esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
## 3 Result Analysis
### Positioning Error Analysis  
In addition to accuracy, positioning error is also an important indicator to measure system performance. We assume that the key points of the default parking spaces are located at the center of the local feature map to infer parking spaces, so positioning error will directly affect the success rate of parking. We recorded the mean value and standard deviation of all the detected true positive targets relative to the real key points as the measurement of positioning error in Table 3. In the comparison in Table 3, the depth-based algorithm still shows a better effect on positioning error, but the effect of Faster-RCNN on positioning error is relatively poor because the prior box size used by Faster-RCNN is larger. However, in terms of the detection of key points in parking spaces, the previous box size is larger. The key point can be considered as a small target detection task relative to the whole image. Although DETR has the best performance in terms of accuracy, it automatically generates target frames based on the network. Although this method greatly simplifies the intermediate process, it is also mentioned in the original text that DETR [35] needs to improve its performance in the face of small-target detection tasks. Compared with DETR, YoloV11 has better performance in positioning error. The reason is that YoloV11 adopts multiple YOLO-Heads as output, and different YOLO-Heads have different receptive fields. The optimal receptive field is selected as output result according to the size of detection target. As a result, YoloV11 can achieve more accurate positioning accuracy. At the same time, we list the time cost of the depth model to detect the marked points in an image in Table 4. It can be seen from Table 4 that among the several depth-based models compared, YoloV11 calculates the least time cost for the key points of the parking space.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Localization Error** & **Localization Error** \\  & **(in Pixel)** & **(in cm)** \\ \hline HoG+SVM & 4.03 \(\pm\) 1.98 & 6.72 \(\pm\) 3.30 \\ ACF+Boosting & 2.86 \(\pm\) 1.54 & 4.77 \(\pm\) 2.57 \\ Faster-RCNN & 3.67 \(\pm\) 2.32 & 6.12 \(\pm\) 3.87 \\ SSD & 1.51 \(\pm\) 1.17 & 2.52 \(\pm\) 1.95 \\ DETR & 1.76 \(\pm\) 1.43 & 3.52 \(\pm\) 2.16 \\ YoloV11-based & 1.12 \(\pm\) 1.05 & 1.46 \(\pm\) 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the offset between the key point and the actual label.  
Figure 7: Performance comparison of multiple target detection algorithms. (**a**) Performance comparison of four target detection algorithms based on neural networks. (**b**) Two traditional machine learning algorithms.
## 3 Results
### Soil Tillage Detection Results
However, despite the severe class imbalance, the model's performance indicates that it has achieved a reasonably good level of discriminatory power, considering the inherent difficulty in detecting soil tillage operations. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rules-Based** & \
\begin{tabular}{c} **XGB** \\ **(Imbalanced)** \\ \end{tabular} &
\begin{tabular}{c} **XGB** \\ **(SMOTE)** \\ \end{tabular} \\ \hline
**Recall** & 46\% & 54\% & 67\% \\ \hline
**Precision** & 6\% & 4\% & 8\% \\ \hline
**F1 score** & 10\% & 8\% & 14\% \\ \hline
**F2 score** & 22\% & 25\% & 27\% \\ \hline
**AUC-ROC** & 51\% & 53\% & 74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics measured on the test set for soil tillage operations. For the XGB-SMOTE approach, the input that contributes the most to the detection of soil tillage operations is the NDVI, with a feature importance of 0.32. The second most important input variable is the \(\Delta\)VH, with a feature importance of 0.22. The VH input has a very low feature importance, less than 0.05.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 4 Study on the Relationship between Physical Layer Parameters and the Performance of LoRa
### LoRa Evaluation Experiment Setup  
The carrier frequency specified by transmitters and the receiver operates at 868 MHz. Transmitters can send LoRa packets, including a preamble with 10 up-chirps, an Start Frame Delimiter (SFD) with 2.25 down-chirps, and the payload with a random number of up-chirps. We used the UHD + GnuRadio library and developed our own LoRa Transceiver. We used white Gaussian noise as interference. The USRP B200 software radio generates noise and the LoRa signal and sends them 10,000 times continuously, as shown in Figure 5. At the same time, we used SX1276 and RN2483 to receive the signal. Additionally, the interval between data packets must not be less than three times the duration of the packet transmission. This constraint is imposed to ensure that each data packet has a long time window, without being disturbed, and the time is long enough to allow the message to be completely transmitted. The power value of the LoRa signal remains unchanged. We changed the amplitude of the noise signal, thereby changing the Signal to Noise Ratio (SNR). The SNR range was from \(-24\) to 0 dB. We also saved the presence of cyclic redundancy check (CRC) errors in the received packets in our traces. Experiments changed the noise amplitude and the SF, BW, and CR of the LoRa signal to evaluate the packet reception performance of the LoRa according to the obtained Packet Error Ratio (PER) and SNR. We also measured the impact of different packet lengths on the packet reception performance of LoRa. The noise intensity of the experiment was increased by 1 dB until the Packet Error Ratio was 1. Therefore, all the measurement parameters in the experiment consisted of three settings for BW \(\in\{125\) kHz, 250 kHz, 500 kHz\(\}\), six for SF \(\in[7,12]\), four for CR \(\in\{4/5,\ 4/6,\ 4/7,\ 4/8\}\), and four for the packet length \(\in\{4,\ 32,\ 128,\ 255\}\) bytes.
