esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Technologies Stack
### Infrastructure
#### 5.3.1. GraalVM  
Regardless of its popularity, Spring Boot has a series of drawbacks that could affect the performance of a real-time document collaboration system [48]. By default, Spring Boot runs a series of tasks before starting an application:  
1. Auto-Configuration: Spring Boot scans the classpath for libraries and automatically configures beans based on the detected dependencies. This can lead to longer startup times, especially in large applications with numerous dependencies.
2. Annotation Processing: Spring Boot heavily relies on annotations for configuration. The process of scanning and processing annotations can contribute to increased startup times, particularly in applications with extensive use of annotations.
3. Reflection: Spring Boot uses reflection to dynamically inspect and instantiate classes. This introspection can impact startup performance, especially in applications with deep class hierarchies [49].
4. Initialization Overhead: Spring Boot applications may have initialization overhead as they setup components, such as the Spring Application Context, which contributes to the overall startup time.  
In the context of a real-time document collaboration application, due to the nature of the system, high spikes of traffic are expected. Waiting for a few seconds before a new microservice instance is up and running can affect the availability of the system. Part of the problem is the way Spring Boot is handled through the Java Virtual Machine (JVM).  
To speed up the start-up process of a microservice, the decision was to switch the compilation target from Java artifacts to native binaries. Native binaries represent a more  
Figure 7: Kubernetes cluster diagram.  
resource-efficient compilation target that does not require the presence of a runtime environment to be executed. Compiling a Spring Boot application to native binaries can be achieved with GraalVM [50].  
GraalVM is a high-performance runtime that provides support for various programming languages and execution modes. Developed by Oracle Labs, GraalVM is designed to improve the performance and interoperability of applications. Regarding the performance boost, GraalVM offers significantly lower (even up to 90%) start-up times of Spring Boot applications and lower CPU and memory consumption.
## 4 Conclusions  
In order to explore the influence of the decorative effect of GNPs on the strengthening efficiency of GNPs/Al composites, GNPs were decorated with rare earth oxide CeO\({}_{2}\) and 0.5 wt.% CeO\({}_{2}\)-GNPs/2024 Al composites were successfully prepared. According to the results of microstructure observation and mechanical properties, the following conclusions can be drawn:  
(1) Reinforcement GNPs decorated by CeO\({}_{2}\) particles were successfully prepared by the alcohol heating method. The rare earth oxides CeO\({}_{2}\) and GNPs were tightly bonded, and this combination was maintained after the composites were prepared by the pressure sintering process.  
Figure 11: Comparison of the theoretical contribution for yield strength of composite with different strengthening mechanisms.  
(2) The addition of CeO\({}_{2}\)-GNPs showed a better grain refinement effect than the original GNPs for the composites fabricated with the same preparation conditions. Moreover, the load transfer mechanism is considered to be the critical mechanism to matrix strengthening.  
(3) The effects of raw GNPs and CeO\({}_{2}\)-GNPs on the mechanical properties of composites were investigated. Results exhibited that the performances of composite reinforced by CeO\({}_{2}\)-GNPs are better than those values of the composite enforced with the raw GNPs of the same quality. The YS, UTS, and hardness of the composites were prepared by adding 0.5 wt.% CeO\({}_{2}\)-GNPs are 184.1 MPa, 271.8 MPa, and 128 Hv, which are increased by 21.1%, 24.7%, and 26.7% compared to these of the matrix, respectively.  
(4) The fracture interface showed the change from GNPs debonding to the tight bonding of GNPs to the matrix, indicating that the combination of the matrix and GNPs has been optimized due to the decorative effect of rare earth CeO\({}_{2}\). The optimization of interface bonding and distribution promotes GNPs to play a more effective role in a variety of strengthening effects, thereby significantly improving the mechanical properties of composites.  
Conceptualization, Z.G.; writing, Q.W.; formal analysis and data curation, S.L. and N.L.; Funding acquisition, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangxi Provincial key laboratory open fund project, grant number \"EJ202003431\" and Major Scientific and Technological Research and Development Projects in Jiangxi Province, grant number \"20194 ABC28001\".  
Not applicable.  
