esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Resource Allocation Algorithm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input:** & **System Area R, Transmit Power of CUs \(p_{c,j}\)** \\
**Output:** & **Actual User Set \(U_{j}^{D}\)** \\ \hline
1: & Initialize, \(\gamma_{min^{\prime}}^{(D)}\), \(\gamma_{min^{\prime}}^{(C)}\), \(G_{min^{\prime}}^{D}\), and \(\beta_{i,j}^{CD}=~{}1,\forall i\in Y,~{}\forall j\in X\) \\
2: & Calculate \(p_{D,j}\) by (14) \\
3: & Obtain \(\gamma_{D,j}\) and \(\gamma_{C,j}\) by (4) and (5) \\
4: & Set \(\beta_{i,j}^{CD}\) to 0 if one of (7) and (8) is not satisfied \\
5: & Obtain \(S_{j}^{C}\) from \(\beta^{CD}\) \\
6: & Calculate \(\omega\Big{(}i\Big{|}~{}S_{j}^{C}\Big{)}\) by (16) \\
7: & **For** each \(j\in X\) do \\
8: & **If**\(\omega\Big{(}i\Big{|}~{}S_{j}^{C}\Big{)}=~{}1\), then \\
9: & Keep \(\beta_{i,j}^{CD}=~{}1\), then go to 13. \\
10: & **Else** \\
11: & Select the D2D pair with maximum weight to be an actual user of \(S_{j}^{C}\) and keep \(\beta_{i,j}^{CD}=~{}1\) \\
12: & **End if** \\
13: & Set the other position of \(i^{*}\)-_th_ row and \(j^{*}\)-_th_ column of \(\beta^{CD}\) to 0 \\
14: & **End for** \\
15: & Obtain \(U_{j}^{D}\) from \(\beta^{CD}\) \\
16: & Return \(U_{j}^{D}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed WE-I-A algorithm.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Bitangent Algorithm  
Figure 14 illustrates a method for generating a bitangent between the two obstacles \(O_{1}\) and \(O_{2}\). Its steps are as follows:  
1. Select arbitrary points \(A\) and \(B\), on the boundary of each of the obstacles. In Figure 13(b), \(A\) is the farthest point along the negative \(x\)-axis for \(O_{1}\) and \(B\) is the farthest point along the positive \(x\)-axis for \(O_{2}\).
2. Generate the glancing tangent \(AC\) from \(A\) to \(-O_{2}\), as shown in Figure 13(b).
3. Generate the glancing tangent \(CD\) from \(C\) to \(+O_{1}\), as shown in Figure 13(c).
4. Repeat Steps (2) and (3), while setting \(A=D\) and \(B=C\), until the pixels of the arrival of the tangents on \(O_{1}\) and \(O_{2}\) remain unchanged.  
Note that the complexity of this algorithm is essentially the same one as the secant method to find the zeros of a function, except that it is somewhat faster because the successive points in the construction are discrete integers.  
Figure 14: Bitangent generation algorithm.  
Figure 13: Glancing tangents.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
###### Abstract  
A methodology based on displacement measurements from monitoring devices is proposed and tested. The goal is to detect the formation of cracks in arch dams and estimate their dimensions using a straightforward approach. The methodology involves interpolating displacement data against representative cases to identify anomalies indicative of cracks. A real dam was used as a case study, which began with the development of a 3D finite element model under several hypotheses. This model was analyzed using finite element method (FEM) calculations to simulate the displacements recorded by monitoring devices. The effectiveness of the methodology was evaluated based on the number and location of monitoring stations, demonstrating its ability to detect cracks and approximate their dimensions with acceptable accuracy.  
arch dam; FEM; cracks; monitoring Article
## 4 UAV Blockage Analysis
### Blockage Probability with Rooftop-Mounted BSs  
The blockage probability heavily depends on the density of mmWave BSs, as well as on the heights of buildings. For some values of these input parameters, the blockage probability might be unacceptably high. In practical deployments, network operators may want to add additional dedicated mmWave BSs. Mounting these BSs on rooftops would allow for an unobstructed LoS of circular shape, drastically reducing blockage probability.  
