esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of the Cost Function: A DER-CAM Case Study
### Proposed two-objective Pareto-based Optimization  
The Pareto-based dynamic evolutionary algorithm attempts to optimize the Pareto front for two objective functions \(f_{1}(x,t)\) and \(f_{2}(x,t)\). The first objective is similar to the cost function for DER-CAM, while the second is an energy consumption minimization function with regard to the DER utilized with a resulting decrease in CO\({}_{2}\) emissions. The mathematical forms of the objective functions are given in Equations (2) and (3). \[f_{1}(x,t)=\min\alpha_{c}(t) \tag{1}\]  
\[\alpha_{c}(t)=\sum_{1}^{m}r_{e}+ \sum_{1}^{t}\sum_{1}^{t}\sum_{1}^{t}\mu_{l}\cdot r_{e}+\sum_{1}^{p} \sum_{1}^{m}r_{e,p}\cdot\mu_{l,p}+\sum_{1}^{s}\sum_{1}^{m}\sum_{1}^{t}P_{gen,der} \tag{2}\] \[\cdot c_{gen,kwh}+\sum_{1}^{s}P_{max,der}\cdot C_{c,der}\cdot F_{a} -\sum_{1}^{s}\sum_{1}^{m}\sum_{1}^{t}P_{gen,der}\cdot r_{exp,der}\]  
where:  
\(r_{e}=\text{electricity rate (peak, week, weekend)}\)  
\(s=\text{no.of DER technologies}\)  
\(P_{gen,der}=\text{power generated by DER}\)  
\(\alpha_{gen,kwh}=\text{cos t of generation/KWh}\)  
\(P_{max,der}=\text{max.power gen.by DER over 20 year period}\)  
\(C_{c,der}=\text{cap.cost of DER over 20 year period (maintenance, etc. )}\)  
\(F_{a}=\text{annuity factor}\)  
\(r_{exp,der}=\text{electricity export rate/DER/day/month}\)  
\(m=\text{number of months}\)  
\(t=1\text{ hr (interval between data points)}\)  
\(n=\text{denotes peak, week or weekend day}\)  
\(\mu_{1}=\text{normalization factor}\)  
\(\mu_{1,p}=\text{normalization factor}\)  
\(p=\text{denotes rates that relate to peak days}\)  
\[f_{2}(x,t)=\sum_{1}^{d}\sum_{1}^{t}\left(e_{b}+e_{py}+e_{hp}\right)+\sum_{1} ^{d}\sum_{1}^{t}\sum_{1}^{u}\eta_{s} \tag{3}\]  
where:  
\(e_{b}=\text{battery energy coefficient, }e_{py}=\text{solar PV panel energy coefficient, }e_{hp}=\text{ air source heat pump energy coefficient, }\eta_{s}=\text{model for load shifting strategy, }d=\text{ day (week, weekend, peak), }n=\text{number of shiftable loads}\) (it is assumed that all \(d\), \(n=3\)). The parameters in Equation (3) are defined as follows:  
\[e_{b}=S(t-1)+\left\{ \begin{array}{c}e_{c}\cdot t\cdot\mu_{b}\\ -e_{d}\cdot(t/\mu_{b})\\ 0\end{array} \right. \tag{4}\]  
where:  
\(S=\text{state-of-charge of battery, }t=\text{charging time, }\mu_{b}=\text{battery efficiency, }e_{c}=\text{ charging power, and }e_{d}\) = discharging power. \[e_{pv}=k\beta_{a}-\left(0.01\beta_{op}\right) \tag{5}\]  
where:  
\(k=\text{Boltzmann constant, }\beta_{a}=\text{adjusted power of the photovoltaic (PV) module due to temperature changes across the module, }\beta_{op}=\text{output power of the PV module under standard operating conditions. }\)  
\[e_{hp}=\frac{\alpha_{he}}{P_{c}} \tag{6}\]  
where:\(\alpha_{he}\) = power generated through heat exchange process, \(P_{c}\) = thermal power of compressor  
\[\eta_{s}=\left\{ \begin{array}{l}\gamma_{1}\delta_{t}(d-1)+\gamma_{2}\delta_{t} [d-2]+\gamma_{7}\delta_{t}[d-7]\\ \gamma_{1}\rho_{t}[d-1]+\gamma_{2}\rho_{t}[d-2]+\gamma_{7}\rho_{t}[d-7]\end{array} \right.
