esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts
## 3 Adopted Machining Cycle  
The machining cycle adopted to produce the airfoil requires 5-axis machining capabilities to ensure the manufacturability of the part. The cycle is constituted by three phases: roughing, semi-finishing and finishing. The tool used for all the operations is a 2-flute 10 mm carbide ball end mill produced by cod. 207280 (Garant, Lengerich, Germany) designed for dry cutting condition on carbon steel. A morphing strategy was applied for the roughing phase to reach an offset geometry of the airfoil starting from the nearly constant thickness geometry produced by WAAM process. A 2 mm axial depth of cut was set for this operation. In Figure 4, the roughing operation is presented. The following machining parameters were selected, based on previous experiences of WAAM parts machining [22]: cutting speed 180 m/min and feed per tooth 0.062 mm/tooth. A machining allowance of 0.5 mm was left on the product, to be removed in the following operations. The Rotation Tool Center Point (RTCP) function was activated for the G-code set-up and a constant 30\({}^{\circ}\) tilt angle of the tool with respect to the airfoil surface was used. As described in Section 2, CMM measurement of the WAAM part was used to optimize the alignment between the final part and the stock. The oriented geometry of the airfoil was then used to create the toolpath thanks to the ESPRIT(r) (DP Technology, Camarillo, CA, USA) Computer Aided Manufacturing (CAM) software. After the roughing cycle, semi-finishing and finishing operations were carried out to complete the manufacturing of the airfoil. The two cycles used  
Figure 3: Coordinates-measurement-machine (CMM) measurement of the deposed airfoil (**a**), measured airfoil geometry (**b**).  
Figure 2: Wire-arc-additive-manufacturing (WAAM) deposition (**a**), airfoil after the deposition (**b**).  
the same tilt angle of the roughing phase. The axial depth of cut adopted for the machining operations was 1 mm for semi-finishing and 0.5 mm for finishing. These steps were selected considering the expected surface finish.
## 2 Supervisory and Control System Based on Real-Time Power Management
### Energy Cost Optimization
#### 2.3.6 Objective Function  
The total energy cost, \(C_{total}\), takes into account the cost of the supplied power from the grid, the profit of injected power into the grid, the cost of the storage degradation when operating, the penalty cost if the EV at departure has not reached its desired SOC, and the cost of the PV shedding power, which represents the PV power that has not taken advantage of it. Therefore, the objective function is to minimize \(C_{total}\), given by Equations (34)-(38):  
\[C_{total}=C_{G}+C_{S}+C_{PVS}+C_{EV\_penalty}, \tag{34}\]  
\[C_{G}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\left[c_{G}(t_{i})\cdot\Delta t\cdot(-p _{G\_I}(t_{i})+p_{G\_S}(t_{i}))\right] \tag{35}\]  
\[c_{G}(t_{i})=\left\{ \begin{array}{c}c_{G\_NH}for\in normal\ hours\\ c_{G\_PH}for\in peak\ hours\end{array} \right.,\]  
\[C_{S}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\left[c_{S}(t_{i})\cdot\Delta t\cdot(p _{S\_C}(t_{i})+p_{S\_D}(t_{i}))\right], \tag{36}\]  
\[C_{PVS}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\left[c_{PVS}(t_{i})\cdot\Delta t\cdot p _{PVS}(t_{i})\right], \tag{37}\]  
\[C_{EV\_penalty}=\sum\limits_{v}^{N_{v}}\left[c_{EV\_p}\cdot(SOC_{EV\_des_{v}} -SOC_{EV\_dep_{v}})\cdot E\right], \tag{38}\]  
where \(C_{G}\), \(C_{S}\), \(C_{PVS}\), and \(C_{EV\_penalty}\) are the grid, storage, PV shedding energy costs, and EV penalty cost, respectively, and \(c_{G}\), \(c_{S}\), \(c_{PVS}\), and \(c_{EV\_p}\) are the grid, storage, PV shedding energy tariffs, and EV penalty tariff, respectively. Lastly, the final optimization problem is given by (39):\[
