esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Machine Learning Models Used
### Autoencoders and Principal Component Analysis  
_Autoencoders_ (AEs) [20] are deep feed forward neural networks which aim to learn to reconstruct the input, being known in the machine learning literature as _self-supervised_ learning systems. An AE has two main components: an _encoder_ and a _decoder_. Assuming that the input space is \(\mathbb{R}^{n}\), the encoder part learns a mapping \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{m}\), while the decoder learns the function \(g:\mathbb{R}^{m}\rightarrow\mathbb{R}^{n}\). If \(m<n\), the network learns to compress the input data into a lower dimensional latent space and to reconstruct it based on the latent representation, by learning the essential characteristics of the data. For avoiding the overfitting symptom (i.e., simply copying the input to the output), L1 regularizationis applied on the encoded state and the model is called a _sparse_ one. Autoencoders have been successfully applied in various tasks ranging from image analysis [26] and speech processing [27] to protein analysis and classification [28; 29].  
Principal Component Analysis (PCA) is a dimensionality reduction statistical technique heavily used in the ML field for tasks such as descriptive data analysis, data visualization or data preprocessing. It can be seen as an unsupervised learning technique that learns to represent the data in a new, lower-dimensional, space. Given a data set with \(n\) samples \((x_{1},\ldots,x_{n})\) and each sample having \(p\) attributes \((a_{1},\ldots,a_{p})\), the PCA algorithm will search for linear combinations of the variables (i.e., \(\sum_{i=1}^{p}a_{i}\cdot c_{i}\) with \(c_{1},\ldots,c_{p}\) constants) such that they are linearly uncorrelated and that the first linear combination has the largest possible variance, the second has the largest possible variance while being orthogonal on the first linear combination and so on. These linear combinations are called _principal components_. The principal components can be found by computing the eigenvectors and eigenvalues of the covariance matrix of the data set, where the constants of each linear combination are given by the eigenvectors and the first linear combination is the one given by the eigenvector associated with the biggest eigenvalue, and so on [30].
###### Abstract  
The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm's search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.  
post-disaster rescue; unmanned aerial vehicle; path planning; IGWO-IAPF algorithm; improved grey wolf optimizer +
Footnote †: journal: Applied Sciences
# Left Ventricular Non-Compaction Cardiomyopathy-Still More Questions than Answers
## 1 Introduction
The prevalence also varied between the diagnostic methods used. The LVNC diagnosis appeared up to 0.26% of echocardiographic examinations but reached 14.8% in healthy volunteers examined with MRI [14]. Significant differences exist also using the same diagnostic modality between different patient cohorts. In the metabasis of [PERSON] et al., the prevalence of LVNC as assessed by echocardiography in healthy controls was 1.05%, in athletic cohorts 3.16%, and in pregnant women up to 18.6% [15]. There are also significant differences in the frequency of incidence of LVNC within one diagnostic method. Using four different diagnostic criteria in the MRI examination, a significant discrepancy in the frequency of diagnosis of LVNC was found. LVNC was diagnosed in 39% of patients using [PERSON] criteria, 23% using [PERSON] criteria, 25% with Jacquier, and only 3% using Captur criteria [16]. Furthermore, a median follow-up of 7 years, showed no differences in outcome between patients with and without LVNC regardless of the evaluation method used. \begin{table}
\begin{tabular}{l} \hline
1. iLVNC. NC morphology in left ventricles with normal systolic and diastolic function, size, and \\ wall thickness; \\ \hline
2. LVNC with LV dilation and dysfunction at onset, such as in the paradigmatic infantile CMP of Barth syndrome \\ \hline
3. LVNC in hearts fulfilling the diagnostic criteria for DCM, HCM, RCM, or ARVC; \\ \hline
4. LVNC associated with congenital heart disease; \\ \hline
5. Syndromes with LVNC, either sporadic or familial, in which the noncompaction morphology is one of the cardiac traits \\ associated with both monogenic defects and chromosomal anomalies, i.e., complex syndromes with several multiorgan defects; \\ \hline
6. Acquired and potentially reversible LVNC, which has been reported in athletes; it has also been reported in sickle cell anemia, \\ pregnancy, myopathies, and chronic renal failure; \\ \hline
7. Right ventricular noncompaction, concomitant with that of the left ventricle, or present as a unique anatomic area of NC. \\ \hline \end{tabular}
\end{table} 
Table 1: Subtypes of NCCM, modified after [PERSON] et al. [10].
