esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[40] investigated the influence of flow rate for side flushing of dielectric fluid on the debris removal and process performance. They reported that the increase in the dielectric flow improved decontamination in the machining gap, flushing away gas bubbles and debris more rapidly, which improved the machining performance in the terms of material removal rate. [PERSON] et al. [41] improved machining performance of the EDM process using a rotating cylindrical electrode. They found that the rotating electrode increased the material removal rate by 41% and reduced surface roughness by 12% compared to using a stationary electrode. [PERSON] and [PERSON] [42] used a rotational magnetic field and rotary electrode in the EDM process. They confirmed that the enhanced flushing of debris from the machining gap increases machining performance. [PERSON] et al. [34; 43] investigated the dimension accuracy of an EDM deep hole using a multi-hole interior flushing electrode. They reported that the improved flushing technique is able to achieve a better closed gap tolerance and higher material removal rate compared with the conventional flushing method. [PERSON] et al. [44] examined how to improve the performance of EDM through relief-angled tool designs on tungsten carbide material. The performance of the relief-angled electrodes was found to be significantly better than the performance of a conventional cylindrical tool. [PERSON] et al. [45] studied the effect of multi-aperture inner flushing on the characteristics of EDM deep hole. They showed that the multi-aperture inner flushing achieved a higher material removal rate, shorter machining time, and lower gap clearance than the side flushing of a conventional electrode. Moreover, various parameters affecting the material removal rate during EDM and the flushing method also play an important role, especially in the case of hole formation [44]. The melt debris accumulates in the internal walls around the drilled hole and forms a solidified recast layer [31; 42]. The conditions of EDM machining depend on several factors, such as the current or duty factor and gap flushing efficiency [46; 47; 48]. Based on previous research, optimization of the machining conditions by adjusting the jump height is able to make machining more stable. However, a large electrode jump height is related to low sparking time, which results in a higher production time. In addition, modifying EDM equipment is a complicated process and has an increased machining cost. In this work, the design of the shape of the electrode was investigated to remove debris in the machining gap. The aim of this study was to examine the influence of a self-flushing electrode (cylindricalstep shape) for EDM deep hole drilling on the improvement of machining performance, as well as machining time, material removal rate (MRR), electrode wear ratio (EWR), the quality of the machined surface (Ra), and the gap clearance of the drilled hole.
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 7 Results
### Visualization of Feature Maps  
Feature maps have been extracted with activation statistics, as shown in Figure 17. The activation statistics were extracted from the model trained with data collected from a distance of 10 m. Regarding convolutional neural networks, the mean of feature maps refers to the average value of elements or the neuron's activations in the feature maps, which provides an insight into the average activation level of the detected features around the spatial dimension. Standard Deviation (std), here, refers to how much the feature maps deviate from the mean. Usually, a higher std value means that the feature map activations have a broader range of values, and a lower std value means that the activations are clustered closer to the mean. Figure 17 shows the features that are read onto the black ice portion of the image.  
Feature map 1 with mean value of 0.03 and std value of 0.05 suggests that the activations are spread around the mean with moderate variability. Therefore, it may mean that the feature maps are capturing patterns with a moderate magnitude and moderate consistency in their activations. Feature map 2 is very similar to feature map 1, indicating a higher concentration and less variability around the mean. Feature map 3 is similar to feature map 1 and feature map 2. With low mean value, feature map 4 indicates that the activations are comparatively small on average, and the std value suggests that the activations are concentrated around the mean. Feature map 5 indicates that the activations have a bit more variability than feature map 4. Feature map 6 is exactly the same as feature map 5. Feature map 7 has a mean value close to 0, which means that the activations are close to zero on average. This feature map is not important for this case. Feature map 8 has  
Figure 17: Feature map visualization of the 6 th layer of the model on the black ice part.  
moderate magnitudes and moderate variability around the mean. Feature map 9 has a high mean value of 0.16, which indicates that the activations are strong on average. The standard deviation suggests that these activations are widely spread around the mean, denoting significant variability. Feature maps 10 and 11 have a moderate spread and variability around the mean. Similar to feature maps 10 and 11, feature map 12 also has the same characteristics. Observing the values of std and mean in Figure 17, it might indicate that this feature map is capturing binary or threshold patterns. Similar to most of the feature maps, it means that the activations are moderately concentrated around the mean. Feature map 16 has a mean value of 0.05, which means that it has moderate magnitudes and significant variability in activations around the mean.  