To assess joint deployment, one may apply the methodology developed in the previous section to rooftop mmWave BSs. The principal difference is that the LoS visibility zone is of circular form with radius \(D/2\) as in (1) with \(H_{B}\) replacing \(h_{T}\). However, as these mmWave BSs are now deployed on the roofs, \(D\) is a RV. Thus, we have  
\[E[D]=\int_{0}^{\infty}f_{H_{B}}(x)2\sqrt{r^{2}-(h_{R}-x)^{2}}dx. \tag{24}\]  
that can be evaluated for a given \(f_{H_{B}}(x)\), \(x<h_{R}\).  
The blockage probability by \(M\) rooftop mmWave BSs is obtained similarly to (22). Finally, in the presence of \(N\) ground-mounted and \(M\) rooftop-mounted mmWave BSs the blockage probability is the product of individual blockage probabilities.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 3 Results
(4) Compared with [PERSON]'s model [15] in a control experiment, the DCC-EL model reduced the RMSE and MSE for predicting power loads over ten days and three months by 5.42% and 15.69%, respectively. The reasons for the discrepancies in the experimental results may be as follows: first, the data may need appropriate preprocessing to fit the input requirements of VMD, LSTM, and LightGBM, which could complicate implementation. Similarly, the outputs might require proper post-processing for integration and interpretation. Moreover, using VMD for feature decomposition could introduce additional uncertainties, and the model's stability might be affected by various model integration methods. (5) Compared with [PERSON]'s model [16] in a control experiment, the DCC-EL model reduced the RMSE and MSE for predicting power loads over ten days and three months by 4.96% and 12.31%, respectively. The reasons for the discrepancies in the experimental results may be: Firstly, ensemble empirical mode decomposition requires appropriate parameter settings and technical expertise for the comparison model. Otherwise, it may lead to inaccurate decomposition of high and low frequencies. Secondly, the low-frequency part might encompass nonlinear changes caused by long-term trends, periodicity, seasonality, or other intricate factors. Employing multivariate linear regression might fall short of capturing such nonlinear relationships or more complicated patterns. If these nonlinear relationships or patterns are crucial in the low-frequency section, relying solely on multivariate linear regression could result in predictive errors. (6) The ablation experiment for DCC in the DCC-EL model showed that the performance of the DCC-EL model is far superior to that of the DCC model. This is because the DCC-EL model adopts an ensemble learning strategy, combining multiple base learners, allowing it to better integrate the characteristics and advantages of different models. This enables DCC-EL to learn a richer feature representation and better capture the complex relationships in the data, thereby improving predictive performance. Furthermore, during the prediction process, the DCC-EL model considers the weight of features, assigning weights to them based on their importance, making the model pay more attention to features that significantly impact the prediction results. This helps reduce the interference of unimportant features on prediction, enhancing the model's stability and accuracy. When using the proposed DCC-EL framework for power load prediction, the results surpassed those of the other six models across nearly all four evaluation metrics (MSE, RMSE, MAE, and MAPE), underscoring the efficacy of the DCC-EL framework.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 3 Methodology  
### Overall Framework  
The overall framework of the proposed approach is illustrated in Figure 1. As seen, the framework consists of two major procedures. In the first procedure, multiple regimes of traffic flow are identified using a probabilistic approach. Each regime characterizes a pattern that describes a homogeneous traffic condition during the study time period. The identified regimes are then used as the representative features for the forecasting modeling. In the second procedure, the training data set and test data set are separately established according to the constructed features. Subsequently, the forecasting model is built based on the training set and an ensemble learning strategy, and utilized to produce the forecasts of the test data set.
# Bluetooth Low Energy Mesh Networks: A Survey
## 5 BLE Mesh Networks: Proprietary Solutions  
In order to exploit market opportunities for BLE mesh network products, several companies have developed proprietary solutions for BLE mesh networks. A majority of these solutions have been designed for the fields of home automation and/or lighting. However, they may also be suitable for other use cases. We next present a comprehensive set of commercial, proprietary BLE mesh network solutions. Due to the proprietary nature of these solutions, the availability of details on the mechanisms used by these solutions is limited. Their main features are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposal** & **Name** & **Year** & **Mesh Paradigm** & **Intended Application** \\ \hline
[19] & CSRmesh & 2014 & Flooding & Lighting, HVAC, switch manager, physical access authorization, smart home \\ \hline
[20] & BLE-MESH.com & 2014 & Routing & Smart city and home automation. \\ \hline
[21] &
\begin{tabular}{c} Wirepas and \\ Nordic \\ Semiconductor \\ \end{tabular} & 2014 & \(\mathrm{N/A}\) & Smart home, smart city, smart door lock, lighting, sport, fitness, health, virtual reality \\ \hline
[22] & NXP & 2015 & Routing & Smart home, smart city, lighting \\ \hline
[23] & Silvair & 2016 & Flooding & Smart lighting \\ \hline
[24] & Cypress & 2016 & \(\mathrm{N/A}\) & Health, fitness, home appliances, toys \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of BLE mesh network proprietary solutions and intended applications (as claimed by each corresponding manufacturer).
