esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 4 Experiments with a Real-World Dataset  
### Real-World Dataset  
Two real-world datasets (i.e., the Shenzhen and Xi'an datasets) are used in this study to illustrate the effectiveness and superiority of the proposed representation learning framework. Each dataset comprises an urban road network and GPS trajectories. To recognize the fine-grained traffic information of road networks, we divide roads according to intersections and specific distances to form several small-road segments as the basic research units of our representation learning process. Thereafter, the ST-matching algorithm [26] is utilized to match GPS points to the corresponding road segments. Details of the datasets are provided in Tables 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Number of** \\ **Intersections** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Segments** \\ \end{tabular} & **Avg. Length** &
\begin{tabular}{c} **Avg. GPS** \\ **Points** \\ \end{tabular} \\ \hline Shenzhen & 44,124 & 8,651,005 & 61.33 m & 1171.45 \\ Xi’an & 299 & 1607 & 111.20 m & 9987.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the two road network datasets.  
The Shenzhen dataset is a large-scale dataset that comprises over a million road segments. This dataset is used to demonstrate the effectiveness of the proposed method in improving the memory usage and computational efficiency. The Xi'an dataset is a small-scale dataset, and the Shenzhen dataset is used to verify the accuracy advantage of the proposed method in downstream traffic tasks. The study areas of the two datasets and their corresponding road networks are shown in Figure 5.
## 4 Results and Discussion
### Classification Performance  
#### 4.6.1 Classification Efficiency  
Data on escaped and trapped particles could be obtained through numerical simulations. To construct the efficiency curve, the percentage of trapped particles within the total mass of feed particles of the same size was taken into account. This aided in determining the degree of separation corresponding to different guide cones. The results are shown in Figure 16.  
Figure 14: Comparison of air axial velocities at Pi. V\({}_{\alpha 1}\), V\({}_{\alpha 2}\), V\({}_{\alpha 3}\), and V\({}_{\alpha 4}\) are the axial velocities corresponding to the guide cone with \(d\) of 708, 808, 758, and 658 mm. (**a**) Axial velocities at points Pi along the rotor inlet. (**b**) Axial velocities at points Pi near the wall.  
The particle classification efficiency with the different guide cones is shown in Figure 16. These curves depict the same pattern of changes in shape, without a fish-hook effect. CHL3 and CHL4 produced steeper Tromp curves and were expected to yield higher classification efficiency. Conversely, CHL1 was anticipated to provide the lowest classification efficiency. The Tromp curves obtained from the simulations were utilized to calculate the cut sizes (\(d_{50}\)) and the classification sharpness indices (\(K\)) under the different guide cones. The cut size is the diameter of the particles that have a 50% probability of becoming either coarse or fine products. The classification sharpness index (\(K\)) was calculated according to the formula \(K=d_{75}/d_{25}\).  
As shown in Table 2, the bottom diameter of the guide cone was adjusted from 658 mm to 808 mm, resulting in Newton efficiencies of 75.06%, 87.26%, 95.5%, and 94.02%, respectively. The Newton classification efficiency was the highest with a bottom diameter  
Figure 16: Tromp curves of the classifier with different guide cones.  
Figure 15: The solid flow patterns when the reaction of solids in the gas flow is considered (two-way coupling). Snapshots were taken at t = 3 s. Particles are colored according to their diameters.  
of 808 mm (CHL3), while the guide cone with a bottom diameter of 758 mm (CHL1) yielded the lowest classification efficiency. In addition, with CHL1, CHL2, CHL3, and CHL4, the pressure drop (\(\Delta P\)) increased gradually from 1438 Pa to 2450 Pa, while the cut size decreased from 0.33 mm to 0.26 mm (Table 2). The energy consumption is represented by the pressure drop [15, 33, 42], which increased gradually. Additionally, the cut sizes for CHL3 were the smallest (0.26 mm). Moreover, the classification sharpness index (\(K\)) under the different guide cone structures ranged from 1.23 to 1.38, which is excellent regarding the classification process [41]. However, CHL4 (708 mm) provided the smallest \(K\). Therefore, when evaluating the classification performance using these typical classification indices, that of the micron air classifier with a bottom diameter of 808 mm was the best.
