esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 AGEI Application to a Case Study  
In this section, the AGEI formulation is applied to evaluate and compare the global efficiency of three hybrid plant configurations. The first configuration is an existing plant located in a real-world context, while the others represent retrofitting solutions designed to enhance the energy performance of the existing plant. Specifically, the second configuration is an innovative practical improvement solution, implementing an advanced integrated AWG machine, described in detail in [23]. The third configuration is a traditional improvement hypothesis, included in the current study for a broader comparative analysis, aimed at better understanding the applications and potentialities of the proposed new evaluation tool. The installation site for the plants is an existing worker village [23], located in the industrial area of the city of Dubai, United Arab Emirates, as shown in Figure 4.  
In this location, thousands of workers are housed in dozens of residential buildings. The village also encompasses various communal kitchens to cook and serve meals to the residents. Each residential building accommodates 200 people, whose daily drinking water needs of 2 L per person are met through a supply of bottled water. The municipal desalinisation plant directly addresses all other domestic water needs, such as toilets, showers, and general washing. In particular, every day, 18,000 L of tap water is heated from 10 \({}^{\circ}\)C to 50 \({}^{\circ}\)C by 24 electric boilers, each characterised by a power of 2 kW. Furthermore, each communal kitchen structure uses a dedicated Liquefied Petroleum Gas (LPG) boiler (125 kW) to heat an additional 22,000 L/day of tap water to 50 \({}^{\circ}\)C. Each communal kitchen building is equipped with a fresh air supply system composed of fans and split units, which cools and dehumidifies an air flux of 12,000 m\({}^{3}\)/h, ensuring adequate air changes and indoor environmental comfort during meal preparation and consumption. For simplicity, the air supply system is referred to as the Air Treatment Unit (ATU) in this paper.  
The AGEI formulation has been applied to the three hybrid plant configurations described in detail in the following sections. Here, it is worth stating that each configuration is capable of meeting all the water and HVAC needs of two residential buildings and one communal kitchen, thereby providing three useful effects:  
1. Supplying drinking water for approximately 800 residents;
2. Heating domestic water for all the aforementioned activities;
3. Cooling and dehumidifying an outdoor air flux for indoor climate control.  
AGEI is calculated in terms of primary energy because the various types of equipment are powered with different energy forms and vectors.  
Table 4 reports the primary energy conversion coefficients, \(f_{i}\), of the energy sources suitable for the UAE.
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Statistical analysis
\[\rho_{xy}=\frac{Cov(x,y)}{\sqrt{D(x)}\sqrt{D(y)}} \tag{45}\]  
where:  
\[Cov(x,y)=\frac{1}{N}\sum_{i=1}^{N}[x_{i}-E(x)][y_{i}-E(y)] \tag{46}\]  
\[E(x)=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{47}\]  
\[D(x)=\frac{1}{N}\sum_{i=1}^{N}\left[x_{i}-E(x)\right]^{2} \tag{48}\]  
The \(x\) and \(y\) are grayscale values of two pixels that are close to each other in the image under test. The results achieved are presented in Table 11.  
Table 11 indicates that the correlation coefficients for the original images are almost one, demonstrating that the pixels are highly related, On the other hand, for the encrypted images, the correlation coefficients are nearly zero, indicating that there is no correlation between the original and encrypted images. As a result, there is no similarity between the original and ciphered images, which demonstrates the high level of confusion achieved by the proposed chaos-based stream ciphers. Based on the analysis of histogram, entropy, and correlation, the proposed chaos-based stream ciphers have a high capability to defend against statistical attacks. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Entropy** & & **Lena** & **Peppers** & **Baboon** & **Barbara** & **Boats** \\ \hline Plain image & & 5.68222 & 7.66982 & 7.72644 & 7.69869 & 7.30541 \\ \hline Ciphered image & LSP-SC & 7.99977 & 7.99976 & 7.99976 & 7.99977 & 7.99977 \\  & LST-SC & 7.99979 & 7.99975 & 7.99977 & 7.99975 & 7.99979 \\  & LSPT-SC & 7.99975 & 7.99977 & 7.99976 & 7.99973 & 7.99973 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Entropy results obtained.
