esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Design Review with Back-Analysis
#### 3.3.3 Parameter Analysis of GSI Using RMR  
The stability of rock slopes is influenced by various factors, including the strength of the rock, discontinuity type, discontinuity spacing, discontinuity continuity, discontinuity surface roughness, the degree of weathering, groundwater status, etc. RMR and GSI values can allow one to define the discontinuity parameters of rock slopes. The instrumental observation data revealed that the ground nail support system offers excellent performance  
Figure 19: Sensitivity analyses of different degrees of weathered sandstone unit parameters: (a) W4–W5; (b) W3.  
based on this design. Thus, considering that the geotechnical parameters of the rock mass are defined, the Hoek-Brown constitutive model, which is the most commonly used numerical analysis model for rock masses [41], was used. According to the results of our parametric analysis, the GSI value is an important parameter for the HB constitutive model. Due to the exceptional performance seen in the soil-nailed support system, a new analysis was conducted using the HB constitutive model. The re-evaluated GSI values were then incorporated into the numerical analyses, ensuring better consistency with the field measurements.  
[PERSON] [42] introduced the RMR rock classification system. Uniaxial compressive strength, rock quality indicator (RQD), discontinuity intervals, discontinuity states, and groundwater are the parameters of this classification system. [PERSON] and [PERSON] [28] introduced the geological strength index (GSI) classification system. [PERSON] [42] defined the relationship between RMR and GSI values. Using the RMR classification, the RMR scores of the completely weathered and moderately weathered sandstone units and GSI values were determined using Equation (14) (Table 9).  
\[\text{GSI}=\text{RMR}-5 \tag{14}\]
# Moving Ad Hoc Networks--A Comparative Study
## 7 Comparison of Short and Long-Distance Wireless Communication Technologies
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **DSRC-ETC** & **DSRC-IVC** & **WLAN** & **IR** & **WMAX** & **GSM/GRPS** & **UMPS** \\ \hline Technical & & IEEE & 802.11p & IEEE & \multirow{2}{*}{ISO 21214} & \multirow{2}{*}{IEEE 802.16} & \multirow{2}{*}{GSM} & \multirow{2}{*}{3 GPP} \\ standard & & IEEE & 802.11a/b/g & & & & & \\ \hline Antenna type & \multirow{2}{*}{Roof antenna} & \multirow{2}{*}{Roof antenna} & \multirow{2}{*}{Roof antenna} & \multirow{2}{*}{Directional antenna} & \multirow{2}{*}{Smart Antenna} & \multirow{2}{*}{Roof antenna} & \multirow{2}{*}{Roof antenna} \\ (MHz) & & & & & & & \\ \hline \multirow{4}{*}{Spectrum range (MHz)} & Europe: & \multirow{4}{*}{11a: 5 GHz} & \multirow{4}{*}{800–1000 nm} & \multirow{4}{*}{2–66 GHz can be} & Europe: & Europe: \\  & & 5855–9925 & United States & \multirow{4}{*}{11 b/g 2.4} & \multirow{4}{*}{800–1000 nm} & \multirow{4}{*}{2–66 GHz can be} & 900/1800 & 900/2100 \\  & (early: 902–92) & & 5850–9025 & & 11 b/g 2.4 & User choice & United States: & United States: \\  & & Japan: 5770–5850 & & GHz (ISM) & & 850/1900 & 850/1700/1900 \\ \hline \multirow{4}{*}{Channel bandwidth} & Control channel: & \multirow{4}{*}{20 MHz} & \multirow{4}{*}{4 independent channels} & \multirow{4}{*}{-} & \multirow{4}{*}{8–time slots/carrier frequency} & \multirow{4}{*}{5 MHz} \\  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{4}{*}{Equivalent omnidirectional  Radiated power} & \multirow{4}{*}{33 dBm} & \multirow{4}{*}{17 dBm/MHz} & \multirow{4}{*}{-} & Depending on & (MS) \(\leq\) \\ \cline{1-1}  & & Vehicle node: & \(\leq\)30 dBm & & & & 2 W(850/90) \\ \cline{1-1}  & & \(\leq\)33 dBm & & & & \(\leq\)1 W(1800/1900) \\ \hline \multirow{4}{*}{Data rate (kbps)} & Downstream & \multirow{4}{*}{3000–27,000} & \multirow{4}{*}{3000–27,000} & \multirow{4}{*}{\(\leq\)54,000 802.11 b:} & \multirow{4}{*}{1000–2000} & \multirow{4}{*}{1000–2000} & (GPS) \\  & & & & & & & Downstream: \\ \cline{1-1}  & & Upstream 250 & & \(\leq\)11,000 & & & 60–800 & environment: 384 \\ \hline \multirow{4}{*}{Communication distance(m)} & \multirow{4}{*}{3–15} & \multirow{4}{*}{\(\leq\)1000} & Access point & \multirow{4}{*}{1–100} & Access point & Base station & Dependent on \\ \cline{1-1}  & & & & Typical & & Coverage & Coverage & base station \\ \cline{1-1}  & & & & & Maximum: & coverage \\ \cline{1-1}  & & & & & 50 km & 35 km & maximum: 2 km \\ \hline \multirow{4}{*}{Connection establishment time(ms)} & \multirow{4}{*}{5–12} & \multirow{4}{*}{Self-organizing} & \multirow{4}{*}{-} & \multirow{4}{*}{10} & Connection & Network access & Network access \\ \cline{1-1}  & & & & delay & time: 10 s & time: 21.2 s \\ \cline{1-1}  & & & & Support for & Support for & Support for \\ \cline{1-1}  & & & & handoff & handoff & handoff \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between long and short-range wireless communication technologies.
