esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Error Analysis of Photogrammetry  
The overall deformation of the structure edge extracted by photogrammetry is compared with the dial gauge as shown in Figure 18. The numerical comparison results are shown in Table 4. From Figure 18 and Table 4, it can be seen that the structural deformation data obtained by photogrammetry are consistent with the data measured by the dial meters, and the maximum error is less than 5%. Compared with the traditional methods, the photogrammetry method has a wider observation range. Thus, the proposed method can obtain the displacement of any section without extensive efforts, which in turn can better reflect the overall displacement and deformation of the structure.  
Figure 16: The load-displacement curve after fitting.  
Figure 17: The load-displacement curve measured by dial meters.  
Figure 18: Error comparison chart.
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Versatility  
There is a potential to utilize PEEV-WS in an extended context. For example, it could support a communication with traffic lights, or coordination of EVs. To consider this possibility in the simulation model, additional data fields carrying general data and thus left for future use were included in the message format.
# Numerical Study of an Ultra-Broadband All-Silicon Terahertz Absorber
## 4 Conclusions  
In summary, we proposed and numerically investigated an ultra-broadband all-silicon THz absorber with a microstructure configured by a periodic array of diamond metamaterial layer and a cubic substrate using the FDTD method. This type of absorber exhibited a nearly perfect absorption response, and had a very wide absorption bandwidth of \(\sim\)1.3 THz for absorption efficiency of \(>\)90%. The electric and magnetic resonances of the silicon layer enabled this nearly perfect broadband absorption characteristic. The advantages of our perfect absorber included polarization independence, wideband absorption, angle insensitivity, and a simple configuration. Moreover, compared with previous metal-dielectric-metal absorbers, the all-dielectric absorber has numerous excellent characteristics. Additionally, the silicon costs less and is less difficult to process, making it possible to apply it in various fields. In future work, we plan to use the absorber for THz imaging and detection.  
[PERSON] wrote the article; [PERSON] reviewed and edited; [PERSON] validated, [PERSON] analyzed the simulations; [PERSON] put forward some comments; [PERSON] checked the spelling, grammar of the article and investigated. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China grant number 61875251, 61875179 and 11874332; in part by the Public project of Zhejiang Province grant number LGG18F050003 and in part by the National major scientific research instrument development project of Natural Science Foundation of China grant number 61727816.  
The authors declare no conflicts of interest.
# Numerical Analysis of the Installation Process of Screw Piles Based on the FEM-SPH Coupling Method
## 2 Basic Theory of SPH
### FEM-SPH Coupling Algorithm  
The contact problem of FEM-SPH is the key to the successful calculation of the numerical model. Presently, the methods to deal with the contact between finite element and SPH particles are mainly the kinematic constraint method and the penalty method. In most  
where \(K,n\) are scalars defined by the contact interface penalty function, \(h_{avg}\) is the average of the distance between particle \(i\) and particle \(j\) and \(N_{a}\) and \(N_{b}\) are the number of SPH particles and FEM nodes in the influence domain.  
The contact between the screw pile and the soil is a point-surface contact, where the SPH particles of the soil model are slave contact points and the FE nodes of the pile model are master contact surfaces. According to the force interactions, the nodal forces of the SPH particles are distributed to the master nodes of the screw pile. Therefore, the total contact force \(F(t)_{interface1}\) generated between the screw pile and the soil can be synthesized from the contact force generated by the SPH particles in contact with the screw pile by the following equation:  
\[F(t)_{interface1}=\sum_{i=1}^{N_{x}}F_{r}(t) \tag{14}\]  
where \(F_{r}(t)\) is the inter-particle contact force of SPH particles, and \(N_{S}\) is the total number of SPH particles in contact with the screw pile during the installation process, which varies with the time.  
For the FE screw pile, a tetrahedral element is adopted to divide the grid, and the total contact force of SPH particles will act on the FE nodes, which can be expressed as:  
\[F_{e}(t)=\frac{2W_{k}F_{r}(t)}{1+w\frac{2}{3}+w_{2}^{2}+w_{1}^{2}} \tag{15}\]  
where \(W_{k}\) is the center coordinate of the contact point between SPH particles and elements relative to the triangular element surface.  
Therefore, the contact force subjected by the FE elements can be calculated through accumulation:  
\[F(t)_{interface2}=\sum_{j=1}^{N_{m}}F_{e}(t) \tag{16}\]  
where \(N_{m}\) is the total number of FE nodes in contact with SPH particles of the soil.  