## 1 Introduction  
Non-thermal plasma discharge properties are used in a wide variety of processes. The main applications include agricultural use [1], medical processing [2], water and exhaust treatment [3, 4], waste air quality improvement, and deodorization [5, 6, 7]. Further research is needed to provide application possibilities in different fields [4]. Regarding the plasma reactor constructions, dielectric barrier discharge (DBD, electrodes separated by a dielectric barrier) and corona discharge (CD, separated electrodes with no dielectric barrier) reactors are most widely used for cold plasma discharge generation under atmospheric pressure. The different properties of both constructions are an important factor in choosing a certain one for industrial applications and have a strong influence on power supply system designs.  
In order to efficiently supply a plasma module, many different power supply topologies and electrical waveforms exist. It is possible to use a simple, high-voltage sine wave source (from several to several tens of kV) with a frequency of 50 Hz to 50 kHz to generate the plasma in a DBD reactor. To generate plasma in CD reactors, it is necessary to have a high-voltage pulsed source (from several to several tens of kV), with a high-voltage steepness (typically in the range from several hundred to several thousand volts/ns [8]. Since the construction of CD reactors is much simpler and they have a smaller flow resistance than a typical DBD reactor, a highly efficient topology is required for a High Voltage (HV) generator. Additionally, when using a HV pulse generator for DBD reactors, it is also possible to significantly improve the energy efficiency of plasma generation [9]. Currently, in the generation of high-voltage pulse systems of rotating gas gaps, gas gaps with trigger electrodes or thyratrons as well as fast ionizing dynistors (FIDs) are often used. From the viewpoint of the dynamic parameters of modern power transistors (such as SiC or GaN MOSFETs), the implementation of HV pulse generators is possible.  
This paper is organized as follows: In the first section, the description of proposed high-voltage resonant converter with simulation results is provided, followed by the design and construction details, and suggestions are discussed for a prototype power supply. Finally, measurement results of the experimental setup and practical verification of properties with different types of loads are presented.
## 2 Material and Methods
### pH Cycle  
The demineralization solution used to form artificial caries was used in the pH cycle. A remineralization solution with a pH level of 7.0 was prepared in one liter of distilled water by adding 1.5 mM calcium chloride (CaCl\({}_{2}\)), 0.9 mM sodium dihydrogen phosphate (NaH\({}_{2}\)PO\({}_{4}\)), 0.15 mM potassium chloride (KCl), and 1 M potassium hydroxide (KOH).  
Toothpastes were weighed on a precision balance (KD-TBC-300, Tebisan, Beyoglu, Istanbul, Turkey) to apply a pea-sized amount (0.32 g) to each enamel sample and were stirred homogeneously with distilled water at a ratio of 1:3 in a magnetic stirrer (Arec Heating Magnetic Stirrer, VELP Scientifica, Usmate Velate, Italy). The samples were kept in the prepared toothpaste solutions for 1 min before the first demineralization cycle as well as before and after the second demineralization cycle (Figure 3).  
Figure 3: (**a**) Measuring the amount of toothpaste on a precision balance; (**b**) preparation of the toothpaste solution in the magnetic stirrer; and (**c**) application of the prepared toothpaste solution to the samples in numbered glass bottles.  
