esa-sceva/satcom-chunk-collection huggingface dataset:
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios  
[PERSON]  
1 Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milano, Italy;  
[EMAIL_ADDRESS]  
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[PERSON]  
1 Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milano, Italy;  
[EMAIL_ADDRESS]  
1  
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detection of asteroids and planets, the target is the only bright spot in the image, making the detection much easier for navigation purposes when the images are processed in a real-time closed loop [3].  
These are just the two biggest advantages of thermal images; many other applications are made possible thanks to thermal infrared (TIR) images such as site selection in dark regions, deployable satellite tracking and navigation, and target marking [1]. Indeed, there is novel literature on the application of thermal infrared images for navigation in adverse illumination conditions in proximity scenarios, for example, for active debris removal [4; 5; 6]. In the same context, an interesting application of TIR images is their combination with visible (VIS) images using fusion algorithms to retain the advantages of both sensors (i.e., high definition for VIS and robustness in low illumination conditions for TIR); on this matter the interested reader can refer to [2; 7; 8].  
Despite the unquestionable advantages of TIR images, the literature on how to create synthetic thermal images is still scarce, but at the same time, the possibility to create thermal images is fundamental for any mission involving thermal cameras for the following reasons:  
1. Thermal image datasets can be created to train and test navigation algorithms in all mission phases.
2. Test software and hardware in the loop electronics, such as image processing boards.
3. Simulate the scientific output of the mission.  
Given the importance of the above points, the paper describes a method to create thermal images starting from high geometrical accuracy thermal simulation results and reports the application of the method in two different scenarios: the first one where the target is a spacecraft, and the second one where the target is an asteroid. The proposed method uses a 3D finite volume method to compute the temperature field of the object, accounting for all the thermal properties of the different materials of the target. The generation of the thermal image is then performed using a rendering engine, and finally, two thermal camera models are proposed as future starting points to simulate the actual output of the instrument.  
The structure of the work is as follows: In Section 2, we report the modeling of the infrared radiation and the infrared camera models. In Section 3, the method to generate the infrared images is presented and applied to a spacecraft and to an asteroid scenario. In Section 4, the method is applied to real case scenarios to show the actual capability of the method and its importance in a real space mission design. In the end, Section 5 gathers the conclusions of the presented work.
## 4 Model and Methodology of the Adaptive Protocol
### Figures, Tables and Schemes
#### 4.3.2 Initialization of the Double-Q-Values  
In Q-learning, the forwarding of data between nodes uses a Q-table to find the best action, where the Q-value is the expectation of nodes when forwarding [35]. In the double Q-value model designed in this paper, the action value functions are divided into life cycle functions and real-time functions \(Q_{L}\) and \(Q_{T}\). \(Q_{L}\) consists of three parts: data aggregation degree, node energy status, and link strength. The first part aims to increase the aggregability of forwarded data packets and reduce the data size to reduce the energy loss caused by data transmission. The second part avoids selecting energy. Nodes with low values are forwarded, and the third part aims to reduce communication overhead and save energy. \(Q_{T}\) consists of two parts: the number of hops reaching the sink node and the forwarding delay estimation, both of which aim to ensure the real-time performance of the data packet reaching the sink node. As shown in (6), double-Q-values are initialized as a weighted sum of the probabilities of their respective parts.  
\[\begin{array}{l}Q_{L}(s,a)=DA(s,a)+E_{s}^{a}+L_{s}^{a},\\ Q_{T}(s,a)=d(a,\sin k)+T_{s}^{a},\end{array} \tag{6}\]  
where \(DA(s,a)\) denotes the degree to which node s aggregates data to the node indicated by its action a; \(E_{s}^{a}\) is the remaining energy of the node pointed to by action a. The link strength between node s and the node pointed to by action a is represented by \(L_{s}a\). \(d(a,sink)\) is the distance between the node indicated by action a and the sink. \(T_{s}^{a}\) is forwarding time from node s to the node pointed to by its action a. Before starting, the Q-values are initialized only by the initial energy and the distance to the sink, and other parameters are updated after running.  
Figure 4: Application of double-Q-learning algorithm in transmission between nodes.