## 5 Discussion and Conclusions
### Influence of Operating Conditions
  
total internal leakage (m\({}^{3}\)/s)  
\(q_{\text{val, rel, tri}}\)  
\(q_{\text{tri,s, tri,r}}\)  
\(q_{\text{r}}\)  
radial leakage of the radial interface of the tooth tips and the crescent fillers (m\({}^{3}\)/s)  
\(q_{\text{rc}}\)  
leakage of the ring-gear/case interface (m\({}^{3}\)/s)  
\(q_{\text{trap}}\)  
trapped flow, flow out of the trapped chamber through the relief groove (m\({}^{3}\)/s)  
\(Q_{\text{max, min}}\)  
maximum and minimum flowrate (m\({}^{3}\)/s)  
\(Q_{\text{kin}}\)  
kinematic outlet flow (m\({}^{3}\)/s)  
\(Q_{\text{wt}}\)  
with-trapped flow by adding the kinematic flow and the trapped flow (m\({}^{3}\)/s)  
\(Q_{\text{out}}\)  
actual outlet flow by adding the kinematic flow, the trapped flow, the leakage and the triangular flow (m\({}^{3}\)/s)  
\(r_{\text{a1, a2}}\)  
addendum radii of the gear shaft and the ring gear (m)  
\(r_{\text{f1, f2}}\)  
distances between the contact point and the centers of the gear shaft and ring gear (m)  
\(r_{\text{fp1, fp2}}\)  
radii of the inner and the outer border of the floating plate (m)  
\(r_{\text{p1, f2}}\)  
pitch radii of the gear shaft and the ring gear (m)  
\(t\)  
time (s)  
\(V\)  
volume (m\({}^{3}\))  
\(V_{\text{d}}\)  
volume of the discharge chamber and the delivery line (m\({}^{3}\))  
\(V_{\text{kin}}\)  
volume of the discharged fluid when the pump operates for a period of time \(t\) from the kinematic  
aspect (m\({}^{3}\))  
\(V_{\text{trap}}\)  
volume of the trapped chamber (m\({}^{3}\))  
\(V_{\text{TS}}\)  
volume of the tooth space (m\({}^{3}\))  
\(z_{\text{1, 2}}\)  
tooth number of the gear shaft and the ring gear (-)  
_Greek symbols_  
\(\alpha_{0}\)  
pressure angle of the gear (\({}^{\circ}\))  
\(\alpha\)  
operating pressure angle of the gear pair (\({}^{\circ}\))  
\(\beta\)  
fluid bulk modulus (Pa)  
\(\gamma\)  
index angle (\({}^{\circ}\))  
\(\delta\)  
radial clearance between the ring gear and the case (m)  
\(\delta_{\text{l}}\)  
lateral clearance between the gears' lateral sides and the floating plates (m)  
\(\delta_{\text{q}}\)  
flow ripple (-)  
\(\delta_{\text{r}}\)  
radial clearance between the gears' tooth tips and the fillers (m)  
\(\Delta t\)  
time interval (s)  
\(\zeta\)  
displacement ratio (-)  
\(\eta\)  
volumetric efficiency (-)  
\(\theta\)  
rotation angle of the gear shaft (\({}^{\circ}\))  
\(\lambda\)  
circumferential angle of one tooth in the gear shaft (\(\lambda=360^{\circ}/z_{\text{1}}\))  
\(\mu\)  
fluid viscosity (Pa-s)  
\(\rho\)  
fluid density (kg/ m\({}^{3}\))  
\(\varphi\)  
angle circumference the sealing area in the ring-gear/case interface (rad)  
\(\psi\)  
sector angle of the annular sector for estimation of the lateral leakage (rad)  
\(\omega\), \(\omega_{\text{r}}\)  
angular velocity of the gear shaft and the ring gear (rad/s)
## 1 Introduction
While the sensor users track animals to gather information on their usual habits, such as hunting or mating, they can also gather data which detects human presence and activity [9]. The challenge is to set up a wireless sensor network in geographically harsh and rugged areas to enable regular military units to use animals as mobile sensor agents. These agents will be indispensable in gathering necessary data to combat terrorist groups hidden in this environment. The design and implementation of the proposed sensor services is evaluated in this paper. Furthermore, this paper reviews mobile sensor network architectures and provides background information for some animals that can be used as mobile sensors in the harsh terrain, including relevant definitions and a technical summary of space-based remote-sensing.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 1 Introduction