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 2 Related Works
### Feature Association in Multi-Object Tracking  
Detection-based multi-object tracking methods mainly focus on how to establish associations between targets across frames. Early methods like SORT [8] only used IoU as a matching metric, lacking the ability to model appearance features. DeepSORT [7] introduced deep features for target representation, but its simple feature comparison method struggles with occlusion and crowded scenes. ByteTrack [18] improved detection omission by handling low-confidence detection boxes but did not deeply optimize feature representation. StrongSORT [19] combined appearance and motion features but still used a local feature matching strategy, unable to establish global semantic associations.  
Recent research has begun exploring the application of attention mechanisms in target association. TransTrack [20] introduced the Transformer architecture into tracking tasks, but its global self-attention computation has high complexity, making it difficult to meet real-time requirements. TransMOT [21] modeled inter-target relationships through spatiotemporal attention mechanisms but lacked effective utilization of historical features. Unlike these methods, the CFAM module proposed in this paper efficiently handles cross-frame feature interactions through a grouped cross-attention mechanism and designs a memory recall mechanism to dynamically compensate for feature degradation, significantly enhancing system robustness in occlusion and dense scenes while maintaining computational efficiency.
## 2 Materials and Methods
### Modelling Method of a Vehicle-Slab Track Dynamic System  
#### 2.1.1 Vehicle Model  
The vehicle was modeled as a multi-rigid-body system (ABAQUS, v. 6.14, Dassault Systems, Velizy-Villacoublay, France) [33; 34], with 6 degrees of freedom in the car body and each bogie (in total, 2 bogies in a model) and 5 degrees of freedom in each wheelset (in total, 4 wheelsets in a model) [35], which is a total of 38 degrees in the model. The kinematic equation of the multi-body system can be derived from the Lagrange and Newton-Euler method, as shown in Equation (2).  
\[M_{v}\ddot{x}+C_{v}\big{(}\dot{x}\big{)}\dot{x}+K_{v}x=F_{v}\big{(}x,\dot{x}, \ x_{track},\ \dot{x}_{track}\big{)} \tag{2}\]  
In Equation (2), \(M_{v}\) is the mass matrix of the vehicle. \(C_{v}\) and \(K_{v}\) are the damping and the stiffness matrices describing the non-linearities within the suspension. \(x\), \(\dot{x}\) and \(\dot{x}\) are the vectors of displacements, velocities and accelerations of the vehicle subsystem, respectively. \(x_{track}\) and \(\dot{x}_{track}\) are the vectors of displacements and velocities of the track subsystem. \(F_{v}\big{(}x,\dot{x},\ x_{track},\ \dot{x}_{track}\big{)}\) is the system load vector representing the non-linear wheel-rail contact forces which are the function of \(x\), \(\dot{x}\), \(x_{track}\) and \(\dot{x}_{track}\).  
There were three simplifications in the model [36; 37; 38; 39]:  
1. There was no eccentricity effect considered during the modelling of the multi-rigid-body system.
2. The bogie was assumed to be connected by rigid beams.
3. The primary and secondary suspension systems were simulated by non-linear connector elements which consider both the stiffness and damping in the longitudinal, lateral and vertical directions.  
The CRH3 vehicle has been established for simulation. The parameters of the CRH3 vehicle are used for the vehicle model [22], which can be found in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Values** \\ \hline Car body/bogie/wheelet mass & kg & 48,000/3200/2400 \\ Car body rolling/nodding/yawing inertia & \(10^{6}\) kg x m\({}^{2}\) & 1.15/2.70/2.70 \\ Bogie rolling/nodding/yawing inertia & kg x m\({}^{2}\) & 3200/2200/6800 \\ Wheelest rolling/yawing inertia & kg x m\({}^{2}\) & 1200/1200 \\ Longitudinal/lateral/vertical stiffness of primary suspension & 103 kW/m & 9.003/0.01/0.4 \\ Longitudinal/lateral/vertical damping of primary suspension & kN x/s & 0/045 \\ Longitudinal/lateral/vertical stiffness of secondary suspension & \(10^{3}\) kN/m & 0.24/0.24/0.40 \\ Longitudinal/lateral/vertical damping of secondary suspension & kN x/s & 5000/30.50 \\ Axle distance & m & 2.50 \\ Boog distance & m & 17.375 \\ Wheel rolling radius & m & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters of high-speed vehicle of CRH3.
###### Abstract  
Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R\({}^{2}\) reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.  
summer maize; cultivated area; phenology difference; multi-temporal MODIS EVI; Huanghuaihai Plain +
Footnote †: journal: Applied Sciences  
1
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Mathematical Modeling of the ISD
#### 2.3.9 Solution Procedure  
The partial differential equations generated for the heating area and the drying area describe heat and heat-mass transfer over small time increments. The numerical integration with respect to time and position can be solved using the finite difference method [22]. The transformation from the physical domain to the computational domain was obtained by creating a numerical simulation model of algebraic equations in one dimension. The finite volume differences of formulated heat and mass balances were replaced by the derivatives in the differential equations.  