## 2 Literature Review
### Classification of WBAN Routing Protocol
#### 2.1.1 QoS Routing  
The QoS routing protocols work based on QoS metrics. The QoS routing is a modular-based protocol where synchronization and network density make it difficult to enhance the QoS protocol. In WBAN, patients' data (critical or normal) is considered very important and transmitted efficiently and securely based on QoS metrics. The QoS routing protocols can be divided into two sub-categories, such as delay-tolerant and reliability-based protocol, where the reliability-based protocol enhances the throughput and minimize the delay and delay-tolerant protocol assure the packets delivery in time. The following are the few QoS-based routing protocols presented such as RL-QRP protocol [24], ENSA-BAN [25], multi-hop protocol using cost function [26], CDR [19], ARBA [27], QPRR [23], and QPRD [28].
## 4 Results and Discussions
### Model Comparison  
To prove the effectiveness of the CFST-LSTM model, its prediction performance is compared with other traditional machine learning models and commonly seen neural networks. These contain three traditional machine learning models, including LASSO regression, Ridge regression, and support vector regression (SVR), and two commonly seen neural networks, including artificial neural network (ANN) and recurrent neural network (RNN) [43; 44; 45; 46; 47]. The parameters of these algorithms were all tuned using the same grid search process. Table 3 presents the results of the model comparison. (1) It can be seen from the first six rows that compared with other models, the two deep learning neural networks, RNN and CFST-LSTM, have lower RMSE/MAE and higher R\({}^{2}\). This is because compared with Lasso and Ridge, neural network-based learning algorithms are better at modeling non-linear real-world relationships. Therefore, ANN, RNN, and CFST-LSTM have better performance. (2) Also, compared with traditional machine learning methods SVR and ANN, the other two deep learning based models (RNN and CFST-LSTM) have lower error and higher R\({}^{2}\). This is because they are specifically designed for time series problems. It is easier for them to learn the impact from historical data. (3) Lastly, CFST-LSTM outperforms RNN, and exhibits the lowest RMSE and the highest R\({}^{2}\). It can be seen that the difference is quite significant. The main reason is that except CFST-LSTM, other models did not consider the influence from nearby stations.  
Figure 8: The goodness of fit plot of the CFST-LSTM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE (\(\mathbf{\mu}\)g/\(m^{3}\))** & **MAE (\(\mathbf{\mu}\)g/\(m^{3}\))** & **R\({}^{2}\)** \\ \hline LASSO & 2.93213 & 1.951243 & 0.915126 \\ Ridge & 2.89453 & 1.932423 & 0.918705 \\ SVR & 2.79312 & 1.892831 & 0.925028 \\ ANN & 2.78484 & 1.882643 & 0.925734 \\ RNN & 2.70294 & 1.853425 & 0.929810 \\ CFST-LSTM & 1.99257 & 1.516072 & 0.958348 \\ CFST-ANN & 2.62591 & 1.79819 & 0.930665 \\ \hline CFST-RNN & 2.53594 & 1.70958 & 0.939712 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model comparison.  
Since the newly designed STF layer in CFST-LSTM is one kind of neural network layer, it can also be added into ANN and RNN. To test how much it can help increase the performance of neural network models, this study also calculated the R\({}^{2}\) value of CFST-ANN and CFST-RNN. The results are shown in the last two columns in Table 3. It can be seen that the R\({}^{2}\) values of these two neural networks have improved. This, on another angle, proved the effectiveness of the proposed STF layer. However, the overall performance of CFST-ANN and CFST-RNN are still behind CFST-LSTM. This is because the LSTM layer in CFST-LSTM is better at learning long-term dependency from time series data.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 2 Background Theory  
### Dynamic Point-Cloud  
A dynamic point-cloud is 3D animation data composed of point-cloud frames. A point-cloud is one of the 3D data representations that symbolize other captured attributes such as spatial coordinates and colors.  