Due to the use of beam elements in bridge modeling, the unique shear lag effect of the box-girder bridge could not be considered, and the uncertainty in shrinkage and creep was neglected in this study. However, previous studies show that the uncertainty in shrinkage and creep models has a significant influence on the evaluation of long-term responses of PSC bridges [20], which should be taken into account in the reliability analysis of PSC bridges. In this study, a random field-based time-dependent reliability method was developed, which is the first combination of random field method and time-dependent analysis for PSC box-girder bridges. Besides, shell elements rather than a beam element were used for accurate modeling of the box-girder bridge, and accordingly the random field analysis of PSC box-girder bridges was firstly extended to two-dimensional. Considering the high computational cost for such a stochastic finite element analysis, the importance sampling (IS) method was adopted to obtain rational efficiency. A case study on the Jinghang Canal Bridge, a three-span continuous PSC box-girder bridge with a main span of 150 m in China, was made as a demonstration.
## 3 Validation of Line-Source Theory and Uncertainty of System
### Uncertainty Utilizing Mass Heat Balance Method  
A thermal balance approach was used as a verification method to confirm that it was reasonable to ensure the accuracy of the experiment on the fluid volume and temperature of the entire system as shown in Table 3. The simplest representation of the heat balance equation is  
\[q_{\text{in}}=V\text{-}C_{p}\text{-}(T_{\text{out}}-T_{\text{in}}), \tag{8}\]  
where \(q_{\text{in}}\) (W) is the calorific value obtained through the measured temperature and the flow rate of the pump. \(V\) (liters per minute, LPM) is the pump flow rate. \(Cp\) is the specific heat of water. \(T_{\text{in}}\) and \(T_{\text{out}}\) are the inlet and outlet temperatures of circulating water. The uncertainty of the temperature measurement for the temperature sensor was \(\pm 0.01\)\({}^{\circ}\)C, and the signal conditioner of the digital display with analog signals was \(\pm 0.04\)\({}^{\circ}\)C. The total uncertainty for temperature measurement is given in Equation (9).  
\[\Delta T=\sqrt{\left(\pm 0.01\right)_{\text{in}}^{2}+\left(\pm 0.04\right)_ {\text{in}}^{2}}+\sqrt{\left(\pm 0.01\right)_{\text{out}}^{2}+\left(\pm 0.04 \right)_{\text{out}}^{2}}\approx\pm 0.0825\text{ }^{\circ}\text{C}. \tag{9}\]  
\[\text{Error}=\frac{\pm 0.0825\text{ }^{\circ}\text{C}}{5\text{ }^{\circ}\text{C}}\times 100\%=\approx\pm 1.65\%. \tag{10}\]  
Using the highest error for the flowmeter taken from Table 4 of \(\pm 2.03\%\), the total uncertainty in the heat balance equation was computed as  
\[\text{Total Error}=\sqrt{\left(\pm 0.0165\right)^{2}+\left(\pm 0.0203 \right)2}\approx 2.62\%. \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Transducer Reading (W)** & **Average q (W)** & **Difference (W)** & **\% of Average Power** \\ \hline A & 2506.6 & 2657.8 & 101.2 & 3.88 \\ B & 3207.2 & 3302.5 & 93.3 & 2.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heat balance check.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 2 Principle and Framework of the ASPF
### Mode Judgment  
In order to quickly determine the similar days, _K-means_ clustering [26; 27] is used to divide the data into several groups firstly, and in every group, clustering center is adopted to judge whether there is a similar day in the current database rapidly, as shown in Figure 2. The input vector \(x\in R^{q\times 1}\) contains \(P_{ori}\) and key variables which affect the original predicting accuracy. \(x\) is then compared with clustering centers \(C_{k}\) to be clustered into group \(k\), if it has one or more similar days, and the closest historical solar power \(P_{sdm}\) can be found in the database. Here, a variable denoted as \(\Delta_{1}\) is chosen as a threshold for the similar historical data, and if the error between \(x\) and \(C_{k}\) is larger than \(\Delta_{1}\), there is no similar day.  
