esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Simulation  
The results were obtained as Digital Imaging and Communications in Medicine (DICOM) files. The files obtained were transferred to the Mimics (Materialise Interactive Medical Image Control System (Mimics 20.00, Leuven, Belgium)) software, and different masks were created for each tooth tissue (dentin and pulp) and other parts (post and core). Once the three-dimensional objects of each mask had been created, they were converted into Standard Tessellation Language (STL) files. The resulting STL files were transferred to the design software (Dental Cad. 3.1 Rijeka, EXOCAD, Darmstadt, Germany), and the STL files were extracted after designing the coronal restoration for each model with the same data. After transferring the resulting STL files to a reverse engineering program (Geomagic Design X 2020/03), a periodontal ligament (PDL) (0.25 mm thick) and cortical bone (0.5 mm thick) were created and extracted as Standard for the Exchange of Product Model Data (STEP) files. The generated elements were transferred to a finite element analysis program (ABAQUS 2020, Dassault Systems Simulation Corp., Johnston, RT, USA) for fatigue performance, and the mesh convergence was determined based on the report by [PERSON] et al. (20). Afterward, 150 N force (on the palatal incisal edge at a 45\({}^{\circ}\) angle) was applied to each group for 1000 cycles. The data were obtained as maximum principal stress values (Pmax).
## 3 Results and Discussion
### Quantitative Analysis and Normalized bOCT Contrast  
In Figures 5 and 6, a clear difference in the biospeckle contrast can be seen for the conditions of soon after heating and 5 min after heating for both males and females in both age groups. There also appears to be a large qualitative difference between the genders. To analyze such differences quantitatively, local biospeckle contrasts were calculated with two sets of ROIs (regions of interest), with one selected from the shallow region or the epidermis-dermis junction layer or papillary dermis layer (<200 \(\upmu\)m) and the other from the deeper region, within the dermis (depth: 600 \(\upmu\)m to 700 \(\upmu\)m from the surface). The size of the ROIs was selected as around 400 \(\upmu\)m (x) \(\times\) 70 \(\upmu\)m (z) (Figure 7). The average speckle contrast was first calculated for each person from two sets of scans obtained under each environmental condition.  
Figure 6: bOCT images obtained from female subjects in different age groups (20s, **above**, and 30s, **below**) under different conditions: before heating (**a**,**d**), immediately after heating (**b**,**e**), and 5 min after finishing heating (**c**,**f**).  
Figure 7: Image with ROIs indicated in black rectangles. ROIs 1 and 2 were selected from the shallow and deeper regions corresponding to the epidermis–dermis border (approximately the papillary dermis layer) and the dermis region, respectively.
## 5 Experimental Verification  
### Experimental System  
In order to verify the effectiveness and feasibility of the proposed control structure, this section uses an experimental design to test the theoretical design. The proposed control structure was tested on a piezoelectric-driven FTS prototype. The experimental platform design is shown in Figure 4. The experimental system uses a piezoelectric amplifier with a fixed gain ratio of 20 to amplify the input signal driving the piezoelectric ceramics. A high-bandwidth encoder with a measurement resolution of 1.2 nm is used to measure the displacement of the end effector in real time. The data acquisition system (NI PCI-6259) is equipped with a 16-bit digital-to-analog converter and a 16-bit analog-to-digital converter to provide voltage to the amplifier and collect data from the encoder. The piezoelectric-driven FTS system runs at a sampling frequency of 10 kHz in a Simulink/Matlab R2021a environment.
### Fouling Mitigation  
Methods for mitigation of fouling build-up have been addressed through means of new materials (such as composites) as lining rehabilitation. This includes but is not limited to Teflon, nanocomposites, plastics, fiber-reinforced polymers, and alloys. The addition of coatings of the aforementioned materials can assist in preventing the build-up due to the lining on the structure's walls. Other preventative means can include the design optimization of the structure to reduce fouling build-up, common in heat exchangers. Another method of prevention is the assistance of chemicals known as 'acid treatment' or 'pH control' [8], as well as the addition of chemical agents into the processed liquid. Magnetic fields [9] and electric fields [10] have both been used for crystallization fouling and biofouling prevention.
