esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 1 Introduction
Furthermore, only the robot determines its pose and in case of an unexpected situation, it might lead to a fault state and connection lost without providing any reliable feedback to the control system of a building, as shown in Figure 1a. The RTLS avoid this situation, but only because of added devices such as communication towers. The marker-based motion capture system, if used in common areas like rooms and corridors of a building, needs many cameras deployed and a specific pattern of IR markers on every device. If there are tens or hundreds of devices, to define and to recognize the markers becomes a difficult task. However, smart buildings may already have their sensor systems, mostly cameras and related devices for security purposes. Nowadays, face-recognition and object detection software has been used in airports (2019), governmental buildings, other public places, and even in smart phones. These capabilities lead us to an approach when the surveillance cameras would be involved in the facial recognition and tracking of any robot or device at the same time, meaning that every device has its unique easily printable marker that characterizes it, so the control system of the whole building has an independent feedback on a device's pose continuously. This also extends the possibility of tracking things in an environment even though they are not electronic devices, for example important packages and boxes. Such a solution acts, as we call it, as a Shared Sensory System. This approach simplifies the tracking of new devices, as only their ID is needed, as well as a new marker to be placed on a device or thing to the control system. Furthermore, the hardware costs are minimized when the devices do not need to carry any computational units or sensors. The image analysis, object detection, localization, and trajectory planning of AGVs may run in the cloud. The scheme of the system is shown in Figure 1b.
## 3 Results
### Technical Potential Estimation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**REs**} & \multirow{2}{*}{\begin{tabular}{c} **Current** \\ **Installed Rated** \\ **Capacity (MW)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Potential Rated** \\ **Capacity (MW)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Battery Storage** \\ **Capacity (MW)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Annual** \\ **Capeacity (MW)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Load Demand (MW)** \\ \end{tabular} } \\ \cline{5-8}  & & & & **Araceli** & & & **Porecasted** \\ Solar PV-Jattery storage & 1.386 & 2.643 & 437 & 60 & 3911.569 & \multirow{2}{*}{124.2335} & \multirow{2}{*}{1612.724} \\ Wind-battery storage & 10.560 & **Balabac** & & & & & \\ Solar PV-Jattery storage & 1.086 & 1.397 & 401 & 60 & 2201.624 & 931.602 & 2147.121 \\ Solar PV-Jattery storage & 3.2 & 11.399 & 3234 & 60 & 17,676.309 & \multirow{2}{*}{7407.427} & \multirow{2}{*}{12,958.001} \\ Wind-battery storage & 8.7 & 4049 & 60 & 31,947.048 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Electricity generation capacity and annual generation potential of solar PV and wind. The best locations for solar PV installations in Araceli, Balabac, and Cuyo have a potential rated capacity of 2.643, 1.397, and 11.399 MW, respectively, and an estimated annual potential generation of 3.912, 2.202, and 17.676 GWh, respectively. For wind renewable technology, Araceli's and Cuyo's best locations for wind turbine installation have an estimated rated capacity of 10.56 and 8.7 MW, respectively with estimated annual potential generation of 34.533 and 31.947 GWh. For all three islands, the solar potential alone can already satisfy the forecasted load demand up to 2030, and it can replace the existing conventional diesel generator sets. However, due to the intermittent characteristics of solar energy, wind power should be installed to back up the energy supply.
# Optimal Configuration of Integrated Energy System Based on Energy-Conversion Interface
## 2 Energy-Conversion Interface Model
### Simple Input-Output Coupling Model  
A simple input-output coupling model can be constructed when all the devices' output energy inside the ECI goes to the demand side. The energy-conversion process inside the ECI is shown in Figure 3.  
In a simple ECI, the relationship between Input **E** and Output **L** can be expressed by Equation (1). For a system with \(m\) types of input energy and \(n\) types of output energy, the input-output relationship can be specifically expressed by Equation (2).  
\[\textbf{L}=\textbf{C}\cdot\textbf{E} \tag{1}\]  
\[\left[\begin{array}{c}l_{1}\\ l_{2}\\ \vdots\\ l_{n}\end{array}\right]=\left[\begin{array}{cccc}c_{11}&c_{12}&\cdots&c_{ 1m}\\ c_{21}&c_{22}&\cdots&c_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ c_{n1}&c_{n2}&\cdots&c_{mm}\end{array}\right]\left[\begin{array}{c}e_{1}\\ e_{2}\\ \vdots\\ e_{m}\end{array}\right] \tag{2}\]  
where \(l_{n}\) and \(e_{m}\) denote the \(n\)-th type of output-energy source and the \(m\)-th type of input-energy, respectively, and \(c_{mm}\) denotes the coupling factor.  
