esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The raw elements were purchased from Alfa-Aesar and had the following purities: Ti (99.95%), V (99%), Cr (99%), Zr (99.95%), and Ni (99.7%). The ferrovanadium alloy (FeV) had a composition of 79.88 wt % V; 16.26 wt % Fe; 3.74 wt % Al; 0.09 wt % Si; and, 0.03 wt % Cr.  
To facilitate the first hydrogenation (activation) 4 wt % of Zr\({}_{7}\)Ni\({}_{10}\) alloy was added to Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) and Ti\({}_{1.56}\)V\({}_{0.36}\)Cr\({}_{1.08}\) alloys using the following synthesis method. In a first step, both xTi-yV-Zr and Zr\({}_{7}\)Ni\({}_{10}\) alloys were melted independently. In the second step, both of the alloys were crushed separately in a mortar and pestle, mixed in the right proportion, and remelted together. This process is thereafter called co-melt. More details about this process could be found in our previous paper [21]. Replacement of 50 at% of vanadium by ferrovanadium was done in both Ti\({}_{1.56}\)V\({}_{0.36}\)Cr\({}_{1.08}\) and Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) alloys. In order to simplify, the name '50% FeV' refer to the composition were 50 at% of vanadium was replaced by ferrovanadium and '100% FeV' to the composition where 100 at% of vanadium was replaced.  
In casting, each sample was turned over and melted three times to ensure homogeneity. The X-ray diffraction patterns were acquired on a Bruker D8 Focus apparatus using Cu k\(\alpha\) radiation. The crystallographic parameters (lattice parameters, crystallite size, and strain) were evaluated by [PERSON]'s refinement of the X-ray diffraction patterns using Topas software [22]. The refined crystal parameters are quoted with three standard deviations.  
The microstructure was investigated with a Tescan Vega3 SEM. An Electron Probe Micro Analysis (EPMA, CAMECA SX 100), coupled with Wavelength Dispersive (WDS), was used for mapping the elements.  
Pressure-Composition-Isotherms were measured using a home-made Sievert-type apparatus. Each sample was crushed using a mortar and pestle under argon atmosphere to prevent oxidation and then exposed to hydrogen without prior treatment. Activations were performed at room temperature under 2 MPa of H\({}_{2}\). Desorptions were performed at 400 \({}^{\circ}\)C under dynamic vacuum for 4 h.
## 1 Introduction
### Structure  
The following section describes the proposed framework for organizing the article's structure. The mathematical simulation of PV models and the objective function utilized are elucidated in Section 2. In Section 3, the suggested ISSAEDO algorithm provides a concise summary of the core EDO and SSA algorithms, the DE technique, and the bound-constraint modification technique. The results of the experiments are presented in Section 4, accompanied by a thorough analysis of the comparisons made with several established competing methodologies. Furthermore, Section 5 presents visual representations to complement the aforementioned discourse. Lastly, Section 6 presents concluding remarks and discusses potential avenues for future research.
# Current-Sensing Topology with Multi Resistors in Parallel and Its Protection Circuit
## 2 Materials and Methods  
### Parallel or Series Multi Resistor Current-Sensing Topology  
In order to meet the requirements of accurate power measurement from no load to full load of various equipment items, the current channel of the power analyzer needs to be designed with multiple measuring ranges from a few milliamps to tens of amps. The typical circuit of the current channel is shown in Figure 1. A resistor \(R_{S}\) is used to convert input current \(I_{IN}\) into voltage. The voltage is amplified by the differential amplifier \(G_{D}\) and the post-amplifier \(G_{N}\) and then fed into ADC for digital quantization. After galvanic isolation, the digital output of ADC is sent to a digital process unit. Pins and solders usually have much higher temperature drift relative to the resistance body. In order to improve measurement accuracy, when the resistance of \(R_{S}\) is low, a four-wire resistor should be used. That is why the first stage may be a differential amplification circuit.  