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 1 Introduction
Several case studies were considered, and the \"Morandi\" bridge (Polcevera Viaduct) in Genoa, Italy, was analyzed in depth, highlighting anomalous vibration modes in the period before the bridge collapse. After conducting a deep analysis of the state of the art, it becomes clear that to date there is a lack of a versatile, cost-effective, and widely applicable tool for internal monitoring of volcanoes. In this context, a new method that employs m-m estimation to perform deep scanning of volcanoes is presented. This approach appears to be tailored to fill this gap technology when today there is direct access to SAR satellite data, which is a day-night sensor, and is also able to penetrate clouds. The experimental results are obtained by processing one SAR Spotlight image, observing Vesuvius, and revealing its internal structure. Tomographic maps show the main crater section, the volcanic conduit, and the main vent. In depth, the magma chamber and the presence of a secondary magmatic fluid conduit are also visible. In addition, the internal structure of the currently plugged main magmatic fluid conduit reveals several different structures of debris layers.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Point-Line Feature Extraction Network
#### 3.1.4 Parallel Attention Module  
To efficiently merge the two branches of point descriptors and line descriptors, we designed a parallel attention module (PAB) based on self-attention and channel attention. As shown in Figure 6, the output features of the key-point description branch contained line-edge information with a strong correlation. In order to improve the accuracy of the line descriptor, a lightweight attention mechanism was used to assign more weights for the features of useful regions. The output feature map of the point description branch was expressed as \(X_{E}\in\mathbb{R}^{C\times H\times W}\). A one-dimensional convolution of \(X_{E}\) was performed to obtain the spatial attention map \(A_{E}\in\mathbb{R}^{C\times H\times W}\). The edge feature map \(X_{E}^{\text{S}}\in\mathbb{R}^{C\times H\times W}\) was calculated as: \(X_{E}^{\text{S}}=a(X_{E}\odot A_{E})+X_{E}\), where \(a\) is a learnable parameter that was initialized to 0. The CAEU module was designed to calculate a channel attention map, which recalibrated the weight of the channel and obtained the fused feature map \(X_{F}^{\text{S}}\in\mathbb{R}^{C\times H\times W}\) from \(X_{F}^{\text{S}}=X_{E}^{\text{S}}\otimes\delta(Conv1\times 1(Conv1\times 1(GAP(X_{E}^{ \text{S}})))\). Finally, the final output \(X_{F}^{\text{SC}}\in\mathbb{R}^{2C\times H\times W}\) of PAB was obtained by concatenating \(X_{T}^{\text{S}}\) and \(X_{F}^{\text{S}}\) together.
## 8 Technical Challenges and Potential Solutions
### Grouped Beams and Columns Gaping Issues  
The gap between the upper unit's floor beam and the bottom unit's ceiling beam provides a functional space, as shown in Figure 12. In such instances, the load transfer would result in the connected columns and beams' strength capacity being underutilized. However, if the spacing between the cluster beams and columns is minimal, the interaction effect will improve the overall strength and stiffness of the system. However, research on the mechanical performance of clustered columns and beams and their interfacial connection is lacking. When this advantage is overlooked, the stiffness of the PFISBs' floor and ceiling beams and stringers during the gravity loads and columns during the lateral stresses is underestimated. Additional research on the cluster columns, beams, and the interfacial connection is recommended to obtain an accurate design model. The authors recommend using Section 5 solutions or employing automated joints for the resisting uplift [71] and beams horizontal joints [79] for lateral interlocking to take advantage of this uniqueness. However, more research is needed to use this inherited feature of the cluster columns and beams.
