esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology
### Stabilization Method
#### 3.3.2 Control Algorithms  
Two control routines are executed in real time to dynamically adjust servo motor positions in response to road surface anomalies: one triggered by GY-521 sensor data and the other by object detection from YOLOv8. Each routine adjusts servo angles to ensure vehicle balance based on different input modalities.  
Algorithm 1 represents the sensor-based control strategy, where the GY-521 accelerometer detects surface depressions and inclines in real-time. Based on the sensed depth or tilt, a linear function maps these values to appropriate servo motor angles, ensuring quick mechanical compensation to maintain balance. On the other hand, Algorithm 2 utilizes YOLOv8's real-time vision-based detection to recognize visual anomalies such as potholes or bumps ahead of the vehicle. It adjusts servo motor angles to avoid instability or collision based on obstacle position and size upon detection. These two control routines operate in synergy: the GY-521 sensor provides fast, low-level corrections based on inertial feedback, while YOLOv8 offers anticipatory, vision-based adjustments. Together, they enhance the system's robustness by fusing inertial and visual modalities for real-time stabilization and obstacle-aware navigation.  
```
0: Height of hill/hole \(H\), GY-521 x-axis value \(X\), pothole detection Result: Adjusted servo motor angle \(A\)
1:functionfetch_gy521_data
2: Read GY-521 sensor values
3: Configure servo motors
4: Set up PID control system
5:return sensor data
6:endfunction
7:functionadjust_servo(X)
8: Compute servo angle: \(A=5.33X-629.55\)
9: Apply servo rotation to stabilize wheel
10:endfunction
11:Main Procedure:
12:if Pothole detected then
13: Call adjust_servo for affected wheels
14:elseif Hill detected then
15: Call adjust_servo to balance vehicle
16:endif
```  
**Algorithm 1** Stability Control with GY-521  
Figure 5: Correlation Between Servo Motor Angle and Road Surface Depressions (Holes).  
```
1:Real-time obstacle detection using YOLOv8 Result: Adjusted servo motor angle for obstacle avoidance
2:functionfetch_yolo_detections
3:Load YOLOv8 model
4:Configure servo motors
5:Perform real-time object detection
6:return detected obstacles
7:endfunction
8:functionadjust_servo_for_obstacle(Obstacle)
9:Identify position and size of obstacle
10:Adjust servo angles to avoid collision
11:endfunction
12:Main Procedure:
13:while Vehicle is moving do
14:Detected Objects \(\leftarrow\) fetch_yolo_detections()
15:if Obstacle detected then
16:Calladjust_servo_for_obstacle
17:endwhile
```  
**Algorithm 2** Stability Control with YOLOv8
# Modulation Decoding Based on K-Means Algorithm for Bit-Patterned Media Recording
## 5 Conclusions  
In this paper, we proposed a modulation decoding method based on the K-means algorithm for BPMR to improve decoding capabilities. In the conventional modulation decoder, the codeword is selected by a decoding algorithm using Euclidean distance. However, the elements of the original codewords consist only of \(-1\) or \(+1\), and the received sequences from SOVA are real-valued data. It is therefore difficult to consider the characteristics of the data sequences. To overcome the mismatch between the received sequence and codeword, we use the K-means algorithm for decoding, which helps to understand data patterns and characteristics. Further, to identify the data patterns and characteristics of the centroid, we show a scatter plot using the t-SNE technique, which is a dimensionality reduction technique. We verify that the K-means algorithm allocates the received sequences into an appropriate cluster and that the K-means decoder exhibits the best performance. Additionally, the K-means decoder is useful for improving the BER performance of the system when TMR occurs. Unlike signal processing schemes with long lists of rules by look-up table, signal processing schemes with machine learning would provide better solutions for complex problems which do not have a good solution using a traditional approach. Therefore, the modulation decoding scheme based on the K-means algorithm could be a  
Figure 6: The number of codeword errors depending on modulation decoding schemes when the SNR is 10 dB.  
good alternative. We will apply this scheme to other storage systems, such as holographic data storage systems, and study the detection and decoding schemes concatenated with the K-means or machine learning algorithm.  
[PERSON] contributed to this work in experiment planning, experiment measurements, data analysis and manuscript preparation; [PERSON] contributed to experiment planning, data analysis, and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2020-0-01602) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflict of interest.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 5 Numerical Analysis
### Performance Measures  
We consider a wireless network system serving two types of radio calls for both primary user (PU) and secondary. The mean system time \(W_{1}\) for PU calls only has the component of service time, i.e., \(W_{1}=1/\mu_{1}\). Since service quality for PUs is not a major concern here, the blocking probability for PU calls (or the forced termination probability) is simply  
\[P_{B_{1}}=\sum_{n_{2}=0}^{\infty}p(c_{1},n_{2})=\sum_{n_{2}=0}^{r-1}p(c_{1},n_{ 2})+\left[P_{r-1}R(I-R)^{-1}\right]_{[c_{1}]} \tag{6}\]  
where \([]_{[c1]}\) denotes the \(c_{1}\)th component of the vector in the bracket.  
