space \(\alpha\). In multiobjective optimization, the optimization effect is best when the minimum values are simultaneously considered. Equations (3) and (4) show the logistics cost and layout area. M\({}_{i}\) is the number of pieces of equipment. n is a large amount of the equipment. In the planning period t from M\({}_{i}\) to M\({}_{j}\), C\({}_{ij}\) is the unit material handling cost per unit distance, D\({}_{ij}\) is the distance, and P\({}_{ij}\) is the demand amount.  
\[\text{Min}LC=\min\sum_{t=1}^{t}\sum_{i=1}^{n}\sum_{j=1}^{n}\left(C_{ij}P_{ij}D_ {ij}\right)\]  
\[D_{ij}=\left|x_{i}-x_{j}\right|+\left|y_{i}-y_{j}\right| \tag{3}\]  
\[\text{Min}\alpha=\sum_{i=0}^{n}L_{d}\times W_{d}\]  
\[L_{d}=\left(L_{r}+L_{l}\right)/2+\left(x_{r}-x_{l}\right)\]  
\[W_{d}=\left(W_{r}+W_{l}\right)/2+\left(y_{r}-y_{l}\right) \tag{4}\]
# mPrivacy: A Privacy Policy Engine and Safeguard Mechanism in Mobile Devices
## 5 Implementation and Evaluation
### Attack Example  
To show information escalation attack and test our protection mechanism, we made an information escalation attack on location. Many researchers made k-anonymity algorithms to protect the user's location privacy [40]. The user's location information is provided as a cloaking region. Our attack aims to escalate the cloaking region using access point (AP) information.  
Figure 4 shows the IEG of this attack. The wireless network has a certain reachable region and unique identification information, which can reveal the geographic information of the network. We define this external information in the graph as AP coverage information. In practice, the AP coverage information can be manually collected as [41]. It is used together with the wireless network connection information from the user's mobile device to detect the AP region, where the user is staying. Then the AP region and the cloaking regions can be drawn on the same map to detect the overlapping region, and hence, we can gain the smaller region. Both AP region detection and overlapping detection processes are kept running from time to time.  
To apply this attack, we have one mobile device, which provides a location blurring service for privacy and one malicious application that can access wireless network information. We pre-define the AP coverage information for the malicious application to utilize. The attack has two cases depending on the contents of the AP coverage information.  
The Wi-Fi signal has various signal strengths in different regions. As in Figure 5, the Wi-Fi signal is full in a circle with a radius of 10 m. The Wi-Fi reachable region is a circle with a radius of 30 m. The overlapping region in the left part of the figure is \(\leqslant\) 21.71 square meters. In the right figure is the overlapping region \(\leqslant\) 740.92 square meters. The rates of escalated regions compared to the cloaking regions are 21.71/2827.43 \(\times\) 1000 \(\approx\) 7.68 and 740.92/2827.43 \(\times\) 1000 \(\approx\) 262.05. We can escalate the cloaking region into a relatively small region with the full signal strength region, but with the weak signal region, a rough region.