Considering the importance of clustering, we propose an improved _k-means_ method to group the data, which is shown in Section 3.1. Please note that in different groups, the key weather variables may be different. For example, solar power is more closely related to temperature in sunny days than cloudy days. Therefore, we adopt a model-selection method called the least angular regression, also known as LARS [24], to analyze and find out the most correlated weather variables in different clusters.  
The predicting network is thus acquired by combining the improved _k-means_ clustering and LARS, which keeps updating for more accurate predictions. Besides, the trained BPNN, the weight \(w\) and the bias value \(b\) are used to determine the value of \(\Delta_{1}\), which is thus closed related to the updated network. As the accuracy of the updated network increases, the similarity judgment is getting more precisely as well. We present the thorough steps to determine and update \(\Delta_{1}\) in Sections 3 and 4.
## 3 Results
### NDVI and Rainfall MK Trends
#### 3.1.1 NDVI MK Trends  
Figure 2 below shows the temporal NDVI MK trend of 30 years in GSDM computed for the wet season. The dots in the figure represent daily wet season NDVI values weighted by the number of days acquired, while the grey-shaded area represents the values that mainly contributed to this NDVI trend. The MK test shows that the NDVI in the GSDM decrease with a Kendall's Tau (indicated as R) of 0.016 (Figure 2).  
The Sen's slope estimator indicates that the magnitude of NDVI for the whole district slightly decreased with a Sen's slope of \(-0.0003\). However, the decrease in NDVI is not significant as the \(p\)-value is above 0.05 (\(p\)-value = 0.92) (Figure 2). The trend shows that the lowest recorded NDVI occurred in 2000, 2004, 2008, 2009, 2014, 2008, 2009 and 2018 and that some of these NDVI decreases occurred during drought events in the district [17].  
The spatial MK trend was performed to determine which areas experienced significant NDVI changes. The results are shown in Table 2 and these represent summary statistics of spatial the NDVI MK trend.  
Figure 3: NDVI MK test trend magnitude (Sen’s Slope) (**a**) and trend significance (\(p\)-value) (**b**) between 1990\(-\)2019.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Slope (Magnitude)**} & \multicolumn{3}{c}{**Significant Trend**} & \multicolumn{3}{c}{**Insignificant Trend**} \\ \cline{2-9}
**NDVI** & \multirow{2}{*}{**Positive**} & \multirow{2}{*}{**Negative**} & \multirow{2}{*}{**Stable**} & **Significant** & **Significant** & **Insignificant** & **Insignificant** \\
**TEND** & & & & **Negative** & **Positive** & **Negative** & **Positive** \\  & & & & **(Degraded)** & **(Improved)** & **(Degraded)** & **(Improved)** \\ \hline Number of 5 & \multirow{2}{*}{260} & \multirow{2}{*}{222} & \multirow{2}{*}{1} & \multirow{2}{*}{3} & \multirow{2}{*}{0} & \multirow{2}{*}{219} & \multirow{2}{*}{259} \\ km pixels & & & & & & & \\ \hline Proportion & \multirow{2}{*}{53.83} & \multirow{2}{*}{45.96} & \multirow{2}{*}{0.21} & \multirow{2}{*}{0.62} & \multirow{2}{*}{0} & \multirow{2}{*}{45.34} & \multirow{2}{*}{53.62} \\ Statistics (\%) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: NDVI MK Spatial statistics on significance of magnitude MK.  
The decreasing NDVI trend was particularly observed in the central to southwestern part of the district in Ephraim Mogale local municipality (Figure 3a) with a Sen's slope of \(-0.001\) and \(p\) value of \(0.28\). NDVI trend magnitude slightly increased, particularly in the northern to north-western part of the district (Figure 3a) in Fetakgomo Tubatse local municipality with Sen's slope of \(0.0008\) and \(p\) value of \(0.51\).  
Approximately \(53.83\%\) of the GSDM recorded slightly increasing NDVI trends and no area experienced significantly positive NDVI trends. These results show that the vegetation in Fetakgomo Tubatse local municipality slightly improved compared to other local municipalities, but the increase in vegetation production was not significant.