esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Industry Facilities  
After describing the primary energy assets, it is also relevant to take into account the energy consumption purchased from the grid, due to the fact that renewable generation is not enough to feed all the demand. This energy usage is predominantly attributed to electric vehicle charging. However, the industrial facilities also consume energy from other sources. These include the electricity consumption of office spaces, mechanical workshops dedicated to vehicle maintenance and repair, and a gas compression plant to charge a few trucks of the fleet still propelled with this fuel. These electrical loads are non-manageable by the industry operator, because they are not equipped with dedicated monitoring and/or control assets. This way, such loads are intended as base loads, leaving EVs and associated chargers as the only electrical consumption to actively manage.  
All in all, the company exhibits significant energy consumption, reaching an approximate annual consumption of 2570 MWh, with slight fluctuations depending on the season.  
The maximum power consumption from the grid is 625 kW, and the energy price changes depending on the period and time of the year.
# 3-D Point Cloud Registration Using Convolutional Neural Networks
## 5 The Proposed CNN Architectures
### 3-D CNN Architecture for Rough Registration  
As shown in Figure 8, to start with, SSPD with the size of \(s\times s\times s\) extracted from an input point cloud is convoluted with each of 16 kernels with the size of \(2\times 2\times 2\) that results in 16 feature maps in the layer C1. Then, to continuously increase the number of feature maps, we apply 3-D convolutions with the same kernel size \(2\times 2\times 2\) as the first convolutional layer for the layer C2 and C3. Consequently, the layer C2 has 32 feature maps while there are 64 feature maps in the layer C3. Additionally, in these layers C1, C2 and C3, zero padding is used in convolutional operations such that the output of feature maps has the same size as the original input. Moreover, after the convolutional operation, the result is added with a bias and then passed through a hyperbolic tangent activation function \(tanh(.)\) to generate the output of each feature map as follows.  
\[\chi_{k}=tanh(b_{k}+\sum_{i}w_{ki}*x_{i}) \tag{6}\]  
where \(\chi_{k}\) stands for the output of the \(k^{th}\) feature map, \(b_{k}\) is a bias of the \(k^{th}\) feature map, \(w_{ki}\) denotes the weight of kernel connected from the \(i^{th}\) input map, \(x_{i}\) means the \(i^{th}\) input map and \(*\) denotes the convolutional operation. In the subsequent layer, the 3-D max-pooling layer S1 with a window size of \(2\times 2\times 2\) is applied to each of the feature maps in the layer C3 that results in the same quantity of feature maps but a reduced spatial dimension, which is two times smaller. Next, the output of  
Figure 8: A 3-D convolutional neural network (CNN) architecture for the rough registration.  
Figure 9: A 2-D architecture for the precise registration.  
the max-pooling layer S1 is flattened to become a feature vector such that each element in this vector is fully connected to each of 2048 neurons in the layer H1. Similarly, the output of the layer H1 is fully connected to the layer H2 with 1024 neurons whilst the fully connected layer H3 has 512 neurons connected from the output of the layer H2. Finally, neurons of the last fully connected layer H3 are connected to 3 neurons representative for the estimation of Euler angles in the output layer. The activation function used in each neuron in these fully connected layers is \(tanh(.)\) function.
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 1 Introduction
A _Converging-Diverging_ (CD) type nozzle or _de Laval_ nozzle is widely used because it converts a larger fraction of the energy present in the propellants into kinetic energy. The gases passing through a CD nozzle can breach the sound barrier (Mach number \(>\) 1) and hence they are also referred to as super sonic nozzles [8]. Specific impulse (shown in Equation (2)) is the impulse (integral of thrust over time) generated per unit weight (at sea level) of propellant and is dependent on the thrust generated and mass flow rate of the propellant (\(\dot{m}\)). Exit velocity (shown in Equation (3)) is the velocity of the propellant at the exit region of the nozzle and can be calculated from the product of the specific impulse and acceleration due to gravity at sea level (\(g_{o}\)). The delta-v (shown in Equation (4)) is obtained from the widely known _Tsiolkovsky Rocket Equation_ that relates exit velocity of a spacecraft to its initial (\(m_{i}\)) and final (\(m_{f}\)) masses [8]. The relationships between the performance factors are summarized below:  
\[\tau=\dot{m}v_{e}+(P_{e}-P_{a})A_{e} \tag{1}\]  
\[I_{sp}=\frac{\tau}{\dot{m}g_{o}} \tag{2}\]  
\[v_{e}=g_{o}I_{sp} \tag{3}\]  
\[\Delta v=v_{e}ln\Big{(}\frac{m_{i}}{m_{f}}\Big{)} \tag{4}\]  
In this paper, micro-propulsion systems have been surveyed, some of which have flown on CubeSats, while others are in the development phase and are in potential consideration for future CubeSat missions. A subset of these have flown on bigger satellites as secondary propulsion systems. Based on the survey conducted, the performance factors of micro-propulsion systems have been summarized in terms of: first, a comparison of thrust and specific impulse for all propulsion systems; second, a comparison of power and specific impulse, as also thrust-to-power ratio and specific impulse for electric propulsion systems. There does exist in the literature a few studies summarizing the state-of-the-art micro-propulsion technologies: a recent survey on micro-propulsion systems--_Lemmer_ (2017) [9] and NASA Mission Design Division Report--_Small Spacecraft Technology State of the Art (2015)_ [10]. These publications provide extensive overview of the performance characteristics and operational data from missions (CubeSats and larger satellites) for various micro-propulsion systems. The main contribution of this paper is to go beyond the technological and operational data and provide a survey that also reflects upon the operating principles and key design considerations, all addressed in a single document. This paper is intended to serve as an introductory learning material for students and young researchers new to the field of micro-propulsion technologies.