### Contributions  
Considering the aforementioned research limitations, this paper presents a novel model for A2A communication scenarios, which aims to capture the channel characteristics that impact the statistics of A2A channels. The model is composed of the LoS, specular reflection, and diffuse components of the channel, effectively representing the influences of UAV vibrations on channel properties. Important statistics such as the spatial-temporal cross-correlation function (CCF), Doppler power spectrum density (PSD), and coherence time are derived. The key contributions include the following:  
* A novel A2A channel model combining LoS, specular reflection, and diffuse components is proposed. The channel properties are characterized in the presence of large-scale path loss and small-scale fading. Additionally, the proposed model can capture the effects of both large-scale and small-scale mobilities of UAVs. The former refers to UAVs' flight, while the latter describes UAVs' random vibrations caused by propeller rotation and engine operation at both ends of the link.
* We derive a closed-form expression that jointly describes the zenith and azimuth angles of diffuse rays as the function of the UAV's location and the dispersion of scatterers. The power of the diffuse rays is calculated by taking into account various factors, such as the path loss, reflection coefficient, and scattering radiation pattern. This is determined based on the grazing angles of impinging rays and the electrical properties and roughness of the reflective materials.
* Key statistics incorporating the spatial-temporal CCF, Doppler PSD, and coherence time are derived. The impacts of model parameters on channel statistics are presented, including the UAV's altitude and the scattering lobe width, as well as the amplitude, direction, and frequency of UAV vibration. We show that the random vibration of UAVs can reduce the temporal correlation and coherence time of the channel and induce extra Doppler frequency components. The influences of UAV vibrations become more pronounced as the carrier frequency increases.  
The rest of this paper is structured as follows. Section 2 outlines the channel model for A2A communication scenarios. A statistical analysis is conducted in Section 3. Section 4 presents the findings and discussion, while Section 5 draws the conclusions.
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
###### Abstract  
People counting in surveillance cameras is a key technology for understanding the flow population and generating heat maps. In recent years, people detection performance has been greatly improved with the development of object detection algorithms using deep learning. However, in places where people are crowded, the detection rate is low as people are often occluded by other people. We proposed a people-counting method using a stereo camera to resolve the non-detection problem due to the occlusion. We applied stereo matching to extract the depth image and convert the camera view to top view using depth information. People were detected using a height map and an occupancy map, and people were tracked and counted using a Kalman filter-based tracker. We operated the proposed method on the NVIDIA Jetson TX2 to check the real-time operation possibility on the embedded board. Experimental results showed that the proposed method had higher accuracy than the existing methods and that real-time processing is possible.  
3-D people counting; stereo matching; NVIDIA Jetson TX2; view projection; Kalman filter tracker; occlusion Article
# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 4 Electric Vehicle Cluster Optimization Model
### Lower Layer Model  
4.2.1. Objective Functions  
With the optimization objective of minimizing the cost to each electric vehicle i owner in cluster j, the charging and discharging strategy of a single electric vehicle is formulated under the premise of satisfying the owner's demand.  
\[F_{2}=\sum_{i=1}^{I}\left(C_{i}^{+}+C_{i}^{B}-C_{i}^{-}\right)+\sum_{t=T_{k,j}} ^{T_{k,j}}\alpha\left|P_{j.t}^{\pm}-\sum_{i=1}^{I}P_{i.t}^{+}+\sum_{i=1}^{I}P_{ i.t}^{-}\right| \tag{16}\]  
\[C_{i}^{+}=\sum_{t=T_{k,i}}^{T_{k,j}}\omega_{t}P_{i.t}^{+}\Delta t \tag{17}\]  
\[C_{i}^{-}=\sum_{t=T_{k,i}}^{T_{k,i}}\omega_{t}P_{i.t}^{-}\Delta t \tag{18}\]  
\[C_{i}^{B}=\sum_{t=T_{k,i}}^{T_{k,j}}\left(P_{i.t}^{+}+P_{i.t}^{-}\right) \lambda_{i.t}\Delta t \tag{19}\]  
where \(F_{2}\) is the cost of the V2G response for the electric vehicle owner, including the charging cost \(C_{i}\), the cost of battery loss \(C_{i}^{B}\), and the gain from discharge \(C_{i}^{-}\) of the electric vehicle \(i\); \(P_{i.t}^{\pm}\) is the charging and discharging power of electric vehicle \(i\) in time period \(t\); \(I\) is the quantity of electric vehicles in the cluster; and \(\omega_{t}\) is the \(t\) -period tariff, and for the convenience of analysis, the tariffs are considered to be the same in this paper; \(\lambda_{i.t}\) is the battery loss coefficient, and \(\alpha\) is the penalty factor for scheduling deviation.  
