esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### PRISMA Flow Diagram  
Figure 2 below reports the PRISMA flow diagram that offers a visual representation of the information flow and specifies the number of identified records processed at each search stage. As shown in Figure 2, the search initially began with 15,816 publications preidentified in all three databases, eventually narrowing down to 15 publications deemed directly relevant to the present review. For the next step, Figure 3 visualizes the keywords and their associations, as determined by the VOSviewer tool [78].  
Figure 3: Keywords co-occurrence—a network visualization diagram. Note: The diagram is generated using the VOSviewer v.1.6.15 software [78].  
Figure 2: PRISMA flow diagram of the literature screening process.  
As Figure 3 shows, the most frequently co-occurring keywords are divided into four clusters. The first group (marked in green) is related to the classification of technology tools; the second cluster (marked in red) is related to the use of monitoring tools by humans; the third cluster (marked in yellow) is related to sleep and general influences associated with it, and the fourth cluster (marked blue) is related to specific environmental factors associated with sleep and is most often investigated using low-cost technology tools, such as noise and artificial light at night (ALAN).
## 4 Adaptation
### Use Cases
#### 4.1.3 KIDE4 CMMS: Information Systems for Maintenance Management  
The third use case consists on interaction with a computerized maintenance management software (CMMS). By using this software, which is typically used to manage maintenance actions, users are able to access maintenance-related information, such as work orders or blueprints. The interaction target language is Spanish.  
KIDE4I has been adapted so as to track work orders, request for blueprints, problem solving protocols or exploded views and check stock for a specific machine or machine component through natural language commands. Furthermore, users can also fill forms on the system's request.  
Figure 4: Bin-picking robot the dialogue system has been adapted for.  
So as to interpret each of the commands directed to the system, the key elements to extract are **actions**, **targets** and **items**. As Example (5) shows, _targets_, along with _actions_, determine the action to perform--in this case, to show a work order--whereas _items_ are the action arguments--the work order identifier and the machine that work order is for.  
* **M****uestrame_action la **orden de trabajor*
* de la **fresadora**item*
* **Show_action me the **work order**target*
* for the **milling machine**item**  
As in the previous cases, the guide use case's definitions were reused, and only 6 specific rules were defined for this use case. To cover the different key element tags, the interpreter was modified to include them.  
For the domain modelling and instantiation, the available error codes--for the problem solving protocols--machines and components were modelled and instantiated, along with the IDs for work orders, which were defined as numerical patterns to be checked by the target system. Each action the system was able to perform was modelled, and the lexical units to identify targets and the rest of the elements of the scenario were modelled as in previous cases, both reusing existing lexical resources and linguistic knowledge. Following the CMMS' requirements, target-system-readable information was also modelled. Finally, action- and frame-related data was instantiated by following, again, the population strategy (more details on the use of this strategy in this use case can be found in [34]).  
Finally, as Tables 1 and 2 show, all classes for this use case have been reused, and a 77.3% of the instances were obtained automatically. For dialogue, the original modelling and instances were totally reused.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 4 Procedure and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Good Weather**} & \multicolumn{3}{c}{**Bad Weather**} \\ \hline \hline  & & **Sunny Day** & **Clear Night** & **Rainy Day** & **Rainy Night** & **Reflection** & **Blur** \\ \hline \multirow{3}{*}{mAP} & Ours & 79.8 & 70.3 & 69.2 & 64.9 & 65.4 & 61.1 \\ \cline{2-8}  & Vanilla ConvNet & 75.3 & 60.5 & 66.4 & 54.2 & 43.3 & 39.9 \\ \hline \multirow{3}{*}{Average mAP} & Ours & 75.05 & & & 65.15 & & \\ \cline{2-8}  & Vanilla ConvNet & 67.9 & & & 50.95 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of two multispectral detectors under different weather conditions. Figure 5: Comparison of all-day detection results reported on the test set. Figure 6: D-F Net performance compared with the vanilla ConvNet: (**a**) Loss function curve of 80,000 iterations; (**b**) precision–recall curve. Figure 7: Some traffic object detection results for good weather and bad weather.