Therefore, for the numerical FEM-SPH coupling model, the torque during the installation process can be expressed as:  
\[T(t)=\sum_{e=1}^{N_{m}}F_{e}(t)\times r_{e}(t) \tag{17}\]  
where \(r_{e}\) is the vector distance between the finite element node and the central axis of the screw pile.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 4 GTCN Algorithm
### GTCN for Traffic Prediction
However, in terms of memory occupancy, since the algorithm in this paper introduced the gated unit to record the characteristic information, the memory occupancy in each segment through TCN and the gated unit training is higher than other moments. The maximum instantaneous memory occupancy of the GTCN algorithm is much higher than other algorithms, which is the cost of improving the accuracy and speed of the algorithm. Figure 6: GTCN Method Structure and ProcessingFlow. Each Segment Was Trained Separately and Shared the Same CGTN Model.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.1 Data Preprocessing  
Every day \(D\), ARPA Sardegna programmatically sends CRS4 a compressed file containing the following meteorological data: the measurements from all stations of the regional network for the previous day \(D-1\) and the forecasts for the same meteorological variables calculated by the BOLAM model for days \(D\) and \(D+1\). Automatic data reception at CRS4 is ensured each morning by an event oriented software system that (i) waits for the archive to be uploaded to the FTP server, (ii) downloads it locally to the storage area of the computing cluster, also creating backup files, and (iii) extracts and processes the data which are then ready to be stored in the InfluxDB database. An email is sent to SMARTerra maintainers, summarizing the contents of the received datasets and issuing an alert in case of anomalies. The entire procedure is performed automatically, without the need for human intervention, all days of the year.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 3 Problem Formulation
### UE-Slice Pairing Sub-Problem  
Then, the optimum pairing for uRLLC UEs and eMBB UEs is achieved to completely maximize the network's total throughput and maintain the QoS requirements for users using the superposition (H-NOMA) scheme between eMBB and uRLLC UEs, additionally considering the intra-slice and inter-slice isolation and the limited capacity for the service's users, whereas we consider only the eMBB and uRLLC users are associated with the same BS and consider the same objective of (19) with fixed \(a^{t}\), \(\forall\ \mathcal{F}\). Therefore, the (U \(-\) S. P) sub-problem can be designed as follows:  
\[\text{O.P.T1}-\text{U}-\text{S. P}:\max_{\{x\}}\ [F_{1}(x),-F_{2}(x)]^{\text{T}}, \tag{27}\]  
\[\text{S.t}\hskip 14.226378 pt(\ref{eq:P1}),(\ref{eq:P2}),(\ref{eq:P3}) \tag{28}\]  
\[x_{u\hat{n}}^{nm}\in\{0,1\}\hskip 14.226378 pt\forall\ m\in M,\ n\in N_{b}^{s},\ s \in\mathcal{S},\ u\in\mathcal{E},\hat{u}\in E,\ b\in\mathcal{B} \tag{29}\]  
\[T_{bl}^{WT}\in\{1,2,\dots,\mathcal{M}\ \}\hskip 14.226378 pt\forall\ l\in\mathcal{E},\ b\in\mathcal{B} \tag{30}\]  
where \(F_{1}(x)=\sum_{b\in\mathcal{B}}\sum_{u,\hat{n}\in U}\sum_{s\in\mathcal{S}}\sum _{n\in N_{b}^{s}}x_{u\hat{n}}^{nm}\ R_{b\mu}\), and \(F_{2}(x)=\sum_{b\in\mathcal{B}}\sum_{l\in\mathcal{I}}x_{u\hat{n}}^{nm}\ \overline{T}_{bl}\). Condition (28) means that the pairing indicators values are binary values. In this case, constraint (29) should take into consideration the superimposed time of pairing in our calculations, which is a random integer mini-slot time. Therefore, a one-to-one matching game [40,55,55] is used to acquire the UE-slice pairing solution, which is explained in Section 4.2.
## 5 Results and Discussion
### Threats to Validity
#### 5.3.4 Conclusion Validity  
Conclusion validity threats are concerned with the relationship between the treatments and the outcomes. We used a variety of statistical comparison tests for comparing ensemble models in this empirical study to examine the effects (i.e., magnitude) of hyperparameter optimization on tree-based ensembles. The underlying assumptions influence the statistical test that should be applied. Any deviation from the statistical test assumptions might result in incorrect results. We utilized the Shapiro-Wilk test for confirming the normality of our data. The non-parametric Wilcoxon effect size test was used because the data were not normally distributed.
