esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Channel  
Assume that the channel is invariant over each block duration. The CIR at block k between the transmitter and the receiver transducer in the discrete time domain is modeled as  
\[h(k)=\sum_{l=0}^{L-1}h_{l}\delta(k-l), \tag{2}\]  
where \(k\) denotes the \(k\)-th block and \(L\) is the channel order. On the receiver side, the Doppler is usually compensated by resampling of the incoming signal [22; 23]. Let \(r_{k}(n)\) be the received baseband signal at the \(k\)-th hydrophone after resampling processing as follows:  
\[r_{k}(n)=e^{i(2\pi f_{k}nT_{s}+\phi_{k,0})}\sum_{l=0}^{L-1}h(k,l)x_{k}(n-l)+w( k,n), \tag{3}\]  
where \(T_{s}\) is the data symbol duration; \(f_{k}\) is the Doppler shift in \(k\)-th block, caused by relative motion between transceivers, A/D, D/A sampling, variant water current, etc.; \(\phi_{k,0}\) is phase rotation caused  
Figure 1: Block diagram: (**a**) transmitter and (**b**) receiver.  
by the symbol timing offset in the \(k\)-th block; and \(w(k,\ n)\) is additional white Gaussian noise with power \(\sigma^{2}\). The \(k\)-th received block is expressed as  
\[\mathbf{r}(k)=[r(k,0),r(k,1),\ldots,r(k,N-1)]^{T}. \tag{4}\]  
For simplicity and to avoid a loss of generality, we eliminated the block index \(k\). The received data block is  
\[\mathbf{r}=\mathbf{D}\mathbf{h}\mathbf{x}+\mathbf{w}, \tag{5}\]  
where \(\mathbf{w}=[w(0),w(1),\ldots,w(N-1)]^{T}\) is the noise vector, and \(\mathbf{D}\) is a diagonal rotated-phase matrix caused by the Doppler shift, which is expressed as  
\[\mathbf{D}=\texttt{diag}\left\{e^{j\phi_{k,0}},e^{j(2\pi f_{2}2T_{s}+\phi_{k,0 })},\ldots,e^{j(2\pi f_{k}2(N-1)T_{s}+\phi_{k,0})}\right\}, \tag{6}\]  
where  
\[\mathbf{h}=\left[\begin{array}{cccccc}h_{0}&0&\ldots&h_{L-1}&\ldots&h_{1}\\ h_{1}&h_{0}&\ldots&\ldots&\ldots&h_{2}\\ \vdots&\ddots&\ddots&\ddots&\ddots&\vdots\\ h_{L-1}\ldots&h_{1}&h_{0}&0&0\\ 0&\ldots&\ldots&\ldots&\ldots&0\\ \vdots&\ddots&\ddots&\ddots&\ddots&\vdots\\ 0&\ldots&h_{L-1}&\ldots&h_{1}&h_{0}\end{array}\right] \tag{7}\]  
is an \(N\times N\) circulant matrix.
## 3 Results and Discussion
### Water Quality Monitoring of Porewater Samples  
The test results showed that the pH values of all porewater samples were between 7.62 and 8.68, which approximated a pH ranging from 6.5 to 8.5. The dissolved oxygen values of 35 sampling sites ranged from 1.04 to 5.44 mg/L. [PERSON] and [PERSON] [42] stated that most crustaceans begin to show elevated mortality when the dissolved oxygen reaches below 2.45 \(\pm\) 0.14 mg/L, whereas dissolved oxygen levels in the rivers of HJ and DG in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multicolumn{2}{c}{**pH**} & \multicolumn{2}{c}{**Dissolved Oxygen**} & \multicolumn{2}{c}{**Conductivity**} \\  & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**End**} & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**End**} & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**End**} \\ \hline BK & 7.78 & 8.86 & 5.81 & 4.93 & 16 & 18 \\  
this study were both lower than 2.45 mg/L and thus were worthy of further discussion of the physiological effects of dissolved oxygen. Conductivity increased due to an increase in salinity. Sampling sites HJ7 and DG1 were closer to the ocean, and the conductivity was relatively higher here than at the inland sites. In addition, site YS crossed the industrial area, and sites such as DB and SY were adjacent to the factory area. Therefore, discharged sewage led to relatively higher electrical conductivity.