## 2 Materials and Methods
### Measurements of Meteorological Factors and Leaf Area Index (LAI)  
Meteorological and soil sensors (see Table 1 for details) were installed in the orchard to automatically measure the air temperature (T\({}_{\mathrm{a}}\)), relative humidity (RH), precipitation (P), photosynthetically active radiation (PAR), soil temperature (T\({}_{\mathrm{s}}\)) at the depth of 5/10/15/20 cm, and soil water content (SWC) at the depth of 20/40/60/80 cm. The leaf area index (LAI) was measured every 1-2 weeks using the LAI-2000 Canopy Analyzer (LAI-2000, Li-Cor, Inc., Lincoln, NE, USA). The measurements were taken at a location 50 cm below the canopy top along the branch-extending direction. Each tree was measured at 10 locations spaced horizontally by 30 cm, and eight trees were measured at each timing point. Daily LAI outside timing points in the measurement were estimated using spline interpolation [40]. To explore the mechanisms of the LAI in mediating environmental factors acting on the variation in carbon flux, we divided the growing season of the kiwifruit into a vigorous and non-vigorous growth period based on the growth rate of the LAI. In 2018, the vigorous growing period was from 21 April to 4 September, while in 2019, it was from 2 May to 19 August, accounting for 55% and 42% of the whole growing season.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Sensor Model** & **Installation Height (m)** & **Abbreviation** & **Unit** \\ \hline Air temperature & PTS-3 & 2.5 & T\({}_{\mathrm{a}}\) & \({}^{\circ}\)C \\ Air relative humidity & PTS-3 & 2.5 & RH & \% \\ Precipitation & L3 & 0.6 & P & mm \\ Photosynthetically & ZF-1 & 3.5 & PAR & W\(\cdot\)m\({}^{-2}\) \\ active radiation & & & & \\ Soil temperature & PTWD-2A & \(-\)0.05, \(-\)0.10, \(-\)0.15, \(-\)0.20 & T\({}_{\mathrm{s}}\) & \({}^{\circ}\)C \\ Soil moisture content & TDR-3 & \(-\)0.20, \(-\)0.40, \(-\)0.60, \(-\)0.80 & SWC & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables and sensor models observed by automatic weather stations.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 2. _Argania spinosa_ Characteristics
### Geographical Distribution of the Argame Stands  
The Argame stands are mainly found in the arid and semi-arid regions of southwestern Morocco [2]. Figure 2 illustrates the distribution area of the Argame forest stands in Morocco. It covers an extensive area of approximately 828,300 hectares [22] to 999,079 hectares [23] and is estimated to comprise around 21 million trees. Its geographical range spans from 29 to 32 degrees north latitude, extending along the Moroccan ocean  
Figure 1: Botanical characteristics of the Argame stands (I. appearance of the Argame tree; 2. bark; 3. root system; 4. flowers; 5. fruits; 6. seeds).  
mouth of the Oued Tensift to the southern mouth of the Oued Draa. The central region where Argane stands are predominant stretches from the northeast of Essaouira to the Souss valley [1]. Today, the Argane stands have significantly expanded its habitat, covering a larger area. It can be found in various regions, including the Souss plain (Taliouine, Aoulouz, Taroudannt, and Agadir) and the southern and western slopes of the Western High Atlas (Haha and Ida-ou-Tanane), extending to Essaouira. The tree also thrives on the northeastern of the Western Anti-Atlas, reaching as far as Sidi-Ifri [24]. Two notable Argane stands areas are the upper Grou valley in the southeast of Rabat and the northwestern foothills of the Beni-Snassen region near Oujda.
