esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulation Results
Whilst not plotted here for reasons of space, for higher incident photon energies we can see even from these plots that they will start to lie above the L-edges of the ions in the heated system, thus again leading to increased absorption at the expense of a reduction in spatial uniformity. Figure 3: Cretin simulations of the temperature (**a**) and ionisation (**b**) distribution along the target as a function of time for the four photon energies of study. The laser pulse is shown in white. Note how, for the cases in which the photon energy of the laser is such that there is strong absorption (1300, 1500 and 1600 eV), the front surface of the target (top region of the images) is much hotter and ionized than the back owing to the rapid absorption of the XFEL radiation before it propagates to the back of the target. However, in the 1400 eV case, as the photon energy lies between the bound-bound (L-M) transitions and below most of the L-edges of the ions, the laser is uniformly deposited along the thickness of the target and gradients are minimised. Figure 4: Cretin simulations of the opacity at the front and back of the targets for XFEL photon energies of 1300 eV (**a**) and 1400 eV (**b**). The orange dashed lines indicate the photon energy of the XFEL in each case, and the laser pulse is shown in white. Additionally, the position of the bound-bound transitions and the L-edges for the cold and hot case are indicated. Each of the inset plots below the figures show a more detailed lineout of the plasma opacity at the photon energy of the XFEL as a function of time. In the 1300 eV case, as the plasma heats up owing to the effect of the XFEL, the bound-bound features shift up in energy until they overlap with the XFEL energy, and thus absorption becomes much more efficient. In contrast, in the 1400 eV case, the XFEL photon energy lies above that of the bound-bound transitions, but below the L-edges of the ions produced as the plasma is heated, thus leading to reduced absorption.
# Cybersecurity in a Large-Scale Research Facility--One Institution's Approach
## 3 Cybersecurity Approach at the NHFML's DCFF in Practice  
A typical DCFF embedded system is chosen as a representative example of how the NHMFL's DCFF implements the presented cybersecurity approach. Such systems interface with large-scale infrastructure, require remote accessibility and are used for data acquisition and equipment protection. The purpose of each embedded system must be individually assessed when identifying appropriate security measures so that the desired level of security can be determined by weighing the level of risk and impact on the facility. The assessment involves determining accessibility needs, understanding technology risks, capabilities, and constraints, and (ease of) use requirements. Specifically, the NHMFL's DCFF uses a device with a high-speed Field Programmable Gate Array (FPGA), a Linux-based real-time operating system, and hot-swappable input/output (I/O) modules for equipment monitoring and control. This setup requires a host PC (Personal Computer) on which software is developed and from which software is deployed to the FPGA or real-time operating system over the network. It also requires the development of custom-built applications that can run on a remote PC and enable user access to the device after deployment. As a result, security measures need to be applied at the physical, OS, application, and network levels. Each layer is related, and failure to protect one layer of the architecture can lead to vulnerabilities in another [21]. Importantly and in addition to the outlined measures, essential parameters monitored by this real-time system are redundantly recorded by the facility's main Distributed Control System to aid in fault or attack recovery. An example of such an embedded system at the DCFF is the magnet power supply monitor, which logs and displays power-system-related instrumentation data. The power supply monitor program runs on an integrated controller, which consists of a real-time embedded processor, FPGA, three analog input modules, and one digital input module. Users can remotely connect to the controller via a custom-built TCP (Transmission Control Protocol) client application to monitor down-sampled power system data in real time and can download data log files through an encrypted web server hosted on the device. The device is secured using a variety of physical and electronic methods. The device is isolated within a wall-mounted box enclosure in a restricted area for physical protection. The device can only be accessed through the DCFF's staff network (Figure 2; Figure 3, Level 4) or, if offsite the FSU campus, the NHMFL's virtual private network (VPN), which requires multifactor authentication and limits access only to staff members who require it. Remote accessibility is limited to a transport layer security/secure socket layer (TLS/SSL)
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 5 Experiments and Results
### Scenarios
#### 5.6.3 Hypothesis 3  
To verify this hypothesis (bidirectional corrections via the middleware provides better results than unidirectional corrections), we compare the scenarios 3 and 5 on the one side (middleware without scheduling, cf. Figures 9 and 11) and scenarios 4 and 6 on the other side (middleware wit scheduling, cf. Figures 10 and 12). The standard deviations between average speeds is zero with the scheduling, while it is positive without it. Hypothesis 3 is valid.  
increase in computational times compared to the regional simulation (which is the longest simulation between the two): 55% (S3 compared to S1) and 65% (S4 compared to S2). However, the use of the middleware with bidirectional correction results in a big increase in the computational times: 301% (S5 compared to S1) and 369% (S6 compared to S2) increase. The difference is big, and Hypothesis 4 is then not valid. Bidirectional correction has clear benefits, but it should be used when there are no tight constraints on execution times.