## 3 Experimental Results
### RF Beating Note and Timing Jitter
\(1/f^{3}\) FFM and \(1/f\) FPM noises are total masked. Above 250 kHz, the frequency independent shot noise dominates at level of \(-96.5\) dBc/Hz. The integrated timing jitter of 564.8 fs is obtained by using Equation (1) in the frequency range from 35 kHz to 20 MHz, which is contributed to by \(1/f^{2}\) WFF and shot noises. Above 20 MHz, the phase noise is affected by the noise floor level of the ESA, thus limiting the contribution of the high frequency components (>20 MHz) to the timing jitter as described in [42].
# Influence of Chinstrap Stiffness on Cerebrospinal Fluid Dynamics and Brain Stress in Helmet Impacts
For MLB players who have undergone the concussion protocol, batters were noted to have a decline in performance both 1 year after concussion as well as throughout the remainder of their career [16]. Therefore, it is essential to minimize the risk of concussion through the proper protective equipment. Currently, baseball helmets serve as the primary equipment designed to protect the batter's head from injury. While helmets significantly reduce the risk of severe injuries, the existing literature indicates that their protective effect is more pronounced against major traumatic brain injury (TBI) than against concussions [17; 18]. Most helmets are equipped with padding intended to absorb impact and enhance safety; however, they are far from flawless in their design and efficacy. For instance, during the 2009 and 2010 MLB seasons, nearly half of the batters who were struck on the helmet by a pitch subsequently received a concussion diagnosis [4]. Despite the variety of helmet designs available, the differences in their effectiveness at mitigating mild and moderate concussions remain poorly understood. Current research in this area is sparse, outdated, or suffers from significant methodological issues such as poor control quality [19]. A study conducted between the 1975 and 1977 MLB seasons found no significant difference in concussion incidence between helmet types; however, the age of this study and inconsistencies in concussion reporting practices during that time limit its relevance and reliability [20; 21]. Many helmet designs focus on modulating padding to improve protection, yet there is little research exploring alternative methods of impact management. Recent advances in sustainable helmet design, such as the use of cork-based or novel composite materials for micromobility applications, have also been reported [22; 23]. However, studies have largely overlooked the potential role of chinstrap design in redistributing impact forces. Modifying chinstraps to facilitate even force distribution throughout the helmet may reduce the impact transmitted to the skull, thereby offering enhanced protection. Given these gaps in knowledge, our study seeks to address this critical issue by investigating how different chinstrap designs influence the forces exerted on the brain during a helmet impact. While recent finite element (FE) studies of football helmets have included the strap system as a component to improve model realism [24; 25], these works have not systematically explored how varying chinstrap stiffness modulates intracranial stress or helmet stability. By examining the effects of force distribution facilitated through chinstrap modulation, we aim to provide new insights into helmet design that could enhance safety for batters and reduce the incidence of concussions.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 3 The Proposed EEMIP Method
### Messages Types  
The nodes in the ZigBee network use three new messages for communication:  
* _Control message-Offer_--the message includes a number of available priorities. The higher the number, the lower the priority--0 is not an option, 1 is the highest priority, 7 is the lowest priority.
* _Control message-Selection_--the message includes selected priority for data.
* _Data transmission message_--the message contains priority, sensor type, and data.
* _Acknowledgement_--the message in the original ZigBee protocol, sent to inform the end device that data have been received.  
Bitwise distribution of message types is illustrated in Figure 2. Based on the first received bit it is decided whether it is a Control message or Data transmission message. In case of the Control message, the second received bit differentiates Offer and Selection messages.  
Figure 1: Overview of the considered architecture.
## 4 System and Threat Model
### Design Goal  
For the federated learning scenario, our goal is to propose a robust, privacy-preserving framework for federated learning networks that enables data aggregation. There are several goals, as follows:  
* **Privacy-preserving:** In our proposed scheme, the utmost importance is given to safeguarding users' privacy in federated learning. At no point can an attacker access any genuine information regarding the user's data.