The mean number of CU calls in the system  
\[N_{2}=\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{\infty}n_{2}p(n_{1},\ n_{2})=\sum_ {n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{r-1}n_{2}p(n_{1},\ n_{2})+P_{r-1}R^{2}(I-R)^{-2 }e+rP_{r-1}R(I-R)^{-1}e. \tag{7}\]  
where the computational procedure on the second item with infinite parameter is also presented in Appendix A. Then, the mean dwell time for CU calls is from [PERSON]'s formula  
\[W_{2}=\frac{N_{2}}{\lambda_{2}} \tag{8}\]  
Another important system performance is total carried traffic. For a given number of channels, a large carried traffic value implies efficient use of bandwidth. The carried traffic (\(E_{1}\)) per PU in B-bands and the carried traffic (\(E_{2}\)) per CU in both A-bands and B-bands can be easily calculated once the state probabilities are determined. Each measure is simply the average number of occupied channels per connection and can be given by  
\[E_{1}=\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{\infty}n_{1}p(n_{1},\ n_{2})=\sum _{n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{r-1}n_{1}p(n_{1},\ n_{2})+\frac{1}{2}c_{1}(c _{1}+1)P_{r-1}R(I-R)^{-1}e. \tag{9}\]  
and  
\[E_{2}=\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{\min[k(n_{1}),r]}n_{2}p(n_{1},\ n_{2}) \tag{10}\]  
Because a PU requires \(m\) sub-bands (B-band), the total carried traffic in a cell is given by  
\[E\ =\ mE1\ +\ E2 \tag{11}\]  
To measure the combined service quality of PU and CU traffics, we also define the service quality factor of system as  
\[QT=\frac{W_{GT}(1-P_{B_{1}})}{\mu_{2}W_{2}} \tag{12}\]  
where \(W_{GT}\) is a weighted factor to adjust the difference between \(P_{B1}\) and \(W_{2}\). Maximization of the quality factor means minimization of the blocking probability and system service delay.
## 2 Transmission Line Modeling Method (TLM)
We can note that in the network, a TLM routine operates on the traveling, scattering and connecting of these thermal pulses. On Figure 1 incident and scattered pulses on each numbered port are labeled \({}_{k}T_{j}^{i}\) with \(j\) indicating the direction and \(k\) the \(k^{th}\) iteration. When we apply the fundamental theorems of electricity to the TLM node (\(n\)), the nodal temperature at \(k^{th}\) iteration is given by the following equation [36; 37]:  
\[{}_{k}^{0}T(n)=\left[\frac{2\big{(}\,_{k}T_{1}^{i}+\,_{k}T_{2}^{i}\big{)}}{R_ {x}+Z}+\frac{2\big{(}\,_{k}T_{3}^{i}+\,_{k}T_{4}^{i}\big{)}}{R_{y}+Z}+\frac{2 \big{(}\,_{k}T_{5}^{i}+\,_{k}T_{6}^{i}\big{)}}{R_{z}+Z}+I(n)\right]\cdot\frac {1}{Y} \tag{5}\]  
Figure 1: TLM 3D Single volume node. where, \(Z\) is the characteristic impedance: \(Z=3\Delta t/C\) and \(I(n)\) is the heat source generator and \(Y\) is the admittance which is given by:  
\[Y=\frac{2}{R_{x}+Z}+\frac{2}{R_{y}+Z}+\frac{2}{R_{z}+Z} \tag{6}\]  
Scattered thermal pulses are the reflected pulses \(\ kT_{j}^{r}\) calculated from a circuit analysis of the nodes that gives the followings equations:  
\[\ kT_{1}^{r}(x,y,z) =\frac{1}{R_{x}+Z}\Big{[}Z\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\  
To resolve the problem of heat diffusion by TLM-3D, we can exploit and evaluate numerically a repeated set of Equations (4)-(15). It is necessary for numerical calculation by TLM to take in account the boundary conditions shown in Figure 2. It is considered as a part of the heat diffusion model that describes the limits interaction of the physical model with its surroundings, and also should be consistent with the description of heat transport inside the medium [38]. For sinking boundary (bottom view), any incident heat pulse on the boundary is reflected as equal in magnitude and reversed in phase. It corresponds to an electrical short-circuit (S/C) where the reflected coefficient \(\Gamma_{j}=-1\). For heat insulating boundary (top view), any incident heat pulse is reflected as equal in magnitude and phase. This case corresponds to an electrical open-circuit (O/C) where the reflected coefficient \(\Gamma_{j}=1\). A symmetry boundary is presented by an electrical open-circuit in TLM modeling [35; 36; 37; 38].