The charge/discharge power and the battery temperature varied less when the electric vehicle was connected to an AC charger. For this situation, the paper treats \(\lambda_{i.t}\) as a function of the current discharge depth \(DOD_{i.t}\) and the frequency of charging/discharging cycles \(l_{i.t}\) to establish the loss model of power battery [27,28].  
\[\lambda_{i.t}=\frac{c_{b}B_{i}+c_{r}}{l_{i.t}B_{i}DOD_{i.t}} \tag{20}\]  
\[DOD_{i.t}=1-S_{i.t} \tag{21}\]  
The SOC of electric vehicle \(i\) in time period \(t\) is shown by the following equation:  
\[S_{i.t}=S_{a.i}+\frac{\sum_{t=T_{a.i}}^{t}\eta_{i}^{+}P_{i.t}^{+}\Delta t}{B_ {i}}-\frac{\sum_{t=T_{a.i}}^{t}P_{i.t}^{-}\Delta t}{\eta_{i}^{-}B_{i}} \tag{22}\]  
where \(c_{b}\) is the acquisition cost for each capacity of the battery and \(c_{r}\) is the renewal budget.  
By fitting the experimental data [29], there is a linear relationship between \(l_{i.t}\) and \(DOD_{i.t}\): the greater the depth of \(DOD_{i.t}\), the lower the number of \(l_{i.t}\).  
\[l_{i.t}=694 DOD_{i.t}^{-0.795} \tag{23}\]
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 4 Discussion  
This study aimed to address three research questions relevant to: (1) the variation of ambient temperatures; (2) predicting temperatures using ground and satellite data; and (3) the role of landscape features in amplifying temperatures. The results indicate that in all of our study regions, the temperatures vary by over 10 \({}^{\circ}\)C, and that those patterns were largely due to specific land cover patterns, including the presence of dense buildings (hottest) and parks/open space (coolest), which is consistent with other UHI studies using similar traverse-based methods [10; 11; 12; 13; 14; 15]. The study went further to illustrate that specific satellite bands can help to inform predictions about the distribution of urban heat. In fact, within the model diagnostics, we observed that the application of a machine learning technique (RF) created high performing models with excellent predictive power, and low variance in error (RMSE).
However, M2M communications face a number of challenges at different levels of M2M architecture as defined by the European Telecommunications Standards Organization (ETSI) [1].  
At the low level of this architecture (i.e., at device network domain), communications take place between constrained M2M devices (sensors, smart objects \(\ldots\)) and gateways or base stations as well as edge nodes depending on the adopted architecture. Due to such constraints (limited energy source, constrained computational capability, access control\(\ldots\)), the management of a constrained device network as defined in RFC 7548, presents different types of challenges compared to the management of a traditional IP network.  
A key challenge for M2M communications is related to the security of M2M devices, which are projected to operate without human intervention and are therefore defenseless. Moreover, the heterogeneity of the technologies and systems implemented on these devices lead to more vulnerability and made difficult their protection against various attacks (Spoofing, Deny of Service, etc.).  
Most studies in this area are based on centralized and weighty security solutions, which are in best part inappropriate for constrained M2M devices.  
Very few works tackle the authentication process with decentralized approaches at the device network level.  
In this paper, a lightweight blockchain-based authentication and authorization mechanism is proposed to secure communications, especially at perception layer, namely M2M device domain. The remainder of this article is organized as follows: in the next section, a survey of different security protocols for wireless M2M communications is presented. In Section 3, after a brief description of a standard M2M communication architecture and its different components, the proposed scheme based on lightweight authentication protocol for M2M device domain is presented. The evaluation of performance parameters using experimental simulation is given in Section 4. Section 5 deals with security analysis of the proposed scheme. Section 6 discusses the obtained results and highlights the introduced advantages of the proposed scheme in comparison with other related protocols. Finally, Section 7 concludes this paper and suggests some future research directions.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Definition of Objectives  
As already mentioned, a Machine Learning project must start with the rigorous and clear establishment of objectives. In this case, the main objective of the models used will be to predict the probability of a failure occurring within the defined time window. More specifically, the probability of a machine failure occurring in the next 24 h (duration of the time window chosen for this application) related to one of the components (components 1, 2, 3 or 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **machineID** & **Model** & **Age** \\ \hline
0 & 1 & model3 & 18 \\
1 & 2 & model4 & 7 \\
2 & 3 & model3 & 8 \\
3 & 4 & model3 & 7 \\
4 & 5 & model3 & 2 \\
5 & 6 & model3 & 7 \\
6 & 7 & model3 & 20 \\
7 & 8 & model3 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Typical example of information for each machine.  