To reduce load effect and temperature rise, the resistance of the current sense resistor is expected to be as low as possible. For an input current of tens of amps, the resistance is only a few milliohms. With a single current sense resistor (e.g., 2 m\(\Omega\)) and a milliampere level input current (e.g., 10 mA), it requires amplifying the sensed voltage (20 \(\mu\)V) several hundred thousand times (\(G_{N}\times G_{N}=10^{5}\)) to several volts (e.g., 2 V) to meet the ADC input range (as determined by the reference voltage \(V_{REF}\)). This puts forward ultra-high requirements to parameters such as power supply noise, amplifier input noise and temperature drift, to guarantee the measurement accuracy. To solve this contradiction, the current channel of the power analyzer usually adopts multiple current sense resistors. Each resistor only covers a section of measurement ranges. Define the range ratio \(R_{R}\) as:  
\[R_{R}=R_{MAX}/R_{MIN} \tag{1}\]  
\(R_{MAX}\) is the maximum range covered by one current sense resistor, while \(R_{MIN}\) is the minimum range covered by the same resistor.  
The lower value of \(R_{R}\) induces lower requirements for the amplification circuit. In power analyzer design, \(R_{R}=2\)\(\sim\)10 is usually taken.
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 4 Experimental Assessment
### Details  
Our suggested model is implemented on the python-based Keras deep learning library. We extract acoustic features using the openSMILE toolbox. We train our model on a machine with a GeForce RTX 2080 Ti and optimize it using Adam [40] with an initial learning rate of 0.0001. The batch size is set to 100 and the epochs to 128. For the testing and evaluation of the framework model introduced in this paper, we use 75% of the dataset for training and 25% for testing and validation. For the generalization of the model, we adopt the k-fold cross-validation method [41] to study the reliability and significance of the model. In the k-fold cross-validation technique, we train the model by splitting the database into five folds using an automated technique.
## 3 Results
### Application of Intelligent Discrimination Models
#### 3.4.1 Model Training  
The detailed records of the tuning parameters and optimal values for each model are documented in Table 3. The parameter tuning for the SVM model includes the regularization penalty coefficient C and the coefficient \(\gamma\) of the RBF kernel function. Random forest (RF) and gradient boosting decision tree (GBDT) models were both based on decision trees as basic classifiers, so in terms of parameter tuning, attention needs to be paid to tree structure-related parameters. The most important among these is the minimum number of samples required to form a leaf node. Additionally, in the construction process of random forest, sample and feature selection were involved, making the maximum number of features another important parameter; GBDT involves an iterative process of updating to fit residuals; thus, while focusing on tree structure tuning, it was also necessary to control the learning rate of the model iteration.  
During each parameter tuning process, the improvement of the model with the ENN-treated dataset was not significant, while over-sampling methods enhanced the training performance of the models. Moreover, the overall training performance of the models on the ADASYN-treated dataset was better than that on the SMOTE dataset, with the training results being the best on the dataset treated by ADASYN+ENN (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameter** & **Searching Range** & **No Balancing** & **SMOTE** & **ADASYN** & **ENN** & **ADASYN+ENN** & **SMOTE+ENN** \\ \hline \multirow{2}{*}{SVM} & C & 1–50 & 5 & 5 & 5 & 10 & 50 & 50 \\  & \(\gamma\) & 0.01–1.0 & 0.02 & 0.05 & 0.5 & 0.02 & 0.02 & 0.02 \\ \hline \multirow{2}{*}{RF} & min\_samples\_leaf & 2–10 & 2 & 3 & 3 & 3 & 2 & 3 \\  & max\_features & 1–6 & 3 & 3 & 3 & 3 & 3 & 3 \\ \hline \multirow{2}{*}{GBDT} & min\_samples\_leaf & 2–10 & 3 & 2 & 3 & 2 & 2 & 3 \\  & learning\_rate & 0.02–0.5 & 0.2 & 0.2 & 0.5 & 0.25 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The tuned parameters for SVM, RF, and GBDT models trained on different datasets.  
Figure 8: The results of parameter tuning for SVM, RF, and GBDT models.