## 2 Theory and Methodology
### Improved Particle Filter Online Recognition Method
Finally, \(j^{(i)}_{k|k-1}\) it can also be obtained through the prior probability density function, which can be written as in Equation (14):  
\[p\Big{(}\lambda_{k}\big{|}j^{(i)}_{k-1|k-1}\Big{)}\ =\ \prod\limits_{l\ =\ 1}^{N_{L}}\sum\frac{1}{3}\delta\Big{(}J^{l}_{k}-n\Big{)}\ \ \ n\in\{0,1,2\} \tag{14}\]  
After the measured vector \(A_{k}\) is obtained at \(k\) moment, the likelihood function \(p\Big{(}A_{k}\Big{|}B^{(i)}_{k|k-1}\Big{)}\) of each particle \(B^{(i)}_{k|k-1}\) is:  
\[p\Big{(}A_{k}\Big{|}B^{(i)}_{k|k-1}\Big{)}\propto\frac{1}{(2\pi)^{N_{L}/2} \big{|}\big{|}^{1/2}}\exp\left\{-\frac{1}{2}\bigg{(}(\varepsilon)\sum\limits ^{-1}_{k}(\varepsilon)^{T}\right\}\ \ \ \ \ i\ =\ 1,2,\cdots,M \tag{15}\]  
where \(\sum\) it can be written as in Equation (16):  
\[\sum\ =\ R_{k}+diag\Big{(}I^{(i)}_{k|k-1,1}\Big{)}\Theta\sum\limits_{k}^{u}+ diag(I^{(i)}_{k|k-1,2})\Theta\sum\limits_{k}^{\
u} \tag{16}\]  
where \(diag\Big{(}I^{(i)}_{k|k-1,1}\Big{)}\) and \(diag(I^{(i)}_{k|k-1,2})\) are the main diagonal elements, and are the diagonal matrix of the vector \(I^{(i)}_{k|k-1,1}\) and \(I^{(i)}_{k|k-1,2}\), \(\varepsilon\) it can also be written as in Equation (17):  
\[\varepsilon\ =\ A_{k}-h\Big{(}Y^{(i)}_{k|k-1}\Big{)}-I^{(i)}_{k|k-1,2}\Theta m^{(i) }_{k|k-1} \tag{17}\]  
Normalize the likelihood probability density of all particles:  
\[\bar{\omega}^{(l)}_{k|k-1}\ =\ \frac{p\Big{(}A_{k}\big{|}B^{(i)}_{k|k-1}\Big{)}}{ \sum\limits_{l\ =\ 1}^{M}\ p\Big{(}A_{k}\big{|}B^{(i)}_{k|k-1}\Big{)}}\ \ \ i\ =\ 1,2,\cdots,M \tag{18}\]  
where \(\bar{\omega}^{(l)}_{k|k-1}\) is the weight. The augmented state vector \(B_{k}\) can be expressed as in Equation (19):  
\[\hat{B}_{k|k-1}\ =\ \frac{1}{M}\sum\limits_{l\ =\ 1}^{M}B^{(i)}_{k|k-1}\bar{ \omega}^{(l)}_{k|k-1}\ \ \ i\ =\ 1,2,\cdots,M \tag{19}\]  
Finally, resampling is carried out. A group of particles are randomly generated based on weighted \(\bar{\omega}^{(l)}_{k|k-1}\) resampling and defined as \(B^{(i)}_{k|k}(i\ =\ 1,2,\cdots,M)\). Thus, an improved particle filtering method incorporating multi-type outlier detection is established. The flowchart of the proposed algorithm is shown in Figure 1.
## 5 Conclusions  
The proposed methodology demonstrated the effectiveness of a continuous-variable quantum key distribution (CV-QKD) framework enhanced by parameter optimization and security enhancement mechanisms. The Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) successfully tuned the system parameters, achieving a key generation rate of 1000 bits per second and a secret key capacity of 950 bits per second. This outperformed recent works such as [16] (key rate: 812 bps) and [18] (key rate: 860 bps), illustrating the efficiency of our approach.  
For side-channel attack detection, the proposed Adaptive Depthwise Separable Convolutional Neural Network (ADS-CNN) achieved an accuracy of 99.05% and a recall of 99.07%, which provides significant improvements over baseline CNN classifiers used in prior studies. Additionally, the CHMAC-EPQH-based device authentication mechanism attained a low False Acceptance Rate (FAR) of 5.794% and False Rejection Rate (FRR) of 0.1751%, demonstrating robust security performance.  
Future work will focus on validating the proposed framework in long-distance transmission scenarios, improving its resilience under high-loss channels. Furthermore, integration with experimental quantum hardware and real-time side-channel attack simulation environments will be explored to test deployment feasibility in practical settings.  