This is a representation of vertex \((v)\):  
\[\textit{vertex}\ v\!\!=(((x,y,z),[c]):x,y,z\ \in\ R,\ [\ c\in(r,g,b)\ |\ r,g,b\ \in N]) \tag{1}\]  
It is represented by \(x,y,z\), which indicate spatial coordinates, and c, which indicates color. \(c\) is a color composed of the elements \(r,g\), and \(b\).  
This is an expression that indicates a point-cloud (\(f\)):  
\[\textit{point}-\textit{cloud}\ \mathrm{f}=\{(v_{i})\ :\ i=0\ldots S-1\} \tag{2}\]  
A point-cloud frame consists of \(S\) number of vertices.  
This is a representation of a dynamic point-cloud (\(V\)):  
\[\textit{dynamic point}-\textit{cloud}\ V=\{(f_{n})\ :n=0\ldots T-1\} \tag{3}\]  
A dynamic point-cloud consists of \(T\) number of point-cloud frames.  
Volumetric capture technology digitizes and shoots several real-world objects, including people and backgrounds. These technologies are being studied using multiple rgb and multiple rgb-d cameras. In [11] we see the findings of a multiple rgb camera study. In [12], high-precision research was conducted that minimized the difference from the actual object. In [13; 14] we see the findings of a multiple rgb-d camera study. The technology for acquiring real-time 3D models using infrared devices was applied in [15; 16; 17]. These scanning devices include Zed, LiDAR sensors, etc., other than Kinect. The difference between Kinect and Zed, which scans the front of the device, was studied in [18]. This study made comparisons based on resolution, lighting, accuracy, speed, and memory. The LiDAR (Light Detection and Ranging) sensor, which can scan the area around the device, provides a point-cloud for distance information that measures the travel time of the light emitted to the ToF (time-of-flight) operating principle. This device is often used, especially in applications that measure the surrounding environment [19; 20; 21]. These scanning technologies can capture real-world movements in multiple frames as digital information.  
However, there are problems in measuring full-quality live-action objects in this way. In order to solve these problems, TSDF (truncated surface distance function) technology was applied in [22; 23]. A point-cloud is generated for each red-green-blue depth data item acquired through a 3D fusion process that creates a single TSDF volume. To implement points again, the TSDF volumes in multiple scanning devices use a feature point algorithm to quickly digitize a complete real object.
# UAV Charging Station Placement in Opportunistic Networks
###### Abstract  
Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.  
U  
; opportunistic networks; charging stations; clustering; DBSCAN; K-means
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)
### Power Control Mechanism Definition
#### 4.1.2 Minimum Power  
It is necessary to establish a lower bound for the transmission power because there are cases in which the mechanism will decrease gradually, and in the absence of such threshold, it could be possible that power takes zero or even negative values. Thus, we include a parameter that defines the minimum power in the mechanism. The minimum power is calculated based on the density of nodes in each scenario andthe simulation area. In this paper, the approximate value of the minimum transmission power of a node is calculated with a simple three-rule, considering the area covered by a transmission.
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 3 Mathematical Formulation
### Power System Supply-Demand Balance Constraint  
To maintain the stable operation of the power system, it is necessary to maintain supply-demand balance. For this purpose, we introduce the power system's supply-demand balance constraint (9) to ensure the balance between supply and demand at each time point. The supply-demand balance constraint can be expressed as the following equation:  
\[P_{\mathfrak{R}}(t)+P_{ESS}(t)+\sum_{n=1}^{N}P_{n}(t)+P_{tolerance}(t)=P_{L}(t) \tag{9}\]  
Here, \(P_{\mathfrak{R}}(t)\) represents the total generation of renewable energy at time t. \(P_{ESS}(t)\) represents the electricity from the energy storage system at time t. \(P_{n}(t)\) represents the power generation from traditional units. \(P_{L}(t)\) represents the total load at time t--the total electricity demand of the system at that moment. To apply this model practically, this study scales up historical data proportionally as the total load. This method allows us to estimate future electricity demand based on past data and predicted growth trends.  