## 5 Performance Evaluation
### Analytic Definitions and Experimental Environment  
Table 1 shows symbol definitions for further convenience in explaining the numerical analysis, which would be illustrated in the next section. For clarification, we assume that all data have same size for protocol utilization.  
Meanwhile, we have performed experiments with the simulation environment presented in Table 2. For simulating a real environment, we consider many variables of data length with MTU (Maximum Transmission Unit) and power consumption in each state (Tx, Rx, Sleep), referring data sheets of CCxx families from TI Inc. [13], which are popularly used for low power communications. IEEE 802.15.6 standard technology was employed for the comparison results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(N\) & Number of Nodes & \(\epsilon^{C},E^{N}\) & Energy Consumption of respective PC and Node \\ \hline \(k\) & Number of Time Slots & \(T_{IFS}\), \(T_{Data}\) & Time delay for IFS and data transmission \\ \hline \(\eta\) & Number of Active Frames & \(R\) & Data Rate \\ \hline \(\Gamma\) & Number of Remained Data & \(L^{n},L^{z}\) & \begin{tabular}{c} Average Latency of respective normal and \\ emergency data transmissions \\ \end{tabular} \\ \hline \(\pi\) & \
\begin{tabular}{c} Number of Data Tx Event on \\ a Node during u-TP \\ \end{tabular} & \(P\) & Power (Watt) value \\ \hline \(\epsilon_{w},\epsilon_{c}\) & Clock Drift at Nodes and PC & \(D\) & Duration of Time Frame \\ \hline \(\omega\) & Packet Length of Constant Data & \(\Phi\) & Duration of Long Preamble Transmission for LPL during Sleep Period \\ \hline \(\lambda\) &
\begin{tabular}{c} Occurrence Rate of Normal \\ Data Transmission \\ \end{tabular} & \(O\) & Delay of Pseudo-random back-off \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol descriptions for analyses.  
Figure 6: Duty cycle mechanism for emergency data reception in Sleep subframe from Coordinator.
## 4 Experimentations
### Word Embedding Process  
To process the Product Space text data, we implemented the Word2 Vec technique [51] which consists of representing each word as a numerical vector (embedding) in its linguistic context. By leveraging distributed word representations, Word2 Vec captures the semantic relationships between words and phrases in the product descriptions, effectively transforming them into dense numerical vectors. The learning is based on specialized neural networks. However, no labels are required for learning, as the ground truth is directly inferred from the proximity of words within the training corpus. Thus, Word2 Vec is self-supervised learning. Word2 Vec has already been used for calculating similarities in industrial waste nomenclatures [52], analyzing taxonomy [53], or providing recommendations [18], although the embeddings cannot be interpreted directly.  
However, to achieve optimal performance and meaningful embeddings for textual descriptions, careful selection and fine-tuning of the hyperparameters are essential.  
One of the key hyperparameters in Word2 Vec is the dimensionality of the word embeddings. Choosing an appropriate dimensionality ensures that the embeddings capture sufficient semantic information while avoiding overfitting or excessive computational overhead. After testing, in our case, 100 embeddings size were sufficient to represent the words of each product code of the Product Space nodes. In addition, the window size hyperparameter determines the context in which words are considered to learn their representations. Adjusting this parameter allows the model to capture different levels of word associations, which can significantly impact the quality of the encoded descriptions. Product descriptions are short, so a window size of 3 is sufficient. Our word embedding has five training iterations or epochs because there is a correct balance between training time and convergence. Indeed, inadequate training may result in incomplete embeddings, while excessive training may lead to overfitting. Furthermore, Skip-Gram was chosen for its ability to capture word co-occurrence patterns effectively.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 4 Factors Influencing Autonomous Vehicle Traffic
For instance, pedestrians often rely on visual cues from drivers, which may be absent in AVs, leading to potential confusion and safety concerns [113]. The presence of AVs also alters human driving behaviors. Studies have shown that human drivers exhibit different car-following behaviors when trailing AVs compared to human-driven vehicles, often reducing their time headways. Pedestrian behavior is influenced by various contextual factors, including the weather, road structure, and social norms, all of which must be considered in AV algorithm development [114]. Driver workload during autonomous driving is another factor, with different non-driving-related tasks and collision warnings affecting the workload and takeover readiness of drivers [115]. Additionally, human drivers' subjective feelings and decision making in mixed traffic environments are influenced by their driving style; for example, aggressive drivers may feel more anxious and behave more aggressively around AVs [116]. Effective interaction modeling between AVs and human-driven vehicles is crucial, as current models often neglect communication and assume passive responses, which can lead to inaccuracies [117]. Public attitudes towards AVs, shaped by factors such as uncertainty, policy impacts, and changes in the road traffic environment, play a critical role in their adoption [118]. Interaction-aware controllers (IACs) in AVs, which predict human drivers' responses, need validation against natural driving behavior to ensure reliability [119]. Fur thermore, demographic, psychological, and mobility behavior characteristics significantly influence public acceptance and the intention to use AVs, highlighting the need for direct experience and education to foster positive attitudes [114].