Figure 5: Representation of the number of components replaced, by type, due to the occurrence of a failure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Datetime** & **machineID** & **Failure** \\ \hline
0 & 2015-01-05 06:00:00 & 1 & comp 4 \\
1 & 2015-03-06 06:00:00 & 1 & comp 1 \\
2 & 2015-04-20 06:00:00 & 1 & comp2 \\
3 & 2015-06-19 06:00:00 & 1 & comp4 \\
4 & 2015-09-02 06:00:00 & 1 & comp 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Typical example of failure records.  
Then, given that a particular and clear objective has already been set, more specific questions can be asked about Machine Learning itself: (1) Should supervised, unsupervised, or reinforcement learning models be chosen or, possibly, combinations of learning modes? (2) Whether supervised learning, classification, or regression? (3) Are models intended to train immediately as new data is obtained (_batch learning_ or _online learning_)?  
After analyzing the problem, and bearing in mind the proposed objective, we opted for supervised learning and, in particular, classification. Furthermore, taking into account the existence of 4 different components under analysis, the problem will be multi-class classification (\"Multiclass Classification\"). It was also considered that it will not be necessary, given the scope of the problem and the nature of the data, for models to train immediately as new data is obtained. Therefore, we are facing a problem of _batch learning_.
## 2 System Description
### Time Synchronization Precision  
Time synchronization precision refers to the accuracy of synchronization between multiple receivers. This metric depends on the synchronization precision of the timing modules. In this design, the synchronization precision requirement between the two receivers is within 10 ns. Therefore, the BeiDou co-view time-frequency transfer technology is proposed to synchronize the two long-wave receiving subsystems [28; 29; 30].  
The principle of BeiDou co-view time-frequency transfer is shown in Figure 10:The BeiDou receivers are positioned at two known locations, A and B. Let the clock time at location A be \(t_{A}\), the clock time at location B be \(t_{B}\), and the BeiDou satellite time be \(t_{BD}\). Let \(d_{A}\) and \(d_{B}\) denote the path delays from the satellite to locations A and B, respectively. The co-view receivers at locations A and B simultaneously receive signals from the same satellite. The second pulses output by the receivers, representing BeiDou time, are sent to a high-precision time interval measurement module and compared with the local clock pulses. This process produces the time difference count values \(\Delta t_{ABD}\) and \(\Delta t_{BBD}\) for the receivers at locations A and B relative to the satellite, as expressed in the following equations:  
\[\Delta t_{ABD}=t_{A}-(t_{BD}+d_{A}), \tag{12}\] \[\Delta t_{BBD}=t_{B}-(t_{BD}+d_{B}),\]  
The data from locations A and B are transmitted to each other's computers through a communication link. The path delays \(d_{A}\) and \(d_{B}\) can be calculated using ephemeris data. By subtracting the two equations above, the time difference between the two locations can be calculated, as shown below:  
\[\Delta t_{ABD}-\Delta t_{BBD}=(t_{A}-t_{BD}-d_{A})-(t_{B}-t_{BD}-d_{B})=(t_{A} -t_{B})-(d_{A}-d_{B}), \tag{13}\]  
BeiDou co-view time-frequency transfer eliminates clock discrepancies by establishing co-view data communication and employing a weighted mutual difference algorithm for co-view fitting values. This reduces path delay errors effectively.  
In this design, BeiDou co-view technology is applied using an optimized high-precision clock disciplining algorithm to tame the built-in high-stability crystal oscillator, thereby achieving high-precision BeiDou timing services. The absolute timing precision is better than 10 ns, and the system has strong independent time-keeping capabilities. Additionally, it outputs timing signals, including 10 MHz and 1 PPS, meeting the precise timing and synchronization requirements of the long-wave receivers.