## 3 Continuous-Time Spline Representation for Fast Motion Estimation
### B-Spline Interpolation  
Splines are an excellent choice for representing a continuous trajectory because their derivatives can be computed analytically. B-splines are chosen in this paper as they are a well-known representation for trajectories in R\({}^{3}\)[22; 23; 24]. B-splines use a series of control points (also denoted as knots) to define a linear combination of continuous function which is constrained by the knots. The interpolation between a series of 3D positions (knots) presented in the B-spline is as follows:  
\[p(t)=\sum_{i=0}^{n}p_{i}Bi,k(t) \tag{14}\]  
where \(B_{i,k}(t)\) is the De Boor-Cox recursive formula, the fractional coefficient for each given control point \(p_{i}\). The formula of \(B_{i,k}(t)\) is:  
\[B_{i,k}(t)=\frac{t-t_{i}}{t_{i+k}-t_{i}}B_{i,k-1}(t)+\frac{t_{i+k+1}-t}{t_{i+k +1}-t_{i+1}}B_{i+1,k-1}(t) \tag{15}\]
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 5 Simulation Results
### MPC Simulation  
In this section, we apply our semi-analytical algorithm to the MPC framework to control the lander. The detailed techniques are listed in Section 4. The initial position of the lander is set to \([x_{0};y_{0};z_{0}]=[1000;0;3000]\) m, and the initial velocity is \(\mathbf{v}_{0}=[-50;10;-75]\) m/s. The other parameters are listed in Table 1. It is noted that 5% thrust is reserved, and the maximum thrust and the mass flow listed in Table 1 is actually equal to 95% of the available thrust and mass flow.  
In this case, a fault mode is considered. The thrust of the lander drops to 70%, with no change in mass flow. There are five possible landing points listed below:  
\[[0;0;0]\quad[0;500;0]\quad[500;0;0]\quad[0;-500;0]\quad[-500;0;0] \tag{42}\]  
We first analyze the reachability of the five landing points, and only the third point is unreachable. Then, we calculate the vacuum solution of the other four landing situations, and the last point is chosen for minimum fuel consumption. The results are presented in Table 3 and denoted as VAS.  
The MPC command update interval is set to 500 ms. The dynamic integral between two sampling instants is used to simulate the real flight of the lander, in which the aerodynamic drag force is considered and modeled as:  
\[\mathbf{D}(t)=-\frac{1}{2}C_{\text{d}}\rho A_{\text{ref}}||\mathbf{v}(t)-\mathbf{v}_{ \text{wind}}||(\mathbf{v}(t)-\mathbf{v}_{\text{wind}}) \tag{43}\]  
where \(C_{\text{d}}\) is the coefficient of drag and set to 0.5, \(\rho\) is the atmospheric density and set to 0.01 kg/m\({}^{3}\), \(A_{\text{ref}}\) is the drag reference area and set to 6 m\({}^{2}\), and \(\mathbf{v}_{\text{wind}}\) is the velocity of the wind and set to \([-5;-5;0]\) m/s. In addition, we add a horizontal acceleration to simulate the actual disturbances and possible faults [38], which is set as follows. When \(z<5\) m, the open-loop control mode is adopted.  
\[\mathbf{f}(k)=[-0.25 sin(\pi k)e^{-0.2k};0.25 sin(\pi k)e^{-0.2k};0] \tag{44}\]  
The control profiles are shown in Figure 13, where the MPC results are labeled as MPC, and the vacuum analytical solution is labeled as VAS. The fuel consumption is 377.9261 kg, and the terminal states are \([x_{\text{f}};y_{\text{f}};z_{\text{f}}]=\{-0.1757;\,0.0229;\,2.3988\times 10^{-4}\}\) m and \(\mathbf{v}_{\text{f}}=[-0.1192;\,0.2304;\,1.6205\times 10^{-4}]\) m/s. The maximum \(\mu\) is 0.9998, less than 1.0526 (1/0.95). It is indicated that our semi-analytical algorithm can still maintain a good accuracy under external disturbances and model uncertainties.  