# Effectiveness of a Nutrition Education Program for Patients with Type 2 Diabetes Mellitus
## 3 Results
### Efficacy of the Nutrition Education Intervention  
At the end of the dietary intervention period, changes were seen in all anthropometric and biochemical variables measured. Body weight and body mass index (BMI) decreased significantly, as did systolic blood pressure, fasting and mean glucose, total and LDL cholesterol and triglycerides (TGs) (Table 3). The most important outcome was the change \begin{table}
\begin{tabular}{l l l} \hline \hline  & & **N \%** \\ \hline  & Occasionally & 3.4 \\  & Weekly & 8.0 \\ Frequency of blood glucose measurement & 1 time/day & 50.0 \\  & 2-3 times/day & 34.1 \\  & \textgreater{}3 times/day & 4.5 \\ \hline  & Once a year & 13.6 \\  & Once per 6 months & 28.4 \\ Frequency of doctor visits for type 2 & Once per 3 months & 35.2 \\ diabetes mellitus & 1 time/month & 17.0 \\  & 2 times/month & 5.7 \\ \hline Update on disease severity/consequences & No & 89.8 \\ of non-regulation & Yes & 10.2 \\ \hline  & No & 11.4 \\  & From the doctor & 52.3 \\ With the onset of the disease, were dietary & By the dietitian with guidance & 36.4 \\ instructions given (immediately)? If so, by whom? & from the attending physician & \\  & From the dietitian during a & 0.0 \\  & visit on individual initiative & \\ \hline  & No answer & 20.5 \\  & Negligence & 31.8 \\ Reason for not visiting a dietitian & The doctor is enough & 36.4 \\  & Cost & 8.0 \\  & No time available & 3.4 \\ \hline  & No & 27.3 \\  & Housework & 6.8 \\ Participation in physical activity & Outside work & 2.3 \\  & Walking & 53.4 \\  & Jogging & 2.3 \\  & Sport & 8.0 \\ \hline  & No answer & 27.3 \\  & 1 time/week & 2.3 \\  & 2 times/week & 4.5 \\ Frequency of exercise & 3 times/week & 10.2 \\  & 4 times/week & 3.4 \\  & 5 times/week & 5.7 \\  & Every day & 46.6 \\ \hline Smoking & No & 75.0 \\  & Yes & 25.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Therapeutic approaches and treatment of T2 DM (%). in body composition (average body fat and vascular fat percentage averages) and waist circumference (Table 3). The atherosclerotic index and uric acid also decreased significantly at the end of the study period, while no significant changes occurred in HbA1c, HDL cholesterol, diastolic blood pressure and muscle mass (Table 3).
## 4 Technologies and the EU \"Sustainable and Smart Mobility Strategy\" Ambitions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Description** & **Projects** & **FS** \\ \hline \multirow{4}{*}{Communication networks} & Communications networks (through dedicated infrastructure or 4G/5G networks) to support the development of ITS and C-ITS for improved traffic efficiency and safety and to support the implementation of new transport concepts. & 5G-CARMEN, 5G-DRIVE, C6-MOBIX, CREATE, IMOVE, MUV, SocialCar & 10 \\ \hline \multirow{4}{*}{Research for road safety} & Monitoring of road traffic events and collection of traffic data to provide inputs to the development of traffic models to improve safety. & i-DREAMS, OSCC, PIONEERS, SENIORS, VIRTUAL & 10 \\ \hline \multirow{4}{*}{Eco-Drive app} & Mobile phone-based app, which supports the vehicle driver to drive safely and economically, by monitoring parameters such as acceleration and braking. & Cloud Your Car UBI, ELVITEN, GlobalBLED & 1 \\ \hline \multirow{4}{*}{Mobility open platforms} & An integrated open data mobility platform & DIGNITY, FreeWheel, INDIMO, \\  & gathers information from all transportation modes and provides information to the user. & MW-R, SELECT for Cities, \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Smart mobility and services technologies, research projects and pertinent SSMS Flagships (FS).