# A Security and Privacy Scoring System for Contact Tracing Apps
## 1 Introduction
### Contact Tracing  
Contact tracing is the process of identifying and informing people that they have been exposed to an individual who tested positive for a virus so that they may take appropriate actions, such as isolating and testing. Traditionally, this was done manually through an interview with a patient about whom they were near during their infectious period. Then,the individuals at risk could be contacted. There could also be a public announcement of a location where a patient was and the time they were there. Digital contact tracing aims to perform an automated version of these interviews and notifications by collecting information from users and comparing this information to determine someone's risk level.
## 2 Materials and Methods
### Analysis Method  
We used the R-peaks of the ECG signal as the ground truth to evaluate our proposed algorithms. However, signals from Group #1 have no ECG signals. For this reason, manual annotation was used to provide ground truth for this group. In some cases, it was challenging to find the correct AO peaks, because infants and children have a higher heartrate than adults and systolic peaks were not always higher than diastolic ones. Segments containing these problems were discarded from analysis.  
In the SCG from children/infants, we only measured accelerations perpendicular to the chest, which was sufficient for the analysis model. During peak detection, it is common to either miss or detect additional peaks. As these two errors are different, they will be handled separately. A missing peak is one that fails to be detected, whereas an additional peak is a fake peak found by the algorithm. To mark these errors, we evaluated the results manually. All computations were done using Matlab 2019b on a desktop computer with Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz, 16 GB RAM. For wavelet decomposition, we employed wavelet package decomposition 1D only.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Evaluation Parameters
#### 4.2.3 Frame Rate  
The frame arrival rate at the receiver of video by each second has been calculated for the proposed method and compared with that for All Frames. The values of the fps are given in Table 2 and Figure 9. The frame arrival rate varies according to the method used and the test video sequence sent. For the Walk and Duval Street sequences, the proposed method has a comparatively smaller fps. Additional processing is carried out upon both videos, that is additional processing occurs in extracting ROMs for nearly every frame. Duval Street's frame size is also large too and, therefore, all in all, the fps lags behind. However, for Ground, Exercise and Football, the proposed method demonstrates higher fps than the All Frames method. The cause of this is that, for Ground, extra processing for ROM identification and extraction is not needed, because the difference between consecutive frames is usually not significant and an empty byte is sent most of the time. Whereas Exercise and Football have the same behavior in terms of ROM processing but, due to small frame sizes, processing time is low as well and so the fps is comparatively higher.  
Figure 9: Average processing overhead at the sender for ROM identification.
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology
### Classification
#### 2.4.4 Vision Transformer with Shifted Patch Tokenization and Locality Self-Attention (ViT-SPT-LSA)  
Building upon the Vision Transformer, we apply modifications to enhance its performance for cry sound classification. These modifications include shifted patch tokenization and locality self-attention following the approach proposed in [43]. Shifted patch tokenization involves shifting the patch positions during tokenization, allowing the model to capture more fine-grained details within the spectrograms. Let \(P^{\prime}\) be the sequence of shifted patches and \(E^{\prime}=(e^{\prime}_{1},e^{\prime}_{2},\dots,e^{\prime}_{N})\) be the embedded sequence of shifted patches. To consider shifted patches and capture temporal information, positional encodings are added to the embedded patches. For the locality self-attention modification, the attention mechanism is modified to attend to local regions of the cry spectrograms. The attention matrix \(A\in\mathbb{R}^{N\times N}\) is calculated as:  
\[A_{ij}=Softmax\Bigg{(}\frac{(E_{i}^{\prime}\cdot(E_{j}^{\prime})^{T})\odot Mask_{ ij}}{\sqrt{E^{\prime}}}\Bigg{)} \tag{5}\]  
The Mask\({}_{ij}\) enforces the local attention constraint by setting non-local positions to zero, allowing the model to capture local dependencies and patterns within the cry sounds. The remaining aspects of the architecture and training process for _ViT-SPT-LSA_ generally follow the same principles as the Vision Transformer discussed in Section 2.4.3.  