* **Verifiability:** Users have the ability to check if the results that the server sends back, after combining all the data, are correct. They can do this by performing a simple calculation. This process is designed to be easy and not too complicated, ensuring that users can confirm the correctness of the server's aggregated results without much cost.
* **Fault tolerance:** Even if some users drop out or fail to upload their local model gradients in time due to various reasons, the federated learning system is still capable of correctly aggregating the gradients from the remaining normally active users to form a global model gradient.
## 4 Simulation and Analysis
### The Impact of FFT Points on Position Detection Accuracy  
It is assumed that the SNR is 31.74 dB and \(\omega\) is 2 mm. Figure 6 shows the relationship between the multi-spot position detection accuracy and \(N\), where \(N=2^{m}\), \(m=5,6,7,\ldots,12\). Figure 5(a) is the relationship when the three spots are located at the same positions. Figure 5(b) is the relative error curve of Figure 5(a). Figure 5(c) is the relationship when the three spots are located at different positions. Figure 5(d) is the relative error curve of Figure 5(c). As can be seen from Figure 5(a),c that the position detection accuracy decreases with the increase in \(N\). The larger \(N\) is, the greater the signal-to-noise ratio gain, which improves the accuracy of position detection. But the impact on the position detection accuracy is smaller when \(N\) increases to 512. The reason for this is that the signal length in this article is 1024. At the same time, the number of FFT points \(N\) also determines the resolution and computational complexity of the spectral analysis. Therefore, the appropriate \(N\) can be selected according to the actual system requirement on the premise of ensuring detection accuracy, such as signal resolution requirements, computational efficiency, and signal length.  
When the number of FFT points is changed, the average relative error of the value by simulation relative to the calculated value by formula is shown in Table 1. As can be seen from Table 1, when the spots are at the same position, the AREs are 5.31%, 5.03%, and 4.49%, respectively. When the spots are at different positions, the AREs are 4.48%, 4.30%, and 5.79%, respectively. This indicates that the simulation value of the position detection accuracy is close to the mathematical model.
## 4 Proposed Hybrid FL-Based Framework
### Discussion on Proposed Framework  
In this section, we present a full review of our proposed framework comprised of the hybrid MABC-SVM with FedMA-based technique for the effective prediction of CVD. Our suggested system model is depicted in Figures 1 and 2. Our suggested system consists of heart disease data-gathering equipment housed within a healthcare facility. Initially, the HSP global model orchestrator distributes a global model to HSP clients. The HSP clients, upon reception of this model, perform classification and optimal feature selection using our proposed MABC-SVM technique, and then perform the local training. The HSP client nodes send their updated local model towards the HSP central orchestrator. A new global model is generated using the FedMA after receiving repeated updates for local models, and it is distributed among the HSP clients. According to our proposed approach, the privacy issues are addressed as all of the CVD data never left the HSP client node, prediction accuracy is raised, and classification mistakes are decreased. Algorithm 1 below shows how our proposed framework functions.  
Figure 1: Overview of proposed hybrid federated learning framework.  
The proposed FedMA-based framework obtains the maximum aposteriori estimate (MAE) using the Bernoulli process described below:  
\[\min_{\{\pi_{li}^{j}\}}\sum_{i=1}^{\mathrm{L}}\sum_{j,l}\min_{\theta_{i}}\pi_{li }^{j}.c(w_{jl})\mathrm{s.t.}\sum_{i}\pi_{li}^{j}=1\forall j,l;\;\sum_{i}\pi_{ li}^{j}=1\forall\;i,\;j. \tag{8}\]  
where the \(w_{jl}\) is \(l\)th neuron of the dataset \(j\) and an appropriate function of similarity is \(c(..)\). The posterior probability of \(c\) (\(w_{jl}\), \(\theta_{i}\)) is computed on the \(j\)th client neuron \(l\) and mean Gaussian \(\theta_{i}\). The total neurons in the federated model can gradually increase in accordance to the sizes of the HSP client models because our suggested inference approach is not reliant on parameters. Our proposed framework is designed for the HSP clients and cloud sites, and it is executed in the following stages:  
1. Stage-I (initial): An initial global model \(\omega\)\({}_{o}\) is disseminated to every HSP client user \(H\)\(C_{N}\). After obtaining this initial model, the HSP client is initiated for initial feature selection using \(X_{i0}\).