## 3 Methods  
### Efficient Channel Attention Temporal Convolutional Network  
In this study, the proposed Efficient Channel Attention Temporal Convolutional Network (ECA-ATCNet) model is an improvement on the ATCNet [32] architecture, and the framework of ECA-ATCNet is shown in Figure 1. Unlike ATCNet, ECA-ATCNet proposes the ECA-Conv block, which not only encodes the low-level spatiotemporal information in MI-EEG signals through three convolution (conv) layers, but also processes the temporal, spectral, and spatial domains of EEG signals at the feature level through Efficient Channel Attention (ECA) [53]. The ECA-Conv block receives raw EEG data, which consists of time-series data from multiple electrode channels. The output of the ECA-Conv block is a time series with higher-level representations. Then, the Sliding Window (SW) block is used to divide the output time series into multiple windows [32]. Finally, multiple windows are input in parallel into separate Attention Temporal Convolution (ATC) blocks [32]. The ATC module consists of Multi-Head Attention (MHA) and Temporal Convolution (TC) blocks. The Multi-Head Attention highlights the most valuable features in each window and feeds the highlighted features into the TC block to extract advanced temporal features. Finally, these features from all windows are aggregated and input into a fully connected (FC) layer with SoftMax activation to convert the input EEG signals into probabilities for each category.
# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 1 Introduction
Parcel delivery missions using a drone were simulated based on heuristic flight path planning (HFP) and other routing algorithms [28]. The results showed that HFPP delivers up to 33% more data packets compared with Encounter-Based Routing and Epidemic routing protocols. Vehicular ad hoc networks (VANETs), which are a type of mobile ad hoc network (MANET), can be used in an intelligent transport system. VANETs allow the mobile vehicles to establish three main categories of communication: vehicle to vehicle, vehicle to infrastructure, and infrastructure to infrastructure. A specific application of VANET applied to a drone network allows messages to be sent via wireless links [32]. A bio-inspired coordination protocol for a drone flying ad-hoc network (FANET) used for agriculture applications [30] has been used in an ad hoc simulator for a preliminary analysis of the feasibility of drone network design. The performance of the branch and bound search-based mode selection (BBS-MS) for drone-based air-to-ground wireless networks was investigated in [31]. In this study, we investigated six routing algorithms in a network of drones with a mobility schedule to ensure communication between isolated areas or in areas with technical damage. We then qualitatively compared the algorithms. The major contributions of this study are as follows:  
* The proposed drone network is independent of the Internet, and is of the DTN type. * A topological map as a collection of regular polygons (squares) was proposed. A time-dependent variant of [PERSON]'s algorithm, which determines the fastest route by taking into account the time when the message reaches the node and the time allocated for data transfer, was developed for the proposed network. * Five classical algorithms for DTN networks were adapted and tested for the proposed network. * Simulations based on flight tests were performed to analyze the efficiency of the data transmission. The results obtained by the single-copy and multiple-copy algorithms were compared, in the case of buffer limited capacity. * Practical application of this work is to facilitate the transmission of information in regions quarantined due to an infectious outbreak, such as COVID-19 pandemic, in regions with technical damage due to a disaster, and in non-urbanized areas without electricity access or communication infrastructure. The remainder of the paper is organized as follows. In Section 2, a drone network architecture based on the mission profile, and drone sensing and communication, are proposed. Then, the algorithms and protocols for the delivery of data using drones are proposed, described, and analyzed. In Section 3, simulation results are summarized and discussed. Finally, conclusions and possible future work are presented in Section 4.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.2 Recurrent Neural Networks (LSTM)  
Due to the effectiveness of deep learning techniques, we also adopted an RNN model consisting of three LSTM layers. The input of the neural network was a vector of 96 MFCC features. Each of these three LSTM layers had 64 filters. We added a fully connected layer and a dense layer of 16 nodes that had an activation function, Softmax.
## 7 Results and Discussion
### Network Jitter  
Without authentication, EIGRP jitter values ranged from 35.66 to 44.11 ms, while OSPF jitter values varied between 37.01 and 45.64 ms. With authentication enabled, EIGRP jitter ranged from 35.96 to 45.05 ms, and OSPF jitter ranged from 35.91 to 45.14 ms. The inclusion of authentication slightly increased jitter in both routing protocols. Figure 18 shows a comprehensive graph of network jitter for visual analysis. Network jitter, which can be calculated as the variance in network delay, is also depicted in the graph.  