In summary, some features have high mean values, indicating strong activations, while others have low mean values; for example, feature map 7 indicates weak activations. The standard deviation offers insights into the variability of these activations.
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 7 Micro-Constituents' Analysis
### Chromatographic Techniques
#### 7.4.2 Untargeted LC-MS Analysis  
Untargeted liquid-chromatography-mass spectrometry (LC-MS) is a powerful approach used to explore the complex chemical composition of milk. Unlike targeted analysis, which focuses on specific known compounds, untargeted LC-MS allows to detect and identify a wide range of molecules without prior knowledge, making it ideal for discovering new bioactive components and understanding the full biochemical profile of milk. The LC-MS generates raw data files for each sample, containing \(m/z\), intensities and retention times for detected ions. In untargeted workflows, extraction solvents with a broad polarity range are used, to avoid specific metabolite selection (Figure 5). The instrument collects full-scan MS data, sometimes alongside MS/MS data if MS operates in data-dependent (DDA) or independent (DIA) acquisition. The processing of untargeted LC-MS data involves some key steps, i.e., peak detection, deconvolution to separate overlapping peaks and align co-eluting compounds, baseline correction and normalization. Normalization accounts for differences in sample concentration or instrument response. Once compounds annotation has been carried out [193], different statistical tools can be applied to identify significant changes across groups. One of the major aims of untargeted LC-MS analysis is the possibility to study and possibly distinguish between sample types or conditions based on their metabolic or lipidomic profiles. For example, [PERSON] et al. [14] developed a comprehensive untargeted metabolomics strategy based on ultra-performance liquid-chromatography  
\begin{table}
\begin{tabular}{l c c} \hline  & **Qualifier Transition (_m/z_)** & **Quantifier Transition (_m/z_)** \\ \hline Vitamin B9 & 442/176 & 442/295 \\ \hline Vitamin B12 & 678/359 & 678/147 \\ \hline Vitamin D2 & 572/298 & 572/298 \\ \hline Vitamin D3 & 560/298 & 560/298 \\ \hline Vitamin E & 431/83 & 431/165 \\ \hline \end{tabular}
\end{table}
Table 7: _Cont._coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to identify potential biomarkers able to distinguish between UHT and reconstituted milk. In another work, [PERSON] et al. [194], evaluated by an untargeted metabolomics approach changes in milk metabolites induced by the replacement of soybean hulls with cocoa husks in the ewes' diet. Cocoa husks appeared to alter levels of milk metabolites involved in thyroid hormone metabolism and the production of ubiquinol-10.
# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 2 Materials and Methods
### Segmentation Model Training
#### 2.3.1 U-Net  
U-Net is commonly used in the context of semantic image segmentation, and its effectiveness in capturing both global context and fine details makes it particularly well-suited for tasks such as medical image segmentation and satellite image analysis, and it is also employed for the segmentation of materials microstructures [28; 29]. U-Net is characterized by a U-shaped architecture with an encoder-decoder structure and skip connections. The encoder, on the left side of the U, consists of down-sampling layers that capture hierarchical features from the input image. The decoder, on the right side, involves up-sampling layers and skip connections that preserve high-resolution details and aid in precise localization. Skip connections connect corresponding encoder and decoder stages, facilitating the retention of spatial information. The bottleneck at the base of the U combines abstract features from the encoder with detailed spatial information from the decoder.  