# Software Fault Localization Based on SALSA Algorithm
## 7 Conclusions  
Fault localization is a critical and complex task in software debugging, especially in manual debugging processes. It not only poses challenges to researchers but also consumes a significant amount of resources. Spectrum-Based Fault Localization (SBFL) technology is a widely discussed method aimed at assisting in the discovery of software defects by  
Figure 6: The influence of different statistical formulas on SA-SBFL on Top-1 (**a**), Top-3 (**b**), Top-5 (**c**), and EXAM (**d**).  
analyzing the execution coverage of programs. However, this method does not fully account for the interactions between program entities and data flow, making it difficult to directly translate its analysis results into precise defect locations.  
This paper proposes a novel software fault localization method called SA-SBFL, which not only leverages traditional spectrum-based fault localization techniques but also incorporates the SALSA algorithm. By analyzing the linking relationships between program statements and using random walk to identify important program statements within the entire linking network, potential defective program statements are pinpointed. Through this method, a ranking list of the suspiciousness of program statements is ultimately obtained to assist researchers in locating fault positions. To validate the effectiveness of this method, this paper examines five open-source benchmark programs from the Defects4J dataset and compares them with six statistical formulas. The experimental results show that compared to traditional SBFL methods, the method proposed in this paper significantly outperforms traditional SBFL methods, narrowing the code detection scope and thereby enhancing software defect localization performance.  
Study conception and design: X.F. and Z.S.; data collection: Z.S. and Z.F.; analysis and interpretation of results: X.F. and Z.S.; draft manuscript preparation: X.F., Z.S. and Z.F.; fund support: Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Fund of the National Natural Science Foundation of China (Grant No. 42261070).  
Not applicable.  
The data that support the findings of this study are openly available at [[https://github.com/riust/defects4j](https://github.com/riust/defects4j)]([https://github.com/riust/defects4j](https://github.com/riust/defects4j)) (accessed on 10 October 2024).  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 6 Future Works
A comprehensive Zero Trust framework could significantly mitigate lateral movement within clusters and fortify the environment against insider threats and supply chain attacks. Developing AI/ML-driven threat detection models designed explicitly for Kubernetes workloads is an emerging and significant trend. These models could acquire foundational behaviors of containers, services, and users, subsequently identifying anomalies such as privilege escalations, reverse shells, or resource exploitation in real time. Research may concentrate on incorporating such models into monitoring tools such as Prometheus or Falco or integrating them into log pipelines for distributed inference. The transient and fluid characteristics of containerized environments render conventional signature-based detection inadequate, while adaptive behavior modeling may address this deficiency. Moreover, federated learning or edge-based inference may improve privacy and performance in extensive, multi-tenant clusters.
## 5 Lessons from International Data Collection
### Equipment  
Some partners had previous experience in soundscape research, but for all this was the first study that featured surveying large number of public participants around a single measurement point. All the research groups found it very important to delegate one researcher/technician to care exclusively about the equipment and the quality of the recordings.  
The intention of the recording stage is to record a first-person experience most representative of the location. Therefore, the researchers are instructed to'make themselves invisible' in the recording. However, at some locations, various research groups decided to put out a sign asking members of public not to touch or come near the measurement point as they experienced passers-by touching the windshield out of curiosity.  
The equipment setup has been designed to be as compact and unobtrusive as possible so as to limit any intrusion on the participant's experience of the space. From our experience, most participants do not end up with the equipment within their field of view during the questionnaire and often do not notice the presence of the stationary equipment. In some locations, this is not possible and participants may comment on its presence; however, over the thousands of surveys collected, only a small number of respondents have commented on the equipment as noticeably impacting their experience.
## 4 Validation Results via Simulation
### Simulation Test 1 (American Standard 120 V @ 60 Hz)  
Figure 10 presents the waveforms at the output of the converter (voltage and current), the capacitor voltages and the inductor currents for the simulation test 1. As it can be observed, the output signal accurately track the high quality sinusoidal in both stationary and transient regimes. After disturbances, the AC component of the capacitor voltages remains unchanged while its average value adapts to the input voltage. Table 5 summarizes the obtained THD (lower than 1%) and RMS error (lower than 0.2%) demonstrating the high performance of proposed control in the five selected operation points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Convention** & **Input Voltage** & **Power Load** & **Test 1 (60 Hz)** & **Test 2 (50 Hz)** \\ \hline Operation condition 1 & 125.0 V & 100\% & 0.10–0.15 s & 0.10–0.16 s \\ Operation condition 2 & 93.75 V & 100\% & 0.15–0.20 s & 0.16–0.22 s \\ Operation condition 3 & 156.2 V & 100\% & 0.20–0.25 s & 0.22–0.28 s \\ Operation condition 4 & 156.2 V & 25\% & 0.25–0.30 s & 0.28–0.34 s \\ Operation condition 5 & 93.75 V & 25\% & 0.30–0.35 s & 0.34–0.40 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Operation conditions and time intervals used in simulation tests.  