Conceptualization, V.P.R., D.P. and E.K.; methodology, V.P.R., D.P. and E.K.; Writing--original draft, V.P.R. and D.P.; Writing--review/editing, D.P., C.P. and E.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The AES HD dataset used in this research is publicly available at [[https://github.com/AESHD/AES_HD_Dataset](https://github.com/AESHD/AES_HD_Dataset)]([https://github.com/AESHD/AES_HD_Dataset](https://github.com/AESHD/AES_HD_Dataset)) (accessed on 7 March 2025).  
The authors declare no conflicts of interest.
# Explaining Deep Learning-Based Driver Models
## 2 Background and Related Work
### Driver Emotion Recognition  
Emotions can be defined as states that comprise feelings, physiological changes, expressive behavior, and inclinations to act [26]. As is described in [27], each emotion has unique features related to signal, physiology, and antecedent events. In addition, each emotion also has some characteristics in common with other emotions such as rapid onset, short duration, unbidden occurrence, automatic appraisal, and coherence among responses. The unique characteristics of an emotion are important to differentiate a specific emotion from other affective phenomena. The Facial Action Coding System (FACS) [28] is a comprehensive, anatomically based system for describing all visually discernible facial expressions or emotions [29]. In this sense, in [30], results on the recognition of seven emotional states (neutral, joy, sadness, surprise, anger, fear, and disgust) based on facial expressions are presented.  
In recent years, a wide variety of techniques have been used for emotion recognition. In [31], a survey of existing works in emotion recognition using electroencephalography (EEG) signals was proposed. Using also EEG signals, in [32], an approach involving the automatic two-stage classification (negative and positive) and three-stage classification (negative, positive, and neutral) of emotions evoked by music is presented. Moreover, a bimodal emotion recognition system using a combination of facial expressions and speech signals is proposed in [33]. In [34], a deep facial expression recognition algorithm for emotions based on CNNs and an ensemble deep learning algorithm to predict facial expressions are proposed.  
In a driving scenario, it is important to analyze the drivers' emotions while driving in order to obtain any kind of information related to their feelings and moods. Psychological studies show that the emotions of the driver play an important role in safe driving [35; 36]. In this sense, the authors in [37] proposed that emotions affect driving directly by promoting aggressive driving, and indirectly by reducing the ability to perform several actions at the same time. The authors in [38] proposed a framework for driver emotion recognition using facial expression recognition. The authors in [39] presented an approach for driver emotion recognition involving a set of three physiological signals (electrodermal activity,skin temperature, and the electrocardiogram). Recently, a complete survey about driver emotion recognition for intelligent vehicles was presented in [40].  
This work aims to go further, starting with models based on deep learning that detect both the emotions and the activity of the driver, and using XAI techniques to analyze and explain the decisions made by the system.
## 3 Results and Discussion
### Kinematic Viscosity  
The RV test results showed that the asphalt binder viscosity increases almost linearly with the increasing of CRM, PP, and TPS contents. Figure 3 explains that the viscosity values acquired from the CRM-modified asphalt binders were higher than those values obtained from the unmodified asphalt binder, TPS- and PP-modified bitumen binders of 124%, 34%, and 31%, respectively, and with a concentration 4% of each modifier. The influence of CRM, PP, and TPS modifiers on the asphalt binder viscosity demonstrated that the addition of these modifiers led to an increase in the kinematic viscosity of the asphalt binder. Our results demonstrated that the kinematic viscosity increased from 0.411 Pa. s (base asphalt) to 0.919, 0.703, and 0.687 Pa. s by adding 4% of the CRM, PP, and TPS modifiers, respectively. Further, our results noted that the increment in viscosity with the addition of 4% CRM is more than two times the base asphalt value. These results indicate that CRM, PP, and TPS are extremely useful when trying to improve the viscosity property of an asphalt binder. A higher viscosity is required to improve the rutting resistance and fatigue/reflection cracking resistance in flexible pavement. When comparing between the viscosity results of the modifiers, CRM has a higher viscosity property than those of both PP- and TPS-modified binders. Hence, CRM is the best modifier to improve the viscosity property of asphalt binders, followed by PP and TPS. As a result, CRM is the most appropriate additive for enhancing the rutting resistance and fatigue/reflection cracking resistance in flexible pavement, followed by PP and TPS modifiers.  
Figure 2: Effect of modifier content on ductility.
# Magnetic Field Penetration Depth in Various Materials and Applications
## 3 Results
### Materials Studied  
Each material has unique properties that drive innovation in different technological areas, from quantum to renewable energy and transport systems. Their study not only enables practical advances but also contributes to understanding the fundamental physical principles that govern diversified matter.  