Figure 2: (**a**) Front side of the enamel sample removed from the acrylic block; and (**b**) back side of the enamel sample removed from the acrylic block.  
After the artificial caries lesion was formed, a demineralization-remineralization cycle was applied to the samples for 7 days to mimic the pH changes in the intraoral environment throughout the day [28]. According to this method, the samples were kept in an oven at 37 \({}^{\circ}\)C for 7 days to imitate body temperature in the demineralization solution twice a day for 3 h; in the remineralization solution for 2 h between demineralization cycles; and in the remineralization solution for the remaining time. The pH values of the solutions were regularly checked using pH measurement papers.  
The demineralization and remineralization solutions were renewed before each cycle; the toothpaste solutions were renewed before each application. After the solution and toothpaste applications, the samples were washed using distilled water for the next step.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 4 Research Challenges
### Mobility in 3D  
Current radio access technologies are not well suited to promote flying radio devices since their formations are largely geared to assist terrestrial UEs. BSs are typically built and modified to provide the best possible performance for ground users. Existing BSs have been modified to achieve the aforementioned goal. The downward tilting antennas produce radiation patterns that are unsuitable for serving aerial UEs, which are expected to be positioned at various heights above ground level. Since the aerial user frequently flies above the BS antenna height, 3D coverage that can adapt to changing UAV elevations is required. The BS antennas of LTE networks may be able to achieve efficient channel gain by utilizing their side lobe antennas. The BS antenna length, UAV height, antenna design,and association criteria all play a crucial role in determining UAV coverage patterns in 3D space. As a result, a 3D coverage model for aerial users corresponding with terrestrial users is required for the network model. UAVs for network services are different from traditional networks in that they use a 3D model rather than a 2D model to create mobility. UAVs are extremely mobile, rendering their control and decision-making processes difficult. Advanced mobility solutions will be required as a result [49; 73; 74; 75; 76].
# Enhancement of Convection Heat Transfer in Air Using Ultrasound
## 3 Mathematical Model  
A simple energy balance shows that energy accumulation in sample \(dU\) is equal to delivered heat \(dQ\):  
\[dU=dQ \tag{1}\]  
If the sample mass \(m\) and its heat capacity \(c\) are constant and the Biot number is small, then inner energy \(U\) is the linear function of internal temperature \(T\):  
\[dU=mcdT=\rho cVdT \tag{2}\]  
where \(\rho\) is the sample's density and \(V\) is its volume. Heat transfer to the sample consists of two parts:  
\[dQ=\Big{[}hA(T_{a}-T)+\dot{Q}_{abs}\Big{]}dt \tag{3}\]  
convective heat flux proportional to the difference of surrounding air temperature \(T_{a}\) and the sample temperature \(T\), and ultrasound absorption \(\dot{Q}_{abs}\). Based on irreversible thermodynamics, [PERSON] [21] introduced the volumetric heat absorption term in the form:  
\[\dot{Q}_{abs}=a_{u}\eta_{u}P_{u} \tag{4}\]  
where \(a_{u}\) is the absorption coefficient of ultrasonic energy, \(\eta_{u}\) is the working efficiency of the ultrasonic transducer, and \(P_{u}\) is the ultrasound power per unit volume, dependent on bed height. If the absorption coefficient is constant and the bed height is not optimal, then the thermal effect is also constant [15; 16; 17; 19]. In Equation (4), \(h\) is the heat transfer coefficient and \(A\) is the sample surface. Rearranging of Equations (1)-(3) gives:  
\[\frac{dT}{dt}=\frac{hA}{\rho cV}\left(T_{a}-T+\frac{\dot{Q}_{abs}}{hA}\right) =\frac{hA}{\rho cV}\Big{(}T_{f}-T\Big{)} \tag{5}\]  
where  
\[T_{f}=T_{a}+\frac{\dot{Q}_{abs}}{hA} \tag{6}\]  
is the final temperature of the heated sample. The solution of Equation (5) has a form:  
\[\ln\vartheta=\ln\frac{T-T_{f}}{T_{0}-T_{f}}=-\frac{hA}{\rho cV}t=at \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Series I** & **Series II** \\ \hline Air velocity (m/s) & & 0.40 \(\pm\) 0.11 & \\ \hline Air temperature (\({}^{\circ}\)C) & 40 \(\pm\) 0.3 & 60 \(\pm\) 0.3 \\ \hline \multirow{3}{*}{Ultrasound power \(P\) (W)} & 0 & 0 \\ \cline{2-3}  & 50 & 50 \\ \cline{2-3}  & 100 & 100 \\ \cline{2-3}  & 200 & 200 \\ \hline \end{tabular}
\end{table}
Table 2: Processes parameters.  
