esa-sceva/satcom-chunk-collection huggingface dataset:
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Distributed Pheromone Repel Mobility Model  
#### 3.5.1 Distributed Pheromone Repel Mobility Model  
The Distributed Pheromone Repel (DPR) model is inspired by ant's pheromone. When ants look for feed, they secrete pheromone in the pathway and return to the nest using their pheromone [12]. The higher the pheromone concentration, the higher the probability in which ants move along that path. Similarly, the work in [12] modeled the scanned unit region by a UAV as the pheromone. Thus, the DPR mobility model is proposed so that each UAV avoids other's pheromones in order to increase the coverage rate. A UAV configures a virtual pheromone map and remembers the pheromone distribution in its map. When each UAV performs a reconnaissance mission, each UAV shares pheromone information by broadcasting its pheromone map periodically (e.g., every 10 s).  
Figure 6 is an example of deciding direction. There are three circles in front of a UAV, which are left, straight, and right area. If there is no pheromone in three circles, the UAV moves based on Random Markov Process model as in Table 2. On the other hand, if pheromone information is located among the circles, the UAV moves based on probabilities of Table 4, where \(L_{p}\), \(C_{p}\), and \(R_{p}\) are the numbers of pheromones in the left, center, and right circles, respectively (\(T_{p}~{}=~{}L_{p}+C_{p}+R_{p}\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Turn Left** & **Straight Ahead** & **Turn Right** \\ \hline \(\frac{T_{p}-L_{p}}{2\times T_{p}}\) & \(\frac{T_{p}-C_{p}}{2\times T_{p}}\) & \(\frac{T_{p}-R_{p}}{2\times T_{p}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: UAV action table with pheromone.  
Figure 5: Examples of changing direction: (**a**) Enhanced Gauss–Markov model; and (**b**) Enhanced Gauss–Markov with overlap avoidance model.  
**Example 1**.: _Let us assume that there are 18 pheromones in the left circle, 17 pheromones in the center circle, and seven pheromones in the right circle. Then, as \(T_{p}\) is 42, the probability of turn-left is given by \(\frac{24}{84}\ =\ (42\ -\ 18)/(2\ \times\ 42)\). Similarly, the probabilities of straight ahead and turn-right are given by \(\frac{25}{84}\) and \(\frac{35}{84}\), respectively. Thus, the UAV has higher probability of turning right according to the action probabilities._  
The DPR model also shows hovering problem as in the Markov Process model since it follows the Markov Process model in case of no pheromone information. Furthermore, when two UAVs meet each other to the same direction, they tend to fly in similar path because of sharing the same pheromone information. It results in overlapped reconnaissance and low coverage rate.
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 3 MCNN Framework for Geophysical Phenomenon Classification
### Multi-Feature Fusion

Table 1: Description of four different image edge filters [58]. \[\phi(k,l)=\frac{\sum_{i=0}^{M}\sum_{j=0}^{N}(IM_{1e,l}(i,j)-IM_{2e}(i,j))}{\sigma_{ 1k,l}\sigma_{2}}, \tag{9}\]  
where the variable \(\phi_{1}(u,v)\) represents the cross-correlation coefficient derived from the low-resolution image pair \(IM_{1e}\) and \(IM_{2e}\). The pair \((u,v)\) denotes the coordinate index of the image \(IM_{1e}\). The image \(IM_{1e,v}\) is a sub-image situated at the \((u,v)th\) position within the image \(IM_{2e}\), with dimensions identical to those of image \(IM_{2}\). \(\sigma_{1 hu,v}\) and \(\sigma_{2l}\) stand for the standard deviations of the respective images. Similarly, \(\phi(k,l)\) corresponds to the cross-correlation coefficient calculated from the low-resolution image pair \(IM_{1e}\) and \(IM_{2e}\), where \((k,l)\) signifies the coordinate index of the image \(IM_{1e}\). The \(IM_{1e,l}\) image is an image segment located at the \((k,l)th\) pixel of \(IM_{2}\) with dimensions matching those of \(IM_{2e}\). \(\sigma_{1k,l}\) and \(\sigma_{2}\) denote the standard deviations of the corresponding images. In the context of oceanic phenomenon detection, the extraction of edge information is significant for capturing structural features [59]. The basic idea of the image edge filter is to detect changes in image intensity by using a discrete differentiation operator [60]. To preserve more useful oceanic characteristics in SAR imagettes, we modify the standard Sobel operator to enhance the correlation between two fused SAR images [61]. In this study, we utilize four new operators instead of the standard two operators for retaining global edges and disregarding smaller edges as follows (Table 1):  