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 3 Engineering Application  
The data used in this study were obtained from a GRP5000 laser scanning survey conducted in a subway tunnel in Hangzhou. The GRP5000 is a mobile tunnel laser scanning system developed by Amberg Technologies, a company based in Regensdorf, Switzerland. This system is primarily used for acceptance inspections of newly completed tunnels, geometric shape assessments during tunnel operation, detection and diagnosis of structural  
Figure 12: Ring plane fitting technology. (**a**) Ring plane fitting schematic diagram. (**b**) Tunnel cross-section extraction effect schematic diagram.  
Figure 13: Tunnel misalignment calculation diagram.  
defects in existing tunnels, as well as digital management and documentation. A portion of the point cloud data acquired from the scan is shown in Figure 14.  
The main hardware components of the GRP5000 system include the following:  
1. Track measurement trolley, used for measuring parameters such as mileage, track gauge, height and superelevation.
2. The Profiler 6012 high-speed laser scanner operates at a scanning rotation frequency of 200 Hz, with a sampling rate of 1 million points per second and an image resolution of 3 mm. For local measurements and short-range scanning, the system's dynamic measurement accuracy can reach \(\pm\)0.5 mm. However, the manufacturer specifies a contour accuracy of \(\pm\)5 mm and a target point accuracy of \(\pm\)2-4 cm, which takes into account larger-scale measurements and environmental factors encountered during field operations, such as vibration and temperature fluctuations.  
The laser scanner performs full-section, high-density measurement of the tunnel using a helical scanning method. The data acquisition software analyzes the intensity of the emitted and received laser signals in real time, thereby obtaining both the image and point cloud data of the tunnel lining's inner surface. Based on these data, further analysis and evaluation can be carried out to assess the structural condition, distribution of defects, clearance dimensions, deformation characteristics, and convergence of the tunnel. Considering the special resolution requirements of this experiment, the point cloud density during scanning must be maintained at no less than 50,000 points per square meter. This requires either reducing the scanning speed under standard mode or switching to a higher-frequency scanning mode.
# RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
###### Abstract  
Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi's dedicated website, and provides instant diagnosis and feedback on the patient's heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future.  
ECG; arrhythmia; convolutional neural network; artificial intelligence; deep learning Article
# Study on Filling Capacity of Optical Glass in a Novel Rapid Hot Embossing Process
## 3 Materials and Methods  
### Glass, Mold and the Mold Kit  
The SCHOTT N-BK7 glass is one of the most commonly used materials for fabrication of optical components, due to its superior optical properties (e.g., high Abbe number and high optical transmittance), and was thus selected as preform material in this hot embossing  
Figure 2: (**a**) Schematic of the rapid heating module and (**b**) the recorded temperature signals at four different positions in the heating test.  
study. The raw N-BK7 glass blanks were purchased from SCHOTT AG. After several mechanical processing procedures, they had a diameter of 7.5 \(\pm\) 0.01 mm, a thickness of 3 \(\pm\) 0.01 mm and a flatness of less than 0.1 \(\upmu\)m. Furthermore, their top and bottom surfaces were mechanically polished to have a root-mean-square (RMS) surface roughness of about 1 nm.  