###### Abstract  
Background: Enhancement of the temperature of sodium hypochlorite (NaOCI) solution would increase its cleaning potential and decontamination of the root canal system. Therefore, the aim of the present in vitro investigation was to compare the efficacy of different methods of NaOCI heating by evaluating the temperature profiles developed at different levels of the root canal system.  
Methods: Five thermocouples were applied at different levels of the root canal system of extracted human premolars. NaOCI solution was heated according to two methods: extraaoral heating (50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C) using a magnetic hotplate heater and intracanal heating by F-06, XF-30/04, and ML-12 pluggers at 100 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 180 \({}^{\circ}\)C. _Results_: The extraaoral heating method was ineffective to produce a significant temperature increase at the root apex. Comparable results were obtained using the intracanal heating method through the ML-12 plugger that showed slightly better results only when set at 180 \({}^{\circ}\)C. On the other hand, negligible differences were observed in terms of temperature maintenance at several levels of the root between the F-06 and XF-30/04 pluggers, even though the time intervals were higher in case of XF-30/04. _Conclusions_: The intracanal heating method provided a better temperature persistence in the middle third of the root canal system. Conversely, extraaoral heating was ineffective to produce a significant temperature increase at the apex of the root. Comparable results were obtained even using the ML-12 plugger.  
endodontics; heating; irrigants; root canal system; sodium hypochlorite; thermocouples +
Footnote †: journal: Applied Sciences
## 2 Datasets and Methods
### Methods
#### 2.3.1 GF-3 Preprocessing
The low entropy value of water bodies such as oceans and rivers indicates that surface scattering is dominant, whereas the high entropy value and low anisotropy of land show a mixture of two or more scattering mechanisms (Figure 4b,c). The estuarine and riverside areas appear red (Figure 4a), mainly due to the volume scattering of vegetation. The Freeman three-component decomposition based on the physical reality was used to establish a polarization covariance matrix with three basic scattering mechanism models, namely, surface scattering, \(P_{S}\); volume scattering, \(P_{V}\); and secondary scattering, \(P_{D}\). The total polarization power was then solved using the above three scattering components, and the formula is as follows [23; 54]:  
\[P_{SPAN}=\left|S_{HH}\right|^{2}+2\left|S_{HV}\right|^{2}+\left|S_{VV}\right|^{ 2}=P_{S}+P_{D}+P_{V} \tag{4}\]  
The second step is to extract texture features from the total polarization power by using gray level co-occurrence matrix (GLCM) and generate eight features, namely, mean, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment, and correlation [55]. Correlation can quantify the directionality of terrain texture. In addition, variance, dissimilarity, and contrast can be used to analyze texture periodicity, whereas entropy, angular second moment, and homogeneity can represent texture complexity [56]. \[Mean_{x}=\sum_{x}\sum_{y}P_{SPAN}(x,y)*x\ \ Mean_{y}=\sum_{x}\sum_{y}P_{SPAN}(x,y)*y \tag{5}\]  
\[Variance_{x}=\sum_{x}\sum_{y}P_{SPAN}(x,y)(x-Mean_{x})^{2}\ \ Variance_{y}=\sum_{x}\sum_{y}P_{SPAN}(x,y)\big{(}y- Mean_{y}\big{)}^{2} \tag{6}\]  
\[Homogeneity=\sum_{x}\sum_{y}\frac{P_{SPAN}(x,y)}{1+(x-y)^{2}},x\
eq y \tag{7}\]  
\[Contrast=\sum_{x}\sum_{y}(x-y)^{2}P_{SPAN}(x,y) \tag{8}\]  
\[Dissimilarity=\sum_{x}\sum_{y}P_{SPAN}(x,y)|x-y| \tag{9}\]  
\[H=-\sum\limits_{k=1}^{3}P_{k}\log_{3}(P_{k})\]0  
\[H=-\sum\limits_{k=1}^{3}P_{k}\log_{3}(P_{k})\]1  
\[H=-\sum\limits_{k=1}^{3}P_{k}\log_{3}(P_{k})\]2  
