esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Fall Detection Application
### Experimental Results
#### 5.2.5 Experimental Summary Results  
Table 2 summarizes the results of the experiments. For each type of movement, the table shows the relevant times and their corresponding slopes. It is possible to see the characteristics of the falling movement and the features of the other movements that can disturb the effectiveness of radar application.The results shown are a typical adult target. The results depend on the characteristics of the target, such as height and age; see for example, [13]. Similar results were obtained from measurements of different heights of 150 cm to 190 cm, with up to 10% difference.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movement Type** & \(m_{p}\) **(kHz/s)** & \(T_{p}\) **(s)** & \(m_{a}\) **(kHz/s)** & \(T_{a}\) **(s)** \\ \hline Falling & 1.042 & 1.535 & 3.764 & 0.425 \\ Sitting & 0.868 & 0.8 & 1 & 0.8 \\ Walking & 3.25 & 0.43 & 2.18 & 0.64 \\ Falling while walking & 2.37 & 0.672 & 5.86 & 0.367 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Movement characteristics.  
Figure 14: Presentation of the measurement results for a man falling while walking. The spectral signature was measured using the MMW radar, and the colored lines indicate the data obtained through tracking the subject’s head, shoulder and right hand in a recorded video.  
Figure 13: Video analysis process of a man falling while walking, tracking motion of the head (red markers), shoulder (green markers) and right hand (blue markers) (**a**) at the beginning of walking; (**b**) during walking, hand forward; (**c**) lying down.  
From Table 2, it can be seen that the slopes for the falling scenarios are significantly higher than those for the other movements. This suggests that the scenario of a falling person can be discerned with certainty using the MMW radar. An application condition could be defined as \(m_{n}>3.7\) kHz/s--that is, when a movement occurs in which the negative slope meets this condition, a fall would be detected by the radar.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 3 Material and Methods
### Description of the Proposed Method: D2D Communication Assisted RF Fingerprint Positioning
For D2D direct communication, responsibility data management and security belong to the participated device, and this topic is under intensive research [53; 54]. According to our proposed method, only position information of other device is needed to be passed through the D2D communication link. Such a small amount of information can easily be fit within 1 kilo bits packet including address, header, encryption etc. To prevent the exploitation of the data devices should communicate with other secured devices, with whom confidentiality, integrity, and privacy are maintained under a common agreement.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Coordination Overhead in Cooperative Techniques  
Techniques like CoMP, eICIC, and joint scheduling [209, 210, 211] require significant coordination between BSs, which may lead to high signaling overhead and increased latency. In rural or infrastructure-limited regions, backhaul capacity and synchronization constraints can limit the feasibility of such solutions.
# Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots
## 2 Materials and Methods  
### Proposed System Overview  
Our proposed system consists of five integrated subsystems. In Section 2.3, we present the central subsystem, the Human-Robot Voice Interaction system, which is based on voice and synthesized text-to-speech technology. The user can talk to the robot and give commands to control the other subsystems. For example, during an exam, a robot or multiple robots are placed in the classroom as illustrated in Figure 1. The robot can then proctor the exam, in which case it will communicate with the navigation subsystem, presented in Section 2.4, which consists of a robot base, Lidar, and 3D sensor. This subsystem allows the robot to autonomously navigate the classroom, find the best paths, map its surroundings, and avoid obstacles. While proctoring the exam, two other subsystems are activated, course material retrieval and recommendation and cheating detection. The instructor, ahead of time, can program the robot to answer specific questions during the exam. The student can then talk to the robot and ask for help. Our robot then uses a camera to detect faces, identify the user and then provide personalized content based on similar queries if the information is programmed to be revealed during the exam, as explained in Section 2.5. The cheating detection subsystem, presented in Section 2.6, is also activated. It consists of 4 cameras mounted on top of the robot, recording a 360-degree view around it. The robot uses the navigation subsystem to navigate the classroom without the need of a teacher, thus maintaining social distancing between students.  