Silicon carbide (SiC) crystal shows high mechanical strength, relatively low coefficient of thermal expansion, and extremely low glass adhesion. As a result, polished 4H-N mono-crystalline SiC discs with a diameter of \(\sim\)10 mm and a thickness of 0.35 mm were used as molds. Furthermore, a through-hole with a diameter of \(\sim\)300 \(\upmu\)m was fabricated at the center of SiC molds by laser drilling technology, with cracks generated at the edge, as seen in Figure 3a.  
In presenting stage, it is hard for the manually placed glass preform and SiC molds to accurately share the same axes, and thus resulting in the misalignment. The misalignment between the glass preform and molds leads to uneven distribution of pressure during the hot embossing process, and thus deteriorates the quality of embossed glass product. To solve this problem, a mold kit is designed, which consists of a zirconia sleeve and a zirconia pin (see Figure 3b). The zirconia sleeve and pin that were fabricated by grinding and lapping are used for the positioning of glass and molds. The sleeve has an inner diameter of 10 \(\pm\) 0.001 mm, an external diameter of 30 mm and a height of 10 mm, and the pin has a diameter of 9.96 \(\pm\) 0.001 mm and a height of 10 mm. The small tolerance of the sleeve and pin enable a fine alignment of glass preform and molds. Moreover, zirconia exhibits high resistance to wear and oxidation at high temperatures, which is responsible for the excellent performance of the mold kit in this study.
## 2 Problem Description and Formulations
### Notations  
Notations used in the model are listed as follows:  
_Sets_  
_V_: Vertex set.  
_V_\({}^{\prime}\): Demand location set.  
_V_\({}_{0}\): \(V^{\prime}\cup 0\).  
_V_\({}_{n+1}\): \(V^{\prime}\cup n+1\).  
_K_: Land vehicle set.  
_A_: Aerial vehicle set.  
_Parameters_  
\(t_{ij}^{l}\): Time cost of transportation from \(i\) to \(j\) by land vehicles.  
\(t_{ij}^{a}\): Time cost of transportation from \(i\) to \(j\) by aerial vehicles.  
\(c^{l}\): The unit cost of land transportation mode.  
\(c^{a}\): The unit cost of aerial transportation mode.  
\(f^{l}\): Fixed cost of land transportation mode.  
\(f^{a}\): Fixed cost of aerial transportation mode.  
\(Q^{l}\): Capacity of a land vehicle.  
\(Q^{a}\): Capacity of an aerial vehicle.  
\(I_{i0}\): Initial inventory level of the demand location \(i\) at the beginning of the planning cycle.  
\(l_{p-1}^{avg}\): Average lead time for the first replenishment in the previous planning cycle.  
_T_: Planning horizon of the planning cycle.  
_H_: Inventory planning horizon, where \(H=T+l_{p-1}^{avg}\).  
_Variables_  
\(l_{ik}\): Arrival time of the demand location \(i\) replenished by vehicle \(k\).  
\(l_{ia}\): Arrival time of the demand location \(i\) replenished by aerial vehicle \(a\).  
\(l_{il}\): Inventory level of the demand location \(i\) at time \(t\).  
\(q_{i}^{l}\): Replenishment quantity of the demand location \(i\) replenished by land vehicles.  
\(q_{i}^{a}\): Replenishment quantity of the demand location \(i\) replenished by aerial vehicles.  
\(x_{ijk}\): 1 if vehicle \(k\) of land transportation mode travels directly from vertex \(i\) to \(j\); 0 otherwise.  
\(x_{ija}\): 1 if vehicle \(a\) of aerial transportation mode travels directly from vertex \(i\) to \(j\); 0 otherwise.  
\(y_{ik}\): 1 if demand location \(i\) is assigned to route \(k\); 0 otherwise.  
\(y_{ia}\): 1 if demand location \(i\) is assigned to route \(a\); 0 otherwise.