where \(T_{0}\) is initial temperature, \(\vartheta\) is dimensionless temperature, and \(a\) is the slope of the line. The knowledge of the slope allows the heat transfer coefficient \(h\) to be calculated:  
\[h=-\frac{\rho cVa}{A} \tag{8}\]  
and then, based on Equation (5), the ultrasound absorption \(\dot{Q}_{abs}\) can be calculated:  
\[\dot{Q}_{abs}=hA\Big{(}T_{f}-T_{a}\Big{)} \tag{9}\]  
Equation (9) is the energy balance at the end of the process. It means that all the energy absorbed by the sample is transferred by convection to the surrounding air (thermodynamic equilibrium).
## 3 System Model
### Overview of Interference Mechanisms  
During signal transmission, several types of interference can degrade the performance of these systems [9; 15].  
* Adjacent satellite interference (ASI): Signals from neighboring satellites can interfere with the desired signal;
* Intermodulation interference (IMI): Non-linear amplification in high-power amplifiers can generate intermodulation products;
* Cross-polarization interference (XPI): Imperfect polarization isolation in antennas can lead to cross-polarization interference, where signals intended for one polarization can leak into the other;
* Adjacent channel interference (ACI): Imperfect filtering or insufficient channel guard bands can result in interference from adjacent channels;
* MVDS interference: Secondary lobes of MVDS antennas can radiate energy towards the satellite, causing interference.  
In the context of this work, we will include a theoretical and practical analysis of the carrier-to-noise ratio that includes all these interferences but focuses on ASI and MVDS interferences. Based on the analysis, we will propose recommendations and methods to mitigate MVDS interference and enhance the carrier-to-noise ratio and energy per bit at the satellite level.
## 4 Numerical Simulation of Asymmetric Failure of Surrounding Rock in Roadway  
### Model Establishment and Monitoring  
1. Overall size of the model: given that the research target of this paper is \"plane stress problems,\" to ensure that the numerical simulation results are more in line with the theoretical calculation results as much as possible and to reduce the error between them, the overall thickness of the model was set at 5 m to simulate the two-dimensional  
Figure 11: The variation curve of the butterfly wing angle \(\beta\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Horizontal Distance Between the Roadway  
stress state. Moreover, boundary coal and rock columns with a width of 100 m were set around the model to mitigate the influence of boundary effects. The coal seam being mined has a rock stratum 200 m high above it. The overall dimensions of the model (as shown in Figure 12) are 400 m \(\times\) 5 m \(\times\) 250 m (\(x\times y\times z\));
2. Model of the surrounding rock of the roadway: to investigate the failure conditions of the surrounding rock of the underlying roadway at different vertical distances from the working face, the \"Ratio-Brick\" meshing technique was employed to establish the models of the surrounding rock of the underlying roadway at different vertical distances (5 m, 12.5 m, 20 m, 27.5 m, and 35 m) from the coal seam;
3. Zone property assignment: the values of the physical and mechanical parameters of the elements are as follows: the elastic modulus is 20 GPa, the cohesion is 4.0 MPa, the tensile strength is 3.0 MPa, the internal friction angle is 30\({}^{\circ}\), the Poisson's ratio is 0.2, and the unit weight is 25 kN/m\({}^{3}\),
4. Stress boundary conditions: in this study, it is assumed that the original stress environment of the roadway is in a \"hydrostatic pressure\" state. Therefore, the lateral pressure coefficient \"stresses-ratio\" is set to 1.0 to simulate the stress distribution of the surrounding rock of the roadway under hydrostatic pressure conditions (as shown in Figure 13, the red zone indicates that the element at this location is in the \"hydrostatic pressure\" state). At the upper boundary of the model, a uniform load \(Q_{\mathrm{z}}\) = 6 MPa equivalent to the load of the overlying strata is applied, and the normal velocities \(V_{n}\) = 0 m/s of all boundaries of the model are fixed to simulate the initial geostress balance under actual geological conditions;
5. Cyclic mining: after the initial geostress balance, the \"loop\" looping statement was used to simulate the coal seam mining process. The single-cycle mining length of the coal seam was 5 m, and the total mining length was 200 m. During the excavation, the built-in Fish language was employed to group the plasticity of the surrounding rock of the roadway at different loop stages to analyze the influence of the working face mining on the surrounding rock of the underlying roadway (as shown in Figure 14).  