## 3 Results and Discussion
### Thermal Stability of the Nanocapsules  
TGA tests were carried out to investigate the thermal stability of the prepared capsules. Figure 5 represents the TGA and derivative thermogravimetric analysis (DTGA) diagrams of capsules containing the healing agent, neat SAN capsules (without core), and neat CAR. The TGA curve of neat SAN nanocapsules shows that the first weight loss (around 100 \({}^{\circ}\)C) of the neat SAN nanocapsules can be attributed to the evaporation of adsorbed moisture and residual solvent on the surface of the nanocapsules. There was a significant thermal decomposition event that initiated at 303 \({}^{\circ}\)C and continued till 452 \({}^{\circ}\)C. The thermal decomposition at 303 \({}^{\circ}\)C is associated with nitrile oligomerization, leading to the production of volatile products, e.g., NH\({}_{3}\), HCN, CH\({}_{3}\)CN, etc., which are the components of acrylonitrile sections of the copolymer [41]. The CAR thermal decomposition occurs between the temperature range of 230-500 \({}^{\circ}\)C. The thermal decomposition of the core-shell nanocapsules started at 270 \({}^{\circ}\)C, contributed to the decomposition of the shell and continued up to 500 \({}^{\circ}\)C due to the decomposition of encapsulated CAR. It can be seen that the thermal decomposition curve of core-shell nanocapsules is contiguous with the mass loss curve of the shell (SAN) in the beginning and is adjacent to the mass loss curve of the core (CAR) at the end of the analysis. This proves the presence of both the components in the prepared capsules (Figure 5a). In addition, the temperature decomposition range reveals the high thermal stability of the produced nanocapsules.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Surface Tension (mN/m)** \\ \hline
5 wt.\% SAN solution in an equal ratio of DMF and DCM (shell) & 30.8 \\ CAR (core) & 33.9 \\ The interfacial tension between CAR and the 5\% SAN solution & 0.074 \\ (calculated from Equation 4)) & 3.02 \(>\) 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface tension of core and shell materials.
## 2 Ground Motion Intensity Measures
### Energy IMs  
The aforementioned IMs depend on structural configuration and are computed from spectral values. However, other IMs use accelerograms directly, which we a refer to as energy IMs. Energy represents the cumulative energy per unit weight absorbed by an infinite set of undamped single degree of freedom oscillators with a uniform distribution of periods on (0, \(\infty\)) [44]. As energy measures of ground motion, root-mean-square acceleration (\(A_{RMS}\)), Arias intensity (\(I_{A}\)) and cumulative absolute velocity (\(CAV\)) are available. \(A_{RMS}\) is calculated as the effective acceleration of ground motion during the significant duration (Equation (14)) and depends on both duration and energy characteristics of the record. In Equation (14), \(T_{sig}\) is the significant duration, and \(a(t)\) is the acceleration time series. Arias intensity (\(I_{A}\)), (Equation (15)) represents the energy dissipated during the entire record duration. Cumulative absolute velocity (\(CAV\), Equation (16)) is calculated as the area under the absolute accelerogram, thus representing the continuous accumulation of acceleration in a ground motion record. \(CAV\) is a useful measure of the onset of structural damage.  
\[A_{RMS}=\sqrt{\frac{1}{Tsig}\int_{t1}^{t_{2}}[a(t)]^{2}dt} \tag{14}\]  
\[I_{A}=\frac{\pi}{2g}\int_{0}^{t}[a(t)]^{2}\mathrm{d}t \tag{15}\]  
\[CAV=\int_{0}^{Tig}\lvert a(t)\rvert\mathrm{d}t \tag{16}\]  
The IMs summarized above incorporate duration and amplitude characteristics. However, some studies refer to IMs accounting for amplitude, duration and frequency content also as energy-based IMs. To equate the motion for a damped SDOF system subjected to ground acceleration, each term is multiplied by differential increments of displacement and are integrated in the time interval (0, \(t\)) to obtain Equation (17) [45]. Equation (17) represents kinetic energy (\(E_{K}\)), damping energy (\(E_{\xi}\)) and absorbent energy (\(E_{A}\)) on the left side, while the right side represents relative input energy (\(E_{I}\)). [PERSON] [46] defined actual damage to structures as the difference between relative input energy and damping energy. Equations (18) and (19) are equivalent velocities that represent the relative input energy and damage energy, respectively.  