The drying air temperature given in Equation (6) was calculated over the finite distance (\(\Delta x\)) to give the change in air temperature in the heating area (\(\Delta T_{air}\)) as:  
\[\frac{\partial T_{air}}{\partial x}=\frac{h_{c,cov-air}\cdot(T_{cov}-T_{air})+ h_{c,abs-air}\cdot(T_{abs}-T_{air})}{\rho_{air}\cdot\delta_{air}\cdot v_{air} \cdot c_{air}} \tag{23}\]  
Introducing auxiliary terms \(A_{1}\) and \(A_{2}\) and noting \(h_{c}\) for \(h_{c,cov-air}\) and \(h_{c,abs-air}\):  
\[A_{1}=\frac{2\cdot h_{c}}{\rho_{air}\cdot\delta_{air}\cdot v_{air}\cdot c_{air}} \hskip 28.452756 ptA_{2}=\frac{T_{cov}+T_{air}}{2} \tag{24}\]  
leads to:  
\[\Delta T_{air}=T_{air\_i}-T_{air\_i-1}=(A_{2}-T_{air\_i-1})\cdot\left(1-e^{-A_ {1}\cdot\Delta x}\right) \tag{25}\]  
The solution for the energy balance of the air in the drying area given in Equation (13) is solved as:  
\[\frac{\partial T_{air}}{\partial x}=-A_{3}\cdot T_{air}+A_{4} \tag{26}\]  
where auxiliary terms \(A_{3}\) and \(A_{4}\) are:  
\[A_{3}=\frac{2\cdot h_{c}}{\rho_{air}\cdot\delta_{air}\cdot v_{air}\cdot(c_{air }+c_{cap}\cdot H)}\hskip 21.681 ptA_{4}=\frac{T_{cov}+T_{air}}{\rho_{air} \cdot\delta_{air}\cdot v_{air}\cdot(c_{air}+c_{cap}\cdot H)} \tag{27}\]  
leading to:  
\[T_{air2}=\frac{A_{4}}{A_{3}}+\left(T_{air1}-\frac{A_{4}}{A_{3}}\right)\cdot e^ {-A_{3}\cdot\Delta t} \tag{28}\]  
The temperature of paddy rice generated in Equation (12) can be solved as:  
\[\frac{\partial T_{pad}}{\partial t}=-A_{5}\cdot T_{pad}+A_{6} \tag{29}\]  
with auxiliary terms \(A_{5}\) and \(A_{6}\):  
square error (RMSE), and mean absolute percentage error (MAPE) were used to assess the accuracy of prediction models.
## 4 Results  
### Results and Analysis of Subsidence Rate  
After processing the 36 Sentinel-1A SAR images, the subsidence rate field and accumulated subsidence in Shanghai from January 2018 to March 2020 were obtained. Figure 4 shows the surface subsidence rate map in Shanghai area derived by PSInSAR (the positive values indicate surface uplift, and the negative values represents surface subsidence), and the map is superimposed on a Google Earth image of Shanghai acquired in 2020.  
The subsidence is relatively stable in most areas of Shanghai. However, the uneven subsidence phenomenon is obvious. Region a is located in the urban area of Shanghai. The subsidence in this region is relatively stable and without obvious subsidence. The main subsidence areas in Shanghai include Region b (S), Region c (FX), and Region d (S). Large-scale subsidence occurs in the three regions. The subsidence range of Region b presents an expanding trend, and the subsidence rates range from \(-15.0\) mm/year to \(5\)mm/year. The overall subsidence rate in Region d is relatively high, which are between \(-25\) mm/year and \(6\) mm/year. Many large industrial areas, such as Jinfengyuan Paper (JFYP), Yadong Petrochemical (YDP), Shanghai Paimilei Industrial Park (SPIP), are found in Region c. Moreover, this region is the most serious subsidence area in Shanghai with multiple significant subsidence funnels.  
Region a is the main urban area of Shanghai. As shown in the subsidence rate map, the subsidence rate in this area is around \(-3\) mm/year, and the subsidence is relatively stable without significant subsidence. According to the \"Shanghai City Master Plan (1999-2020)\" released by the Shanghai Municipal Urban Planning Administration, Shanghai has made strict restrictions on the population and urban construction land in the main urban area. Moreover, the central urban area has been built earlier, in which the subsidence has tended to stabilize.  