\[H=-\sum\limits_{k=1}^{3}P_{k}\log_{3}(P_{k})\]3 GF-3 polarization features in the YRD include (**a**) alpha, (**b**) anisotropy, and (**c**) entropy. As shown in Figure 5, a false color image with three texture features can be used to display the surface texture information, river extension, and tidal Creek development in the YRD. Red land and blue water indicate that the land surface is rough and ground types vary with obvious texture, whereas the texture difference of the water area is slight. Due to the significant morphological differences between estuarine and riverside vegetations, such as _Phragmites australis_ and _Tamarix chinensis_, the texture changes rapidly.
## 2 Overview of the Study Area
### Historical Deformation of the Baige Landslide  
The interpretation of landslide deformation phases through the analysis of satellite photos has become increasingly clear and visually intuitive thanks to advancements in remote sensing technology. The preparation process of landslide deformation is categorized into five distinct stages, as derived from the analysis of 15 historical images of the Baige slip area collected by [PERSON] [48]. These stages include the stable stage, obvious deformation stage, the uniform deformation stage, the accelerated deformation stage, and the secondary accelerated deformation stage. Table 1 displays the deformation characteristics for each step.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time** & **Deformation Stage** & **Stage Characteristics** \\ \hline –1996 & Stable state & There is no obvious deformation on the slope surface [49]. \\ \hline
1996–2011 & Obvious deformation & The shallow soil mass in the main sliding area is subject to creep deformation, local collapse, settlement grooves at the top of the trailing edge, and obviously discontinuous tension cracks. \\ \hline
2011–2015 & Uniform deformation & Settlement deformation and collapse deformation are the main ones. The tension cracks at the back edge are basically connected. The rear edge of the main sliding area creeps and slips downward, forming a platform silk at the back. The tension cracks at the back edge gradually expand and connect, and the collapse deformation areas are further developed. \\ \hline
2015–2017 & Accelerated deformation & Collapse deformation intensifies, and all deformation areas are connected and merged. The tension crack at the rear edge of the landslide is deepened and widened under the effect of rainfall, the tension crack surface is continuously penetrated downward, and the front slope of the main sliding area swells, causing the front edge terrain to become steeper and the sliding force of some rock and soil mass to increase, aggravating the collapse deformation in the middle of the landslide. \\ \hline
2017– & Secondary accelerated deformation & Deformation areas are basically merged. The landslide deformation tends to be whole, and the landslide enters a sliding state. \\ \hline \end{tabular}
\end{table}
Table 1: Division of historical development stages of the Baige landslide.  
Figure 3: The Baige landslide after partial clearing [46].  
Simultaneously, [PERSON] identified the path on the landslide through satellite, interpreted the road on the image by remote sensing, compared and analyzed the road positions in different periods, and calculated the displacement of the landslide surface, as shown in Figure 4 [47].
# Drive on a Greener Way: A Case Study on Navigating Cross-Regional Traffic Networks in South China
## 4 Dynamic Route Planning Method
### Static A* Algorithm  
The A* algorithm is a heuristic search algorithm that can find the shortest route between the origin and the destination. The applicable scenario is one of a search for the optimal route with a known traffic network and nodes. Compared with the Dijkstra algorithm, it does not need to search all nodes and is more efficient.  
The processes of static route planning consist of determining the road network and weights \(g(n)\), calculating the prediction cost function \(h(n)\), and calculating the shortest route by \(f(n)=g(n)+h(n)\).  
Step 1: Determining the road network and weights, \(g(n)\)  
The road network is abstracted from the map. Because we focus on cross-regional traffic networks, the network consists of highways. The weights of each road are calculated in Section 4.2.  