Figure 12: FLAC\({}^{\mathrm{3D}}\) model.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 6 Conclusions  
In this paper, we introduced the energy consumption problem caused by the scheduling request procedure in the NB-IoT network. NB-IoT characteristics such as repeating transmission of NPUSCH and NPRACH, increase energy consumption in an IoT device, thus notably decreasing the battery life. As a solution, we propose the PBESM to reduce energy consumption by decreasing the number of scheduling request procedures. The PBESM predicts the uplink occurrence through inspection of the packet header and classification of the predictable message. Furthermore, it measures the response time for each session and predicts the processing delay following the proposed algorithm. A network-level simulation showed that the PBESM can achieve from 10% to 34% battery saving in different scenarios and improve the total active time per session by up to 16%, if compared to the legacy NB-IoT. Even in the worst case that corresponds to a very short time session with good channel quality, the active time increased to some extent, but the energy consumption was dramatically reduced.  
Further works will include a software-defined network architecture to enhance packet inspection. Moreover, we will investigate the effect of contention resolution in a multiuser scenario. We expect that the failure of contention resolution will decrease and the PBESM will raise the cell capacity.  
Overall, the proposed mechanism does not require any special hardware on an IoT device, but a simple modification in the network entities including packet inspection and delay prediction. We believe that our mechanism can contribute to successful deployment of NB-IoT networks.  
This work was supported by the ICT R&D program of MSIP/IITP, Korea. [B0101-17-1276, Access Network Control Techniques for Various IoT Services].  
Jinseong Lee designed the algorithm, performed the simulation and experiment, and wrote the paper. Jaiyong Lee conceived and led the research, analyzed the results, and wrote the paper.  
The authors declare no conflict of interest.
## 4 Control Algorithm Evaluation
### Numerical Simulation  
Due to the presence of friction and hysteresis damping, accurately measuring the system response to low-frequency excitations in experiments is challenging. Therefore, numerical simulation is used as a complementary approach to analyze the performance of both OL and CL systems in the frequency domain. To demonstrate the effectiveness of the proposed controller in reducing vibration transmissibility, a 2-DOF driver-seat model, as shown in Figure 2, is employed. For simplicity, an ideal actuator is assumed in the numerical simulation. For comparison, the passive case is simulated by replacing the actuating mechanism with an ideal viscous damper of 600 N\(\cdot\)s\(\cdot\)m\({}^{-1}\). The nominal parameter values for the driver-seat system are provided in Table 1.  
The existence of Coulomb friction may cause the system to be magnitude- and frequency-sensitive. Therefore, two groups of sinusoidal excitations with different magnitudes (10 mm and 20 mm) are employed to investigate the acceleration transmissibility of the seat suspension. The excitations range in frequencies from 0.5 Hz to 5.0 Hz, which can cover the main component of excitation from cabin floor to seat suspension. The steady-phase acceleration transmissibility, which is defined as the ratio between the RMS values of input acceleration and output acceleration, is used as the evaluation index. In order to achieve better control effect, the PPF of the controller is set to be relatively compact: \(\varphi_{1}(t)=(0.05-0.02)e^{-3.5t}+0.02\). The control gains are selected as \(k_{1}=5\), \(k_{2}=5\).  
The simulation results are summarized in Figure 4, from which one can see that the active seat suspension with the proposed control algorithm can significantly reduce the transmissibility throughout the simulated frequency range. With the increase in excitation magnitude, the active suspension becomes more effective in suppressing the vibration. One can also read that the passive system reflects a resonance at about 1.5 Hz, while the active system shows monotonically decreasing curves. Furthermore, the active suspension has a better control effect for excitations at 1-3 Hz.