## 2 Preliminaries
### Ring-LWE-Based Secure Matrix Multiplication  
[PERSON] et al. proposed secure matrix multiplication [18] using the packing method proposed by [PERSON] et al. [19]. For an \(\ell\)-dimension vector \(U=[u_{0},u_{1},\ldots,u_{\ell-1}]\), the following two polynomials are defined:\[\mathsf{poly}_{1}(U) =\sum_{m=0}^{\ell-1}u_{m}x^{m}, \tag{14}\] \[\mathsf{poly}_{t}(U) =-\sum_{m=0}^{\ell-1}u_{m}x^{n-m}.\]  
Note that for any two vectors \(A=[a_{0},a_{1},\dots,a_{\ell-1}]\) and \(B=[b_{0},b_{1},\dots,b_{\ell-1}]\), using the above packing method, we have  
\[\mathsf{poly}_{1}(A)\times\mathsf{poly}_{t}(B)=\langle A,B\rangle+\mathsf{ other\ terms\ of\ degree}, \tag{15}\]  
where the constant term of \(\mathsf{poly}_{1}(A)\times\mathsf{poly}_{t}(B)\) provides the inner product \(\langle A,B\rangle\).  
Let \(\mathbf{U}\) be a (\(k\), \(\ell\)) matrix and let \(U_{1},\dots,U_{k}\) denote the row vectors of \(\mathbf{U}\). For matrix \(\mathbf{U}\), the packing method is defined as follows:  
\[\begin{split}\mathsf{poly}_{mat}(\mathbf{U})&=\mathsf{ poly}_{1}(U_{1})+\dots+\mathsf{poly}_{1}(U_{k})x^{(k-1)\ell}\\ &=\sum_{i=1}^{k}\mathsf{poly}_{1}(U_{i})x^{(i-1)\ell}.\end{split} \tag{16}\]  
Assume that \(k\ell\leq n\) (\(n\): dimension of polynomial \(x^{n}+1\)). Letting \((k,\ell)\) matrix be  
\[\mathbf{A}=\begin{bmatrix}A_{1}\\ \vdots\\ A_{k}\end{bmatrix} \tag{17}\]  
and letting \(B\) denote an \(\ell\)-dimensional vector, we have  
\[\mathsf{poly}_{mat}(\mathbf{A})\times\mathsf{poly}_{t}(B)=\sum_{i=1}^{k}\mathsf{ poly}_{1}(A_{i})\times\mathsf{poly}_{t}(B)x^{(i-1)\ell}, \tag{18}\]  
where the coefficient of \(x^{(i-1)\ell}\,(i=1,\dots,k)\) is \(\langle A_{i},B\rangle\), the inner product of vectors \(A_{i}\) and \(B\).
## 3 Results and Discussion  
### Temperature Resulting  
For a clean air speed of 0.1 m/s and the lowest POSS bio-char moisture content (15% d.b.), Figure 3 presents the \(Tp\) and temperature values of the POSS bio-char combustion at varying distances above the fire base. The POSS bio-char combustion had three phases: preheating, degassing, and charcoal, the same as para rubber wood combustion [17] and others [38; 39; 40]. Figure 3 illustrates that the locations above the fire base had a prime influence on the measured temperature values. Figure 3 shows the highest temperature value of about 1145 \({}^{\circ}\)C was found 300 mm above the fire base in the preheating phase. The temperature value decreased with the increasing the distance above the fire base, while the highest \(Tp\) value was about 1382 \({}^{\circ}\)C in the same place as the distancing temperature experiment.  
Figure 3: The temperature under the condition of 0.1 m/s clean air speed and 15% d.b. of POSS bio-char moisture content for the particles and different distances above the fire base.  
For a clean air speed of 0.1 m/s, Figure 4 shows that the POSS bio-char moisture content had a prime influence on increasing temperature values above the fire base (the chart is not shown for 45% d.b.). The temperature value decreased with increasing POSS bio-char moisture content. This is the same result as para rubber wood combustion [17] and others [38; 39; 40]. From Figure 5, where the clean air speeds were raised to 0.2 m/s and 0.3 m/s, respectively, the rise in clean air speed affected both the temperature values of the POSS bio-char combustion, \(Tp\), and the temperature at each location above the fire base. At the same POSS bio-char moisture content (the chart shown for the lowest POSS bio-char moisture content, 15% d.b.), clean air speeds increased with decreasing \(Tp\) and temperature value at 300 mm above the fire base, but at 500 mm and 800 mm above the fire base, the chart shows that the temperature increased. For this reason, the rise in clean air speed affects the point of complete combustion, temperature, and end of the burning period. This agreed with the results reported by [41; 42; 43].