Once the exam is over, our proposed Image-Based Exam Paper Scanning subsystem, described in Section 2.7, scans students' exam papers in a contactless way by recording a video of the student flipping through their exam papers in front of the camera. The video is then uploaded to the cloud along with the proctoring video. Uploaded videos are then processed using our computing server, using an AI Vision model to find instances of cheating and extract each exam page and student ID to be graded. Reports are then generated for the examiners to review manually.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Azimuth Thruster
#### 2.1.1 Measurements  
Piezoelectric accelerometers (stud-mounted, 0.5-8000 Hz frequency response with \(\pm 3\) dB deviation) were used to measure vibration on different positions in the thrusters, but only the data from the \"pinion shaft gear end\" position are considered here. The data were collected into separate samples, where the length of times series was fixed to 4096 points. The sampling rate in data acquisition varied based on the rotational speed of the shaft, and here it was typically 1.5 or 3 kHz, resulting in a varying running time in the separate samples. The rotational speed was taken from the beginning of the time series recording by using an inductive sensor, and in this study, it is assumed that the speed was then constant during the rest of the recording. The data were transferred to a data store typically once a day or more frequently during the alarm situations detected by the monitoring system onboard.  
Figure 1: Illustration of azimuth thruster and its main components with approximate placement of accelerometer utilized in this study.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 5 Results
### E\({}_{\text{b}}\)/N\({}_{0}\) versus Time According to Polarization Techniques  
From another point of view, we can analyze the behavior of the ionospheric waves and the polarization techniques by the mean E\({}_{\text{b}}\)/N\({}_{0}\) received during different hours of the day. In Figure 7, we can observe the results obtained by a 4 QAM transmitted at 50 W. As we can see, the E\({}_{\text{b}}\)/N\({}_{0}\) received by the ordinary and extraordinary waves differs by a maximum of 3 dB. We can analyze that meanly, the selection combining E\({}_{\text{b}}\)/N\({}_{0}\) is always higher than the E\({}_{\text{b}}\)/N\({}_{0}\) received by the ordinary and extraordinary waves. Rarely, as we can see at 11 UTC, the mean of the selection combining is under one of the E\({}_{\text{b}}\)/N\({}_{0}\) received mean values of an ionospheric layer. On the other hand, for 50% of the hours by equal-gain combining, we received a higher E\({}_{\text{b}}\)/N\({}_{0}\) than the ionospheric waves, and for 12% of the hours, a higher E\({}_{\text{b}}\)/N\({}_{0}\) was received than by selection combining. For the other 50% of the hours, we received an E\({}_{\text{b}}\)/N\({}_{0}\) between the received E\({}_{\text{b}}\)/N\({}_{0}\) of the ordinary and extraordinary waves. On the other hand, we can analyze that the ordinary and extraordinary waves can have an E\({}_{\text{b}}\)/N\({}_{0}\) difference up to 3 dB.
# The Conditional Probability of Correlating East Pacific Earthquakes with NOAA Electron Bursts
## 4 Discussion
Figure 10 shows that an increase in conditional probability is observed at about 40 h from the NOAA detection, around 54 h the probability magnitude overcomes the 99% significance level up to around 59 h. Finally, the probability decreases at an unconditional level around 70 h after the EB detection.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 5 Experimental Results
### Processing Performance
BL-NMS method has a little performance decrement over the traditional NMS, since BL-NMS enlarges some detection boxes for small targets to a bigger size, which has the IoU overlap less than 0.5 with ground truth. To evaluate the quality of the ship patches generated by our method, strict recall and strict precision are used as the evaluation metrics. The ground users need to scan the whole parts of ships, hence our detection boxes should cover the ship completely, which means that the minimum bounding rectangle of each ship needs to be contained within the detection boxes. A detection box is counted as true positive in strict recall and strict precision, only when its IoU overlap with ground truth is greater than 0.5 and it also contains the ground truth. The stricter metrics lead to a large performance decrement for traditional NMS while it has only a little impact on BL-NMS. The results demonstrate the effectiveness of BL-NMS in improving the quality of the ship patches generated by the on-board processing method. We also evaluated the effect of the threshold of BL-NMS. The smaller threshold of BL-NMS can reduce more redundant detection boxes, which can achieve a higher precision. For on-board ship detection task, all the ship patches by the detection boxes are downlinked to the ground receiver, therefore the users can still scan the ships treated as false alarms. Finally, we choose 0.3 as the threshold of BL-NMS. If the satellite has a better transmittability, we can increase the threshold of BL-NMS to downlink more target patches.