## 2 Materials and Methods
### Procedures  
#### 2.3.1 Tensiomyography Assessment  
The radial displacement (Dm), contraction time (Tc), and delay time (Td) were measured and recorded in both the biceps femoris (BF) and rectus femoris (RF) muscles of the dominant leg, using a TMG device (TMG Measurement System, TMG-BMC Ltd., Ljubljana, Slovenia) as described elsewhere [37; 38]. An accurate pressure transducer (TransTekwGK40, Ljubljana, Slovenia) was positioned perpendicular to the muscle axis. The radial displacement was assessed in the muscle belly after an external electrical stimulus. To induce twitch responses, adhesive electrodes (5 \(\times\) 5 cm; Compex Medical AS, Ecublens, Switzerland) were connected to an electric stimulator and positioned on the muscle surface, following the alignment of muscle fibers [38; 39]. The electric pulse was set to 1 ms, and the signal amplitude started at 20 mA. For each pulse, the current amplitude was increased by 10 mA, until maximal displacement of the muscle belly was reached. To avoid fatigue or potentiation effects, a 15 s rest period was allowed between the successive electrical stimuli. All TMG measurements were conducted by the same experienced researcher. The TMG-derived velocity of contraction (Vc) was calculated by dividing Dm by the sum of Tc and Td.  
Figure 1: A schematic presentation of the study design. CMJ: countermovement jump; DJ: drop jump; TMG: tensiomyography; COD: change of direction.  
Figure 2: A National Team rugby player performing two conditioning activities: (**2.1**) the 45-cm drop jump–dropping (**A**), landing (**B**), and jumping (**C**); (**2.2**) the flywheel squat–starting the eccentric phase (**A**), at the end of the eccentric phase (**B**), and at the end of the concentric phase (**C**), respectively.
# Estimation and Adjustment of Interface Stiffnesses for Machine-Tool
## 3 Approach Methods and Procedures
### Convergence Analysis  
In order to ensure the stability of the FMA results, it is necessary to carry out a convergence analysis to find out the suitable element mesh size for different FE models of the body. In finite element analysis, the finer mesh will cause the computer to spend a lot of resources to solve the problem, and using a smaller mesh to grid the geometrical model may lead to unstable analysis results in the case of determining whether the solution will converge or not. In the ANSYS Workbench, the tetrahedral element is selected in the finite element mesh of the model, and the body sizing may be gridded sequentially in descending manner (element size from 50 mm to 7.5 mm in this study), and the related parameters of mesh-average and mesh nodes are deduced accordingly. Furthermore, the convergences of the 1 st mode natural frequency results and the mesh-average associated with different element sizes are always noted as an index for mesh convergence judgement. The mesh-average parameter in the software for element mesh quality index is finally integrated as the basis for the selection and examination of a suitable element size fulfilling convergent demand.  
Figure 6 shows the relationship between mesh-average, 1 st mode natural frequency, and the number of nodes for the saddle subsystem. When the element mesh size is smaller than a certain size, the analysis results will begin to stabilize, that is, the natural frequency difference of the 1 st mode shown in Table 1 will begin to converge within a certain range. The tendency of mesh-average also approaches a certain value simultaneously. The suitable element mesh size is different from the various volume and construction of the part or subsystem, but the suitable mesh size and corresponding mesh nodes may be determined based on the parameter of the mesh-average. Usually, it can be found that the natural frequency difference of all parts or subsystems of the whole machine-tool falls within 1% when the mesh-average is around 0.8, approximately.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Saddle**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Natural Frequency (Hz)**} & \multirow{2}{*}{**Difference (\%)**} \\ \cline{2-2} \cline{4-4}  & **Before** & & **After** \\ \hline
1 & 314.75 & 314.92 & 0.05 \\ \hline
2 & 327.63 & 327.7 & 0.02 \\ \hline
3 & 441.17 & 441.25 & 0.02 \\ \hline
4 & 600.64 & 600.88 & 0.04 \\ \hline
5 & 665.59 & 664.49 & \(-\)0.17 \\ \hline
6 & 754.63 & 754.78 & 0.02 \\ \hline \hline