\[m\int_{0}^{t}\ddot{u}\dot{u}dt+c\int_{0}^{t}\dot{u}\dot{u}dt+k\int_{0}^{t} \dot{u}udt=-m\int_{0}^{t}\ \dot{u}\ddot{u}_{g}dt \tag{17}\]  
\[V_{E}=(2E_{I}/m)^{0.5} \tag{18}\]\[V_{D}\ =(2E_{D}/m)^{0.5} \tag{19}\]
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Energy Efficiency  
Multiple routes can communicate a node and the sink. The aim of energy-aware algorithms is to select those routes that are expected to maximize the network lifetime. To do so, the routes composed of nodes with higher energy resources are preferred.
## 3 Field Trialed System
### Remote Controlled Tractor
#### 3.2.1 Remote Controlling Tunnel between the Tractor and the Remote Control Cabin  
The target for the remote control was to verify the remote controllability of the tractor through different trial networks. This was done by transmitting CAN messages over the network. While exploring the possible technologies, SocketCAN [49] was found. SocketCAN allows for controlling and programming the CAN interface as a standard network interface. SocketCAN userspace utilities and tools make it possible to set up and test the CAN network with ease. Furthermore, the Cannelloni tool allows the transfer of CAN data over an Ethernet tunnel.  
Cannelloni supports tunneling over user datagram protocol (UDP) or stream control transmission protocol (SCTP). The UDP is faster but unreliable, while the optional SCTP provides reliable transport. At this point in the research, there was a perception that all the messages should go through. Therefore, SCTP was chosen.  
Initial testing was conducted using virtual CAN interfaces from SocketCAN. Functionality was then tested using real hardware. For this, Kvaser Leaf Light v2 was chosen, which has a SocketCAN driver available directly on the Linux kernel. Kvaser adapters were verified to work using a test CAN network. The test CAN network was a cable made using a single termination resistor between the high and low CAN lines. After CAN tunneling was possible in a local area network, the next goal was to conduct traffic securely over the public network. The SSH port forwarding was chosen to transfer the CAN traffic over the network and encrypt it. However, SSH port forwarding does not work with SCTP. Therefore, one more step was required to convert SCTP to the transmission control protocol (TCP). Protocol conversion was done using the Socat utility. Figure 5 shows the components and protocols used to tunnel CAN traffic over networks.  
Figure 4: Simplified overview of the network from a remote controlling perspective.  
Both the tractor and remote control cabin have Raspberry Pi 4 computers connected to the local CAN using a Kvaser USB adapter. The Raspberry Pi on the tractor side works as a client to establish a connection to the server Rasberry Pi computer on the remote cab. The idea is that the remote control cabin server has a known address on the internet, while the tractor can be behind the mobile operator's network address translation (NAT) and or firewall. The software on the tractor side will consistently attempt to establish a connection to the server until the connection is made. If the connection to the server is lost, the client continues to try to make a new connection.  
Devices on the tractor generate a lot of CAN traffic, most of which are not relevant for remote control. The same is also true for the remote control cabin. Therefore, message filtering was added for both ends to pass through only the required messages. Filtering was implemented by modifying the Cannelloni software to accept filtering rules for outgoing and incoming traffic. During the field trial, it was sufficient to filter outbound traffic at both ends.