Additionally, \(P_{tolerance}(t)\) represents the permissible error margin in the solution. This means that a certain level of deviation is acceptable in the calculations to maintain the system's flexibility and operability in actual operation. With such a setup, we can ensure that the power system maintains a supply-demand balance under various circumstances, thereby ensuring the system's stable operation.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Software-Defined Network (SDN)
#### 3.1.2 Functionality of SDN  
The SDN controllers act as the centralised brain for the network, overseeing the management and orchestration of network traffic. It facilitates seamless communication between devices by dynamically programming and applying network policies throughout the infrastructure. The controllers' key role is to centrally program and manage the forwarding actions of SDN devices within the network. This is accomplished using a detailed database that contains all the necessary information regarding the network's operations. As the network's central authority, the SDN controllers direct data flow and enforce policies based on insights gathered from various network components [36].
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 6 Conclusions  
In this paper, we proposed an object-tracking algorithm that leverages motion direction and time series information. To account for the lack of consideration of target motion direction, we introduced a loss function that incorporates directional guidance. Furthermore, we designed a tracking result scoring module (TRSM) based on the attention mechanism by integrating tracking result time series information. The TRSM module improves the algorithm's ability to evaluate the reliability of tracking results and facilitates template updates. Our algorithm's performance was assessed through various experiments conducted on LaSOT, TrackingNet, GOT-10K, and VOT2020 datasets, and it demonstrated notable improvements in tracking accuracy. The ablation experiments and qualitative analysis also validated the effectiveness of the proposed method. Our findings have important implications for the research aimed at enhancing the accuracy and success rate of deep learning-based object-tracking algorithms and improving the reliability of tracking results. However, the TRSM module still requires the support of the object-tracking algorithm during training. To address this limitation, in our future research, we aim to improve the TRSM module by exploring alternative training methods that do not involve object-tracking algorithms. Additionally, we will investigate the effect of various hyperparameters on object-tracking performance in the loss function. Moreover, we will apply the proposed method to long-term object-tracking scenarios that more closely resemble real-life situations.  
Conceptualization, J.S., C.W. and S.Y.; methodology, J.S.; software, J.S.; validation, J.S.; formal analysis, J.S., C.W. and S.Y.; investigation, J.S. and C.W.; resources, C.W. and S.Y.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, J.S. and C.W.; visualization, J.S.; supervision., C.W. and S.Y.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Thanks to friends for their suggestions on this paper and the program.  
The authors declare no conflict of interest.
## 3 Dynamic Analysis
### Transmissibility for Force Excitation  
The case of the QZSPVI is subjected to a harmonic forcing excitation \(F=F_{e}\cos(\omega)\). Through the LBM, SCM, and air damping, the force is transmitted to the base and can be written in dimensionless form as below:  
\[F_{t}=2\xi\hat{a}^{\prime}+\hat{a}+\sum_{n=2}^{5}\alpha_{n}\hat{a}^{n} \tag{35}\]  
It reveals that the transmitted force depends on the velocity and position of the load plate given in Equation (26). This force may be rewritten as:  
\[\hat{F}_{t}=-2\xi a_{u}\Omega\sin(\Omega\tau-\gamma)+\left(\hat{a}_{u}+\frac{ 3\alpha_{3}\hat{a}_{u}^{3}}{4}+\frac{7\alpha_{5}\hat{a}_{u}^{5}}{8}\right) \cos(\Omega\tau-\gamma) \tag{36}\]  
The maximum force is transmitted to the base, being:  
\[\hat{F}_{\rm{max}}=\sqrt{\left(-2\xi\hat{a}_{u}\Omega\right)^{2}+\left(\hat{a }_{u}+\frac{3\alpha_{3}\hat{a}_{u}^{3}}{4}+\frac{7\alpha_{5}\hat{a}_{u}^{5}}{ 8}\right)^{2}} \tag{37}\]  
The transmissibility for forcing excitation is defined as below:  
\[\left|T_{F}\right|=\left|\frac{\hat{F}_{\rm{max}}}{\hat{F}_{e}}\right| \tag{38}\]  