\[S_{1}=(\Delta y)^{T}= \begin{bmatrix}1&0&-1\\ 2&0&-2\\ 1&0&-1\end{bmatrix} ,S_{2}=(S_{1})^{T}=\Delta y= \begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix} , \tag{10}\]  
\[S_{3}= \begin{bmatrix}2&1&0\\ 1&0&-1\\ 0&-1&-2\end{bmatrix} ,S_{4}= \begin{bmatrix}0&1&2\\ -1&0&1\\ -2&-1&0\end{bmatrix} . \tag{11}\]  
By applying convolutionally the four operators into the original images, we can obtain:  
\[E_{l}(i,j)=\sum_{m=1}^{3}\sum_{n=1}^{3}IM(i+m-1,j+n-1)*S_{k}(m,n)\quad k=1,2,3,4 \tag{12}\]  
\[E(i,j)=\underset{l}{max}(E_{l}(i,j)). \tag{13}\]  
In Equation (13), \(E(i,j)\) stands for the appropriate edge of the point \((i,j)\) in the whole image \(IM\). Hence, a better correlation of the two fused images can be achieved, in contrast to the correlation between them generated from standard Sobel operators or other edge detection methods.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 3 Deep Learning Model for the Prediction of Ocean Weather  
As explained in Sections 2.2 and 2.3, a deep learning model developed using the ConvLSTM and the denoising AutoEncoder is efficient in predicting eight parameters of ocean weather worldwide. This section describes the model used in this study to predict the actual ocean weather and the input data required to train the model.
## 1 Introduction
Moreover, with a spectrum fitting method in system identification, we can directly deduce the platform's transfer function, which is very suitable for the frequency-domain analysis and model reference control. In this paper, we propose to use the complementary filter method to twice fuse the signals of the CCD and MEMS accelerometer, respectively getting a fusion acceleration and velocity without additional sensors. Based on the original ADLC structure, the new acceleration with no drift and less noise, would be used in fusion DOB(FDOB), while the velocity nearly with no delay is to build an additional velocity loop. Since the DOB method is often used to compensate the low and medium disturbance and the MEMS accelerometer's high-frequency noise would also affect the accuracy of observed disturbance, we modified the traditional complementary filter method and only combined the MEMS accelerometer's medium-frequency signal with the CCD's low-frequency signal to rebuild the acceleration model and get a relatively low-bandwidth acceleration. To avoid the low-bandwidth acceleration decreasing the system's bandwidth, the acceleration feedback loop would still use the MEMS accelerometer's original signal. Series of analysis and experiments verified that the virtual sensors have very high precision and the proposed way would apparently enhance the system's DS, especially in low and medium frequency. This paper is organized as follows. Section 2 respectively describe the previous enhanced-DOB(EDOB) method and the novel FDOB method and gives out the basic theory of the complementary filter. Section 3 focuses on the added virtual velocity loop and the system's robustness. Section 4 discusses the modified complementary filter and the details of the fusion acceleration and velocity. Section 5 is the experiment part which respectively presents the DS improvement from the FDOB and the virtual velocity loop. Concluding remarks are presented in Section 6.
## 5 Conclusions  
One of the most precise industries in agriculture is the production of mushrooms due to the need for strict control over humidity and temperature. The air conditioning system represents a predominant share of energy consumption, water, and CO\({}_{2}\) emissions in mushroom production. A new system with a psychrometric design and using the new, more sustainable R-454B refrigerant gas was simulated. Compared to a typical AC system, the proposed design generates significant improvements in three indicators: energy consumption, water consumption, and a 70% reduction in CO\({}_{2}\) emissions. This reduction can exceed the target of the EU Green Deal policies aimed at reducing net greenhouse gas emissions by at least 55% by 2030 [60]. It was predicted by the US Environmental Protection Agency that gradually decreasing HFCs could reduce global warming this century by up to 0.5 \({}^{\circ}\)C [61].  