## 3 Problem Formulation
### QoS-Aware Service Composition  
The QoS-aware cloud service composition problem aims to select and combine suitable cloud services to meet the user's QoS expectations. In this context, the QoS value of the composite service is aggregated from the QoS values of individual services. We consider a sequential structure and calculate the QoS value of a cloud service composition using the following equation:  
\[QoS(MCECSC)=\sum_{i=1}^{n}\sum_{j=1}^{r}w_{i,j}\times q_{i,j} \tag{7}\]  
where \(MCECSC\) represents the cloud service composition plan, and \(QoS(MCECSC)\) is the aggregated QoS value of the cloud service composition. We use \(q_{i,j}\) to represent the \(j\)-th QoS attribute of the cloud service selected for the \(i\)-th subtask; \(r\) represents the number of QoS attributes, and \(n\) represents the number of subtasks.  
To select QoS attributes, we consider cost, response time, and reliability, which are commonly used by most researchers in this field. However, these attributes have different ranges, making effective comparison challenging. Specifically, the necessity of normalization lies in the fact that normalizing various indicators can reduce the differences between different QoS indicators, making them easier to be comprehensively evaluated and compared. This helps achieve effective and efficient QoS estimation, improves system reliability, stability, and maintainability, and further enhances user satisfaction. Therefore, normalization has a high practical value [39]. Hence, normalization is necessary to map the range of QoS to the [0, 1] interval, enabling more effective comparison. Additionally, some QoS attributes are positively correlated (such as reliability), while others are negatively correlated (such as cost and response time). Therefore, we use Equation (8) for positive correlation QoS normalization and Equation (9) for negative correlation QoS normalization.  
\[V_{normositive}=\left\{\begin{array}{ll}\frac{\max-\min}{\max-\min}&,\ \max \
eq\min\\ 1&,\ \max=\min\end{array}\right\} \tag{8}\]  
\[V_{normositive}=\left\{\begin{array}{ll}\frac{\max-v}{\max-\min}&,\ \max \
eq\min\\ 1&,\ \max=\min\end{array}\right\} \tag{9}\]  
Our goal is to maximize the QoS value of the cloud service composition and minimize the energy consumption of the cloud service composition to ensure high-quality and low-energy service composition. This is a multi-objective optimization problem, which we approach with the following objective formula:  
\[Obj=\left\{\max QoS(MCECSC),\min EC(MCECSC)\right\} \tag{10}\]
## 2 Overview of the Experiment  
The data of this paper were derived from a deep-sea communication experiment in the South China Sea conducted in July 2021. The experimental area is displayed in Figure 1.  
O1 and S1 in Figure 1 are the transmitting and receiving points, respectively, and the range between them is 14.8 km. The sea floor between these points is relatively flat, with an approximate sea depth of 3790 m.  
The receiving array consists of 44 self-recording hydrophones arranged at unequal intervals. The receiving depth ranges from 124 to 3605 m. Figure 2 is a schematic of the experiment.  
The linear frequency modulation (LFM) signal has good autocorrelation performance and is not sensitive to Doppler, so it is usually used for channel estimation in underwater acoustic communication. In this experiment, LFM signals were transmitted by an underwater acoustic transducer suspended at a depth of 44.8 m from an experimental ship at point S1  
Figure 1: Area of the experiment in the South China Sea.  
Figure 2: Schematic of the experiment in the South China Sea.  
in Figure 1. The center frequency of the transmitted signal was 10 kHz and the bandwidth was 4 kHz. Figure 3 shows the time-frequency diagram of the transmitted signal.  
The duration of the LFM signal was set to 1 s and the time interval between adjacent LFM signals was 4 s. The transmitted signal consisted of 41 LFM signals. Figure 4 shows the sound speed profile measured during the experiment. The approximate depth of the sound channel axis was 1110 m. The sound speeds at the bottom and the surface were 1523 and 1544 m/s, respectively. As the sound speed at the sea surface was greater than that near the seabed, it was a typical incomplete sound channel. Incomplete sound channels commonly exist in the South China Sea, so it is of significance to study the channel characteristics in such a scenario.  