# Performance Evaluation of an API Stock Exchange Web System on Cloud Docker Containers
## 3 Container-Based Web System Architecture and Experiments
### Hardware Elements  
This subsection details the hardware test environment utilized for the analysis, which involved deploying the system on the Docker container platform. Our system comprises a logic tier and a database, with the application (SEWS) deployed as a benchmark (as shown in Figure 2) within a container with a Linux OS to simulate a realistic container setup using the Docker engine in Swarm mode. The request load generator (SELG) was deployed in a separate container. Depending on the test, we used different number of containers (1 (Figure 2a), 5 (Figure 2b) and 10 (Figure 2c)).  
We used a pool of containers running on a machine that had: 4, 8, and 12 CPUs; 10, 20, and 30 GB RAM (\(Exp\). 1: \(12-30\) (12 CPU and 30 GB RAM), \(Exp\). 2: \(8-20\), and \(Exp\). 3: \(4-10\) cases), and Linux Debian 10 (Buster) 64-bit OS. We used the standard kernel provided by Linux Debian in version 4.19.0-16. Tables (Tables 2-4) include the hardware configuration (\(ET1-ET9\)). A separate network was established to interconnect all the containers. We executed only the required programs for our tests and gathered data on the arrival and execution times of each request and the average CPU utilization during each experiment. SELG measured the benchmark execution times, and we used the _psutil_ library to measure the CPU utilization.  
Figure 2: Container system (horizontal scaling) with back-end: (**a**) one container \(C1\), (**b**) five containers \(C5\), and (**c**) ten containers \(C10\).
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 3 Results
### Influence of Ultrasonic Treatment on the Ascorbic Acid (VC) in Pumpkin Juice  
Ultrasonic waves had a certain influence on the VC content in pumpkin juice fermented by yeast. The cavitation effect and micro-jet generated by ultrasonic waves might have accelerated the interactions between ascorbic acid and the surrounding substances, such as contact with oxygen, enzymes, or other active ingredients, thus affecting its stability ([PERSON] et al., 2018). As can be seen from Figure 2L in the results, when treated with ultrasonic waves at a low power, there was no obvious change in the VC content in the pumpkin juice. When the ultrasonic power was 300 W, the VC content in the pumpkin juice fermented by yeast significantly increased by 1.381 mg/mL compared with that at 200 W. From the experiment, it is known that after ultrasonic treatment, it was beneficial to increase the VCcontent of the pumpkin juice after yeast fermentation. This is consistent with the research findings of [PERSON] et al. [29] that ultrasonic treatment increased the content of ascorbic acid. [PERSON] et al. [17], when exploring the changes in the ascorbic acid content in fruit juices treated with ultrasonic waves and pasteurization, found that the ascorbic acid content in the fruit juice after ultrasonic treatment decreased from (37.21 \(\pm\) 2.18) mg/100 mL to (27.10 \(\pm\) 1.84) mg/100 mL, but that its retention rate was higher than that of the sample after pasteurization. The difference in the ascorbic acid content after ultrasonic treatment might have been related to the treatment conditions (temperature, time, ultrasonic power, etc.), and the specific mechanism of action still required further investigation.
