esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 3 Solution Design
### Mobile Network Edge Deployment
Finally, to further optimize computational efficiency, the system adopts a parallel processing mechanism. In the MEC servers, computational resources are dynamically allocated through task scheduling, distributing signal data processing tasks across multiple nodes. The optimization of task scheduling can be expressed using Equation (3):  
\[T_{total}=\sum_{i=1}^{N}T_{i}\cdot\frac{C_{i}}{C_{max}} \tag{3}\]  
where \(T_{total}\) is the total computation time, \(T_{i}\) is the computation task time for the \(i\)-th node, \(C_{i}\) is the computation capability of the \(i\)-th node, and \(C_{max}\) is the maximum computation capability of all nodes. With this approach, the system can efficiently allocate tasks, ensuring that the computational resources of each MEC node are fully utilized, thereby improving the system's response speed and processing efficiency. In the 5G wireless signal ranging process, the user's environmental sensing data and personal information must be adequately protected to prevent potential data leakage or privacy infringement risks. Therefore, the design incorporates measures such as data encryption, identity authentication, and access control to ensure the security and privacy of user information, especially when sensitive data are being transmitted. The schematic of edge deployment for environmental sensing is shown in Figure 2.  
The edge deployment architecture implements privacy protection through three layers:  
1. **Device layer**: Data are encrypted during transmission through routers and base stations (BS). 2. **Edge layer**: CDN edge servers perform identity verification and control data access rights via gateways. 3. **Core layer**: The core network only receives processed non-sensitive data.
## 3 MG Communication Protocols and Standards
### IEC Standards
#### 3.1.2 IEC 61400-25-2  
This standard presents a uniform platform of information exchange and a data model for all participants in the wind power station, and serves as a communication stack for mapping this information. This standard deploys common data classes of IEC 61850 to be corporate in SG. Its data object has been extended in the recent version [43].
# A Review of Sound Field Control
## 3 Personal Audio Systems
### Algorithm Improvements
The performance of the acoustic contrast at each frequency was compared as provided in Figure 3. To demonstrate the acoustic contrast at non-control points, the observed frequency interval is 10 Hz. Note that the acoustic contrast of TACC is high at control frequencies but rapidly decreases when the frequency deviates from the control frequency. BACC and BACC-RV both alleviate this problem, reducing the difference in contrast between uncontrolled and controlled frequencies. However, compared with BACC, BACC-RV has a larger acoustic contrast. Figure 4 shows the frequency response at the center of the microphone array in the bright zone. The frequency response of TACC has obvious fluctuations over the entire frequency range. Moreover, the frequency response of BACC is similar to line spectrum,  
Figure 2: Design framework for robust sound reproduction in [43]. which performs worst. To maximize the acoustic contrast in the time domain, the BACC method only retains the frequency component with the highest acoustic contrast in the frequency domain. With the RV constraint, BACC-RV achieves the best frequency response consistency among all three methods. The above methods implemented in the frequency domain or in the time domain utilize the cost function at discrete spatial control points. Thus, the performance at the spatial non-control points is degraded. The method designed in the modal domain can solve this problem [46]. The energy in the bright and dark zones is transformed into the modal domain. Modal coefficients contain complete sound field information at any point in a space. Therefore, the energy is controlled in the entire region, rather than at discrete control points. Moreover, the reproduced sound field has a more uniform energy distribution.
## 3 Results  
### Effectiveness of the Light Intervention  
#### 3.1.1 Spectrometer  
The spectrometer collected data in one baseline measurement to compare the intensity and CCT of the light in the three rooms of each participant's home for phases A\({}_{1}\) and B\({}_{1}\).  
For each of the three rooms, a randomization test was performed to test the significance between the A (without lamp) and B phases (with lamp). The results for illuminance, CCT, and EDI\({}_{\rm mel}\) are shown in Table 3. For the kitchen, the observed mean difference in illuminance was 674 lx, \(p<0.001\) (356 lx EDI\({}_{\rm mel}\)), with higher values in phase B. For the bedroom, the observed mean difference in illuminance was 1261 lx, \(p<0.001\) (653 lx EDI\({}_{\rm mel}\)), with higher values in phase B. For the living room, the observed mean difference in illuminance was 812.7 lx, \(p=0.03\) (559 lx EDI\({}_{\rm mel}\)), with higher values again in phase B.  