herein from Equation (4), the non-dimensional magnitude of the excited force \(\hat{F}_{e}=1\)
## 2 Methods
### Singular Spectrum Analysis
After SVD operation of \(\mathbf{X}\) in the diagonal average step, the decomposition matrices \(\mathbf{X}_{i}\) of the trajectory matrix \(\mathbf{X}\) should be converted back to a set of reconstructed time series \(\mathbf{Y}^{(i)}\) by the diagonal average, an inverse operation of time lagged embedding. In order to obtain \(k\)-th element \(y_{k}^{(i)}\) of the reconstructed time series \(\mathbf{Y}^{(i)}\) from the decomposition matrix \(\mathbf{X}_{i}\), we need to take an average value of the elements on the \(k\)-th vice diagonal line. Then, any element of a reconstructed signal \(y_{k}^{(i)}\) can be described by the following formula:  
\[y_{k}^{(i)}=\left\{ \begin{array}{ll}\dfrac{1}{n+1}\sum\limits_{j=0}^{n}a_{j \mu-j},&0\leq n\leq L-1\\ \dfrac{1}{L}\sum\limits_{j=0}^{L-1}a_{j\mu-j},&L-1<n<N-L\\ \dfrac{1}{N-n}\sum\limits_{j=n-N+L}^{L-1}a_{j\mu-j},&N-L\leq n\leq N-1 \end{array} \right. \tag{6}\]  
where \(L\) represents the embedding dimension, \(N\) is the length of the original signal and \(a_{i,j}\) is the element of the decomposition matrix \(\mathbf{X}_{i}\) on the \(i\)-th row, \(j\)-th column. Finally, in the grouping step, reconstructed time series \(\mathbf{Y}^{(i)}\) are divided into several classes and summed according to the category. In general, the SSA method can be classified as trend, oscillation and noise. In practice, it is very common that the leading component corresponds to the trend. In our application, the distance signal can be grouped into the classes of signal or noise. Hurst exponent \(H\) can be used as the grouping basis of basic SSA, allowing signal extraction from white noise automatically. The case of \(H>0.5\) indicates that the reconstructed time series is a long-term memory series, a part of the reconstructed signal; while the case of \(H\leq 0.5\) implies that the reconstructed time series is white noise that needs to be eliminated. In the calculation, we used the aggregated variance method to calculate the Hurst exponent [29].
## 4 Conclusions  
In this study, a series of laboratory experiments were conducted to investigate the physical and mechanical properties, strain field evolution, failure mode, and micro-mechanism of red sandstone under different drying\(-\)wetting cycles and acidic solutions. The following main conclusions were drawn:  
(1) With the increase in the number of drying\(-\)wetting cycles and the decrease in the solution pH values, the mass, P-wave velocity, elastic modulus, and uniaxial compressive strength of red sandstone decreased monotonically, while the water absorption increased. The stress\(-\)strain relationship showed a tendency to change from brittleness to slight ductility.  
(2) Under the axial load, the strain field of red sandstone underwent a process from a uniform distribution to an inhomogeneous distribution. As the number of drying\(-\)wetting cycles increased, the apparent strain in the strain localization band increased, and the failure mode transitioned gradually from tensile failure to shear failure or tensile-shear composite failure. The influence of solution pH values on the failure mode was milder than that of drying\(-\)wetting cycles. The decrease in the solution pH values contributed to an increase in the secondary cracks and the occurrence of shear behavior.  
(3) As the drying\(-\)wetting cycles progressed, the microstructure of red sandstone became increasingly loose with the development of pores and fissures, and a few minerals, such as feldspar and calcite, were gradually dissolved and separated from the rock matrix. The decrease in the solution pH values aggravated the changes in the microstructure and mineral composition. These changes provide a favorable explanation for the mechanical property deterioration of red sandstone under the coupling effect of drying\(-\)wetting cycles and acidic solutions.  
All authors contributed to the study's conception and design. Sample preparation and data collection and analysis, G.Z., L.W., Z.L. and N.W.; Writing\(-\)original draft, G.Z. and Z.L.; Writing\(-\)review and editing, L.W. and N.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research of Education Department of Liaoning Province under Grant LJKZ0404 and the Research Fund for the Doctoral Program of Liaoning Province under Grant 2019-BS-160.