## 4 Distributed Power Control for Spectrum Sensor Nodes
### Problem Formulation
It is assumed that the available energy of the \(m\)-th SSN is derived from its battery of limited capacity and the harvesting energy from the renewable energy sources by exploiting the energy harvesting technology. Thus, the energy consumption of the \(m\)-th SSN evolves according to a linear differential equation given as:  
\[\frac{dE_{m}(t)}{dt}=E_{A}(t)-\tau_{rp}p_{m}(t)-E_{m}(t) \tag{21}\]  
Then the cost of energy consumption for the \(m\)-th SSN at time \(t\in\left[t_{0}^{\prime},t_{0}^{\prime}+\tau_{rp}\right]\) can be given as:  
\[U_{m}^{C}=\wp_{m}^{C}E_{m}(t) \tag{22}\]  
Therefore, based on the revenue and the cost formulated in Equations (19) and (22), the utility function of the \(m\)-th SSN at time \(t\in\left[t_{0}^{\prime},t_{0}^{\prime}+\tau_{rp}\right]\) can be constructed as follows:  
\[U_{m}=U_{m}^{R}-U_{m}^{C}=\wp_{m}^{R}(P_{m}^{\max}-p_{m}(t))-\wp_{m}^{C}E_{m} (t) \tag{23}\]  
We denote the discount factor by \(r\), for \(0<r<1\). Our optimization objective is to maximize the utility function \(U_{m}\) by choosing optimal transmit power \(p_{m}^{OP}(t)\) of the \(m\)-th SSN during the slot \(\tau_{rp}\) according to \(\wp_{m}^{R}\) and \(\wp_{m}^{C}\), at time \(t\in\left[t_{0}^{\prime},t_{0}^{\prime}+\tau_{rp}\right]\), i.e.,  
\[\text{Maximize}:\ \int_{t_{0}^{\prime}}^{t_{0}^{\prime}+\tau_{rp}}\left(\wp_{ m}^{R}(P_{m}^{\max}-p_{m}(t))-\wp_{m}^{C}E_{m}(t)\right)e^{-r(t-t_{0}^{\prime})} \tag{24}\]  
It is noteworthy that the discount factor \(r\) is an exponential factor between \(0\) and \(1\) by which the future utility must be multiplied in order to obtain the present value with the underlying structure of differential game theory in mind. Therefore, each SSN is required to maximize its discounted utility \(U_{m}\) function by discount factor \(r\), implying that discount factor will have a stronger impact on the utility obtained by each SSN in the future. To this end, the power control problem for all IT and MT SSNs in the physical layer can be formulated as a differential game model defined by \(\mathcal{G}=\left\{\mathcal{N},\left\{p_{m}^{OP}(t)\right\}_{m\in\mathcal{N }^{\prime}}E_{m}^{OP}(t),\left\{U_{m}\right\}_{m\in\mathcal{N}}\right\}\), where \(\mathcal{N}=\mathcal{N}_{IT}\cup\mathcal{N}_{MT}\) is the set of players involving all IT and MT SSNs, \(p_{m}^{OP}(t)\) is the strategy of player \(m\), \(\left\{p_{m}^{OP}(t)\right\}_{m\in\mathcal{N}}\) is the set of strategies or strategy space related to all players, \(E_{m}^{OP}(t)\) is the state variable associated with optimal transmit power \(p_{m}^{OP}(t)\), and \(\left\{U_{m}\right\}_{m\in\mathcal{N}}\) is the set of utility function of all players with their strategies.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 4 Experiment on a Lab-Scale Caisson-Foundation System
### Vibration Data Acquired from Accelerometers  
The time-history responses were acquired at a sampling rate of 1.0 kHz. For each impact load, the time-history responses of five sensors (i.e., ACC1z, ACC 2z, ACC 2x, ACC 3x, and ACC 2y) were recorded simultaneously. The observation period was set to 1 s, resulting in 1000 data points per response (90 data points before and 910 data points after the impact load). Comparing the five accelerometers on each caisson, the acceleration magnitude in the z-direction was smaller than in the x- and y-directions. The time-history responses from the five sensors were then concatenated into a single response of 5000 data points. The representative time-history responses of five accelerometer sensors in eight foundation damage levels (D0-D7) are plotted in Figure 10.  