For the kitchen, CCT remained virtually unchanged (\(-28\) K, \(p=0.532\)). For the bedroom, CCT was 567 K higher, though this difference was not statistically significant, \(p=0.169\). The same holds for the living room, where the average CCT was lower (\(-431\) K), though not significantly so, \(p=0.88\).  
The EDI\({}_{\rm mel}\) differences were substantial. The observed mean differences in the kitchen, bedroom, and living room were 356, 653, and 559 lx, respectively, with significantly higher values in phase B.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 5 Experiments
### Radiometric Variation at the Azimuth Scene Center  
As previously analyzed, the minimum radiometric variation occurs at the azimuth scene center, where the reference EAP is usually derived. Seven point targets are set at the scene centerline, 1.5 km apart in the range direction. The RCS of each point target is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Azimuth location (m) & \(-4500\) & \(-3000\) & \(-1500\) & 0 & 1500 & 3000 & 4500 \\ \hline Integrated energy (dB) [1] & 1.01 & 0.51 & 0.12 & \(-0.19\) & \(-0.42\) & \(-0.58\) & \(-0.68\) \\ \hline \end{tabular} \({}^{\dagger}\) The integrated energy of each point target is normalized by the value at the scene center.  
\end{table}
Table 2: Imaging results for targets at swath edge, calibrated with the reference EAP.  
Figure 8: Comparison of azimuth radiometric variation: about 1.69 dB calibrated using the reference EAP, about 0.1 dB calibrated using the proposed method accounting for the 2D antenna pattern.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Azimuth location (m) & \(-4500\) & \(-3000\) & \(-1500\) & 0 & 1500 & 3000 & 4500 \\ \hline Integrated energy (dB) [1] & 1.01 & 0.51 & 0.12 & \(-0.19\) & \(-0.42\) & \(-0.58\) & \(-0.68\) \\ \hline \end{tabular} \({}^{\dagger}\) The integrated energy of each point target is normalized by the value at the scene center.  
\end{table}
Table 3: Imaging results for targets at swath edge, calibrated by the proposed method.  
the same. After the correction with the reference EAP, parameters of each point target are measured, see Table 4. The results show that the relative radiometric variation is very low when EAP is precise. After the proposed method of antenna pattern correction is applied, the imaging results are given in Table 5. A careful comparison between the parameters in Tables 4 and 5 shows that even at the reference squint angle, the proposed correction method still has better performance than the conventional correction using the reference EAP.
# Impact of Acceleration and Acceleration-Initial Speed Profiles on Team Success in LaLiga
## 1 Introduction
This approach evaluates accelerations from training and match data within 5 km\(\cdot\)h\({}^{-1}\) speed intervals, measured using GPS, rather than relying on specific field tests [18]. Alternative methodologies had been proposed to analyze the relationship between acceleration and running speed with GPS technology. The acceleration-speed (AS) profile proposed by [PERSON] et al. (2021) [19] to evaluate in-situ sprint performance in real context is close to the horizontal force-velocity profile [20]. Recent studies used the AS profile to analyze team and individual seasonal changes in performance, analyze the differences between positions and age groups, and analyze the variability of the AS profile according to the microcycle day [21, 22, 23]. Moreover, [PERSON] et al. (2023) [24] and [PERSON] et al. (2024) [25] proposed a new quantification method of metabolic and mechanical load to improve the traditional analyses based on acceleration or distance covered per speed zone alone yield partial information. The mechanical and metabolic power combine at the same time acceleration and speed to assess performance. To date, we do not have evidence that characterizes the acceleration-initial speed running (AS\({}_{0}\)) profiles of the teams according to success and the position role in soccer players. Moreover, it is unknown if high-intensity accelerations, traditionally quantified by absolute thresholds that exceed 3 m\(\cdot\)s\({}^{-2}\) in LALIGA [26, 27], are associated with the team's success in competition. For this reason, the aims of the study were (a) analyze by position role the maximal acceleration capabilities relative to initial speed running on the team's ranking at the end of the season and (b) analyze the relevance of the number of accelerations above 3 m\(\cdot\)s\({}^{-2}\) according to the final league position. We hypothesized that successful soccer teams have players with higher acceleration and velocity capacities relative to initial speed running than less successful teams, and high-ranked teams performed more accelerations above 3 m\(\cdot\)s\({}^{-2}\).