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 4 The Principle of the PPF-OM Scheme
### Signal Processing on the Receiver Side
\begin{split}\mathbf{x}_{\mathrm{R}}&\!\!=\!\! \mathbf{P}_{k}^{\mathrm{H}}\mathbf{Q}_{x}\bigg{[}\begin{array}{c}\mathbf{F}_ {\mathbf{R}}^{\mathrm{o}}(k,:)\\ \mathbf{F}_{\mathbf{R}}^{\mathrm{e}}(k,:)\end{array}\bigg{]}=\mathbf{x}_{k}+ \mathbf{P}_{k}^{\mathrm{H}}\mathbf{Q}_{x}\mathbf{n}_{k}\\ \mathbf{y}_{\mathrm{R}}&\!\!=\!\!\left(\mathbf{P}_{k}^{\perp}\right)^{\mathrm{ H}}\mathbf{Q}_{y}\bigg{[}\begin{array}{c}\mathbf{F}_{\mathbf{R}}^{\mathrm{o}}(k,:)\\ \mathbf{F}_{\mathbf{R}}^{\mathrm{e}}(k,:)\end{array}\bigg{]}\!\!=\!\!\mathbf{y }+\left(\mathbf{P}_{k}^{\perp}\right)^{\mathrm{H}}\mathbf{Q}_{y}\mathbf{n}_{k} \end{split}  \tag{24}\]  
From Equation (24), the \(k\)-th component of both two signals were obtained, and the other components were then obtained in the same manner. Therefore, the two signal matrices could be written, as follows:  
\[\mathbf{X}_{\mathrm{R}}\!\!=\!\!\left[ \begin{array}{c}\mathbf{x}_{\mathrm{ R}1}\\ \mathbf{x}_{\mathrm{R}2}\\ \vdots\\ \mathbf{x}_{\mathbf{R}_{\mathrm{I}}^{\perp}}\end{array} \right]_{\frac{1}{2} \times\frac{\gamma_{2}}{2}},\mathbf{Y}_{\mathrm{R}}\!\!=\!\!\left[ \begin{array} []{c}\mathbf{y}_{\mathrm{R}1}\\ \mathbf{y}_{\mathrm{R}2}\\ \vdots\\ \mathbf{y}_{\mathrm{R}_{\mathrm{I}}^{\perp}}\end{array} \right]_{\frac{1}{2} \times\frac{\gamma_{2}}{2}} \tag{25}\]  
Finally, two signals were demodulated based on the MLE rule. On the transmitter side, the amount of computation was concentrated in Equations (11)-(13). Equation (11) required \(2u\) penalty operations. Equation (12) required \(2u\) addition operations, and Equation (13) required \(Mu\) penalty operations and \(Mu\) addition operations. On the receiver side, the amount of computation was concentrated in Equations (16) and (22). Equation (16) required \(Mu\) penalty operations and \((M-1)u\) addition operations; Equation (22) required \(4u\) penalty operations and \(2u\) addition operations. Based on above analysis, we found that the amount of computation was proportional to \(u\) and \(M\); thus, in real practice, we could adjust the parameters according to the actual calculation power of the communication equipment. In other words, the computational complexity was adaptable.
# Therapy of Neurophysiological Changes after Oral and Maxillofacial Surgery--A Systematic Review
## 2 Materials and Methods
### Eligibility Criteria  
Original articles were selected based on the PICO (Population Interventions Comparison Outcomes) criteria (Table 1):  
**Population:** Participants included in this review were adults (>18). All of them had undergone oral and maxillofacial surgical procedures with consecutive neurophysiological disturbances of the trigeminal nerve. Alteration must be assessed by neurophysiological diagnostic tools (quantitative sensory testing/QST, neurosensory testing, minimal measurement such as two-point discrimination/needle sensation). Subjective findings, such as visual analogue scale/VAS or index-related findings, had to be documented. The sensory changes were defined as altered sensation (hypoesthesia, hypeesthesia, anesthesia, paresthesia, dysesthesia, pain).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Population** & **Interventions** & **Comparison** & **Outcomes** & **Study Design** \\ \hline \hline \multirow{8}{*}{
\begin{tabular}{c} Adults (>18) with \\ neurophysiological changes after oral and \\ maxillofacial surgery \\ \end{tabular} } & & \(\bullet\) Recovery & \\  & & (complete/residual. Type) & \\  & & of neurosensory disorder & \\  & & & in residual cases: \\  & & \(\bullet\) Various medical & paresthesia, allodynia, \\  & & interventions (e.g., VitB) & \\  & & \(\bullet\) Surgical interventions & \\  & & \(\bullet\) Placebo & \\  & & \(\bullet\) Others & \\  & & CAVE adverse effects & \\  & & & patients or \\  & & & neurophysiological \\  & & & diagnostic tools) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of PICO (Population/Interventions/Comparisons/Outcome).  
**Interventions:** All participants underwent interventions to improve their sensory disturbances, such as medical therapy (steroids, vitamin B, others) or surgical interventions (nerve reconstruction, laser therapy). Adverse effects were considered.  
**Comparison:** Improvement in sensation after various interventions was compared to no treatment (time management) or placebo.  
**Outcomes:** Primary outcome was defined as the status of recovery (complete/partial and residual sensory disturbances such as hypoesthesia, hyperesthesia, anesthesia, pares-thesia) and relief of pain states under various therapies. It was defined as the proportion of the number of patients who showed significant/complete recovery compared to the general population of the included studies. The intention was to show the superiority of a therapy for nerve injury after surgical procedures.  
Secondary outcome was defined as the status of no recovery. It was labeled as the proportion of the number of patients who showed no improved altered sensation compared to the general population of the included studies.