\begin{table}
\begin{tabular}{c c c c} \hline \([\mathbf{t_{1}},\mathbf{t_{\text{f}}}]\) & \([\mathbf{\mu_{\text{x}}},\mathbf{\mu_{\text{y}}}]\) & \([\mathbf{t_{\text{x}}},\mathbf{t_{\text{y}}}]\) & **Fuel Consumption** \\ \hline \([3.5261,58.4313]\) s & \([0.1810,0.0916]\) & \([56.3076,42.3248]\) s & 377.0066 kg \\ \hline \end{tabular}
\end{table}
Table 3: Vacuum solution of Example 3.
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 3 Dynamic Modeling of 3-UPRU+UP with Prismatic Joint Clearance
### Dynamic Modeling with Ideal Joints
#### 3.1.4 Constraint Equation of the Composite UP Joint  
The composite UP joint has three DOFs, which restricts relative rotation in one direction and relative movement in two directions between the two connected rigid bodies, resulting in three independent constraint equations. Figure 6 shows the constraint vector diagram of the composite UP joint. Points \(P\) and \(Q\) are the centers of the static and moving platforms, respectively. The dotted line represents the moving axis of the P-joint; \(\mathbf{d}_{oi}\) and \(\mathbf{d}_{oj}\) are the rotation axis of the U-joint. During motion, the line connecting \(P\) and \(Q\) is always perpendicular to \(\mathbf{d}_{oi}\) and \(\mathbf{d}_{oj}\) on the moving platform. Simultaneously, \(\mathbf{d}_{oi}\) and \(\mathbf{d}_{oj}\) are perpendicular to each other. The constraint equation for the composite universal and prismatic joint can be expressed as:  
\[\mathbf{\Phi}_{UP}(q)=\left[\begin{array}{c}(\mathbf{R}_{i}\mathbf{d}_{oi})^{\mathrm{T} }\overline{QP}\\ (\mathbf{R}_{i}\mathbf{d}_{oj})^{\mathrm{T}}\overline{QP}\\ (\mathbf{R}_{i}\mathbf{d}_{oi})^{\mathrm{T}}(\mathbf{R}_{j}\mathbf{d}_{oj})\end{array}\right]= \mathbf{0}_{3\times 1} \tag{12}\]  
The polishing robot is actuated by three prismatic joints. The mechanism has three DOFs, allowing the three actuators to produce specific movements. To conduct dynamic analysis, the trajectory of the moving platform needs to be determined. Through kinematic analysis, the displacement curves \(f_{n}(t)\) (\(n=1\), \(2\), \(3\)) of the three actuators are determined, and the derivation of the driving constraint equation is presented as follows:  
\[\mathbf{\Phi}_{D}=\left[\begin{array}{c}|\mathbf{\rho}_{1}-\rho_{5}|-f_{1}(t)\\ |\mathbf{\rho}_{2}-\rho_{6}|-f_{2}(t)\\ |\mathbf{\rho}_{3}-\rho_{7}|-f_{3}(t)\end{array}\right]=\mathbf{0}_{3\times 1} \tag{13}\]  
where \(\rho_{\mathrm{i}}\) (\(i=1\), \(2\), \(3\), \(5\), \(6\), \(7\)) denotes the vector position of \(U_{\mathrm{i}}\) (\(i=1\), \(2\), \(3\), \(5\), \(6\), \(7\)) in \(W\)-\(XYZ\).  