The authors declare no conflict of interest.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 3 QR\(-\)LoRa Design
### Joint Frequency-Amplitude Modulation  
#### 3.2.1 QR\(-\)LoRa Symbols  
A QR\(-\)LoRa chirp can be expressed as  
\[s_{E_{0}}(t)=\left\{\begin{array}{l}\mathrm{e}^{j2\pi\left(\frac{\mathrm{i} }{T}\left(\mathrm{i}\hat{t}^{2}+\frac{F_{0}}{\mathrm{i}\hat{m}}t\right)-\frac{ \mathrm{i}\hat{m}}{2}t\right)},\;0\leq t<T-\frac{F_{0}}{\mathrm{i}\hat{m}}\\ \mathrm{e}^{j2\pi\left(\frac{\mathrm{i}\hat{m}}{T}\left(\mathrm{i}\hat{t}^{2}- T+\frac{F_{0}}{\mathrm{i}\hat{m}}t\right)-\frac{\mathrm{i}\hat{m}}{2}t\right)},T-\frac{F_{0}}{ \mathrm{i}\hat{m}}\leq t<T\end{array}\right. \tag{5}\]  
Then, we perform a cyclic right-shift of \(T_{0}\) on a ramp signal, which can be written as  
\[A(t)=\left\{\begin{array}{l}\frac{1}{T}(t+T-T_{0}),0\leq t<T_{0}\\ \frac{1}{T}(t-T_{0}),T_{0}\leq t<T\end{array}\right. \tag{6}\]  
Multiplying \(A(t)\) with \(s_{F_{0}}(t)\), we obtain the QR\(-\)LoRa chirp with the amplitude encoding value \(T_{0}\) and the frequency encoding value \(F_{0}\), which can be given as  
\[s_{(F_{0},T_{0})}(t)= A(t)s_{F_{0}}(t) \tag{7}\]  
Since there are two discontinuity points, which are the maximum and minimum of the envelope, we segment the signal into three parts to represent it, respectively. Assuming \(T_{0}<T-\frac{F_{0}}{\mathrm{i}\hat{m}}\), the complete expression of QR\(-\)LoRa chirp is given as follows  
Figure 4: System overview of QR\(-\)LoRa.  
\[s_{(j_{0},T_{0})}(t)=\left\{\begin{array}{l}\frac{1}{T}(t+T-T_{0})e^{2\pi\left( \frac{tW}{2T}(t^{2}+\frac{tW}{2T})-\frac{tW}{2T}\right)},0\leq t<T_{0}\\ \frac{1}{T}(t-T_{0})e^{2\pi\left(\frac{tW}{2T}(t^{2}+\frac{tW}{2T})-\frac{tW}{ 2T}\right)},T_{0}\leq t<T-\frac{t_{0}}{2W}\\ \frac{1}{T}(t-T_{0})e^{2\pi\left(\frac{tW}{2T}(t^{2}+T_{0}+\frac{tW}{2T})- \frac{tW}{2T}\right)},T-\frac{t_{0}}{2W}\leq t<T\end{array}\right. \tag{8}\]
## 1 Introduction
### Pathophysiology and Diagnosis of SCI  
After an SCI, pathological changes affect recovery. Initial vascular disruption causes hypoperfusion and ischemia, leading to neuronal cell death. Inflammatory responses, oxidative stress, and apoptotic pathways contribute to tissue damage. Neurogenic shock (hypotension and bradycardia) may occur due to autonomic dysfunction. Tissue loss leads to cavity formation, complicating recovery [4]. Diagnosing SCI involves clinical assessments and imaging. Emergency evaluation includes testing movement, sensation, breathing, responsiveness, and muscle strength. Magnetic resonance imaging (MRI) detects spinal trauma, herniated discs, vascular abnormalities, and ligament damage [5]. Computerized tomography (CT) scans identify fractures, bleeding, and spinal stenosis [6]. X-rays quickly identify vertebral misalignment or fractures, aiding immediate decision-making [7].
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 4 Experiments and Analysis
### Test Collection and Retrieval Model  
The CLEF 2018 eHealth Evaluation Lab Consumer Health Search (CHS) task (Kvariations issued individually by 6 research students with no medical knowledge (The queries and the process to obtain them are described in [[http://www.khresmoi.eu/assets/Deliverables/WP7/KhresmoiD73.pdf](http://www.khresmoi.eu/assets/Deliverables/WP7/KhresmoiD73.pdf)]([http://www.khresmoi.eu/assets/Deliverables/WP7/KhresmoiD73.pdf](http://www.khresmoi.eu/assets/Deliverables/WP7/KhresmoiD73.pdf)), accessed on 15 February 2021).  