2. Stage-II (HSP clients): The client nodes will perform feature selection and classification of each fragmented local data of size \(\beta\) using a hybrid MABC with the RB-SVM technique. The updated weights of the local solution are returned to the HSP global orchestrator from every HSP client.
3. Stage-III (HSP global orchestrator): Upon reception of the weights from every HSP client, it performs the matched averaging and obtains an updated weight \(\omega_{N}\) for the current round of communication.
4. Stage-IV (finalization at HSP global orchestrator): The updated weights \(\omega_{N}\) are computed until there is no evolution in the HSP client models.  
Figure 2: Illustration of model computation at HSP global model orchestrator and user ends.
## 2 Proposed Feed-Forward NN Structure-Based Estimation Methods
### Proposed NN-Based Estimation Method 2: CFNN Structure  
An alternative NN-based model, the cascade forward neural network (CFNN), has been shown to boost model accuracy and learning speed in cases with complicated relationships. CFNN is similar to FFNN, except that the former also includes a weighted link between the input and output layers. The additional connection means that the inputs can directly affect the output, which increases the complexity of the model but also increases its accuracy. Likewise, the CFNN model uses the BP algorithm to update the weights but has a drawback in that each layer of neurons is related to all previous layers of neurons [57]. The proposed CFNN-based modeling method is illustrated in Figure 4. The output of CFNN is expressed as follows:  
\[\hat{g}_{CFNN}=g_{2}\left\{\sum_{o=1}^{l}q_{o}\left[f_{2}\left(\sum_{i=1}^{k}r _{oi}x_{i}+c_{o}^{hid}\right)\right]+\sum_{i=1}^{k}z_{1i}x_{i}+d\right\} \tag{12}\]  
Figure 4: Proposed CFNN-based RD modeling method.  
Figure 3: Early stopping technique.  
Again, the hyperbolic tangent sigmoid and linear functions are usually used as the transfer functions in the hidden and output layers, respectively. In this case, the derived mean and standard deviation functions are expressed as  
\[\hat{\mu}_{\text{CFNN}}(\mathbf{x})=\sum_{o=1}^{l\_mean}\left\{q_{o}^{ mean}\left[\frac{2}{1+exp\left(-2\left(\sum_{i=1}^{k}r_{oi}^{mean}x_{i}+c_{o}^{ mean}\right)\right)}-1\right]\right\}+\sum_{i=1}^{k}z_{1i}^{mean}x_{i}+d_{ mean}^{\text{CFNN}} \tag{13}\] \[\hat{\sigma}_{\text{CFNN}}(\mathbf{x})=\sum_{o=1}^{l\_std}\left\{ q_{o}^{std}\left[\frac{2}{1+exp\left(-2\left(\sum_{i=1}^{k}r_{oi}^{std}x_{i}+c_{o}^{ std}\right)\right)}-1\right]\right\}+\sum_{i=1}^{k}z_{1i}^{std}x_{i}+d_{std}^{ \text{CFNN}} \tag{14}\]  
where \(l\_mean\) and \(l\_std\), \(c_{o}^{mean}\) and \(c_{o}^{std}\), \(d_{mean}^{CFNN}\) and \(d_{std}^{CFNN}\), \(q_{o}^{mean}\) and \(q_{o}^{std}\), \(r_{oi}^{mean}\) and \(r_{oi}^{std}\), \(z_{1i}^{mean}\), and \(z_{1i}^{std}\) denote the number of hidden neurons, bias at hidden node \(o\), bias at the output neuron, weight connecting hidden node \(o\) to the output neuron, weight connecting input factor \(x_{i}\) to hidden node \(o\), and weights directly linking input factor \(x_{i}\) to the output neuron of the CFNN for the mean and standard deviation, respectively.