According to Equations (8)-(13), the complete constraint equation of the polishing parallel manipulator is as follows:  
\[\mathbf{\Phi}(q)=\left[\begin{array}{cccccc}\mathbf{\Phi}_{U11}&\mathbf{\Phi}_{U2}&\mathbf{ \Phi}_{U13}&\mathbf{\Phi}_{U5}&\mathbf{\Phi}_{U6}&\mathbf{\Phi}_{U17}&\mathbf{\Phi}_{UP}\\ \mathbf{\Phi}_{P1}&\mathbf{\Phi}_{P2}&\mathbf{\Phi}_{P3}&\mathbf{\Phi}_{R1}&\mathbf{\[PERSON]}_{R2}&\bm {\Phi}_{R3}&\mathbf{\Phi}_{D}\end{array}\right]^{\mathrm{T}}=\mathbf{0}_{60\times 1} \tag{14}\]  
Figure 6: Constraint vector of the composite UP joint.
## 3 IVR-Based SDO Streaming Distance Education System
### IVR-SDO Streaming Education System Architecture
#### 3.1.2 SDO Image Server  
When a request is received by the SDO image transfer module, the SDO images in the repository are sent to the relay server. The images are sent with a frame size of approximately 1 MB using multiple channels. This method can fully utilize the network resources. The sender splits the total image into the maximum data size that can be sent for each channel, and the receiver merges them to the original size. This study transmits 10 types of 1 MB SDO images in 30 fps using six channels.
###### Abstract
* **The optimal solution is determined by the whole utterance set [21]:** To extend the idea of the previous point, using the MSE as the objective loss function tends to consider the portions in utterances with different characteristics in an _averaged_ manner, which might alleviate the distinct and discriminative components that highlight the speech. For example, an ordinary utterance set possesses low- and high-pitch utterances. Minimizing the MSE at these utterances indiscriminately very likely causes these utterances to have median pitches, which is not our goal at all. Inspired by the observations mentioned above and some other literature [15; 18; 22], in this study, we propose a novel deep-learning-based noise-robust speech feature extraction algorithm with an MSE-irrelevant loss function. The used loss function is directly associated with the performance of a speech recognition system in noisy environments. Briefly speaking, a deep neural network is trained to perform the speech feature mapping that maximizes the posterior state probability associated with the back-end acoustic models in the speech recognition system. The resulting new speech feature representation is expected to outperform the original feature in recognition accuracy and possess noise robustness. It is noteworthy that many research efforts have employed deep neural networks to engage in an ASR system to improve its noise robustness. For example, an end-to-end (E2E) ASR framework with a single DNN directly maps an acoustic feature sequence to a word sequence [23; 24; 25]. This E2E DNN is learned to optimize criteria directly associated with the ASR performance, such as the word error rate (WER). Such an E2E model can behave better and be more compact than the conventional hybrid ASR model, consisting mainly of acoustic models, a lexicon, and language models. The authors in [26] claim that the improvements in speech quality metrics such as PESQ by speech enhancement techniques do not translate into better ASR performance. They propose a joint optimization of mask-estimating network-based speech enhancement and acoustic modeling to reduce the WER. In addition, a deep learning scheme for _a priori_ SNR estimation, termed Deep Xi, is presented in [27; 28] to facilitate the conventional minimum-mean-squared-error (MMSE)-based SE methods for a robust ASR. Compared with these complicated and fine-grained techniques, our newly presented method is a relatively lightweight network that is much easier to be learned, while it is likely less effective. However, our method is a DNN-wise transformation in the same feature domain and thus can be easily integrated with these advanced methods to boost the ASR performance. In the following sections, we first introduce the original acoustic features and the associated acoustic models in a pre-processing stage and then present the novel feature extraction algorithm. Next, we compare our method with various speech features created by the utterances enhanced by some state-of-the-art enhancement methods. Finally, we give a concluding remark.
## 6 Conclusions  
We analyzed the orbit determination and maneuver estimation of the TH-2 satellite system based on spaceborne GPS and BDS2 observations. The analysis of the data quality  
Figure 16: 3D RMS of overlap comparisons of BDS2-only, GPS-only, and GC combined POD for TH-2A (**top panel**) and TH-2B (**bottom panel**).  
shows that the spaceborne receiver of the TH-2 has a stable data tracking ability and a good observation quality.  