Step 2: Calculating the prediction cost function, \(h(n)\)  
The Dijkstra algorithm is used to calculate the shortest route from the destination to any node in the network to reduce the calculation process and improve the results. We use the shortest route calculated by the Dijkstra algorithm from the destination to the other nodes as a prediction cost function. In the calculation, the real-time road condition is notconsidered. The Dijkstra algorithm is more expensive, but it does not need to be recalculated during dynamic adjustment, only once at the beginning of the navigation process.  
Step 3: Calculating the shortest route  
The A* algorithm is used to calculate the optimal route for the OD in the road network. In this problem, the cost function \(g(n)\) represents the weights of each road, which are calculated in Step 1; the prediction cost function \(h(n)\) is calculated in Step 2. When a road is selected at a node, the function \(f(n)=g(n)+h(n)\) is used to calculate the expected optimal route from the current node to the destination. The next road is then chosen based on this calculation. This process continues until the destination is reached.
# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 4 Cramer-Rao Bound for the TDOA Synchronous Architecture
Therefore, we present the characterization of the \(\mathbf{h}\) and \(\mathbf{R}\) for the TDOA architecture:  
\[h_{TDOA_{i}}=\|TS-CS_{i}\|-\|TS-CS_{j}\|\ \ \ \ \ i=1,\ldots,\ N_{CS}\ \ \ i=1, \ldots,\ N_{CS}\ \ \ i\
eq j \tag{9}\]  
\[\sigma_{TDOA_{j}}{}^{2}=\frac{c^{2}}{B^{2}\Big{(}\frac{p_{j}}{P_{ \alpha}}\Big{)}}PL(d_{0})\bigg{[}\bigg{(}\frac{d_{i_{LOS}}}{d_{0}}\bigg{)}_{CS _{i}}+\bigg{(}\frac{d_{i_{NLOS}}}{d_{0}}\bigg{)}_{CS_{i}}^{\frac{n_{NLOS}}{4 _{LOS}}}+\bigg{(}\frac{d_{i_{LOS}}}{d_{0}}\bigg{)}+\bigg{(}\frac{d_{j_{NLOS}} }{d_{0}}\bigg{)}^{\frac{n_{NLOS}}{4_{LOS}}}\bigg{]}^{n_{LOS}} \tag{10}\] \[+\frac{1}{l}\sum\limits_{k=1}^{l}\big{\{}|T_{i}-floor_{TR}(T_{i} +U_{i}-U_{0}+T_{0}(\eta_{i}-\eta_{0})+T_{i}\eta_{i})|c^{2}\big{\}}\] \[+\frac{1}{l}\sum\limits_{k=1}^{l}\big{\{}|T_{j}-floor_{TR}(T_{j} +U_{j}-U_{0}+T_{0}(\eta_{j}-\eta_{0})+T_{j}\eta_{j})|c^{2}\big{\}}\]  
where \(CS_{i}\) and \(CS_{j}\) are the two coordinator sensors involved in the time measurement, \(N_{CS}\) is the number of coordinator sensors receiving an effective coverage of the positioning signal emitted from a determined TS location, \(c\) the speed of the radioelectric waves, \(B\) the signal bandwidth, \(P_{T}\) the signal transmission power, \(P_{N}\) the mean noise level, \(PL\) (\(d_{0}\)) the path-loss in the reference distance (\(d_{0}\)) from which the Log Normal path loss model of [47] is considered, \(d_{i_{LOS}^{\prime}},\ d_{i_{NLOS}^{\prime}},\ d_{i_{LOS}}\) and \(d_{j_{NLOS}}\) the distances from the TS to the \(CS_{i}\) and \(CS_{j}\) in LOS and NLOS conditions respectively calculated through the ray tracing algorithm presented in [48]; \(n_{LOS}\) and \(n_{NLOS}\) the path loss exponents in LOS and NLOS conditions, \(l\) the number of iterations of the Monte Carlo simulation performed for estimating the clock uncertainties [25], \(T_{i}\) and \(T_{j}\) are the total time of flight of the positioning signal from the TS to the \(CS_{i}\) and \(CS_{j}\) and \(U_{i}\), \(U_{j}\) and \(U_{0}\) and \(\eta_{i}\), \(\eta_{j}\) and \(\eta_{0}\) the initial time offset and the drift of the \(CS_{i}\), \(CS_{j}\) and the reference clock for the system synchronization respectively. The computation of the inverse of the _FIM_ allows the finding of the lower bound for the Root Mean Squared Error (\(RMSE\)) in a determined target location given the spatial distribution of sensors under analysis:  
\[RMSE\ (TLE_{i})=\sqrt{trace(J^{-1})} \tag{11}\]
## 6 Summaries and Outlook  
### Summaries  
In this paper, we give a comprehensive survey of the localization algorithms for UWSNs. From Table 4, we can see that each algorithm has its own characteristics and no one is absolutely the best. It is shown that:  
* Although GPS does not work properly underwater and equipping sensor nodes with GPS is often expensive, most researchers still adopt anchor nodes to localize unknown nodes. Meanwhile, unknown node in sub-sea environment can be localized by the hierarchical approaches (e.g., LSHL, LSLS, MS-DNR, MS-AUV and 3 DUL) or mobile anchor nodes (e.g., DETs, DNR anchor nodes and AUVs).