In the conducted experiments with U-Net, the bias term of all convolutional layers is initialized to zero. Additionally, the convolution layer weights in the encoder and decoder subnetworks are initialized using the 'He' weight initialization method [30]. The encoder-decoder has a depth of 3, resulting in a U-Net comprising 46 layers with 48 connections. The most relevant hyperparameters configured for training include the Adam optimizer, a learning rate of 0.001, L2 regularization, and a maximum number of epochs set to 2. Experiments were conducted by increasing the number of epochs, yet substantial improvements were not achieved; instead, there was an increase in computational time. The loss layer utilizes cross-entropy loss to quantify the disparity between the predicted values and their corresponding actual data. The formula is expressed as follows in Equation (1).  
\[\text{loss}=-\frac{1}{\text{N}}\sum_{\text{n}=1}^{\text{N}}\sum_{\text{i}=1}^{ \text{K}}\text{w}_{\text{i}}\text{t}_{\text{ni}}\text{ln}\,\text{y}_{\text{ ni}} \tag{1}\]  
Here, N represents the number of samples, K is the number of classes, w\({}_{\text{i}}\) denotes the weight for class i, tni is the indicator of whether the nth sample belongs to the ith class, and yni represents the output for sample n for class i.
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 1 Introduction  
Wireless sensor networks are used in various applications like environmental monitoring, child education, smart manufacturing, infrastructure monitoring and many others. A wireless sensor network usually consists of many small self-powered sensor nodes that measure their environment and communicate data to other nodes or a base station [1].  
Recently, more and more wireless sensor networks consist of wireless sensor nodes that include wake-up receivers [2; 3; 4; 5] that reside in a low-power stand-by state until they receive a wake-up massage. Only after receiving this message, or a sensor event, a sensor node wakes up to full operation. This approach can be more favorable, with respect to energy consumption, than duty-cycling [6].  
The low-frequency wake-up message is usually modulated on a high-frequency carrier signal using on-off-keying modulation. To ensure ultra-low power consumption, the wake-up message can be demodulated passively [6; 7]. For instance, the wireless sensor nodes presented by [6; 8] use the AS3932 wake-up receiver that listens permanently to a 125 kHz signal that is modulated on a 868 MHz carrier frequency and demodulated using a passive rectifier and a low-pass filter. Changing between communication and low-power wake-up listening is implemented by using an antenna switch.  
However, resulting from the passive demodulation, wake-up receivers usually exhibit a lower sensitivity than communication radios [6; 9; 10]. To improve robustness and reliability of wake-up messages, [PERSON] et al. presented in [11] a technique to transmit concurrent wake-up messages by purposefully interfering signals of two or more wireless sensor nodes. This creates a wake-up signalby using the beat frequency of the superposed and slightly out of tune carrier signals and increases the transmitted signal strength.  
In this article, we present a wireless sensor node with wake-up receiver, based on the work presented in [12]. The novel design uses equal-gain diversity in the wake-up path as introduced in Section 2. Superposition of the diversity signals is realized at 125 kHz after the passive demodulation of the incoming signals. As a result, there are almost no additional active parts required to achieve constructive interference as phase-alignment of the signals is not required, as demonstrated in Section 3. In Section 4, we demonstrate experimentally the improved wake-up signal robustness and reliability in multipath propagation environments. Finally, the article is concluded in Section 5.
## 3 Results
### Graphene-Based Sensors
#### 3.2.2 Sensing Properties and Potential Applications  
Graphene-based sensors possess remarkable sensing properties due to their unique electrical, mechanical, and surface characteristics. The exceptional electrical conductivity of graphene allows for high sensitivity and low noise in sensing applications. Additionally, graphene's large surface-to-volume ratio and atomic-scale thickness provide an ideal platform for sensing interactions at the nanoscale.  
One prominent sensing mechanism in graphene-based sensors is the change in electrical conductivity upon exposure to various analytes [47]. This can be achieved through several sensing mechanisms, including the field-effect transistor (FET) configuration, where the electrical properties of graphene are modulated by the presence of analytes, leading to changes in conductance or resistance [48].  