The bars in Figure 4 show the distribution of studies by material type: type II superconductors clearly dominate, with 30 studies, reflecting their scientific and technological relevance. Natural materials are in second place, with 11 studies, highlighting their importance in specific applications. Type I conductors and superconductors have only four studies, indicating a lower interest compared to type II. Semiconductors are under-represented, with only one study, which may reflect a narrower focus in this review.  
There is a growing interest in advanced insulating materials to improve the sensitivity and functionality of devices in the presence of magnetic fields. That is, the number of studies and materials available varies by material type, with notably more research on superconductors and insulators than on conductive materials.
## 3 Results
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 3.4.3 Functionalization of FnPEGAuTSNPs and pH Monitoring  
Prior to pH adjustment assays, AuTSNPs were coated with polyethylene glycol (PEGAuTSNP) to minimize contact between the Fn and the nanoplates' surface to avoid potential impact in the protein conformational behavior. Conformational changes in the protein were monitored by suspending the Fn-PEGAuTSNP in two phosphate buffers at pH 7.2 and pH 4.1, respectively. In Figure 11, the LSPR\({}_{\lambda\text{max}}\) of Fn-PEGAuTSNPs at pH 7 was measured at 633 nm; upon pH adjustment from pH 7 to pH 4, the LSPR\({}_{\lambda\text{max}}\) was measured to be 643 nm.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Optimization  
Finally, many authors have considered the distribution of sensors and energy optimization as a multi-objective optimization problem. The authors in [29] for instance proposed an Efficient Cover Set Selection (ECSS) approach, which is an improved version of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) [30]. The goal of the proposed scheme is to maintain full coverage by using fewer sensors. They highlighted that having more active sensors results in more coverage but with a lower overall lifetime, and vice versa.  
The authors concluded that the ECSS algorithm had advantages such as the ability to reset all the sensors' statuses with a one-time approach. It could also be made flexible by providing it with parameters such as the requested field coverage and the model.  
In another approach named the Multi-Objective Set Cover Problem (MO-SCP) [31], the authors considered reliable coverage as an additional conflicting objective to be optimized. They tried to solve it using a multi-objective evolutionary algorithm to select the set with the minimum number of sensor nodes, such that it will provide coverage of all the targets. To meet the requirement of staying active for long periods, the authors proposed to alternate nodes between idle and active states through something called 'wake-up scheduling' of the sensors. Their objective was to achieve a WSN system which can actively sense its predefined targets almost all the time. With the implementation of this method, only a single disjoint set is active while the others are kept in sleep mode (in a specific interval), resulting in an energy conservation scheme. When the active set goes out of power, another set is scheduled to be in the active state. Therefore with this approach, the more DSCs that there are the better it is for the entire system to stay on power. Moreover, they adopted a heuristic crossover operator designed specifically to improve the performance of the algorithm, and conducted simulation scenarios to test the effectiveness of the proposed algorithm.  
In this paper, we propose an approach that combines a set cover method with a min-cost max-flow algorithm using a linear programming technique.
# A Rapid Bridge Crack Detection Method Based on Deep Learning
## 5 Conclusions  
A method combining DCGAN and YOLOv5 which can detect bridge cracks rapidly and accurately was proposed. Moreover, we described the theory, training environment, parameter setting, and neural architecture of the DCGAN and YOLOv5. This work investigated the performance of the proposed model and compared the training results of the extended dataset with the original dataset. The main findings are presented as follows:  
1. The trained DCGAN can learn the characteristics of cracks and quickly generate a large number of artificial bridge crack images which are used to extend the real dataset. The time cost of generating 1000 artificial crack samples was on the order of 10 s. The generated images were balanced and feature-rich.
2. The YOLOv5 target detection neural network can perform crack identification and rapid detection. The time cost of detecting one crack image is on the order of 10 ms.
3. The results indicate that when YOLOv5 was trained on extended dataset, it had a similar detection accuracy compared with when it was trained on the original dataset (real dataset), which provides a new idea for the cost control of maintenance and monitoring of large-scale concrete structures.  
The proposed method of combining DCGAN and YOLOv5 has been proven to be acceptable, especially in terms of cost-effectiveness. However, the quality of the images generated by the DCGAN and the detection accuracy of YOLOv5 need to be improved. These factors are influenced by factors such as the computing power of the computer, hyperparameter optimization, and training strategy. In future work, advanced training strategies and optimization algorithms for neural networks will be the focus of our research.  