We define the energy-flow distribution ratio from one energy-output port J (supply-side output or device output) to energy-input port K (demand-side input or device input) at different energy-transfer paths as the energy-flow distribution coefficient. The coupling coefficient can be expressed by the  
Figure 3: Simple energy-conversion interface (ECI) energy-flow diagram.  
Figure 2: Integrated energy system schematic.  
energy-flow distribution coefficient, the energy-conversion efficiency of the device, and the associated state of the device and the load. The energy-conversion efficiency of the device is determined by the characteristics and operation situation of the device itself. The connection between the device and the load is the correspondence relationship between the energy converted by the conversion device and the load energy form. Then, the input-output relationship of the energy-conversion interface can be expressed by Equation (3).  
\[\mathbf{L}=(\mathbf{F}^{\mathsf{T}}\mathbf{H}\mathbf{A}^{\mathsf{T}}+\mathbf{ A}^{\mathsf{\prime}}\mathbf{T})\cdot\mathbf{E} \tag{3}\]  
where \(\mathbf{H}\) is the efficiency matrix and \(h_{ii}\) represents the energy-conversion efficiency of the \(i\)-th device; \(\mathbf{A}\) and \(\mathbf{A}^{\prime}\) are the distribution matrices; \(\alpha_{ij}\) indicates the allocation ratio from \(e_{i}\) to the \(j\)-th device; \(\alpha^{\prime}_{ij}\) indicates the allocation ratio from \(e_{i}\) to the \(j\)-th load, and when there is one and only one energy-flow path, the value is \(1\); \(\mathbf{F}\) is the branch connection characteristic matrix; \(f_{ij}\) indicates the relationship of the \(i\)-th device and the \(j\)-th load, and its value is \(1\) when the branch exists. Otherwise, its value is \(0\).
## 1 Introduction
Results show that applications are badly reputed lacking effective measures for confidentiality, integrity, authentication and availability. Analysis of security-related claims show that confidentiality is the service the most neglected during the development of Android apps. Moreover, apps display good reputation in terms of functionality. This means that developers put a lot of efforts in functional aspect than security aspect. A quantitative comparison with SERS and Google rating shows that our approach is more precise and robust to estimate trust in terms of security. CIAA-RepDroid is able to recommend developers exact CIAA aspects to improve within their source codes. This work provides two main contributions. * CIAA-RepDroid, a fine-grained security-related reputation based on sentiment analysis of security-related reviews and probabilistic classification model of polarities;
* a decomposition of reputation into confidentiality, integrity, authentication and availability reputations;
* experiments against existing rating system approaches demonstrating effectiveness of CIAA-RepDroid to related security flaws in Android apps. The terms app and application are interchangeably used in the document. The rest of the paper is structured as follows. Section 2 provides a broad overview of authors who conducted similar works. Section 3 presents the proposed reputation model. Section 4 presents and discusses results obtainedwith real applications and comparison against similar approaches. We conclude and provide further research to enhance our methods.
## 2 Impedance Analysis of Inter-Turn Fault of Induction Motor
### Impedance Analysis with \(\Delta\) Connection  
Figure 3 illustrates the terminal configuration of an IM with a \(\Delta\) connection when an ITF occurs in phase A. As shown in Figure 3, three terminals are available, enabling various inter-terminal connection configurations. Depending on the selected terminal pair, the measured impedance varies due to the presence of fault resistance in the affected phase. The impedance measured between phases A and B, as shown in Figure 3a, is expressed in Equation (10).  
\[Z_{a-b}=Z_{f}\,||\,2Z_{h}=\frac{2Z_{f}Z_{h}}{Z_{f}+2Z_{h}} \tag{10}\]  
In the same way, the impedance values for terminal pairs _b-c_ and _c-a_, corresponding to Figure 3b,c, are described in Equation (11).  