Graphene-based sensors have demonstrated remarkable capabilities in detecting a wide range of physical and chemical parameters relevant to bridge maintenance [49]. Furthermore, graphene's unique surface properties enable the functionalization of graphene-based sensors, allowing selective sensing of specific analytes [50]. Functionalization can be achieved by attaching or coating graphene with various molecules, nanoparticles, or polymers, enhancing the sensor's selectivity and sensitivity towards target substances.  
The potential applications of graphene-based sensors in bridge maintenance are vast. They can be integrated into bridge components to monitor structural behavior, detect stress concentrations, and provide real-time data on strain and deformation. Additionally, graphene-based sensors can be utilized for corrosion detection, gas sensing to detect pollutants or leaks, and environmental monitoring to assess temperature and humidity changes.  
Figure 3: Relative prevalence of various graphene fabrication techniques estimated based on an extensive review of the available literature. It is crucial to note that the specific percentages assigned to each technique—mechanical exfoliation (30%), CVD (40%), epitaxial growth (15%), chemical synthesis (10%), and molecular beam epitaxy (5%)—are approximations derived from the authors’ observations across numerous studies and do not represent precise quantitative data from a single source or dataset.
# An Overview of CubeSat Missions and Applications
## 4 The Use of CubeSats as Telecommunication and Asset-Monitoring Platforms
### Optical Communication CubeSat Missions and Applications
If successful, the ISOC is expected to provide inter-satellite data transfer rates reaching 1 Gbps up to distances of 200 km while maintaining multiple different laser-optical connections simultaneously [199]. Figure 11: A simplified illustration of the operations concept of the Pathfinder Technology Demonstrator 3 (PTD-3) mission. Illustration by NASA/ Dave Ryan [194]. This rising interest in laser-optical communication technologies for CubeSat applications has facilitated the development of various COTS laser-optical communication products that are now available for integration on CubeSat missions. Examples include Astrolight's (Vilnius, Lithuania) Atlas-1 Free-Space Optics assembly and its accompanying fiber amplifier which take up a volume of 1.2U and 0.6U, respectively. According to its manufacturer, Atlas-1 can be utilized for space-to-Earth laser communication and it is capable of achieving a transmit data rate of up to 1.25 Gbps using a 1550 nm wavelength laser beam [200]. Another example of a commercially available laser-optical communication system is the SCOT20 terminal which has been developed by Tesat-Spacecom GmbH & Co. (Backnang, Germany). SCOT20 fits in a 1U volume, has a mass of approximately 1.6 kg, and is able of supporting 100 Mbps DirectTo-Earth (DTE) or bidirectional Inter-Satellite dualinks. Furthermore, SCOT20 is capable of transmitting data reliably over distances of up to 2000 km while its power consumption is just 8 W [201]. One more COTS laser-optical communications terminal available for use by CubeSat platforms is the AAC Clyde Space's (Uppsala, Sweden) CubeCAT terminal which is designed to fit in a 1U volume. It can support bidirectional space-to-Earth optical communication operations with downlink data rates reaching 1 Gbps and an uplink data rate of up to 200 Kbps [202]. Finally, there is also the choice of Stellar Project's (Padova, Italy) LaserCube, a two-way laser-optical communications terminal which is designed to fit inside a 2U volume and is intended to be integrated into CubeSats with a size larger than 3U [203]. All available commercial laser-optical communication solutions such as the aforementioned ones, coupled with new developments and experimental hardware are expected to enable a multitude of future CubeSat missions and applications involving data transfer via laser-optical link.