The received signals were obtained from the hydrophones. The CIRs were obtained by processing the received data. Then, the vertical structure change, coefficient of variation, RMS delay spread, and temporal coherence of channels were analyzed according to the CIRs of different positions and times. Specific analytical methods are described in the next section.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 2 Pull-Out Test
### Details of Perfobond Ribs  
The details of the conventional perfobond rib and the notched perfobond rib were illustrated in Figure 3. Both types of perfobond ribs were identical in terms of the hole diameter (\(d_{p}\) = 75 mm), the rib length (\(l_{p}\) = 250 mm), the rib height (\(h_{p}\) = 150 mm), and the rib thickness (\(t_{p}\) = 20 mm). The conventional perfobond rib of CPT specimens made a closed circular hole on the steel plate, while the notched perfobond rib of NPT specimens made an open hole with a cut on the hole edge. In this pull-out test program, the cut width \(c_{w}\) was designed as 37.5 mm, representing half of the hole diameter \(d_{p}\). Thus, the cut ratio \(c_{w}/d_{p}\) of the notched perfobond rib was equal to 0.5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Specimen** & \(d_{p}\) **(mm)** & \(c_{w}\) **(mm)** & \(c_{w}\)**/\(d_{p}\)** & \(d_{r}\) **(mm)** & \(l_{p}\) **(mm)** & \(h_{p}\) **(mm)** & \(t_{p}\) **(mm)** & **Notch** \\ \hline CPT–1 & 75 & 0 & 0 & 20 & 250 & 150 & 20 & No \\ CPT–2 & 75 & 0 & 0 & 20 & 250 & 150 & 20 & No \\ CPT–3 & 75 & 0 & 0 & 20 & 250 & 150 & 20 & No \\ NPT–1 & 75 & 37.5 & 0.5 & 20 & 250 & 150 & 20 & Yes \\ NPT–2 & 75 & 37.5 & 0.5 & 20 & 250 & 150 & 20 & Yes \\ NPT–3 & 75 & 37.5 & 0.5 & 20 & 250 & 150 & 20 & Yes \\ \hline \end{tabular}
\end{table}
Table 1: Pull-out test specimens.  
Figure 3: Details of perfobond ribs. **(a)** Conventional perfobond rib; **(b)** notched perfobond rib.  
Figure 2: Layout of push-out test specimen. **(a)** Front view; **(b)** side view; **(c)** top view; **(d)** 3D view.
# Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
## Abbreviations  
### Acronyms  
ANN Artificial Neural Network  
MPE Mean Percentage Error  
MI Meteorological Index  
MLR MultiLinear Regression  
m.a.s.l. meters above sea level  
NOAA National Oceanic and Atmospheric Administration  
_nRMSE_  
Normalized Root Mean Square Error  
_nMBE_  
Normalized Mean Bias Error  
_RSE_  
Relative Standard Error  
SURFRAD Surface Radiation Budget Network  
**Meteorological variables**  
\(\cos\)Z Solar azimuth cosine (\({}^{\circ}\))  
\(\Delta\) [PERSON]'s brightness factor (dim)  
\(\epsilon\) [PERSON]'s clearness index (dim)  
_RaGH_  
Global Horizontal Irradiance (W\(\cdot\)m\({}^{-2}\))
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work
### Object Recognition  
#### 2.2.1 Template Matching  
This object recognition technique is widely used in machine vision for the purposes of inspection, registration and manipulation as stated by [PERSON] in [13]. Template matching is very effective for predefined environments where the object position and illumination are tightly controlled. However, this approach is ineffective or computationally infeasible when the object is rotated, scaled, is partially invisible or the illumination varies a lot.  
According to [PERSON] [14], a template is referred to as anything shaped or designed to serve as a model for a special purpose. The term matching describes the tasks of comparing something in respect to its similarity, i.e., to examine the difference between a template and the extracted snippet.  
From a more algorithmic viewpoint template matching is a very simple technique where a sub-image is searched within a given image. The sub-image is typically some object of interest [6].  
Conforming to [PERSON] [11], there are several methods to calculate an indicator in order to measure how similar two images are which is then internally used by the template matching process. The used methods are highly depending on the area of operation. The most prominent ones are the square difference matching, correlation matching and correlation coefficient matching.  
[PERSON] at al. presented an algorithm in [15] for vehicle segmentation and classification applying deformable templates. The algorithm was used to both segment the vehicle from a complex background or from other moving vehicles in an image sequence. By defining a polygonal template to characterize a general model of a vehicle they could detect vehicles with different shapes by adjusting the template parameters.  