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 4 Discussion and Conclusions  
Considering that breast tumor screening using DM has some consequences and limitations because a higher number of unnecessary biopsies and ionizing radiation exposure endangers the patient's health [12], along with low specificity and high FP results, which imply higher, recall rates and higher FN results [191]. This is why US is used as the second choice for DM. Thus, US imaging is one of the most effective tools in breast cancer detection because it has been shown to achieve high accuracy in mass detection, classification [38], and diagnosis of abnormalities in dense breasts [192]. For the abovementioned reasons, we have addressed using both kinds (DM and US) of images in this review, focusing on different ML and DL architectures applied in breast tumor processing, and offering a general overview of databases and CNNs, including their relation and efficacy in performing segmentation, feature extraction, selection, and classification tasks [192]. Thus, according to the research shown in Table 1, the most utilized databases for DM images are MIAS and DDSM, and for US image classification, the public databases BUSI, DDBUI, and OASBUD are most used. The DM images contributed to 110 and 168 published conference papers for the DDSM and MIAS databases, respectively [5]. However, the databases report some limitations and advantages; for example, the MIAS database contains a limited number of images, strong noise, and low-resolution images. In contrast, the DDSM contains a big dataset. Likewise, INbreast contains high-resolution images but has a small data size. BCDR, in comparison with DDSM, has been used in a few studies. Some details about the others strengths and limitations of these databases are discussed in [PERSON] [65]. Thereby, Table 2 shows a summary of traditional ML-CAD systems that use public and private databases of DM and US breast images. It covers (i) image preprocessing and (ii) postprocessing steps. This is in contrast with Table 5, which shows a brief summary of DL-CAD systems based on CNN architectures in both types of digital breast images. Thus, in Table 5, various DL architectures and their training strategies for detection and classification tasks are discussed. Based on the most popular datasets, CNN seems to perform rather well, as demonstrated by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [48, 174, 169]. Furthermore, [169, 173] used several preprocessing and postprocessing
## 3 Parametric Analysis of RF Rectifier Circuits Efficiency
The output DC voltage has been measured with a N6781A SMU in a four-wire voltage measurement configuration by averaging the output voltage. \(R_{L}\) values have been generated with a standard resistor decade box. Experimental measurements show a good agreement with the simulated results. The above analysis shows that the diode capacitor circuit is the most efficient rectifier operating at \(-30\) dBm, achieving efficiencies higher than 10% for loads between 1 k\(\Omega\) and 10 k\(\Omega\).
# Effect of Heat Treatment on Water Absorption of Chinese fir Using TD-NMR
## 3 Results and Discussion  
### Relationship between MC and NMR Signal  
The gravimetrically determined MC against the integral peak area of the T\({}_{2}\) relaxation curve and their linear fitting curves, as well as the regression equations are displayed in Figure 2. As shown in the figure, the dry-basis MC has a significant linear correlation with the integral peak area of the T\({}_{2}\) relaxation curve with R\({}^{2}\) higher than 0.99 for all samples. This provides a clear illustration that the integral peak area of the T\({}_{2}\) relaxation curve is proportional to the total water content of the sample, and hence it is a feasible method to investigate the water uptake process of the heat-treated wood.  
Figure 2: Relationship between the moisture content (MC) obtained by the gravimetric method and the integral peak area of T\({}_{2}\) relaxation curve according to the Carr-Purcell-Meiboom-Gill (CPMG) experiment (HT\(x\) refers to the heat-treated sample, where \(x\) is the treated temperature in Celsius degrees).
## 2 Method
### Score Mapping Module  
This module weighs the output of the spatiotemporal feature parameter processing module to compute the final speech quality score. The score mapping module, as shown in Figure 7, takes as input a feature matrix of size \(\mathrm{dft}\times\mathrm{L}\), where \(\mathrm{dft}\) represents the feature dimension after processing for each segment, and \(\mathrm{L}\) represents the number of segments along the time axis. Through a fully connected layer, attention scores are obtained. These scores are masked and normalized using zero-padding and the softmax function. They are then multiplied with the input matrix, \(y\), through matrix multiplication to obtain a weighted average feature vector, z. Finally, z is passed through another fully connected layer to estimate the overall speech quality [33].