Figure 2 illustrates the shifted patch tokenization process employed in our modified Vision Transformer (ViT) model, and Figure 3 demonstrates the architecture of the final modified model. As illustrated, the original input Mel spectrogram is augmented through techniques such as horizontal flipping, random rotation, and random zoom. The SPT component divides augmented images into smaller patches. Each patch represents a portion of the image and is treated as a token for further processing. These patches are shown as individual blocks. Next, the Patch Encoder processes these tokenized patches, preparing them for further transformation within the model. Finally, a series of transformer blocks are repeated eight times within the model. Each transformer block captures hierarchical features within the image. These blocks typically consist of components like Multi-Head Self-Attention, Layer Normalization, Multi-Layer Perceptron (MLP), and Skip Connection layers. These components collectively enable the model to understand and process the input image in a hierarchical manner. Within each transformer block, Multi-Head Self-Attention is a core component. It allows the model to capture dependencies between patches by assigning different attention weights. It is worth noting that Layer Normalization is applied before each attention mechanism and MLP, enhancing training stability.
## 3 Object System by Using HS Theory
### HS Searching Procedure
In the ergodic results, \(\beta_{\text{opt}}\) values from Sanya, Shanghai, Zhengzhou, Harbin, Mohe, and Lhasa ranges from \(-18.1^{\circ}\)(Jun) to \(49.9^{\circ}\)(Dec), from \(-7.6^{\circ}\)(Jun) to \(61.4^{\circ}\)(Dec), from \(5.5^{\circ}\)(Jun) to \(64.3^{\circ}\)(Dec), from \(12.6^{\circ}\)(Jun) to \(73.7^{\circ}\)(Dec), from \(16.6^{\circ}\)(Jun) to \(80.0^{\circ}\)(Dec), and from \(-8.9^{\circ}\)(Jun) to \(59.9^{\circ}\)(Dec), respectively. The values of \(\beta_{\text{opt}}\) are negative from May to July. The results also show that \(\gamma_{\text{opt}}\) values obtained with the HS method are exactly the same as those obtained with the PSO method and ergodic method, and that the trend of \(\beta_{\text{opt}}\) values in each month is roughly the same. Figure 2: Monthly daily extraterrestrial solar radiation for various tilted angles and orientations in Shanghai: (**a**) Jan; (**b**) Feb; (**c**) Mar; (**d**) Apr; (**e**) May; (**f**) Jun; (**g**) Jul; (**h**) Aug; (**i**) Sep; (**j**) Oct; (**k**) Nov; and, (**l**) Dec.  
Table 3 compares the optimum tilt angles and monthly total extraterrestrial solar radiation of Shanghai using the ergodic, PSO, HS, and some traditional methods. The results indicate that radiation on \(\beta_{\text{opt}}\) obtained with the HS method is clearly higher than that obtained using the PSO method and methods, taking \(\beta\) to be equal to \(\Phi\) and \(\Phi\pm 15^{\circ}\). Hence, the tilt angle obtained using \(\Phi\) for the PV modules should not be adopted for different places to obtain the maximum overall solar energy. However, in Table 3, the results of the HS method are closest to the standard values. These data show that the HS method can be used as a substitute for the ergodic method. For further comparison, the statistical errors of PSO and HS for six typical climatic stations in China are shown in Table 4, obtained using Equations (21)-(24). According to MPE, MAPE, MABE, and RMSE, the errors of the HS model are lower than those of the PSO method. More specifically, the computational results show that HS errors are about 1 order of magnitude smaller than the PSO errors. The MPE of the HS method is between \(-0.0450\%\) and \(0.4376\%\), the MAPE ranges from \(0.1105\%\) to \(0.4510\%\), the maximum of MABE is \(0.0867^{\circ}\), and the minimum of MABE is \(0.0317^{\circ}\). The RMSE value, which measures the accuracy of estimation, stays well between \(0.0385^{\circ}\) and \(0.1279^{\circ}\). The data in Table 4 starkly illustrate that the monthly optimum tilt angle data obtained from the HS method are more accurate and reliable than that from PSO. The results indicate that the HS method is applicable to the optimization problem of the tilt angle and orientation for PV modules.