The assessments of the maneuver handling show that the residuals of the POD for the maneuvering TH-2A are comparable to those of the maneuver-free TH-2B. Maneuver handling can avoid the precision degradation of the GPS- and BDS2-based POD caused by thrusts. The GPS-based POD solutions have good accuracy, which are better than 0.7 cm in 3D RMS. The orbit precision of the BDS2-based POD is smaller than 8 cm compared with the GPS-based orbits. This result indicates that the precision of the BDS2-based orbits achieves the subdecimeter level and satisfies the accuracy requirement of 1 m for the TH-2. In addition, the relative error of velocity changes between the GPS- and BDS2-based POD is less than 7.0%, which indicates that the maneuver estimation by using regional BDS2 data can be on par with using global GPS data.  
In the fusion of the GPS and BDS observations, we found that the independent receiver clock offsets need to be estimated for each system due to the different time tag corrections for the GPS and BDS2 data obtained by the TH-2. Compared with the GPS-only solutions, the precision of the GPS and BDS2 combined SPP solutions is improved by 12-14% when the PDOP values of GPS exceed three. For the GPS and BDS2 combined POD, the 3D RMS of the overlap comparison is 0.64-0.65 cm. Nevertheless, due to the weaker consistency of the BDS2 precise ephemerides, the orbit precision of the combined POD is only improved by 3-4% compared with the GPS-only orbits.  
Conceptualization, H.Z. and B.J.; data curation, Z.H.; formal analysis, H.Z., B.J. and K.S.; funding acquisition, X.D.; writing--original draft, H.Z.; writing--review and editing, D.G., B.J., K.S. and B.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China (2020 YFA0713502) and the National Natural Science Foundation of China (41874028, 11771450, and 61803018).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We are very grateful to the GFZ for providing the GPS and BDS2 precise orbit and clock products. We would like to thank [PERSON], [PERSON], and [PERSON] for the valuable information about the receiver.  
The authors declare no conflict of interest.
## 5 Experiments
### Test Results
#### 5.3.1 Property 1  
A packet should not be routed to the same node more than once before reaching the intended destination. By using the syntax and semantics we mentioned before, this property can be simply formalized as:  
\[\forall_{y>x}(receive(y)!=message(x))\]  
where \(receive(y)\) represents any message received after message \(x\).  
The testing results are shown in Table 1; unsurprisingly, no fail verdict has been found. The column \"Total Messages\" represents all of the messages we captured from each node. The columns 'Pass', 'Fail' and 'Inconclusive' represent the messages satisfying/not satisfying/uncertainly satisfying the requirement condition accordingly. Sometimes, there is an amount of messages concluded to be in \"Total Messages\", but not relevant to the property being tested. Correspondingly, these messages are not counted in the final verdicts. That is also the reason that the total number of 'Pass', 'Fail' and 'Inconclusive' does not equal the 'Total Messages' for some properties. For this property, all of the messages in the traces satisfied the condition and passed this property. Since this property is just a simple one for all of the WSN routing protocol, for verifying the efficiency of our approach, we test more complex properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traces** & **Total Messages** & **Pass** & **Fail** & **Inconclusive** \\ \hline
101 & 67,409 & 67,409 & 0 & 0 \\
102 & 95,206 & 95,206 & 0 & 0 \\
103 & 81,709 & 81,709 & 0 & 0 \\
104 & 84,590 & 84,590 & 0 & 0 \\
105 & 69,274 & 69,274 & 0 & 0 \\
106 & 70,113 & 70,113 & 0 & 0 \\
107 & 79,807 & 79,807 & 0 & 0 \\
108 & 78,483 & 78,483 & 0 & 0 \\
109 & 87,196 & 87,196 & 0 & 0 \\
110 & 62,235 & 62,235 & 0 & 0 \\ Gateway 500 & 60,805 & 60,805 & 0 & 0 \\ Gateway 501 & 52,444 & 52,444 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results for Property 1.