\[Z_{b-c}=Z_{c-a}=Z_{h}\,||\,(Z_{f}+Z_{h})=\frac{Z_{h}(Z_{f}+Z_{h})}{Z_{f}+2Z_{h}} \tag{11}\]  
Under ITF conditions, the impedance of the fault phase decreases due to the fault resistance being placed in parallel with the stator winding. As a result, the impedance of  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Fault Phase**} & **Maximum Impedance** & **Minimum Impedance** \\  & **among \(Z_{a-b}\), \(Z_{b-c}\), and \(Z_{c-a}\)** & **among \(Z_{a-bc}\), \(Z_{b-ca}\), and \(Z_{c-ab}\)** \\ \hline A & \(Z_{b-c}\) & \(Z_{a-bc}\) \\ B & \(Z_{c-a}\) & \(Z_{b-ca}\) \\ C & \(Z_{a-b}\) & \(Z_{c-ab}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impedance analysis results for a Y connection with a phase A fault.  
the _a-b_ connection becomes lower than those of the _b-c_ and _c-a_ connections, as demonstrated in Equation (12).  
\[\begin{array}{l}Z_{a-b}-Z_{b-c}=\frac{Z_{b}(Z_{f}-Z_{h})}{Z_{f}+ZZ_{h}}<0\\ Z_{a-b}<Z_{b-c}=Z_{c-a}\end{array} \tag{12}\]  
The impedance for the terminal connection between c and the midpoint of _a-b_, denoted as _c-ab_ and shown in Figure 3f, is calculated as in Equation (13).  
\[Z_{c-ab}=Z_{h}\,||\,Z_{h}=\frac{1}{2}Z_{h} \tag{13}\]  
Likewise, the impedances for connections _a-bc_ and _b-ca_, represented in Figure 3d,e, are given by Equation (14).  
\[Z_{a-bc}=Z_{b-ca}=Z_{f}\,||\,Z_{h}=\frac{Z_{f}Z_{h}}{Z_{f}+Z_{h}} \tag{14}\]  
A comparison of Equations (13) and (14) reveals that the impedance of the _c-ab_ configuration is greater than those of the _a-bc_ and _b-ca_ connections, as summarized in Equation (15).  
\[\begin{array}{l}Z_{c-ab}-Z_{a-bc}=\frac{Z_{h}}{2}\frac{Z_{h}-Z_{f}}{Z_{f}+Z _{h}}>0\\ Z_{c-ab}>Z_{a-bc}=Z_{b-ca}\end{array} \tag{15}\]  
When a fault occurs in phase B or C, the impedance becomes either a maximum or a minimum, depending on the specific terminal connection. Table 2 presents the maximum and minimum impedance values for each fault phase and terminal configuration. This information enables effective ITF diagnosis based on impedance characteristics.  
However, in IMD systems, such as those used in electric vehicles, mechanical access to motor terminals is often limited or impractical. A diagnostic approach based on space voltage vector control is proposed to address this limitation by emulating terminal connection conditions electrically, without physical rewiring.
## 3 Methods
### Long Short-Term Memory Neutral Network  
LSTM is a special kind of recurrent neural network (RNN) proposed in 1997, mainly to solve the problem of gradient disappearance and gradient explosion in the long time series training process. In comparison to RNNs, LSTM performs better for longer time series data. In recent years, LSTM has been widely used in various fields, such as traffic flow prediction [40], stock yield forecast [41], trajectory prediction [42; 43], and earthquake forecasting [37].  
The memory unit structure of LSTM, consisting of the forget gate, the input gate, and the output gate, is illustrated in Figure 5. At the current time step, the memory unit takes in the hidden variable \(h_{t-1}\), the memory variable \(C_{t-1}\), and the input \(x_{t}\). Then, the calculation of the forget gate, the input gate, and the output gate yields the output variables \(h_{t}\) and \(C_{t}\), which are then fed to the next unit.  
The forget gate first adds \(h_{t-1}\) and \(x_{t}\) and passes the result through a sigmoid function to obtain the forget factor, which is then multiplied with \(C_{t-1}\). The forget factor is calculated as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
Similarly, the input gate firstly adds \(h_{t-1}\) and \(x_{t}\), and passes the result through a sigmoid function and a tanh function to obtain \(i_{t}\) and \(\widetilde{C}_{t}\), respectively. The input gate then multiplies the results of the two functions and adds the results with the output of the forget gate to obtain \(C_{t}\). The calculation formula of \(i_{t}\), \(\widetilde{C}_{t}\), and \(C_{t}\) is as follows:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{3}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{4}\]  
Finally, the output gate adds \(h_{t-1}\) and \(x_{t}\), and passes the result through a sigmoid function to obtain the forget factor. The cell state is then passed into the tanh function and  
Figure 5: The structure of one memory block in LSTM neural network.  
multiplied by the forget factor to obtain the new hidden state, which is then passed into the next unit. The calculation formula of \(o_{t}\) and \(h_{t}\) is as follows:  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\times tanh(C_{t}) \tag{6}\]  
Here, \(W_{f}\), \(W_{i}\),\(W_{C}\), and \(W_{o}\) are weight parameters and \(b_{f}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\) are bias parameters.