Figure 10: Simulated results during Experiment 1 using the proposed DS-SMC.  
Figure 11 shows a comparison between the DS-SMC approach developed in this work and the SS-SMC approach developed in [4]. As it can be noted, the voltage of the capacitors which is the same voltage applied to open semiconductors in each boost cell is always lower for the DS-SMC. It is relevant to mention that two features of the proposed control are responsible of the improvement: (a) the harmonic content included in the capacitor voltages and (b) their average component. The DC component cannot be accessed using the SS-SMC approach while using the proposed DS-SMC this component allows to enforce the minimum value of the output capacitors to be almost equal to the input voltage. In this simulation test \(V_{dc}=V_{in}+0.338V_{m}+V_{+}\) being \(V_{+}\) settled to 5 V.
# Reliable Semantic Communication System Enabled by Knowledge Graph
###### Abstract  
Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.  
semantic communication; knowledge graph; semantic extraction; semantic restoration +
Footnote †: journal: _entropy_
This model can be used to evaluate all the parameters. Furthermore, the model is validated using simulation, and it has very good agreement, which validates the proposed method. Figure 1: The proposed asymmetric carrier divider with irregular RF phase on DD-MZ modulator. Nevertheless, the current paper has some limitations: (1) We used the comparison between numerical and simulation models without experiment due to the unavailability of devices, but this model was successfully verified and had a good agreement; (2) Our proposed method was focused on optical signals modulated by RF sine waves [22; 26; 27; 28; 29]; (3) To eliminate the dispersion, we focused on the received RF power; (4) We assumed that the optic arm length is less than 1 m; therefore, the phase did not change significantly; (5) In this study, we investigated up to 16 scenarios, and the DD-MZM modulator was used with the best performance in the C4 scenario; and (6) As a consideration, the paper's proposed method focused not only on asymmetric power divider but also on the optimal phase. Therefore, the minimum DF was obtained by combining the irregular phase and the power divider to reduce DF by 96.7%. We think that the global optimum DF results will also be similar to the proposed case in this scenario. Moreover, we also must consider the availability of the optical divider in the market.
## 3 Results and Discussion
### Flame Kernel Development in Quiescent and Turbulent Conditions  
Figure 3 presents high-speed schlieren images for time evolutions of the early flame kernel development under laminar and turbulent conditions at five different values of \(u^{\prime}\) varying from 0 to 2.1 m/s, all for successful ignition events. The first four columns have a 24 \(\times\) 24 mm\({}^{2}\) view field to see the embryonic kernel development after the spark discharge; the fifth column has a larger view field of 42 \(\times\) 42 mm\({}^{2}\) for the observation of self-sustained flame propagation.  
For the laminar case, the initial rod-like kernel at \(t\) = 0.04 ms develops into an oval flame kernel and continuously propagates to form a laminar self-sustained spherical flame at \(t\) = 20 ms (the last image of the first row in Figure 3). For the turbulent cases at \(u^{\prime}\) = 0.46 m/s (the 2 nd row) and \(u^{\prime}\) = 0.92 m/s (the 3 rd row), the initial spark kernels at \(t\) = 0.04 ms and their subsequent development of flame kernels at \(t\) = 1.2 ms are very similar to that of the laminar case, indicating that weak and/or modest turbulence have almost nearly negligible effect on the embryonic kernel development at least up to 1.2 ms. This is possibly due to the suppression effect by the emitted shock wave [30]. However, such a shock wave suppression effect seems to become unimportant, as the kernel is wrinkled earlier by stronger turbulence. This is evidenced by the schlieren images shown in the 4 th and 5 th rows of Figure 3 at \(u^{\prime}\) = 1.4 m/s and 2.1 m/s, where the flame kernels at \(t\) = 1.2 ms have largely wrinkled by turbulence with higher values of \(u^{\prime}\). For the early development of the flame kernel, the higher the value of \(u^{\prime}\) is, the larger the wrinkled flame kernel area is, and the higher the flame kernel speed is. Furthermore, the wrinkled flame kernel with negative curvature segments could be induced by turbulence that can enhance reaction rate of \(Le>>\) 1 mixtures through differential diffusion [25]. We observe that turbulence can enhance the flame kernel speed to be much greater than its laminar flame speed for successful ignition events, resulting in a shorter \(\tau_{\rm delay}\) for the present \(Le>>\) 1 flames, as seen by comparing the lapsed times indicated in the last column of Figure 3.