# Estimation of Changes in Sediment Transport along the Free-Flowing Middle Danube River Reach
## 2 Description of the Study Reach  
The subject of this study is an approximately 300 km long reach spanning from rkm 1581 in Dunaujivaros, Hungary, to rkm 1255 in Novi Sad, Serbia (Figure 1), and it belongs to the Middle Danube. The reach is situated between the Gabcikovo (rkm \(\sim\)1800) and Iron Gate I (\(\sim\)rkm 1000) dams and hydropower plants. The Danube River is alluvial downstream of \(\sim\)rkm 1600, although the transition from gravel bed to sand bed is around Paks in Hungary (rkm 1531). Since the Danube is a free-flowing river along this reach, in the course of creating the Danube waterway, now part of the European TEN-T network, there was extensive river-training work along the reach in the past. This included cutting off mostly overdeveloped meander bends and the installation of different stone structures after the 1870s (e.g., groynes and longitudinal stone toes). These river-training measures were known for increasing flow energy and sediment transport capacities, and they resulted in deepening erosion in the riverbed. Additionally, there were intensive dredging activities in the upstream part of the study reach between Dunaujivaros (rkm 1581) and Paks (rkm 1531) in the second half of the 20 th century until the beginning of the 2000s, which provoked further morphological changes in the river channel.  
To the best of the authors' knowledge, no comprehensive analysis of the changes in the water regime or, at the same time, the morphological parameters of the entire free-flowing alluvial reach of the middle Danube River has been performed to date. To fill this gap, available time series of the water level and discharge data for the entire free-flowing alluvial reach of the Danube River and the morphological evolution of cross-sections at corresponding gauging stations (GSs) are analyzed in the present study. Moreover, the digital database of surveillance cross-sections from the state institutions authorized for hydrographic surveys along the international waterway is used to assess variations in the long-term sediment transport along the reach.
## 4 Results and Analysis
### Algorithm Accuracy  
After comparing the convergence and speed of the model, we verified the accuracy of the lip-recognition model based on the self-made dataset and analyzed the pattern of experimental data in each epoch. Figure 11 shows the accuracy curve of the original network and different pruned networks on the training set.  
Figure 11: Comparison curve of accuracy.  
Figure 10: Average recognition time.  
As is shown in the picture, the accuracy of the original model was lower than that of the pruned model, because the parameters of the original model were so large that it led to overfitting during the training process. As the number of iterations increased, the accuracy curve gradually rose. Furthermore, when the number of iterations reached about 20 times, the value of recognition accuracy reached an excellent level, which indicated that the network model fit the distribution of training data well and reached an optimal level.  
In order to compare the performance of different models after pruning, we compared the accuracy of ResNet-18, ResNet-34, and VGG16 with a pruning rate of 0.6 and baseline, which is shown in Figure 12. We found that different models with a large number of parameters could reach convergence well under the self-made dataset after pruning. Moreover, because the VGG16 network had a large number of parameters, the overall performance of the model was greatly improved compared with the other models after pruning.  
Furthermore, the main factors affecting the accuracy rate in lip recognition are as follows. Firstly, the lip region is small and there is little difference between lip movements. Secondly, there are not enough researchers and insufficient datasets for lip recognition. We made recall statistics for each English number pronunciation prediction result, as shown in Figure 13.  
Figure 12: Accuracy comparison between different models at a pruning rate of 0.6.  
Figure 13: Recall statistics of the English independent pronunciations.
# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 6 Conclusions  
The paper designs a multilevel transformer network and an adaptive multitask decoder, which addresses the challenge of extracting edge details of water leakage identification in tunnels and realizes intelligent and high-precision detection of tunnel water leakage diseases in complex environments. The experimental results indicate that the article model has excellent performance. Our contributions are as follows:  
1. The encoder is a multilevel transformer to address the limitations of ViT.
2. An adaptive multitask decoder is proposed to accurately segment the water seepage and wet stains from water leakage images in tunnels.
3. A converged network is designed to fuse the coarse images of the adaptive multitask decoder.  
Our methodology has been found to exhibit a commendable degree of efficacy in the segmentation of water leakage, as demonstrated through its comparison to other state-of-the-art segmentation techniques. However, it remains apparent that the method necessitates further enhancement. Specifically, the computational cost of the model is still substantial and renders it incapable of achieving real-time segmentation. As such, in future research endeavors, we shall persist in our efforts to improve the image segmentation of water leakage in tunnels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **F1** & **PA** & **MIOU** & **FWIOU** & **Dice** \\ \hline Adaptive Multitask Network & 0.958 & 0.988 & 0.906 & 0.962 & 0.948 \\ Single-task Network & 0.912 & 0.951 & 0.852 & 0.923 & 0.921 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics of adaptive multitask network and the branch of the water seepage.  