# Semantic Microservice Framework for Digital Twins
## 4 Proof-of-Concept: Automatic Sensor Data Evaluation
### Use-Case: Thermal Heating Process  
As depicted in the pipe- and instrumentation diagram in Figure 3, a simple thermal heating process is used to evaluate the proposed service architecture. The ventilation unit \"F1\" blows ambient air through an electric heater \"H1\" into a vessel called \"SiPro\" where a thermal process takes place. The temperature of the process is controlled by modifying the power of the heater. The air is retrieved from the vessel by the ventilation unit \"F2\". The heat exchanger \"HE1\" is used for heat recovery of the exhaust air. Five temperature and two mass flow sensors are placed in the supply and return ducts. The ambient temperature surrounding the vessel causes heat loss of the vessel. The temperature after the heat exchanger \(T_{tx}\), the supply temperature \(T_{sup}\), and the process temperature \(T_{p}\) are measured by sensors with the IDs 102, 105, 106.1. The mass flow into the process is measured by the sensor \(m_{in}\). Those are the most relevant data points for the presented sensor evaluation showcase.  
The data for the service evaluation is generated by a simulation, and implemented in Open Modelica [46]. The simulation was used to produce data for a sensor fault as well as abnormal behavior of the plant caused by a clogged duct. During a regular operation, the temperature inside the process \(T_{p}\) is controlled and is following the setpoint trajectory shown in Figure 4. As shown, there are three operating points for \(T_{p}\): 50 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and a standby mode, in which the temperature is held at 30 \({}^{\circ}\)C. During standby, the pressure setpoint of the ventilation unit \"F1\" is reduced. Thus, the mass flow \(m_{in}\) is also decreased.  
Figure 4: Setpoint for the ventilation unit \"Fan 1\" and the process temperature \(T_{p}\).  
Figure 3: Use-case: Pipe and instrumentation diagram of the heating process.  
To emulate the behaviour of real sensors, random noise is added to the simulated values, which is modeled as a normal distribution with zero mean and a standard deviation of 0.2 \({}^{\circ}\)C and 0.01 kg/s, respectively. The Open Modelica simulation model is available at the GitHub repository [47].
## 2 Principle
### Propagation Mode Analysis Based on the Short-Time Fourier Transform  
The stress wave will be excited to propagate along the bar when the pressure-bearing end of the bar is subjected to the action of explosion shock wave or bullet impact. In light of the above research results, there are infinite propagation modes of the stress wave in theory, but in engineering, only the first to fourth orders are generally considered. It is impossible to distinguish whether a propagation mode of the stress wave has only one order or is composed of multiple orders on the basis of a single experiment result of a specific pressure bar. However, the first to fourth order normalized frequencies and the propagation time curves in Figure 5 provide an analysis idea that the time to reach the specified point in the bar of the frequency components with different propagation velocities are in sequence  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **Non-Dimensional Number** & **Peak Value** & **Valley Value** & **Asymptotic Value** \\ \hline First & \(D/L\) & 0 & 0.857 & 10 \\  & \(C_{g}/C_{0}\) & 1 & 0.378 & 0.576 \\ Second & \(D/L\) & 0.891 & 1.825 & 10 \\  & \(C_{g}/C_{0}\) & 0.850 & 0.452 & 0.616 \\ Third & \(D/L\) & 1.465 &  
and the short pulse will become the long one with the increase of the propagation distance when the stress wave propagates along the axis of the bar for a certain distance. The mechanical parameters (strain, stress, displacement, or velocity) are measured at a certain point in the bar, where the frequency components and their corresponding arrival time in the signal are obtained by time-frequency joint analysis. Then, comparing those results with the curves shown in Figure 5, one can see which modes of the stress wave are excited in this bar.  
In reference [7], the photoelectric signal of the PDV is analyzed by the short-time Fourier transform method, and the pressure waveform can be obtained by multiplying the conversion coefficient given by the elementary theory with the velocity waveform of the free surface of the pressure bar obtained by inverse deduction from the frequency shift. In this paper, the short-time Fourier transform method is used to analyze the free surface velocity waveform of the pressure bar, so that the time-frequency joint spectrum of velocity waveform can be obtained.  
In the process of continuous time signal processing, the short-time fourier transform (STFT) is defined as:  
\[\text{STFT}_{x}(t,\Omega)=\int x(\tau)g^{*}(\tau-t)e^{-\hat{\Omega}\tau}d\tau \tag{8}\]  
where \(g(\tau-t)\) is the window function and \(g(\tau)\) is obtained by moving the window function to the right by t. Equation (8) can be understood as Fourier transform of the signal multiplied by the window function. Since the window function is shifted in the time domain, STFT has both time domain and frequency domain positioning functions. The window function is selected as the Hanning window, also known as the raised cosine window. Compared with the Gaussian window, the main beam width of the spectrum is narrower, thus providing better frequency resolution.