## 2 Short Introduction to Pareto Optimization and Two Aggregation Methods
### \(\varepsilon\)-Constrained Method  
The \(\varepsilon\)-constrained method is based on the optimization of one selected objective function \(f_{j}\) and treating the others as constraints [7]. The optimization problem now has the form  
\[\begin{array}{c}\text{maximize }\,f_{j}\left(x\right),\,\,\,x\in S,\,\,\,\,\,j \in\left\{\text{l},...,k\right\}\\ f_{i}\left(x\right)\geq\varepsilon_{i}\,\,\,\text{for all }i=1,\,\ldots,\,k,\,i\
eq j\end{array} \tag{6}\]  
The \(\varepsilon_{j}\) are the lower bounds for those objective functions that are treated as constraints. For practical applications, the appropriate bounds must be selected carefully. In particular, it must be ensured that the bounds are within the feasible objective region, because otherwise the resulting problem would have no solutions. [PERSON] gives suggestions for a systematic selection of values for the \(\varepsilon_{j}\) and illustrates them with sample applications [8].  
Figure 5 gives an example based on the feasible region shown in Figure 3. In Figure 5\(f_{2}\) is treated as a constraint with the lower bound \(\varepsilon_{2}\). Thus, the remaining Pareto front is the section between the points F1 and F2. The figure also shows the main movement direction of solutions in \(Z\) that have exceeded the threshold \(\varepsilon_{2}\). The main movement direction results from the optimization. Move components up- and downwards are also possible, but are not considered by the assessment procedure of this method as long as they do not drop below \(\varepsilon_{2}\). A too large value of the constraint like \(\varepsilon_{bad}\) would make the problem unsolvable.  
A decision vector \(x^{\prime}\in S\) is Pareto-optimal, if and only if it solves Equation (6) for every \(j=1,\ldots,k\), where \(\varepsilon_{i}=f_{i}\left(x^{\prime}\right)\) for \(i=1,\ldots,k\), \(i\
eq j\), see [9]. This means that \(k\) different problems must be solved for every member of the Pareto front, which can be expected to be computationally costly. If the task can be relaxed to weak Pareto optimality, only one solution of Equation (6) per member of  
Figure 4: Example of a penalty function. It turns constraint violations into a penalty value between 1 and 0, which serves as a factor for decreasing the weighted sum.  
the front is required [9]. On the other hand, the method does not require convexity for finding any Pareto-optimal solution.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 2 Theoretical Formulation
### Geometry and Kinematics of Three-Dimensional Beam
#### 2.2.2 Kinematics of Deformed Beam
\begin{array}{c}\omega_{x}=\frac{1}{\phi^{4}}\{d\chi/ds\big{[}\theta^{2}( \varphi\chi+\psi-\psi\cos\theta)-\theta\varphi\chi\sin\theta\big{]}\\ +d\psi/ds\big{[}\theta^{2}(-\chi+\varphi\psi+\chi\cos\theta)-\theta\varphi \psi\sin\theta\big{]}\\ +d\varphi/ds\big{[}\theta^{2}\varphi^{2}+\vartheta(\chi^{2}+\psi^{2})\sin \theta\big{]}\}\end{array}  \tag{9}\]  