Conceptualization, Y.L. and W.G.; Methodology, Y.L. and W.G.; Software, T.Z.; Validation, W.G., T.Z., [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Formal analysis, Y.L.; Investigation, Y.L.; Data curation, Y.L.; Writing--original draft, Y.L.; Writing--review & editing, W.G., T.Z., [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Supervision, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Funding acquisition, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 12102294, 12272257), the Natural Science Foundation of Shanxi Province (202203021211169), and the special fund for Science and Technology Innovation Teams of Shanxi Province (Nos. 202204051002006).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Analysis of Disturbance Influence Profile  
Based on the above analysis, the stress and deformation of the surrounding rock mass are analyzed and studied along the main inclined shaft-cutting face under the influence of excavation disturbance, as shown in Figure 22. It mainly shows the evolution of stress and displacement in the Z direction of the section taken by the excavation model of the main inclined shaft (along the section of the main inclined shaft). Under the condition that the main inclined shaft is not affected by excavation disturbance, the stress range of floor-surrounding rock increases with the increase of buried depth. The maximum roof stress is 2.62 \(\times\) 10\({}^{7}\) MPa, and the maximum floor stress is 8.37 \(\times\) 10\({}^{6}\) MPa. After excavating the electromechanical chamber at the bottom of the well, the obvious bottom heave appears in the main inclined shaft section adjacent to the electromechanical chamber. In addition, after the excavation of the head chamber, the stress range of the surrounding  
Figure 21: Supporting state after excavation of belt roadway head chamber.  
rock of the main inclined shaft moves to the wellhead direction, and the stress increases. The maximum roof deformation is 0.247 m and the maximum floor deformation is 0.599 m without excavation disturbance. After the excavation of the electromechanical chamber, the obvious bottom heave phenomenon occurs in the horizontal section of the main inclined shaft. The deformation range of the floor near the chamber is 0.5-1.06 m, and the maximum deformation range of the roof is 0.862-1 m. The bottom drum range of the inclined shaft behind the chamber of the excavation head tends to increase. The literature points out that when the floor deformation reaches 1 m, the roadway section size will be greatly reduced, and high roadway maintenance costs will be incurred [33]. In summary, the serious deformation and failure area of the main inclined shaft mainly occurs near the intersection of the falling position, and the main causes are the deterioration of the surrounding rock fragmentation and the bottom heave caused by tensile stress. It is suggested that grouting reinforcement and the bottom anchor cable cooperative support method can be used for comprehensive treatment.
# Human Attribute Recognition--
## 5 Evaluation Metrics  
This section reviews the most common metrics used in the evaluation of HAR methods. Considering that HAR is a multi-class classification problem, Accuracy (_Acc_), Precision (_Prec_), Recall (_Rec_), and \(F\)1 score are the most common metrics for measuring the performance of these methods. In general, these metrics can be calculated at two different levels: label-level and sample-level.  
The evaluation at label-level considers each attribute independently. As an example, if the gender and height attributes are considered with the labels (male, female) and (short, medium, high), respectively, the label-level evaluation will measure the performance of each attribute-label combination. The metric adopted in most papers for label-level evaluation is the mean accuracy (_mA_):  
\[mA=\frac{1}{2N}\sum_{i=1}^{N}(\frac{TP_{i}}{P_{i}}+\frac{TN_{i}}{N_{i}}), \tag{1}\]  
where \(i\) refers to each of the \(N\) attributes. _mA_ determines the average accuracy between the positive and negative examples of each attribute.  
In the sample-level evaluation, the performance is measured for each attribute disregarding the number of labels that it comprises. _Prec_, _Rec_, _Acc_, and \(F\)1 score for the \(i^{th}\) attribute are thus given by:  
\[Prec_{i}=\frac{TP_{i}}{P_{i}},\quad Rec_{i}=\frac{TP_{i}}{N_{i}},\quad Acc_{i} =\frac{TP_{i}+TN_{i}}{P_{i}+N_{i}},\quad F_{i}=\frac{2*Prec*Rec}{Prec+Rec}. \tag{2}\]  
The use of these metrics is very common for providing a comparative analysis of the different attributes. The overall system performance can be either measured by the mean _Acc\({}_{i}\)_ over all the attributes or using _mA_. However, these metrics can diverge significantly, when attributes are highly unbalanced. _mA_ is preferred when authors deliberately want to evaluate the effect of data unbalancing.