The FFD method was used to extract the SVD from the time-history responses. Figure 11 plots the SVD responses calculated simultaneously from all five accelerometers (i.e., 1z, 2z, 2x, 3x, and 2y). The SVD responses were relatively uniform under all foundation damage levels (D0-D7). Three peaks were identified to represent the caisson's behavior: the first peak frequencies ranged from 25.9 to 27.3 Hz, the second from 183.6 to 186.8 Hz, and the third from 279.8 to 281.2 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Gravel Removed** & **Sand Removed** & \multirow{2}{*}{**Descriptions**} \\  & **(\% of Gravel Armor)** & **(\% of Sand Mound)** & \\ \hline D0 & - & - & Undamaged intact state \\ \hline D1 & 0.37 kN (2.3\%) & - & \\ D2 & 0.46 kN (2.8\%) & 0.44 kN (0.7\%) & \\ D3 & 0.56 kN (3.4\%) & 0.91 kN (1.5\%) & \\ D4 & 0.58 kN (3.6\%) & 1.14 kN (1.9\%) & \\ \hline D5 & 0.58 kN (3.6\%) & 1.35 kN (2.2\%) & \\ D6 & 0.58 kN (3.6\%) & 1.56 kN (2.5\%) & \\ D7 & 0.58 kN (3.6\%) & 1.77 kN (2.9\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of foundation damage scenarios.  
Figure 10: Time-history responses of five accelerometer sensors.
# Survey of Smart Parking Systems
## 7 Discussion and Open Problems
### Rescheduling Routes  
Related to the previous point about algorithms, we uncovered another gap in the research. No solution proposed, in the work analyzed (when applicable), that the routes established by the SPS can be re-planned according to events that can be triggered when executing the path of a vehicle toward the destination parking slot. This algorithmic and data processing characteristic can bring greater value to SPS.  
The main reason why we consider this type of action necessary in the system algorithms is the fact that, during the real driving of a vehicle, events may occur that cause the route established a priori or in real time to not be completed. Such events could include traffic jams, detours, construction sites, accidents, damaged traffic lights, etc.  
Although there were some studies that mentioned deviations as variables to take into account when calculating routes, they did not specify how these variables should be implemented or how the system would react to changes in them [308].  
To implement this feature in the system, a real-time algorithm would be essential. Moreover, other types of data sensors would need to be added to provide the system with information on the status of the routes.
# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
###### Abstract  
Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase.  
intagistics; robotic bin-picking; simulation model; ADAMS; pick-point determination; MATLAB/Simulink; 2-F robotic gripper; performance analysis Article  
Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper  
[PERSON], [PERSON] and [PERSON]  
1 Faculty of Logistics, University of Maribor, Slomskov trg 15, 2000 Maribor, Slovenia  
2  
Footnote 1: email: [EMAIL_ADDRESS]  
28 December 20228
## 4 QoS and QoE
### QoS and QoE Guarantee in Traditional Networks vs. ICN  
The Internet Engineering Task Force (IETF) has defined two models to support quality of service in IP-based networks: Integrated Services (IntServ) and Differentiated Services  
Figure 2: QoS and QoE metrics in traditional networks and ICN.  