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 5 Transport Management with SDN
### Traffic Load  
The use of traffic in the control and data layers in SDN is significant for the distribution of traffic according to the load. In SDN, the forwarding of decision-making is centralized with the appropriate load balancing. Splitting traffic loads at the package level rather than at the flow level is better for traffic management.  
Another work has been proposed as a Quality of Experience (QoE) using the SDN-controlled framework, as shown in [23]. However, the authors studied SDN traffic control using another approach. They studied TE in terms of TE management, which is implemented through traffic load balancing, Quality of service (QoS), EE scheduling, and traffic management in an IP/SDN network. Secondly, they used TE measurements and have  
Figure 5: Managing vehicular data traffic within 5G environments.  
focused on the application and network layers. Nevertheless, the authors of [24] proposed a new architecture for supporting spectrum management in heterogeneous wireless networks in 5G environments.
## 4 Simulation Results
### The Localization Error of ACFL and PCFL under Different Parameters
#### 4.1.1 The Threshold (\(\upmu^{t}\))  
Figure 5 shows the localization error for both ACFL and PCFL changing with the threshold \(\upmu^{t}\). The density of anchors is set to be 4 and the number of samples is set to be 400 in Figure 5. In general, the larger the threshold, the less number the task anchors which can communicate with mobile nodes. The localization error of ACFL (PCFL) reaches the minimum, when the threshold is set for 0.0142 and 0.01, respectively. The localization error is increasing with the growth of the threshold. In general, the average localization error of ACFL is bigger than that of PCFL as shown in Figure 5. When the threshold is changed from 0.01 to 0.03, the average localization error of PCFL is less than 2 m, while that of ACFL is more than 4 m.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## Materials
### RV Xuelong Ice Navigation and Position Data  
RV _Xuelong_ is a polar ice-strengthened research vessel (Class B1) serving the four CHINARE research stations in the Antarctic. Every year, the ship arrives at the outer ice edge off Zhongshan Station, Prydz Bay, in late November or early December for the annual resupply. Its speed through the sea ice strongly depends on the SIC (Figure 2). In the open ocean its cruise speed may be up to 18 knots, but once in ice-covered seas, her progress slows down gradually with increasing ice cover. The navigation speed reduces significantly once SIC exceeds 70%. Maintaining a speed of 1.5 knots the RV _Xuelong_ can break through 0.9-m-thick level ice with a 0.2-m snow load. Ice conditions with SIC \(>95\%\) and SIT \(>1.0\) m will typically block the ship's travel ([PERSON], Captain of RV _Xuelong_, personal communication). Hence, optimal navigational routing is critical to minimize delays due to environmental conditions.  
The half-hourly samples of latitude, longitude, speed, and heading of RV _Xuelong_ were utilized to assess the impact of sea ice conditions during the travel. The different routes across the Prydz Bay during the 28 th, 29 th and 30 th CHINARE voyages are indicative of the large inter-annual variability encountered during the Novembers from 2011 to 2013 (Figure 1).
## 2 Materials and Methods
### First Phase
#### 2.2.2 Constructing the Xml File  
To create the necessary XML file for the Haar Cascade algorithm, the process begins by preparing a dataset consisting of positive and negative images. Positive images contain the target object, in our case, minerals, while negative images include images that do not contain minerals. The next step involves training the cascade classifier, using OpenCV's algorithms [34]. During training, the algorithm extracts Haar features from the positive images and uses an AdaBoost model to combine weak classifiers, iteratively improving their ability to distinguish between positive and negative samples [15]. The cascade structure is built by arranging classifiers into sequential stages, with each stage designed to reject non-mineral regions rapidly. Once training is complete, the classifier was saved as an XML file, which was later deployed for mineral detection in the Mineral Quest quiz game.  
Figure 2: Examples of minerals images.
## 4 Numerical Simulation and Result Analysis
### Numerical Simulation Parameters Setting  
The simulation computer is an IBM desktop, P4 2.8 GHz CPU, 760 MB of memory, WD400 BB hard disk, Intel 82,865 G integrated graphics. The running environment is Windows XP SP3, in which JAVA 6 JDK has been configured and QualNet5.0.2 & VS2005 installed. The simulation environment and parameters are shown in Table 1.  