Figure 17: Network delay using 13 Routers—EIGRP vs. OSPF.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods  
### Datasets  
The training of complex machine learning models that comprise large number of variables is particularly cumbersome. Here, model architecture and the selection of relevant data are crucial considerations [26]. If not chosen properly, the artificial intelligence may produce a model that causes overfitting or underfitting, which often leads to bad predictions of new, unseen, data [72].  
Few datasets that comprise high-quality and uncompressed facial recordings with reference physiological measurements (e.g., heart rate from an electrocardiogram or pulse rate from a finger oximeter) are currently available. Compression, even at a low factor, has a significant impact on the measurement. Highly compressed video streams lead to low-quality and corrupted PPG signals [43].  
MANHOB-HCI [73] is a dataset that contains a large number of facial videos along with electrocardiographic recordings. The video streams are, however, compressed. PURE, a dataset introduced in [47] by [PERSON] et al. contains 60 one-minute videos along with pulse signals recorded with a finger oximeter. The videos streams have not been compressed. Like PURE, the COHFACE dataset [74] also contains one-minute videos along with pulse oximeter recordings. The video streams are, however, highly compressed. More recently, [PERSON] et al. proposed the UBFC-RPPG video dataset [20], which contains 43 uncompressed videos along with finger oximeter signals. The participants played a time-sensitive mathematical game that supposedly raises their pulse rate. [PERSON] et al. proposed ECG-Fitness [60], a dataset that contains 204 uncompressed videos. Electrocardiograms were simultaneously recorded.  
In the context of automatic learning of artificial intelligence models, the databases presented above are limited: only a small volume of data are available and the datasets include notable differences (e.g., sampling frequency, compression, and image resolution). Thus, preprocessing operations (e.g., spatial and temporal resampling) are required to unify the data.  
In the following, we propose a new strategy dedicated to the simulation of synthetic PPG videos. The pulse signal is first approximated using data that are fitted to real iPPG signals.
# Fiber Bragg Gratings Based Cyclic Strain Measuring of Weld Toes of Cruciform Joints
## 6 Conclusions  
Aiming at the difficulties of local strain measuring for welded joints, an FBG sensor based method was proposed, and the corresponding fatigue test was conducted. Strains at some characteristic points along the weld toe under the cyclic tensile load were recorded, and the strain time histories were drawn and analyzed. The following conclusions can be drawn:  
1. FBG has small section size, good flexural resistance and can be arranged across the characteristic points at the weld toe. Compared with other strain sensors, it can obtain the complete cyclic strain time history of the weld toe more accurately and reliably, which has great advantages for the experimental investigation and SHM for welded structures.
2. Under the cyclic loading, the strain time history of the weld toe can be divided into three distinct phases, in which cyclic hardening effect in the early phase and plastic flow behavior in the final phase are obvious and influence greatly on the mechanical behaviors for welded joints.
3. Both experiment investigation and theoretical analysis show that the local stress and strain state at the weld toe is the vital factor which affects the fatigue performance of welded joints without considering the weld defects. Geometric points such as convex vertexes of the arc segment are potential positions for fatigue damage, which should be paid more attention to.
4. Generally, the weld toe curves of welded joints are irregular, which result in variable SCF along the weld toe. Under cyclic loads, different geometry characteristic points along the weld toe satisfy the crack initiation condition one after another, resulting in multiple cracks of the weld toe and their unsynchronized initiation moment.  
Conceptualization, G.W., S.L., Y.G., and Z.D; Methodology, G.W. and Y.G.; Software, S.L.; Data Curation, G.W. and S.L.; Validation, G.W., S.L., and Y.G.; Formal Analysis, G.W. and S.L.; Investigation, S.L.; Resources, G.W., Y.G., and Z.D; Visualization, G.W. and S.L.; Writing--original draft, S.L.; Writing--review & editing, G.W. and Y.G.; Supervision, G.W., Y.G., and Z.D.  
This work is supported by National Natural Science Foundation of China (No. 51575408).  