Collection The collection contains 5,535,120 Web pages and it was created by compiling Web pages of selected domains acquired from the CommonCrawl [20].  
Relevance Assessments For each information need, the organizers of the task provided about 500 documents assessed for a total of 25,000 topic-document pairs.  
Retrieval Model The index provided by the organizers of the task, an ElasticSearch index version 5.1.1, comes with a standard BM25 model with parameters b = 0.75 and k1 = 1.2 ([[https://sites.google.com/view/clef-ehealth-2018/task-3-consumer-health-search](https://sites.google.com/view/clef-ehealth-2018/task-3-consumer-health-search)]([https://sites.google.com/view/clef-ehealth-2018/task-3-consumer-health-search](https://sites.google.com/view/clef-ehealth-2018/task-3-consumer-health-search)), accessed on 15 February 2021).  
Notice That, among the queries of the CLEF 2018 eHealth CHS collection, the two identified by ids 160006 and 164007 will not retrieve any document in common with the other variants of the same information need (at least for \(N\leq 1000\)). This is because the text of query 160006 is \"nan\", while query 164007 has a typo \"pros and cons spirculina\", instead of spirulina, a type of algae. We stress on this aspect since, for those queries, it will not be possible to compute the value of the gain by definition, since the intersection of their ranked list with the ones for other formulations of the same topic will be empty.
## 6 Study Limitations  
### Source of Subject Recruitment  
This study recruited students with high-functioning autism and typical development with social difficulties through announcements on the national autism association's website and related pages, with the help of special education teachers from various schools. Regional, cultural, and individual differences may affect the results, preventing generalization to all related groups.
## 3 The UDVRP Model
### Problem Description  
The problem (called UDVRP) involves a distribution center, \(n\) delivery stations, and \(K\) identical unmanned delivery vehicles. These vehicles depart from the distribution center, visit multiple delivery stations, and subsequently return. To minimize the total cost of delivery, the vehicles must consider many elements, including transportation costs, charging expenses, and penalty fees. In addition, they must also comply with specific constraints, such as the maximum load capacity, delivery time, and driving distance, when devising their routes.  
This instance can be represented on a graph \(G=(N,X,Y)\), where \(N=\{0,\ldots,n\}\), with node \(i=0\) being the depot and \(i\in\{1,\ldots,n\}\) representing customers. Additionally, \(X\in\{0,1\}\) and \(Y\in\{0,1\}\) denote two binary variables that indicate the connectivity of nodes. The depot node is connected to the coordinate \(x_{0}\), while each customer node \(i\) is linked to a two-dimensional feature vector \(x_{i}=\{x_{ci},x_{di}\}\), where \(x_{ci}\) represents the coordinate and \(x_{di}\) represents the demand. Every edge is linked to the distance between the nodes it connects. \(X=1\) and \(Y=1\) indicate that there is vehicle service between two assigned points.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 1 Introduction
Furthermore, through comparative analysis with related schemes, our evaluation indicates superior performance in terms of time consumption and communication overhead. The subsequent sections of this paper are structured as follows: Section 2 outlines the related works, while Section 3 provides the preliminaries. The proposed scheme is elaborated upon in Section 4, followed by security analysis and performance analysis in Sections 5 and 6. Ultimately, we conclude the paper in Section 7 and point out future research directions in Section 8, respectively.
# On-Road Driver Emotion Recognition Using Facial Expression
## 5 Results and Discussion
### Ablative Analysis  
The FERDERnet model this study proposes contains of three modules: the FD module, which performs the processing of face detection, crop, and image format transform; the ABR module, which performs facial image augmentation-based re-sampling; the ER module, which employs the deep network backbone and adopts the fine-tuning strategy to perform emotion classification.  
Hence, to validate if and how much the ABR module impacts the emotion recognition task and compare it with the augmentations that torch transforms provide, ablative studies were conducted. The proposed FERDERnet was modified and evaluated by including or removing the ABR module in the architecture. The ablative analysis adopts precision, recall, and F1-score as metrics.  
The ablative analysis result is presented in Table 5. As can be observed, the model suffers significant performance loss when the ABR module is removed; the F1-score shows a decline by 4.6%. Furthermore, the confusion matrix of FERDERnet_X without the ABR module is demonstrated in Figure 9, and the classification results significantly drop in classes like Disgust, Fear, and Sad compared with Figure 7, which also shows the importance of the ABR module. The probable reason is that the ABR module performs not only adjustment of imbalanced data, but also enhances the images' diversity of lightness, angle, and contrast, which improves the model's recognition performance of night images and images under hard conditions in the dataset. This demonstrates the importance of data re-balance and diversity that the ABR module performs in the on-road driver emotion classification task.