Figure 15: Visual results of the adaptive multitask network and the single-task network. (**a,e**) Original image; (**b,f**) Ground truth; (**c,g**) Single-task network; (**d,h**) Adaptive multitask network.  
**Author Contributions:** Conceptualization, L.Z. and X.Y.; Funding acquisition, S.L.; Investigation, X.Y.; Methodology, L.Z.; Project administration, S.L.; Resources, S.L.; Software, X.Y.; Validation, L.Z. and J.W.; Visualization, J.W.; Writing--original draft, L.Z. and J.W.; Writing--review & editing, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China, No. 51209167, 12002251; Natural Science Foundation of Shaanxi Province, No. 2019 JM-474.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 4 Discussion
### Apparent Influence of Vegetation Differences in SIF Responses
Thus, denser and taller vegetation has greater resistance to drought effects (i.e., weaker response to changes in soil moisture at the measured depth) if compared to shorter physiognomies such as shrublands and savannas: this is an emergent property of forest ecosystems and it is closely related to the concept of ecosystem resilience [92; 93]. Furthermore, the observed correlations between GlobCover physiognomies and response to environmental variables were greater with SIF\({}_{FR}\) than with SIF\({}_{FR}\)-Yield, in agreement with our assumptions regarding the expected effects of normalizing SIF by a structure and biochemistry vegetation proxy like NDVI. This is, to our knowledge, the first study to show results substantiating an emergent ecosystem property through remotely sensed measurements of chlorophyll fluorescence. Considering the consistency of SIF responses with observed climate, we believe that our study did not suffer from the limitation of using land cover data from a single year in reference to SIF and other reflectance data from an eleven year period. However, this limitation and the mixed representativeness of GOME-2's large pixels, preclude a more detailed discussion of SIF results in relation to the emittingvegetation. More studies using higher-definition SIF data (i.e., with other instruments like OCO-2, TanSat, TROPOMI, OCO-3 or the upcoming FLEX) accompanied by more precise land cover data, should be employed to advance knowledge of vegetation ecophysiological responses.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 1 Introduction
To that aim, our main contributions are:  
* A framework proposal for the joint EN and UPF's placement optimization problems. * A novel solution approach to the UPF's placement problem (UPFFP) considering optimally located edge service infrastructure, user mobility, latency and reliability requirements.
## 2 Materials and Methods
### Structural Characteristics  
The structure was studied by scanning electron microscopy (SEM) using an S-3400 microscope (Hitachi, Tokyo, Japan) equipped with a Noran-7 Thermo attachment for energy-dispersive spectroscopy (EDS). The phase composition was studied by X-ray diffraction (XRD) on an Advance D8 Bruker diffractometer (Bruker, Billerica, MA, USA) using CuK\(\alpha\) radiation. The fine structure of the coatings was examined by transmission electron microscopy (TEM) on a JEM-2100 (Jeol, Akishima, Japan) microscope. Lamellas for studying the fine structure were prepared using an FIB Quanta 200 3D scanning electron-ion microscope (FEI, Hillsboro, OR, USA). The TEM study produced bright-field (BF) images of the structure, and selected area electron diffraction (SAED) patterns of the coatings were obtained at an accelerating voltage of 200 kV. The processing of SAED and BF images, including the determination of interplanar distances and the crystallite size, was performed using ImageJ 2.5.1.0 and Olympus Radius 2.0 software. X-ray photoelectron spectroscopy (XPS) studies were carried out using a Kratos XPS spectrometer (Shimadzu, Kyoto, Japan) equipped with a monochromated Al K\(\alpha\) X-ray source (hv = 1486.6 eV). During the XPS measurements, pressure inside the chamber was maintained constant (-5 \(\times\) 10\({}^{-9}\) torr). The coatings were etched using an Ar gas cluster ion source (GCIS) at a voltage of 20 kV. An etched crater had a size of 1.9 mm \(\times\) 1.0 mm; the time of each etching step was 300 s. The processing and calibration of the XPS spectra were carried out using Casa XPS 2.3.15 software.