Water is one of the major indicators of sustainability and, in addition to CO\({}_{2}\) and energy emissions, has become important. In climate control, water use was reduced by 2.83 L per kg of mushroom [62]. Water and energy are interconnected, generating a relationship called the water-energy nexus. Reducing water waste by increasing the evaporation temperature, in addition to reducing water consumption, is also capable of reducing energy consumption [62, 63].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{3}{*}{**Condition**} & **Air Conditioning** & **Consumption Water** & & **Energy Consumption** \\  & **Energy Consumption** & **Air Conditioning** & & **kwh per Kg of** \\  & **Service Life 10 Years** & **Useful Life 10 Years** & **TEWI kgCO\({}_{2}\)/10 Years** & **Mushroom per 21-Day** \\  & **kWh (Air** & **Liters (Only Air** & & **Cycle** \\  & **Conditioning Only)** & **Conditioning)** & & **Cycle** \\ \hline In current operation & 189,000 & 2,616,240 & 18,219 & 0.7515 \\ Improved design & 135,138 & 18,690,000 & 5324.81 & 0.537 \\ \hline \end{tabular}
\end{table}
Table 10: Demonstration of consumption reduction in 120 cycles of 21 days.  
These actions of efficient equipment in the psychometric part and the use of a more sustainable refrigerant gas can decentralize the production of mushrooms, making countries such as Portugal and Brazil self-sufficient and competitive.  
Conceptualization, A.F.S.; methodology, A.F.S.; validation, A.F.S. and H.J.L.d.S.; formal analysis, A.F.S. and P.D.G.; investigation, A.F.S.; resources, H.J.L.d.S.; data curation, A.F.S. and P.D.G.; writing--original draft preparation, A.F.S. and H.J.L.d.S.; writing--review and editing, P.D.G.; visualization, H.J.L.d.S.; supervision, A.F.S.; project administration, A.F.S. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 3 HAWK Optimization Through Profiling
### Profiling of the HAWK Signature Scheme
#### 3.1.3 Processs of Verify  
The analysis of the operations used during the verification process is summarized in Table 7. The computational processes that contribute the most significant overhead during the verification process are the mp_poly_to_ntt and decode_gr processes. The mp_poly_to_ntt process accounts for 27.3% of the overhead in the verification process, while the decode_gr process accounts for 20.1%. The mp_poly_to_ntt function is responsible for converting polynomials to the NTT domain. The decode_gr function is responsible for decompressing data by decoding values from a given buffer using the Golomb-Rice coding method. Additionally, the mp_poly_to_NTT_autoadj process accounts for 12.3% of the overhead. As a result, it can be concluded that the NTT conversion process contributes the most significant overhead during the verification process.
# IoT-Enabled Smart Sustainable Cities: Challenges and Approaches
## 3 Improving the Sustainability of a Smart City
### Tourism and Cultural Aspects
As an example, tourists can be provided with a mobile App exploiting several technologies on the mobile device during a tourist trip, such as (i) rear camera, for Augmented Reality (AR) on the artworks, and frontal camera, for emotional analysis, when visiting an exhibition; (ii) Near Field Communication (NFC), for paperless ticket and augmented content fruition; and (iii) Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), in order to give them a feedback on their traversed path inside exhibition's location (through the INS) and outside around the city, visiting different exhibition places (by means of GNSS).
## 2 Materials and Methods
### Pixel-by-Pixel Scan Matrix Method
This technique intelligently pads the image's edges with zeros, effectively extending the image's dimensions to enable comprehensive de-blurring without compromising result accuracy. In the case of the depth estimation process using the same \(3\times 3\) blurred image, processed with the identical \(2\times 2\) green kernel matrix as the output of a depth estimation model, the methodology also includes four sliding steps (I, II, III, and IV), as illustrated in Figure 4B. In the pixel located at the coordinates (2, 2), the computed depth estimation values for these steps are 5.0, 5.1, 5.2, and 5.0, respectively. These values are derived from the training data of the depth estimation model, which includes blurred images and corresponding depth labels ranging from 0.1 to 100.0 mm, in 0.1 mm increments. Therefore, the model predicts a set of depth values. Following Equation (3), the depth value of the pixel (2, 2) is determined to be 5.0 mm. This step-by-step process is applied similarly to the other pixels in the \(3\times 3\) matrix. The CNN model is responsible for depth estimation, a critical aspect of the 3D reconstruction process. The CNN model operates on an estimation  
Figure 4: Examples of the estimated pixel value in the de-blurred mode (**A**) and the depth estimation mode (**B**). The letters I, II, III, and IV represent the steps in the pixel scanning process, and the numbers in the center represent the estimated value. Figure 3: Principle of the pixel-by-pixel scan matrix method. The green matrix is a scanning matrix with dimensions \((S-1)\times(S-1)\) pixels, the gray matrix is a transmitted image matrix with dimensions of \((B\times B)\) pixels and the blue arrow shows the scanning direction. of the process  
matrix of size \(224\times 224\) pixels, and, similar to the FCN model, it slides through the image with a one-pixel step size. This approach enables the CNN model to analyze the depth of each pixel, facilitating an accurate estimation of the spatial distribution and characteristics of the absorbing structures.