A different approach for vehicle detection is used by [PERSON] and [PERSON] [16]. Through recognizing and reading the license plate on cars and trucks it was possible to identify vehicles uniquely. After locating and segmenting the license plate and their characters, template matching is then used for classifying each of the characters.
## 1 Introduction
### Objectives of the Paper
In summary, to answer the research question for the Bucharest case study, the objectives of the study were 1. to develop a digital map of the Bucharest public transport network, supported by an open-source GIS application, and 2. to use the digital map to explore the elements contributing to public transport performance in Bucharest, Romania, namely, 2a, the accessibility of public transport stations in 400 m isochrones (5-min walking distances) for residential areas, more specifically presenting the analysis of the correlation of the transport schedules of the tram network in Bucharest, and 2b, the optimization of the headway of vehicles using the QGIS software and PostGIS database, allowing the simulation of vehicle movement in the existing urban public transportation network andimproving the public transport operation schedule, thus avoiding crowding in public transport stations. The rest of the paper is organized as follows: First, in Section 2, the materials and methods utilized are detailed. Second, in Section 3, the results and discussion are presented, including the digital map of public transport in Bucharest, outlining the accessibility of public transport for residential areas, and optimizing the headway of vehicles using the QGIS software and the PostGIS/PostgreSQL database. Last, Section 4 presents a short conclusion based on the results and discusses future endeavors.
## 4 Discussion and Findings
At the center point, when it comes to examining the agglomerations, the produced data from the study reveal that they are at a minimum state. There is a need for more research on different color compositions from various grades where the best processing settings are identified to achieve the best reduction in waste [27; 28; 29; 30; 31; 32]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **S (rpm)** & **T (\({}^{\circ}\)C)** & **FR kg/h** & **L\({}^{\star}\)** & **a\({}^{\star}\)** & **b\({}^{\star}\)** & **dE\({}^{\star}\)** \\ \hline a & 700 & 255 & 25 & 68.10 & 1.100 & 15.330 & 0.630 \\ b & 725 & 255 & 25 & 68.24 & 1.410 & 15.200 & 0.570 \\ c & 750 & 255 & 25 & 68.42 & 1.470 & 15.350 & 0.350 \\ d & 775 & 255 & 25 & 68.77 & 1.340 & 15.840 & 0.330 \\ e & 800 & 255 & 25 & 68.12 & 1.070 & 15.360 & 0.620 \\ f & 750 & 230 & 25 & 67.91 & 1.400 & 14.760 & 1.100 \\ g & 750 & 240 & 25 & 68.62 & 1.520 & 15.403 & 0.320 \\ h & 750 & 255 & 25 & 68.42 & 1.470 & 15.350 & 0.350 \\ i & 750 & 270 & 25 & 68.43 & 1.320 & 15.460 & 0.320 \\ j & 750 & 280 & 25 & 68.66 & 1.520 & 15.440 & 0.300 \\ k & 750 & 255 & 20 & 68.89 & 1.400 & 15.900 & 0.440 \\ l & 750 & 255 & 23 & 68.81 & 1.390 & 15.960 & 0.540 \\ m & 750 & 255 & 25 & 68.42 & 1.470 & 15.350 & 0.350 \\ n & 750 & 255 & 27 & 68.93 & 1.460 & 15.810 & 0.440 \\ o & 750 & 255 & 30 & 68.80 & 1.510 & 15.640 & 0.320 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GT extrusion parameter effects at 255 \({}^{\circ}\)C, 25 kg/h and 750 rpm, at center points. Figure 5: FR = 24.44, between speed (B) and temperature (A) overlay-plot contours. The findings identify pigment dispersion in polycarbonate composite materials in an analysis of the specimens' characteristics for three process conditions with pigment distribution. SEM was also used to develop a quantitative methodology. As shown in Figure 6, compounded grade contains primary particles and agglomerates at three different levels; each of these samples was individually altered. To better reveal the primary agglomerated pigment, lower processing settings were used in comparison to higher processing parameters. Figure 6 shows that polycarbonate pigment samples were found to be agglomerated when examined by SEM, as evidenced by micrograph photographs showing a uniform dispersion. In the high shear zone, de-agglomeration occurs, at the center points, due to the increase in extrusion parameters at 750 rpm, 25 kg/h and 255 \({}^{\circ}\)C. The material had a lower shear heat or rate at a low extrusion process. As a result, agglomeration can occur when the shear zones are lowest.