###### Abstract  
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver's post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios. In this paper, the signal records of five disruptions (pure, continuous wave interference (CWI), multi-tone continuous wave interference (MCWI), multipath (MP), spoofing, pulse, and chirp) are examined, and the most influential features in both the time and frequency domains are identified with the help of explainable AI (XAI) models. Different Machine learning (ML) techniques were employed to assess the importance of the features to the model's prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. This XAI model reveals the black-box ML model's output prediction and provides a clear explanation of the specific signal occurrences based on the individual feature contributions. By using this black-box reveler, we can easily analyze the behaviour of the GNSS ground-station signals and employ fault detection and resilience diagnosis in GNSS post-processing.  
GNSS; jamming; explainable AI; interpretability; interference +
Footnote †: journal: Accepted: 13 December 2024  
Footnote †: journal: Accepted: 17 December 2024
## 4 Results  
### Monitoring of the RCA and Calibration Bias  
We monitored Z\({}_{\rm H}\) based on SC and GC monitoring. The RCAs for each radar within the KMA network were calculated to monitor the radar system status, as shown in Figure 6. The BRI and JNI radar on the KMA network were excluded from this analysis. The Z\({}_{\rm H}\) of BRI radar was recorded with a resolution of 0.5 dBZ due to the data transfer problem related to network capacity during an analysis period. For the JNI radar, there is no GC echo near the radar. The periods (P1, P2, and P3) were divided by considering the change in RCA (ARCA). The RCA of the KWK radar during P2 was higher than 5 dB. The mean and standard deviation (STD) of the RCA for each period are shown in Table 4. The STD of RCA for each period was less than 0.3 dB. It was proven that RCA was stable despite the short period for constructing the GCM. The absolute calibration bias calculated based on SC for each radar within the KMA network during the entire period is shown in Figure 7. The \(\epsilon_{\rm SC}\) (blue dot) and \(\epsilon_{\rm GC}\) (red dot) were compared when the RCA changed significantly owing to the change in calibration bias.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radar 1** & \multirow{2}{*}{**Radar 2**} & **Distance between** & **Number of Overlapping** \\ (Reference) & & **Two Radars (km)** & **Volumes** \\ \hline KWK & BRI & 214 & 575 \\ GDK & KWK & 86 & 1240 \\ KSN & KWK & 160 & 1064 \\ JNI & KSN & 176 & 762 \\ MYN & GNG & 183 & 500 \\ MYN & KSN & 200 & 595 \\ PSN & MYN & 118 & 1595 \\ GSN & JNI & 132 & 846 \\ SSP & JNI & 131 & 987 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distance and the number of overlapping volumes for the matched radar pair.  
Figure 6: Time series of radar calibration adjustment (RCA) at each radar within the KMA network from 1 May 2020 to 31 July 2020. The P1, P2, and P3 is the period with the similar RCA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multicolumn{2}{c}{**P1**} & \multicolumn{2}{c}{**P2**} & \multicolumn{2}{c}{**P3**} \\ \cline{2-7}  & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** \\ \hline CDK & \(-0.11\) & 0.17 & 0.69 & 0.16 & - & - \\ KWK & \(-0.11\) & 0.15 & 22.62 & 2.79 & \(-0.59\) & 0.16 \\ GNG & \(-0.14\) & 0.16 & – & – & – & – \\ MTN & 0.13 & 0.26 & 3.10 & 0.29 & 3.59 & 0.29 \\ KSN & \(-0.02\) & 0.2 & \(-1.09\) & 0.18 & \(-1.44\) & 0.20 \\ PSN & \(-0.17\) & 0.1 & \(-0.31\) & 0.12 & \(-0.60\) & 0.13 \\ SSP & 0.03 & 0.14 & \(-0.35\) & 0.23 & \(-0.04\) & 0.15 \\ GSN & 0.06 & 0.09 & 4.16 & 0.10 & 0.12 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean and standard deviation (STD) of RCA at each site. The periods are divided by the change in RCA.  