###### Abstract  
The use of Acousto-Optical Tunable Filters (AOTFs) is well known in ground- and space-based applications. These devices are used in several optical instruments and payloads for monitoring and other purposes. To make use of the filter capability of the AOTF, a dedicated Radio Frequency (RF) chain, consisting of an RF generator and RF amplifier, is needed. An RF generator can be designed in several ways. However, the design of these steering devices for space applications comes with several difficulties and limitations. The mechanical stress due to shock and vibration, the temperature variation, as well as the vacuum environment and radiation levels in which these devices have to perform limits the selection of possible techniques. This paper aims at giving an in-depth overview of space-qualified RF generator techniques using Commercial-Off-The-Shelf available components that usable in the harsh environment of space and applicable in driving AOTFs. Several analog as well as digital generator principles are discussed, substantiated by test results.  
Acousto-Optical Tunable Filter (AOTF); Radio Frequency (RF); space-qualified; imaging; phase-locked loop (PLL); Single Side Band (SSB); Direct Digital Synthesis (DDS); Application-Specific Integrated Circuit (ASIC) 2020
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 4 Proposed Method
### Evaluation Metrics  
To evaluate the performance of the proposed sign language recognition system, we used standard metrics for object detection and sequence classification. The evaluation occurred in two steps: first, we assessed the YOLOv8-Pose model for hand detection and keypoint extraction, and second, we evaluated the Bi-LSTM model for classifying gesture sequences using keypoints and motion data. The YOLOv8-Pose model's pose loss measures keypoint prediction accuracy, with lower values indicating better localization. As shown in the training loss and metric plots, key loss components (box, pose and classification loss)  
Figure 7: Training loss and accuracy over epochs for Bi-LSTM model.  
gradually decreased. The validation pose loss (Figure 8a) decreased from 2.5 to about 1.0, indicating strong hand keypoint detection.  
The precision and recall metrics were consistently high, with keypoint (P) and bounding box (B) (Figure 8b,c) scores exceeding 0.8 and 0.9, respectively. The mAP metric, which summarizes the precision-recall curve at different IoU thresholds, showed excellent results, which show the YOLOv8-Pose model's effectiveness in detecting hand regions and key points.  
For sequence classification, we used a standard classification report, including precision, recall, F1-score, and accuracy, similar to prior studies evaluating LSTM-based sign language recognition [25]. These metrics provide a thorough assessment of the system's ability to recognize both static and dynamic signs, including those with high visual similarity.