The running interface of the simulation system is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Remarks** \\ \hline Name of simulation scenario & Tactical Sensor Network & Animation update period for the scene: 0.1 s \\ Simulation time display accuracy & 0.1 ms & \\ Simulation time & 3000 s & \\ Simulation area & 20 km \(\times\) 20 km & Map background \\ Initial number of nodes & 21 & \\ Node communication range & \(\leq\)2 km & The initial value, can be changed to 10 km \\  & & during simulation \\ Antenna type & Omnidirectional antenna & \\ Routing model & DTN-AODV & DTN-AODV.class \\ Node movement rate & 0-10 m/s & Random value \\ Mobility model & Integrated mobility model & Map-based road data \\ DTN overlay cache & 5 MB & \\ Service type & Data & Ping application, video data \\ Network Management Mode & Cooperation & Awareness combat network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameter settings for simulation.  
Figure 12: Operation results of the simulation system.
# Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures
###### Abstract  
The potential offered by the Internet, combined with the enormous number of connectable devices, offers benefits in many areas of our modern societies, both public and private. The possibility of making heterogeneous devices communicate with each other through the Internet has given rise to a constantly growing scenario, which was unthinkable not long ago. This unstoppable growth takes place thanks to the continuous availability of increasingly sophisticated device features, an ever-increasing bandwidth and reliability of the connections, and the ever-lower consumption of the devices, which grants them long autonomy. This scenario of exponential growth also involves other sectors such as, for example, that of Artificial Intelligence (AI), which offers us increasingly sophisticated approaches that can be synergistically combined with wireless devices and the Internet in order to create powerful applications for everyday life. Precisely for the aforementioned reasons, the community of researchers, year by year, dedicates more time and resources in this direction. It should be observed that this happens in an atypical way concerning the other research fields, and this is because the achieved progress and the developed applications have practical applications in numerous and different domains.  
Keywords:internet; wireless; multimedia; artificial intelligence; machine learning; ubiquitous computing; wireless sensor; networks; Internet of Things; security; deep neural networks; big data +
Footnote †: journal: _Future Internet_
###### Abstract  
The aim of this study was to the evaluate volumetric and linear adaptation of an indirect adhesive restoration, comparing a novel chairside 3D printer to conventional milling techniques. An intact upper premolar was selected, prepared for an overlay restoration, and replicated. A standardized overlay restoration was designed with CAD software (Cerec inLab CAD SW 4.5.2, Dentsply Sirona, Charlotte, NC, USA), maintaining equal morphology for each sample. Restorations were produced with three CAM processes: chairside 3D printer (D-FAB, DWS, Thiene, Italy), chairside milling unit (Cerec MCXL, Dentsply Sirona, Charlotte, NC, USA), and an industrial milling machine serving as control (Micro 5x, [PERSON], Mader, Austria). Once cemented, specimens were scanned using micro-computed tomography to assess volumetric, internal, and external adaptation. Data were statistically analyzed with ANOVA and post hoc Bonferroni tests. CAM technique significantly affected volumetric adaptation (\(p<0.001\)), with the chairside 3D printer performing the best and chairside milling unit the worst. Concerning internal adaptation, the chairside milling unit performed significantly worse than the other groups (\(p<0.001\)). No significant differences were reported for external adaptation (\(p>0.05\)). In conclusion, CAM technique influenced volumetric and internal adaptation, with the 3D printer showing optimal volumetric adaptation and chairside milling poor internal adaptation.  
chairside; 3D printing; micro-CT; interfacial adaptation; overlay; milling 202515828  
1 Dental School, University of Turin, Via Nizza 230, 10100 Turin, Italy; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (T.R.); [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (G.F.); [EMAIL_ADDRESS] (P.C.)  