\[ \begin{array}{c}\omega_{y}=\frac{1}{\phi^{4}}\{d\varphi/ds\big{[}\theta^{2} (\varphi\chi-\psi+\psi\cos\theta)-\theta\varphi\chi\sin\theta\big{]}\\ +d\psi/ds\big{[}-\theta^{2}(-\varphi-\chi\psi+\varphi\cos\theta)-\theta \chi\psi\sin\theta\big{]}\\ +d\chi/ds\big{[}\theta^{2}\chi^{2}+\vartheta(\varphi^{2}+\psi^{2})\sin \theta\big{]}\end{array}  \tag{10}\]  
\[ \begin{array}{c}\omega_{z}=\frac{1}{\phi^{4}}\{d\psi/ds\big{[}\theta^{2} \psi^{2}+\vartheta(\varphi^{2}+\psi^{2})\sin\theta\big{]}\\ +d\varphi/ds\big{[}\theta^{2}(\chi+\varphi\psi-\chi\cos\theta)-\theta\varphi \psi\sin\theta\big{]}\\ +d\chi/ds\big{[}\theta^{2}(-\varphi+\chi\psi+\varphi\cos\vartheta)-\theta \chi\psi\sin\theta\big{]}\big{\}}\end{array}  \tag{11}\]  
The change rate of rotation with respect to the arc length at deformed configuration can be obtained by solving Equations (9)-(11) as follows:  
\[ \begin{array}{c}d\varphi/ds=\frac{1}{2\theta^{2}}\big{[}2\omega_{x}\varphi^{ 2}+\theta^{2}(\omega_{z}\chi-\omega_{y}\psi)+2\varphi(\omega_{y}\chi+\omega_ {z}\psi)\\ +\vartheta(-\omega_{y}\sigma\varphi\chi-\omega_{z}\tau\varphi\psi+\omega_{x} \kappa(\chi^{2}+\psi^{2}))\cot\frac{1}{2}\big{]}\end{array}  \tag{12}\]  
\[ \begin{array}{c}d\chi/ds=\frac{1}{2\theta^{2}}\big{[}2\chi(\omega_{x}\kappa \varphi+\omega_{y}\sigma\chi)+\omega_{x}\kappa(\varphi^{2}+\chi^{2})\psi+ \omega_{x}\kappa\psi^{3}\\ -\omega_{z}\tau(\varphi^{3}-2\chi\psi+\varphi(\chi^{2}+\psi^{2}))+\mathrm{J}( -\chi(\omega_{x}\kappa\varphi+\omega_{z}\tau\psi)+\omega_{y}\sigma(\varphi^{2 }+\psi^{2}))\cot\frac{\theta}{2}\big{]}\end{array}  \tag{13}\]  
\[ \begin{array}{c}d\psi/ds=\frac{1}{2\theta^{2}}\big{[}(\omega_{y}\sigma \varphi-\omega_{x}\kappa\chi)(\varphi^{2}+\chi^{2})+2(\omega_{x}\kappa\varphi+ \omega_{y}\sigma\chi)\psi\\ +(2\omega_{z}\tau+\omega_{y}\sigma\varphi-\omega_{x}\kappa\chi)\psi^{2}+ \vartheta(\omega_{z}\tau(\varphi^{2}+\chi^{2})-(\omega_{x}\kappa\varphi+ \omega_{y}\sigma\chi)\psi\cot\frac{\theta}{2}\big{]}.\end{array}  \tag{14}\]In addition, from Equations (3)-(5), the relationships between the fixed global coordinate and the rotation vector \(\vartheta\) at the deformed configuration can be expressed in terms of the rotation vector as follows:  
\[dX/ds=\frac{\varphi\psi}{\vartheta^{2}}(1-\cos\vartheta)+\frac{X}{\vartheta} \sin\vartheta \tag{15}\]  
\[dY/ds=\frac{\psi X}{\vartheta^{2}}(1-\cos\vartheta)-\frac{\varphi}{\vartheta} \sin\vartheta \tag{16}\]  
\[dZ/ds=1-\left[1-(\frac{\psi}{\vartheta})^{2}\right](1-\cos\vartheta) \tag{17}\]
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 2 Related Work  
As the fundamental research of affective computing that integrates emotion with AI, SER has become an active research area for its wide applications in other fields that range from NLP, HCI, psychology, and deep learning. Generally, SER contains the undermentioned steps: corpus recording, signal preprocessing, emotion feature extraction, and classifier construction [9], etc. The emotion feature extraction is a principal step that extracts representative features from input data for the sake of downstream classification. As the key component of an SER system that produces final SER results, the classifier can be learning machines from ensemble learning, kernel-based learning, deep learning, or their integration [7, 8].  