Region b is located in Songjiang District, Shanghai. According to the \"Shanghai Songjiang District Master Plan and Land Use Master Plan (2017-2035)\" published by the People's Government of Songjiang District of Shanghai, Songjiang District will adhere to the development trend of combining multiple towns and Songjiang New City as the center. Under the condition of ensuring the steady growth of the regional ecological area, the construction of urban infrastructure and manufacturing factories should be strengthened.  
Figure 4: Surface subsidence rate map in the study area obtained by PSInSAR; The white curve area denotes the urban area of Shanghai (SQ); The red rectangles depict characteristic areas.  
These conditions have led to the rapid development of the industry in Songjiang District from 2017 to 2020. Population data show that by 2020, the region's permanent population reached 1582,400. Industrial and domestic water mainly come from the exploitation of underground water. In Section 5.3, the relationship between the surface subsidence and the change of the water table will be further discussed.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 2 Space Debris Detection
### Spatial Feature Enhancement  
As contextual information is crucial for finding small objects [46], the context-aware features were obtained first. The light-weight atrous spatial pyramid pooling [43] module (LR-ASPP), with a large pooling kernel and a \(1\times 1\) convolution, was employed to generate context features. Thus, four LR-ASPP modules were connected to the first four blocks of the  
Figure 3: Spatial feature extraction module. The black squares on each line denote the first five outputted feature maps of each block. The number at the end of each line means the number of extracted feature maps.  
backbone, respectively, to capture the image-level global context. Inspired by the scale enhancement structure in [47], we delivered the outputted context-aware features to the scale enhancement module to further strengthen the scale cues of small objects. The generated scale-aware features \(F_{k}^{o}\) of the given context-aware features \(F_{k}^{i}\) can be computed as:  
\[F_{k}^{o}=(1+F_{k}^{i})\odot C_{k} \tag{1}\]  
where \(C_{k}\) are the feature maps outputted by SFM at different blocks, as shown in Figure 3. '\(\odot\)' and '\(+\)' refer to element-wise multiplication and element-wise addition, respectively. Since the spatial features are also vital to saliency detection, the skip connection structure was adopted to mitigate the feature degradation around the objects caused by model singularities. Lastly, we attached the residual skip connection layer [40] to each SFEM to enable the connection between the extracted spatial features and the saliency prediction model. The residual skip connection layer here is different from the above skip connection structure, which only skips the element-wise multiplication operation. The residual skip connection layer consists of three convolutional layers, which could downsample the channels of feature maps at different layers to the same channels as well as share powerful target information with the spatial-temporal feature fusion module.  
Figure 4 shows the enhanced spatial features of space debris. Compared to the spatial features generated by SFM, more spatial features are obtained through SFEM. For example, many colors and corners are revealed in column 2 and column 5 of block 2 in Figure 4, while no features are extracted by SFM in Figure 3. The higher layers can extract high-level features, such as the shapes and contours of objects (e.g., row 4 in Figure 4) and more complex textures (e.g., row 5 in Figure 4), while the SFM cannot, as shown in Figure 3.
## 7 Open-Pit Mine Application  
### Introduction  
After the design and requirements study, this section presents the current state of the smart energy management system's proof of concept at the experimental open-pit mine. Before implementing the architecture in the field, a test bench was designed in order to test the communication protocols, the database connection, the SCADA system and the prediction algorithm.
###### Abstract  
The Integrated Satellite-Terrestrial Networks (ISTNs) with Software-Defined Networking (SDN) incorporated have become the emerging architecture and have attracted considerable attention recently. Therein, the joint gateway and controller placement problem is of fundamental significance for designing ISTNs with flexible and efficient management capabilities. Hence, how to achieve network reliability maximization with low network latency in the joint placement problem is worthy of in-depth study. In this paper, a network partition algorithm, Simulated Annealing Partition-based K-means (SAPKM), is proposed and analyzed for further ameliorating the problem. Experiments are performed on real network topologies to validate the effectiveness of our approach for the joint placement problem. Compared with the state-of-the-art existing works, numerical results show that SAPKM outperforms when deploying four or more controllers in terms of network reliability performance, network latency, and inter-plane latency with less than 2 ms to converge.  
Integrated Satellite-Terrestrial Networks (ISTNs); Software-Defined Networking (SDN); joint gateway and controller placement; propagation latency; network reliability +
Footnote †: journal: Sensors
# An Efficient Framework for Securing the Smart City Communication Networks
## 4 Simulation Network and Setup  
In this section, we first describe our used simulation tool called OPNET. We then discuss the simulation setup of our proposed framework for enhancing the security control in smart city communication networks. We also use the OPNET simulator to study the performance of the smart city communication network with and without authentication and key exchange mechanisms. Various performance measures are defined in this study such as throughput (packet per seconds), delay (packet per seconds), load, and traffic received (packet per seconds). We finally present and analyze the simulation evaluation.