## 2 Theoretical Modeling
### Significance of Physical Neutral Surface and Classical Plate Theory (CPT)  
As per the theory of rectangular thin plate's bending [1] using classical plate theory, the displacement components along with all three directions \((x,y,z)\) are given by  
\[\begin{array}{l}u=u_{0}(x,y)-z\frac{\partial w}{\partial x};\\ v=v_{0}(x,y)-z\frac{\partial w}{\partial y};\\ w=w(x,y)\end{array} \tag{4}\]  
where \(u,v,\) and \(w\) are the displacement components in each of the three directions, respectively, \((x,y,z)\). Because of variations in material properties in the thickness direction, there is an eccentric gap between the physical neutral surface and the geometric mid-surface of the P-FGM plate. Hence, by constructing a new reference coordinate \(z_{ns}=z-z_{0}\), the stretching-bending coupling can be neglected [1]. Here \(z_{0}\) denotes the distance between the physical neutral surface and the geometric mid-surface of the P-FGM plate. As a result, Equation (4) changes to  
\[\begin{array}{l}u=-z_{ns}\frac{\partial w}{\partial x}=-(z-z_{0})\frac{ \partial w}{\partial y};\\ v=-z_{ns}\frac{\partial w}{\partial y}=-(z-z_{0})\frac{\partial w}{\partial y} ;\\ w=w(x,y)\end{array} \tag{5}\]  
As a result, the strain caused by the displacements as mentioned earlier is of the form  
\[\begin{array}{l}\epsilon_{xx}=-z_{ns}\frac{\partial^{2}w}{\partial x^{2}}; \\ \epsilon_{yy}=-z_{ns}\frac{\partial^{2}w}{\partial x^{2}};\\ \gamma_{xy}=-2z_{ns}\frac{\partial^{2}w}{\partial x\partial y}\end{array} \tag{6}\]  
where \(\epsilon_{xx},\ \epsilon_{yy}\) represent the normal strains in the \(x\) and \(y\) directions, respectively, and \(\gamma_{xy}\) represents the shear strain along the \(x\), \(y\) plane. Assuming that the P-FGM plate obeys generalized Hooke's law, the stress-strain relationship is  
\[\begin{array}{l}\sigma_{xx}\\ \sigma_{yy}\\ \tau_{xy}\end{array}\end{array} \tag{7}\]  
where the normal stresses are \(\sigma_{xx}\) and \(\sigma_{yy}\) and the shear stress is \(\tau_{xy}\), and the reduced stiffness matrix \(\mathbf{Q}_{ij}\) is given by  
\[\begin{array}{l}Q_{11}=Q_{22}=\frac{E(z_{ns})}{1-\
u^{2}};\\ Q_{12}=Q_{21}=\frac{\
u E(z_{ns})}{1-\
u^{2}};\\ Q_{66}=\frac{E(z_{ns})}{2(1+\
u)}\end{array} \tag{8}\]  
The physical neutral surface \((z_{0})\) can be computed by setting net force along a direction to zero. Thus,  
\[\sum F_{x}=\int_{-h/2-z_{0}}^{h/2-z_{0}}\sigma_{xx}dA \tag{9}\]  
which simplifies [1] to  
\[z_{0}=\frac{kh(E_{rat}-1)}{2(k+2)(E_{rat}+1)} \tag{10}\]  
where \(E_{rat}=\frac{E_{r}}{E_{m}}\) is the modulus ratio, and as observed, for \(E_{rat}\) is one, the P-FGM plate becomes an isotropic plate. Therefore, the volume fraction and material properties of the FGM plate is  
\[\begin{array}{l}\left(V_{f}(z_{ns})=\left(\frac{1}{2}+\frac{Z_{m}+Z_{0}}{h} \right)^{k}\right)\\ \text{and}\ \ P(z_{ns})=V_{f}(Z_{ns})P_{c}+\left(1-V_{f}(Z_{ns})\right)P_{m} \end{array}\]where \(V_{f}\) is the volume fraction of P-FGM material constituents, while \(P\) represents the material properties of the P-FGM plate considering the physical neutral surface.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Technological Evolution of DES in Healthcare  
The integration of emerging technologies has transformed healthcare discrete event simulation (DES) from standalone modeling tools into sophisticated decision support systems. Our analysis highlights a progressive shift toward data-driven, interconnected simulation environments that enhance both model accuracy and practical applicability.
## 3 Results
### Drought Events and Characteristics Analysis in the HHHP  
#### 3.2.1 Run Theory Identifies Drought Events  
From 2001 to 2020, the average mISDI in the HHHP showed a significant downward trend (slope = \(-0.0009\)/month, \(p\) < 0.01), indicating that the drought in the HHHP worsened. In the five drought events (E1-E5) from 2001 to 2020, the corresponding drought characteristics (DD, DI, and DS) were calculated using the run theory based on the regional mean mISDI. The identification results of the run theory are shown in Table 7.  
As shown in Table 7, the duration of recent droughts was shorter, while the number of drought periods increased (Figure 5). There was a gradual decline in the average mISDI over the same period from being slightly positive (\(-0.4\)) to being slightly negative (\(--0.4\)), indicating that drought conditions in the HHHP gradually deteriorated. E1 had the longest DD and the strongest DI among the five drought events. From 2001 to 2020, the DD gradually became shorter, but the HHHP showed a significant downward trend. The reason for this may be that the run theory identified drought events in the HHHP as a whole, but usually, droughts only occur in local areas. With climate change, there was an increased variability with swings from parched to soaked (Figure 4). Flash drought events should be the focus of attention, and short-term drought events that occur during critical periods of crop growth can also have a significant impact on grain yields.