Figure 7: Time series of radar absolute calibration bias estimated from SC (blue dot) and change in the RCA (red dot) at each radar site from 1 May 2020 to 31 July 2020. The vertical dotted lines represent the period with similar RCA (P1, P2 and P3) shown in Figure 6.
## 5 Shaking Table Test of a Hollow Concrete Gravity Dam Containing Saturated Sandy Soil
### Analysis of Test Results  
Let us define the scenario with an acceleration amplitude of 0.1 g as Case 1 and the scenario with an acceleration amplitude of 0.2 g as Case 2.  
Figure 13 depicts the displacement-time history curve at point \(C\) for the Case 1 condition. Initially, the conventional gravity dam model records a peak displacement at the crest of approximately 0.85 mm, with a maximum absolute value reaching 0.89 mm. The new-type gravity dam model mirrors this behavior for the initial 5 s, after which there is a notable reduction in displacement. This indicates that significant liquefaction of the sand occurs around the 5 s mark. Beyond this point, the maximum absolute value of the peak displacement for the new-type dam model is 0.781 mm, which corresponds to an 11.94% decrease compared to the conventional dam model.  
Figure 14 presents the acceleration-time history curve for point \(C\) under Case 1 conditions. It is observed that the acceleration of the new-type gravity dam, with a maximum of 0.281 g, is notably lower than that of the conventional gravity dam, which has a maximum of 0.316 g, leading to an 11.07% reduction.  
Figure 15 presents the displacement-time history curve for point \(C\) under the Case 2 condition. The conventional dam model's displacement peak is consistently around 1.45 mm, with a maximum absolute value reaching 1.782 mm. In contrast, the new-type dam model exhibits a significant decrease in displacement after 5 s, with the maximum absolute displacement at point \(C\) being 1.61 mm post-5 s, indicating a 9.65% reduction compared to the conventional gravity dam. Figure 16 illustrates the acceleration values over the first 20 s. Despite minor discrepancies in recording times due to manual operations,  
Figure 13: The displacement–time history plot of point \(C\) under the Case 1 condition.  
it is apparent that the new-type dam model exhibits a maximum acceleration of 0.518 g, which is significantly lower than the conventional dam's maximum acceleration of 0.56 g, indicating a 7.5% reduction.  
Figure 16: The acceleration–time history plot of point \(C\) under the Case 2 condition.  
Figure 14: The acceleration–time history plot of point \(C\) under the Case 1 condition.  
Figure 15: The displacement–time history plot of point \(C\) under the Case 2 condition.  
In both scenarios, the displacement of the new-type dam model notably decreases around 5 s, indicating that the liquefaction of saturated sandy soil is achieved within about 5 s post-vibration initiation, which reflects a swift response to seismic activity. Both the maximum displacement and acceleration of the new-type dam model show a significant reduction compared to those of the conventional gravity dam model, suggesting that liquefied saturated sandy soil can effectively contribute to seismic damping during an earthquake.
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 3 Analysis of the Control Strategy  
### LCL-Type Three-Phase Grid-Connected Inverter Control Structure  
The control structure diagram of the LCL-type grid-connected inverter is shown in Figure 2. After collecting the voltage and current of the three-phase power grid from the external large power grid, through [PERSON] transformation, \(u_{\alpha}\), \(u_{\beta}\) and \(i_{\alpha}\), \(i_{\beta}\) are obtained. According to the instantaneous power calculation principle, the current reference values \(i_{2\alpha}\), and \(i_{2\beta}\) of the current loop can be obtained and then compared with the grid-connected feedback currents \(i_{2\alpha}\) and \(i_{2\beta}\). After the error signal is adjusted by the improved \(G_{\text{QPIR}}\)(s) controller, the PWM controller controls the on and off status of the switch tube.  
According to Equation (3) and considering the control system structure given in Figure 2, the s domain model of the three-phase LCL grid-connected inverter controlled in the \(\alpha\)-\(\beta\) coordinate system can be obtained, as shown in Figure 3. \(K_{\text{PWM}}\) is the transfer function of the voltage-source three-phase inverter bridge modulated by PWM, and \(H_{\text{i1}}\) and \(H_{\text{i2}}\) are the capacitance current feedback coefficient and grid-connected current feedback coefficient, respectively.  
Figure 3: Control block diagram of the grid-connected inverter with LCL filter in the \(\alpha\)-\(\beta\) coordinate system.  
Figure 2: Threephase LCL-type grid-connected inverter with active damping in the \(\alpha\)-\(\beta\) coordinate system.