* In UWSNs, bandwidth constraint, sensor nodes' mobility, and unpredicted variation in channel behavior make range methods based on received signal strength (RSS) and angle of arrival (AoA) inaccurate or unapplicable. Generally speaking, ToA-based or TDoA-based localization schemas are preferable even though they require time synchronization.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline Algorithms & Anchor type & Ranging method & Message communication & Time synchronization & Localization coverage & Localization time & Localization accuracy & Computational complexity & Energy consumption \\ \hline
3D-MALS & Surface & Range-free & Silent & No & Large & Depend & Low & Low & High \\
[58, 59] & buoys,DETs & & & & & & & & \\ SLUM & Anchor-free & Range-free & Silent & No & Large & Short & Low & Low & Low \\
[66] & & & & & & & & & \\ RSMB & One mobile & & & & & & & \\  & anchor & & & & & & & \\
3 DUL & Surface & Two-way & Active & No & Large & Long & Low & Low & High \\
[32] & buoys & ToA & & & & & & \\ UDB & AUV & Range-free & Silent & No & Small & Long & Depend & Low & Low \\
[71] & & & & & & & & & \\ LDB [72] & AUV & Range-free & Silent & No & Small & Long & Depend & Low & Low \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of Hybrid Localization Algorithms.  
* In centralized localization algorithm, unknown nodes must stay \"active\" to transmit related localization information. In contrast, many distributed algorithms make unknown nodes passively listen to anchor nodes' message, since nodes in silent communication spend much less energy and communication overhead.
* Most localization algorithms require time synchronization to estimate distances between unknown and anchor nodes. Due to nodes' mobility, the mobile and hybrid localization algorithms often provide larger localization coverage than that of stationary localization algorithms, although they generally require longer localization time and give coarser location estimation.
* On the whole, the localization algorithms for UWSNs are computationally light. The energy consumptions of existing localization algorithms are generally high. Therefore, it is essential to design new energy efficient algorithms for UWSNs.
## 3 Act
### Compression
#### 3.3.3 Compression in ACT  
Figure 4 shows the usage of data compression techniques in the ACT. The ACT first transforms the data from the time domain to the frequency domain by using the DWT, reduces the range of the data with the help of ADPCM, and then reduces the number of packets by employing RLC before transfer of data in source node. Then, it introduces the DWT for priority-based congestion control because DWT classifies the data into four groups with different frequencies. Subsequently, it assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a less number of packets for a packet with a low priority. In the relaying node, the ACT reduces the number of packets by increasing the quantization step size of ADPCM in case of congestion. The destination node (usually a sink node) reverses the compression procedure. A sink node should apply Inverse Run-Length Coding (IRLC), Inverse Adaptive Differential Pulse Code Modulation (IADPCM), and then Inverse Discrete Wavelet Transform (IDWT).  
Figure 5 shows an example of compression in the ACT. First, several packets are grouped into a square array with the same width and height. Then, the DWT transforms the data from the time domain to the frequency domain, and the array can be divided into four sections, LL (low and low frequency) on the upper left side, LH (low and high frequency) on the upper right side, HL (high and low frequency) on the lower left side, and HH (high and high frequency) on the lower right side. If the DWT is applied one more time, the LL section is divided into four sections, as shown in the Figure 5. The LL section presents the shape of data in a reduced resolution, and the original data can be  
Figure 4: Data compression procedure.  
reconstructed without a high frequency. The ACT assigns a high priority to the section with a low frequency. The data transformed by the DWT should be scanned from a low frequency to a high frequency, as shown in the Figure. After scanning, ADPCM and RLC are applied, reducing the number of packets. A sink node applies the following reversed procedures: IRLC, inverse scanning, and IDWT.