202515828
## 5 Heuristic Algorithm Design
### LEO-First Topology Abstraction-Based Routing Algorithm  
The process of the LEO-first topology abstraction-based routing (LF-TAR) algorithm is depicted in Algorithm 2. In the LF-TAR algorithm, the requests are firstly routed on the abstracted topology of LEOs and ground stations. If the rest of the key resources on the abstracted topology cannot fulfill the requirements of the request, then we utilize the GEOs to establish corresponding quantum channels on the physical topology for QKD to fulfill the requirements. The GEO-based QKD is used as an alternative plan for the LEO-based QKD. The abstracted topology of GEOs and ground stations is not established, while the GEOs establish the quantum channels for QKD according to the requests. Lines 1-2 serve as the same function in the GF-TAR algorithm. The time resources of the links on the physical topology of GEOs and ground stations are initialized in line 3. It should be noted that one GEO can establish \(l_{\text{h}}\) quantum channels to the ground stations. Hence, the time resources of links from the GEO to the ground station is \(\Delta\cdot l_{\text{h}}\) in numerical terms. Line 5 serves as the same function of the GF-TAR algorithm, i.e., to firstly try to fulfill the requirements of the requests on the abstracted topology of LEOs and ground stations. If it failed to fulfill the requirements in line 5, we try to implement the requests by the GEO-based QKD. In lines 6-16, we update the physical topology of GEOs and ground stations based on the rest of the time resources of the links. If the rest of the time resources cannot fulfill theminimum requirements of the set of secret keys required, we remove this link from the physical topology. The QKD relaying path is obtained by routing on the physical topology \(G_{\rm c}(V_{\rm c},E_{\rm c})\) of GEOs and ground stations in line 17. If the time resources are sufficient on the links along the QKD relaying path, we allocate the time resources to the request in lines 18-32.
## 5 Comparative Analysis  
In this section, we execute some performance comparison experiments to highlight the advantages of \(\mathcal{CI}\)STMD network. To quantitatively assess the model's detection capability, we assess the identification capabilities of four current STMD-based frameworks, namely, \(\mathcal{CI}\)STMD, ESTMD [16], DSTMD [17], and \(\mathcal{F}\)STMD [21]. We will evaluate each model's detection performance by plotting the ROC curve of \(D_{A}-F_{A}\), and the detection rate \(D_{A}\) and false alarm rate \(F_{A}\) are depicted as follows:  
\[D_{R}=\frac{T_{D}}{T_{N}},\ \ F_{A}=\frac{B_{D}}{I_{N}}, \tag{18}\]  
where \(T_{D}\), \(T_{N}\), \(B_{D}\), and \(I_{N}\) represent the number of true detections, number of targets, number of false detections, and number of images, respectively. First, we conduct a comparative study of the proposed visual network with the ESTMD, DSTMD, and \(\mathcal{F}\)STMD models across different video simulation variables, including object velocities, sizes, brightness, background movement velocities, movement orientations, and backdrop images. The information regarding the simulation frame sequences can be found in Table 2. In Figure 15a, the receiver operating characteristic (ROC) curves that depict the detection performance of the comparison models on small objects in the simulated image sequences are presented. As we can see, the performance of \(\mathcal{CI}\)STMD surpasses that of the other three models. More precisely, \(\mathcal{CI}\)STMD yields a higher detection rate at different \(F_{A}\) levels. In Figure 15b-f, we provide the ROC curves of the comparison networks under different simulation video parameter conditions, where \(F_{A}=5\) to maintain a fair comparison. As exhibited in Figure 15b, the ROC curves of the comparison models for detecting various sizes object in the simulation image sequence are given. The \(\mathcal{CI}\)STMD model clearly shows superior detection capabilities over the other three models when evaluated using different object sizes, varying from \(1\times 1\) to \(11\times 11\) pixels. As presented in Figure 15c, \(\mathcal{CI}\)STMD performs best according to different object brightnesses. More exactly, the ROC curves of the four models all decrease when the object brightness ranges from 0 to 0.15. Figure 15d demonstrates the ROC curves for different moving object velocities. It can be seen that \(\mathcal{CI}\)STMD significantly outperforms the ESTMD, DSTMD, and \(\mathcal{F}\)STMD models. We can find that \(\mathcal{CI}\)STMD has a higher \(D_{A}\) compared to the other visual networks, while the \(F_{A}\) is low when target velocity increases from 0 to 300 pixels per second. As shown in Figure 15e,f, the \(\mathcal{CI}\)STMD model has a higher performance level than the ESTMD, DSTMD, and \(\mathcal{F}\)STMD models in diverse conditions of background velocity and movement orientations.