## 3 Results
### Evaluating the Efficiency of the YOLO v10 XE Model in Analyzing Building Roof Topologies
#### 3.2.1 Modeling Parameter Calibration and Data Augmentation  
In order to enhance the model's efficacy in identifying roofs on raster images, hyperparameter tuning and data augmentation techniques were employed. This was performed with the objective of augmenting the diversity of the training set and preventing overfitting, which is of particular significance given the limited amount of data. Hyperparameter tuning was conducted iteratively to refine the model's performance. The learning rate was initially set to 0.01 but was reduced to 0.001 after preliminary experiments showed that a higher learning rate led to unstable loss behavior in the early epochs. The weight decay  
Figure 6: Statistics of training data from YOLO: (**a**) Centroid distribution of training objects. (**b**) Bounding box dimensions of training objects (source: author’s own elaboration).  
parameter, set to 0.0001, was used to regularize the model by limiting the magnitude of the weights, thereby reducing the risk of overfitting. These values were determined based on a combination of empirical tests and cross-validation on the training dataset.  
The techniques employed were as follows:  
**Rotation**: The images were rotated at random angles, thereby enabling the model to learn invariance to the orientation of objects.  
**Scaling**: The size of the objects in the image was altered, thus facilitating the detection of objects of varying scales and aligning with the theory of scalability in neural networks.  
**Changing brightness and contrast**: The model's immunity to environmental changeability was enhanced through the simulation of different lighting conditions.  
As a result of image augmentation (each image in each epoch was randomly duplicated using 5 sets of augmentation types), the final result was \((1+5)\cdot 227=1362\) images in the training set. The augmentation of data was implemented exclusively on the training dataset, with the objective of ensuring an unbiased evaluation on the validation and test sets.
## 2 Proposed Scheme  
In this section, the proposed reversible data hiding scheme in encrypted 3D mesh models is detailed. The main techniques are based on the two techniques multi-group partition (MGP) and closest pair prediction (CPP). Figure 1 shows the schematic framework of our proposed scheme. First, the content owner detects the redundant room, which is reserved for additional data embedding in the original 3D mesh models using CPP and MGP before preprocessing, and then encrypts the 3D mesh models and uploads the encrypted models into the cloud. Then, the cloud server can embed some additional data into the encrypted models. When a third-party receives the embedded encrypted models, based on different keys, the embedded data can be extracted or the original 3D mesh models can be recovered. The details of preprocessing, multi-group partition, closest pair prediction, 3D mesh model encryption and uploading on the content owner side, data embedding on the cloud server side, data extraction, and 3D mesh model recovery on the third-party side are described in the following subsections. The symbols used in this paper are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(V/v\) & Vertex set/vertex & \(F/f\) & Face set/face \\ \((x,\,y,\,z)\) & Three-dimensional coordinate & \(n_{V}/n_{F}\) & Number of vertex/face \\ \(i/j\) & Index of vertex/face & \(\sigma\) & Number of preserving valid bits \\ \(\Psi\) & Integer vertex & \(\theta\) & Recovered vertex \\ \(G/g\) & Vertex group/each group & \(\alpha\) & Number of vertices in each group \\ \(\Psi_{g}\) & Vertex in each group & \(\Psi_{u}\) & Usable vertex \\ \(\Psi r\) & Reference vertex & \(R\) & Reference vertex set \\ \(n_{r}\) & Number of reference vertices & \(l\) & Index of \(R\) \\ \(d\) & Distance between \(\Psi u\) and \(\Psi r\) & \(\Psi r^{c}\) & Closest reference vertex \\ \(d^{c}\) & Closest distance & \(\beta\) & Maximum length of three-dimensional \\ \(\theta\) & Bits used to record \(\beta\) & \(\mathbb{R}\) & closest distance \\ \(\delta\) & Length of binary bits & \(K_{m}/K_{h}\) & Recovery information sequence \\ \(\overline{\mathbb{R}}\) & Encrypted \(\mathbb{R}\) & \(\gamma\) & Embedding rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: The List of Symbols.  
Figure 1: Schematic workflow.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 1 Introduction
The influence of magnetic field intensity on the near field thermal radiation for SiC nanowires and plates is quantified discussed in detail. We also analyzed the influence of magnetic field intensity on the near field thermal radiation of SiC nanowires/plates.