## 3 System Model
### Transportation Network Architecture
Energy deficiency can be compensated for by an energy-storage device, while excess energy can be stored. ESs communicate the service status information (e.g., the number of charging AEVs, the reserved number of AEVs, and arrival time) to the nearest RSU. RSUs cache the static and dynamic information of road segments and charging infrastructures within the communication range. The static information includes the length of the road segment, the ID of the road segment, and the location of charging infrastructures. The dynamic information includes the traffic conditions and the charging infrastructure service status information. RSUs can obtain information about cached road segments and charging infrastructures on other RSUs through wired communication.
## 3 Results and Discussion  
### Visual Inspection, UV-Visible Spectroscopy, and Quantification of Synthesized AgNPs  
The synthesis of AgNPs using 1% latex extract was indicated by a slight color change. Initially, the mixture (1% latex + AgNO\({}_{3}\)) appeared white (Figure 1A); however, following the incubation period, a light greyish color was observed (Figure 1B). [PERSON] et al. [28], reported a similar color change for the green synthesis of AgNPs inside centrifuged natural rubber latex. These visible color changes are usually related to the reduction in silver (Ag) ions by the capping of biomolecules present within the respective extracts [5; 28]. Moreover, the literature has often associated a rapid and extreme color change with the production of AgNPs; however, this may vary based on several factors such as plant organ type, solvent/extract, concentration, duration of incubation, and the presence of reducing agents [29; 30]. Moreover, according to studies, it appears that the concentration (%) of the latex largely influences the production of AgNPs, since [PERSON] et al. [3] reported that a 2% latex solution was insufficient for AgNP synthesis compared to a 3% latex solution, which displayed optimal results during the green synthesis.  
Coupled with the visual assessment, the formation, distribution, and stability of the synthesized AgNPs using latex was further confirmed using UV-visible spectroscopy. The analysis showed prominent silver surface plasmon resonance (SPR) bands at 410 nm with an absorbance of 0.947 (Figure 2). The wavelength of the AgNPs fell within the accepted range, which is often detected between 400 nm and 460 nm [31]. Similarly, [PERSON] et al. [5], displayed broad SPR peaks at 410 nm due to the collective oscillations of electrons; however, the absorbances were much higher, possibly related to the longer reaction times (72 h).  
The yield of synthesized AgNPs using latex extracts amounted to 14.80%, which is a moderate quantity of AgNPs (Table 1). It was noted that several studies used higher concentrations of latex, increased temperature, and incubation times, and most importantly, each latex-bearing species contained a variable chemical composition comparable to the current study [3; 32; 33; 34; 35; 36]. Several of the above-mentioned variables may likely influence the quantification of AgNPs.
## 3 Reduced Dynamic Orbit Determination of COSMIC-2
### Orbit Determination Strategy for COSMIC-2  
In this study, the RD method POD for six COSMIC-2 satellites with five-day space-borne GPS observation is carried out with Bernese 5.2 software [41]. The orbit determination arc section is 24 h. Due to the serious lack of observation data on DOY 140 and DOY 141 of Sat-1, the orbits of this satellite in these two days are not solved. In order to evaluate the accuracy of the solved orbits of COSMIC-2 using the RD method, three orbit accuracy analysis methods are applied, these three methods include the analysis of carrier-phase observation residuals, and the comparison of overlapping orbits and the comparison with reference orbits. The orbit determination strategy is shown in Table 4.
###### Abstract  
In the area of low-power wireless networks, one technology that many researchers are focusing on relates to positioning methods such as fingerprinting in densely populated urban areas. This work presents an experimental study aimed at quantifying mean location estimation error in populated areas. Using a dataset provided by the University of Antwerp, a neural network was implemented with the aim of providing end-device location. In this way, we were able to measure the mean localization error in areas of high urban density. The results obtained show a deviation of less than 150 m in locating the end device. This offset can be decreased up to a few meters, provided that there is a greater density of nodes per square meter. This result could enable Internet of Things (IoT) applications to use fingerprinting in place of energy-consuming alternatives.  
IoT; localization; LoRaWAN; deep learning Article