Conventionally, speech emotion recognition systems are trained with supervised learning models or their variants [7, 8]. The generalization of the models is often emphasized by training on a variety of samples with diverse labels [10]. Generally, labels for emotion recognition tasks are collected with perceptual evaluations from multiple evaluators. The raters annotate samples by listening to or watching the stimulus. This evaluation procedure is cognitively intense and expensive [11]. Therefore, standard benchmark SER datasets usually have a limited number of sentences with emotional labels, often collected from a limited number of evaluators.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 2 System Model and Tool Analysis
### Multi-Point Cooperative Transmission Mechanism for Vehicular Users  
In the vehicular network, the core idea of multi-point cooperative transmission is that the vehicular network users make use of other vehicular network users to cooperate when communicating with the base station. The multi-point cooperative transmission can transmit signals to the target which is unable to communicate normally due to serious interference or high path loss. It also can establish communication links in special occasions and improve communication efficiency by coordinating the information of multiple neighboring vehicular network users and carrying out multi-point cooperative transmission services through other neighboring vehicular network users. In the vehicular cooperative network, as shown in Figure 2, when some vehicular network users are in a state of suspended communication--that is, when they do not effectively communicate with the base station--other vehicular network users in the surrounding can use it to carry out cooperative communication services when communicating with the base station [6]. This technology not only occupies the original channel resources of other cells, but also has high technical requirements for capacity, delay, and signaling overhead. This paper studies the collaborative multi-point transmission for the vehicle network users by using surrounding suspended communication in particular, which can make full use of channel resources, improve the transmission rate of the normal communication vehicle network users, and minimize the interference to vehicular networks users in other areas. Therefore, the application of multi-point cooperative transmission technology in the communication between users and base stations in such vehicular networks can improve the signal transmission efficiency and the overall performance of the system.
## 6 Discussion
### Applicability of WebAssembly Analysis Techniques in Other Domains  
The comparison between WebAssembly and native programs raises important questions about the transferability of analysis techniques across these domains. Specifically, can tools like MineSweeper, designed for WebAssembly, be adapted to detect malicious application software in native binaries? Conversely, can traditional virus scanning technologies be effectively used for scanning malicious WebAssembly binaries?  
Tools like MineSweeper focus on analyzing WebAssembly binaries for patterns indicative of cryptojacking activities, leveraging the specific structure and execution model of WebAssembly. Adapting such tools for native programs would require addressing the broader spectrum of malicious behaviors that native applications might exhibit, along with the consideration of different binary formats, execution flows, and interaction with the operating system. Thus, analysis techniques for WebAssembly binaries are likely not directly transferable to native programs.  
Traditional virus scanning technologies are designed to detect a wide range of malicious signatures and behaviors in native applications. These technologies could potentially identify known malicious patterns or signatures within WebAssembly binaries. However, the effectiveness of this approach may be limited by the specific WebAssembly architecture. That is, the specific exploitation techniques and vulnerabilities relevant to WebAssembly might not align with those typically encountered in native applications, and vice versa.