The authors would like to thank the reviewers' efforts in reading the manuscript.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Color Measurement  
The color was registered using a Minolta CR200 colorimeter (Minolta Corp., Ramsey, NY, USA). Before use, the instrument was calibrated with a ceramic reference, CIE-L*a*b* coordinates, and D65 illuminant. Registered parameters were L* (lightness: L* = 0 means black, L* = 100 means maximum light intensity), a* (\(-\)a* = greenness, +a* = redness), and b* (\(-\)b* = blueness, +b* = yellowness). Three measurements were made on the tip and middle portion of each sample, and the mean values for the tip and middle are reported in the table separately. The difference between the color of the control and the color of the UV-C sample is calculated by Equation (1) according to [PERSON] and [PERSON] [31]:  
\[\Delta\mathrm{ELab}\;=\;\sqrt{\left(\Delta\mathrm{L}^{*}\right)^{2}\;+\;\left( \Delta\mathrm{a}^{*}\right)^{2}\;+\;\left(\Delta\mathrm{b}^{*}\right)^{2}} \tag{1}\]  
where \(\Delta\mathrm{L}^{*}\) = L* sample (UV-C sample during each storage time) \(-\) L* control (at T = 0); \(\Delta\mathrm{a}^{*}\) = a* sample (UV-C sample during each storage time) \(-\) a* control (at T = 0); and \(\Delta\mathrm{b}^{*}\) = b* sample (UV-C sample during each storage time) \(-\) b* control (at T = 0).
# Wear Model of Silicon Nitride Ceramic Balls in Three-Body Coupling Grinding Mode
## 6 Conclusions  
In this paper, the application of a three-body coupled lapping method in the batch processing of silicon nitride ceramic balls was introduced. Then, based on the traditional two-body grinding model, the relevant parameters were analyzed, and the wear model of the three-body coupled grinding mode of silicon nitride ceramic precision balls was optimized. This model can be used to predict material removal during the grinding process of silicon nitride ceramic balls. The basic idea behind this model is \(\text{MRR}=\text{Z}\cdot\text{A}\cdot\text{p}_{\text{b}}\). Compared with previous modeling methods based on the two-body wear model, this model not only combines the hardness, geometry, and fracture toughness of the ball billet itself, but also incorporates the abrasive concentration, mechanical motion, and structural parameters into the formula to predict the material removal rate. Through theoretical derivation and experimental verification, the appropriate load, abrasive concentration, and other related parameters can be given according to the model control, so as to obtain the required wear mode. The elimination of other adverse factors and the optimal design of the speed of the disk are also discussed in other articles.  
Conceptualization, W.Y. and B.L.; methodology, W.Y.; software, W.Y.; formal analysis, B.L.; investigation, B.L.; resources, J.Y.; data curation, W.Y.; writing--original draft preparation, W.Y.; writing--review and editing, W.Y.; visualization, J.Y.; project administration, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Public Welfare Program of Zhejiang Province] grant number [LZY23E050003], and [Natural Science Foundation of Zhejiang Province] grant number [LGG2F030013].  
The study did not require ethical approval.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Load (N)**Concentration (wt\%) & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline
2 & 95.6 & 47.55 & 31.53 & 23.53 & 18.72 * & 15.52
## 5 Architecture of the Proposed System
### Pre-Processing  
The developed algorithm concept requires one to first apply sequences of actions making up signal pre-processing. The standardization procedure assumed standardizing signals relative to their maximum values. The pre-processing process often also involves so-called sound file segmentation, which results in a speed signal division into short fragments called frames. The frame length of the speech signal was set at 40 ms. The overlap was 10 ms. A speech signal acoustic analysis, which aims at capturing changes in the excitation signal (i.e., sound generated directly by vocal folds), requires selecting a speech signal in terms of its sonority due to a commonly known speech signal generator model associated with the speech signal production/generation process. The detection of voiced sounds was achieved with the use of the autocorrelation function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **PD Patients** & **NON-PD Patients** \\ \hline Male & 8 & 19 \\ Female & 16 & 5 \\ Age (average) & 55.5 & 40.0 \\ The disease duration (average) & 5.3 & N/A \\ The symptom severity—based on part III UPDRS & 20.25 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information about patients with PD and control group (NON-PD).  
Figure 1: Architecture of the proposed system.  
Voiced fragments are characterized by the occurrence of regular peaks (with a period of basic tone). The voiceless parts are similar to an aperiodic signal. In the system, the classification of the speech signal into voiced or unvoiced parts is performed using the autocorrelation function. To verify whether a sound is voiced, the second global maximum is determined and checks one level (the first maximum is zero). If the level is higher than a reference value \(p_{v}\), then this part is considered to be voiced; otherwise, it is deemed voiceless.  
Another problem for the pre-processing stage is the detection of speaker activity. During registration, parts of the signal in which the speaker is not active occurred. The use of another parameter responsible for the rejection of frames without speech is aimed at eliminating the silence of the recording and the rejection of frames that are potential noise, which can cause erroneous feature extraction for the dedicated speech description. The authors decided to base their decision on the power of the variable component (the variance of the signal by threshold). If the level of power is higher than a reference value (pp), then this part is considered to be speech; otherwise, the frame is deemed silent.