###### Abstract  
In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO satellite networks, the traditional single shortest path algorithm can no longer meet the optimal path requirement. Therefore, this paper proposes a multi-path routing algorithm based on an improved breadth-first search. First, according to the inter-satellite network topology information, the improved breadth-first search algorithm is used to obtain all the front hop node information of the destination node. Second, all the shortest paths are obtained by backtracking the path through the front hop node. Finally, according to the inter-satellite network, the bandwidth capacity of the traffic and nodes determines the optimal path from multiple shortest paths. However, due to the high dynamics of low-orbit satellite networks, the topology changes rapidly, and the global topology of the network is often not available. At this time, in order to enhance the adaptability of the algorithm, this paper proposes an inter-satellite network dynamic routing algorithm based on reinforcement learning. Verified by simulation experiments, the proposed algorithm can improve the throughput of the inter-satellite network, reduce the time delay, and the packet loss rate.  
inter-satellite network; multi-path routing; Q-learning +
Footnote †: journal: Proceedings  
1
# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
## 4 Methodology
### Technologies for Model Compression
#### 4.1.3 Matrix Factorization  
To reduce the time complexity, tensor factorization is a commonly used method. It is usually based on low rank approximation theory, and a high-dimension tensor can be approximated by multiple one-dimensional tensor products.  
[PERSON] et al. [44] proposed a canonical polyadic (CP)-decomposition based method that decomposing one network layer into five layers with low complexity. The optimal solution was hard to compute with Stochastic gradient descent (SGD) weight fine-tuning. [PERSON] et al. [45] exploited redundancy of convolutional layer and a tensor decomposition method was devised. It treated two-dimensional tensor decomposition as singular decomposition, and three-dimensional tensor decomposition as two-dimensional decomposition.  
[PERSON] et al. [46] used Singular Value Decomposition (SVD) decomposition for parameter matrix, and proposed a nonlinear optimization method with non-SGD. The cumulative reconstruction error of previous layer is considered in asymmetric reconstruction. [PERSON] et al. [47] used rank 1 convolutional filter to generate M independent basic feature map, and then K \(\times\) K convolutional filters can be decomposed into 1 \(\times\) K and K \(\times\) 1 filters. The output is linearly reconstructed with learned weights. [PERSON] et al. [48] proposed a method for training low rank constraint network. A global optimizer is used for matrix factorization and the redundancy of convolutional filter can be reduced.  
[PERSON] et al. [49] proposed a model with one or more tensor trained layer. Tensor is trained for tensor compressing and its filters are generated based on SVD approximation. According to redundancy inside and among channels, sparse decomposition was conducted on channels [31]. Convolutional operation with high cost can be transformed into matrix multiplication. The matrix is then sparsified with regularization term.  
Among these model compression methods, matrix factorization based methods are most widely used. Many top compressed CNNs models focus on this, which is illustrated in the next subsection.
## 3 Results  
### Agroecosystem Gas Fluxes  
Mean rates of GPP were approximately 2-fold higher and ER rates were more than doubled in vegetated versus unvegetated ditches (Figure 2). Gross primary production was significantly higher in vegetated drainages than unvegetated drainages across all hydraulic residence times (\(F_{1,4}\) = 14.81, \(p\) = 0.02; Figure 2A). Vegetated ditch GPP was highest in the 6 h HRT (8.84 \(\pm\) 1.56 g O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\), \(\mu\) \(\pm\) se), followed by the 4 h (7.91 \(\pm\) 1.56) and 2 h treatment (7.08 \(\pm\) 1.16). Gross primary production was not significantly different among HRT treatments (\(p\) = 0.23) nor were there significant interactions between vegetation and HRT (\(p\) = 0.91).  
Vegetation also significantly increased rates of ER (F\({}_{1,4}\) = 25.95, \(p\) = 0.01) and HRT had a marginally significant influence on ER (\(p\) = 0.06). Again the highest rates of ER were observed in the 6 h HRT treatment within vegetated streams (-7.20 \(\pm\) 1.47 g O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\), \(\mu\) \(\pm\) se) followed by the 4 h (-5.69 \(\pm\) 1.30) and 2 h (-4.72 \(\pm\) 1.34) treatments. A similar pattern was observed for unvegetated draines, with 6 h HRT treatment exhibiting the highest respiration values (-2.56 \(\pm\) 1.09 g O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)) followed by 4 h and 2 h HRT treatments. Though fluxes in unvegetated ditches were smaller than vegetated ditch fluxes, no significant interaction between vegetation and HRT (\(p\) = 0.78) was observed (Figure 2B).  