## 3 Results and Discussion
### Narrative Review
It was found that 4 Web Medical's implants are produced using EBM, while Restor3D employs SLM. The Meshworks producer ([PERSON] et al., 2014) also uses SLM to manufacture customized implants with latticed titanium alloy structures. Additive Orthopaedics LLC (Little Silver, NJ, USA) produces customized implants with EBM ([PERSON] et al., 2014). A discussion of the differences between these AM metal-based processes is presented in Section 3.2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
\begin{tabular}{c} \\ \end{tabular}
\end{table} 
Table 1. Cont. The reported follow-up periods across the studies varied significantly, ranging from 7 months to 60 months, with most studies reporting favorable outcomes in terms of implant integration and functionality. For instance, [PERSON] et al. [17] and [PERSON] et al. [18] documented successful osseointegration within a 1-year follow-up period, while [PERSON] et al. [33] reported good results with CT-confirmed bone integration at 60 months post-surgery. In the study of [PERSON] et al. [29], CT scans were used to calculate the defect sizes and design 3D-printed spherical cages with diameters tailored accordingly. [PERSON] et al. [18] presented both custom and prefabricated 3D-printed titanium trusses, while [PERSON] et al. [34] reported exclusively on prefabricated 3D-printed cages. Prefabricated trusses were used when their standard shapes and sizes matched the anatomical requirements of specific cases, providing a less tailored yet still effective solution. This highlights the range of customization in 3D-printed implants, which can range from patient-specific designs to prefabricated options, depending on the clinical scenario. While the custom trusses described in [35,39] demonstrated extensive personalization, prefabricated solutions served as a practical alternative in appropriate cases, balancing customization and cost efficiency. In all reviewed papers, the design of the open structures of the implants facilitated the use of bone grafts for bone integration. Moreover, the porous surfaces of the implants (all based on powder sintering) contributed to osseointegration and positive outcomes. Based on the information in Table 1, the reviewed studies showed a wide geographic distribution of 3D-printed titanium cage applications. While the relatively small number of reported applications in scientific journals limits the identification of clear trends, some observations still can be made. The majority of implants are produced and used in the United States, with leading manufacturers like 4 WEB Medical and Restor3D. In Europe, in the United Kingdom, Meshworks implants were reported, with the analyzed studies emphasizing their cost efficiency. China represents another important player, as illustrated by implants from Shenshi Medical Device, which align with local regulatory frameworks. Furthermore, these studies show some regional preferences, with the US favoring the EBM process and Europe and China also exploring the SLM process.
## 3 Database and Models  
### Data Collection  
To explore the effects of steel fibers on the compressive strength and the flexural strength of UHPFRC, a reliable database was established. 162-group compressive strength experimental data and 102-group flexural strength experimental data were gathered from the literature [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20] (Tables 1 and 2). The specimens for compressive strength testing varies in shapes and sizes, due to lack of proper standards. According to GB/T 31387 [47], cube specimen of 100 \(\times\) 100 \(\times\) 100 mm is considered as standard for homogeneity of data, The test specimens were converted to standard cube compressive strength by the correlation techniques suggested by researchers [48; 49; 50; 51] and the equations are as follows. \[f_{cu,100}=0.959f_{cu,70.7} \tag{5}\]  
\[f^{\prime}_{c}=0.845f_{cu,70.7} \tag{6}\]  
\[f_{cu,150}=0.91f_{cu,100}+3.62 \tag{7}\]  
\[f_{cu,150}=f_{cylin,100\times 200}+6.41 \tag{8}\]  
\[f_{cylin,50\times 100}=1.07f_{cylin,100\times 200} \tag{9}\]  
where, \(f_{cu,70.7}\)\(f_{cu,100}\), and \(f_{cu,150}\) are the compressive strength of 70.7 mm cube, 100 mm cube, and 150 mm cube, respectively; \(f_{cylin,50\times 100}\) and \(f_{cylin,100\times 200}\) are the compressive strength of 50 \(\times\) 100 mm cylinder and 100 \(\times\) 200 mm cylinder, respectively; \(f^{\prime}_{c}\) is the axis compressive strength of UHPFRC, the size effects are not obvious in axis compressive strength. Figure 1: System of typical artificial neural networks (ANN) model. A typical ANN model has input, sum function, log-sigmoid activation function, and output. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{ \begin{tabular}{c} **Specimen** \\ **Dimension** \\ \end{tabular}
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 3 Proposed Method
If \(predicted\_distance\approx real\_distance\), the GPS location correlates with the frame, indicating that there was no GPS spoofing attack on the drone. If \(predict\_distance>real\_distance\), there is no correlation between the GPS location and the frame, confirming that there has been a GPS spoofing attack. This process is presented in Figure 2 with an example in which we collected five frames (\(frame_{0}\) to \(frame_{4}\)), along with their GPS locations, and tried to predict the distance for \(frame_{5}\) (\(i=0\), \(n=4\)).