(DiffServ). IntServ follows the signaled QoS model, where the end hosts signal their QoS needs to the network. On the other hand, DiffServ operates on a provisioned QoS model, configuring network elements to handle multiple traffic classes with different QoS requirements. However, these QoS models face significant limitations. IntServ, for example, reserves the necessary resources for specific traffic flows but is hampered by the high cost of signaling, resource reservation, and the maintenance required for each flow, which greatly reduces its scalability. Conversely, DiffServ is better suited for access networks, requiring a mapping of flows to classes at each edge router. This approach may lead to substantial overhead, even though the burden is confined to edge routers to improve scalability in the core network [41].  
MPLS traffic engineering (MPLS-TE) enhances QoS by using DiffServ Traffic Engineering (DS-TE) models to manage aggregated traffic in core networks [42]. MPLS-TE allows for the creation of label-switched paths (LSPs) based on specific performance requirements, such as latency, bandwidth, or reliability. When integrated with DiffServ, this enables differentiated handling of traffic classes, so critical or high-priority data can be routed along paths that meet strict QoS criteria. While DS-TE offers better scalability than IP-based DiffServ and IntServ, it is still limited by overhead [43]. Additionally, hierarchical networks require complex DiffServ mappings, increasing edge node processing demands [41].  
The primary challenges associated with IP and MPLS-based QoS models stem from the network's limited information awareness. However, ICN can largely mitigate these limitations, as the network identifies the requested content using a distinct addressing scheme. This native content identification feature of the ICN architectures simplifies the process of mapping information to QoS requirements, and the need for deep packet inspection is significantly reduced or even eliminated [41]. In addition, the QoS description can be added to content chunks using the content namespace with negligible overhead. However, various ICN models may adopt different QoS approaches, each with distinct features based on their architectural design.  
For instance, the PURSUIT architecture provides routing capabilities that facilitate IntServ-like paths without the need for signaling overhead, which is useful for scaling networks [44]. The CCN and NDN architectures support hierarchical and human-readable naming of contents, which makes differentiating between different types of data, i.e., QoS classes, much easier than the current Internet architecture. Moreover, chunk-level load balancing in CCN and PURSUIT multi-path communication can lead to much better multimedia streaming and higher QoE compared to TCP/IP [45].
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 3 System Model
### Problem Definition  
Figure 1 presents an example of UCANs, in which primary users (PUs) denoted by red circles can be natural acoustic systems (e.g., marine mammals) or man-made acoustic communication systems (e.g., underwater sensors), while secondary users (SUs) denoted by blue triangles can be cognitive acoustic communication nodes. SUs can transmit data only when their transmissions do not hamper the communications of PUs. Therefore, some SUs must be silent if there are PUs close to them. For example, as shown in Figure 1, SU\({}_{5}\) cannot establish a communication link with SU\({}_{6}\) due to the possible interference to PU\({}_{3}\), as PU\({}_{3}\) is in close proximity to SU\({}_{5}\). Similarly, SU\({}_{1}\) and SU\({}_{2}\) cannot connect with each other successfully because of the existence of PU\({}_{2}\), which is close to both SU\({}_{1}\) and SU\({}_{2}\). As illustrated in this typical UCAN, we observe that the link connectivity of SU pairs is more difficult to ensure that of PUs, and consequently SU pairs have a lower probability of coverage. Therefore, we aim to investigate the link connectivity and the probability of coverage of SUs in UCANs in this paper.  
Figure 1: Underwater network model. PU: primary user; SU: secondary user.