Rates of denitrification were higher in vegetated ditches across all HRTs though differences were not statistically significant (\(F_{1,4}\) = 3.17, \(p\) = 0.15). The highest values were estimated for vegetated draines in the intermediate 4 h residence time (9.27 \(\pm\) 3.95 mg N\({}_{2}\)-N m\({}^{-2}\) h\({}^{-1}\)) with the lowest rates estimated for unvegetated draines in the fastest 2 h residence time treatment (3.44 \(\pm\) 0.20 mg N\({}_{2}\)-N m\({}^{-2}\) h\({}^{-1}\)). 6 h treatments had similar rates of denitrification between vegetated (4.93 \(\pm\) 0.85 mg N\({}_{2}\)-N m\({}^{-2}\) h\({}^{-1}\)) and unvegetated ditches (3.73 \(\pm\) 0.82 mg N\({}_{2}\)-N m\({}^{-2}\) h\({}^{-1}\)). The effect of HRT on N\({}_{2}\) fluxes was insignificant as  
Figure 2: Drainage (**A**) gross primary production (GPP) and (**B**) ecosystem respiration (ER) rates by hydraulic residence time and vegetation treatment for all runs. Negative rates (**B**) indicate oxygen consumption whereas positive rates represent oxygen production (**A**). Bars are the means and error bars represent the standard error of the mean.  
the intermediate HRT showed the highest rates for vegetated and unvegetated drainages (\(F_{1,10}\) = 0.01, \(p\) = 0.9).
## 1 Introduction
### Bridging the Gap: From Stadiums to Urban Communities  
In light of the current emphasis on sustainable practices, researchers have predominantly concentrated on assessing how photovoltaic panels on stadium roofs cater to the energy needs of these sporting arenas. While acknowledging the significance of such endeavors, there exists an intriguing yet unexplored avenue--the potential for this renewable energy to transcend the confines of stadiums and extend its benefits to the broader urban community. The energy generated by photovoltaics, often exclusively utilized by the stadiums and their attendees, presents an untapped resource that could be harnessed to meet the energy demands of the urban neighborhood in which these sporting venues are embedded.  
This fundamental transition from a localized energy focus to a more expansive urban perspective is the main aim of the present study. As sustainability gains precedence globally, football stadiums, positioned as iconic symbols of sports culture, find themselves at the forefront of this renewable evolution. This study aims to unravel the possibilities of integrating the energy produced by photovoltaics within stadiums into the broader urban fabric. This shift in perspective aligns with the overarching commitment to sustainable development and environmental stewardship.  
In essence, the present study seeks to bridge the existing gap between stadium-centric energy solutions and the comprehensive energy requirements of urban communities. By investigating avenues for the effective utilization of photovoltaic energy on a broader scale, the paper aims to contribute valuable insights to the discourse on sustainable urban development. As the world increasingly prioritizes environmental responsibility, football stadiums emerge not just as venues for sporting spectacles but as potential catalysts for a greener and more sustainable urban future.
## 5 Experimental Results
### Training and Testing Configurations  
We use Intel (R) Core (TM) i7 CPU, GeForce GTX 1080 GPU, Windows 10, and Linux 16.04 to accomplish the overall experiment. We conduct MATLAB 2019b and Python 3.5 as the programming language. We perform the preprocessing (spatio-temporal image representation) using MATLAB 2019b and implement deep learning using PyTorch toolbox in Python 3.5. To use the pre-trained model, we resize the STIF images generated from UTD-MHAD and MSR-Action3D datasets into 224 \(\times\) 224 \(\times\) 3.  
We evaluate the proposed system by applying it on the UTD-MHAD and MSR-Action3D datasets as described in Section 2.2. The dataset is partitioned into training and testing data as described in Section 2.2. At the initial step, we set the learning rate as 0.001,and the learning rate decreases at the interval of 10 epoch with a factor of 0.9. We set the batch size to 16 that shuffles during the reading. The Adam optimizer is conducted for optimization purposes with a momentum value of 0.999. The training process continues until 100 epochs. Table 1 lists the hardware, software, and parameters used for training and testing the proposed system.