esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Result Analysis
#### 4.2.1 Identification of Disturbance Boundaries
Figure 4: ISODATA unsupervised classification results. Determination and analysis of disturbance boundaries of SDAs  
During the preliminary identification of SDAs, this study identified three primary issues: first, certain internal regions of SDAs were misclassified as other land classes, resulting in noise within the identification outcomes; second, transportation land and urban construction land were grouped together with SDAs, causing classification confusion; and third, some areas initially identified as SDAs were sporadically distributed within vegetated regions, leading to numerous speckle noises. To address these issues and achieve precise boundaries for SDAs, this study first applied a 7 \(\times\) 7 spatial filtering technique to the preliminary identification results, effectively eliminating misclassifications caused by local noise and thereby resolving the first and third issues. Subsequently, the spatially filtered classification results were subjected to spatial overlay analysis with land use data from 2016 (the year preceding the commencement of the GS-HSR construction) to eliminate areas where SDAs were confused with transportation land, urban construction land, and other land classes due to misclassification. This approach effectively reduced the phenomenon of other land classes being erroneously classified as SDAs. Finally, based on the spatial distance relationship of SDAs relative to the railway engineering projects, further misclassified areas were excluded. Since SDAs are constructed within a 500 m range on both sides of the railway, the research team utilized GIS-based spatial buffer analysis and overlay analysis techniques to remove misclassified patches within the 500 m range on both sides of the GS-HSR. Through a series of manual judgments and analyses, this study ultimately obtained precise disturbance boundary delineations for soil and water erosion in SDAs, as shown in Figure 6.
## 2 Study Site  
The Taquari River drains a catchment area of 65,000 km\({}^{2}\) and is located in the midwestern region of Brazil, in the Upper Paraguay Basin, whose watershed area is approximately 600,000 km\({}^{2}\), spreads across three countries (Brazil, Bolivia and Paraguay) and includes Pantanal, one of the world's largest freshwater wetlands (140,000 km\({}^{2}\)[22]). Known for its biodiversity, Pantanal is considered a World Natural Heritage site by UNESCO, reinforcing the importance of better understanding sediment dynamics in the region.  
The study site is located in the upper reach of the Taquari River (18\({}^{\circ}\)31\({}^{\prime}\)36.26\({}^{\prime}\)S, 54\({}^{\circ}\)43\({}^{\prime}\)56.90\({}^{\prime}\)W), in the city of Coxim, Brazil. In Figure 1, P1 is the monitored cross-section within the surveyed river reach, P2 is a control point at the Coxim River and P3 is a stream gauging station called Coxim (code 66870000, in the Brazilian National Water and Sanitation Agency) at the Taquari River. Historical data available at P3 include water level, water discharge and cross-section bathymetric profiles.  
Sediment transport (in suspension and bedload) in the study area is high during floods, increasing the morphology dynamics and frequently altering the bedforms geometries. At the Coxim gauging station (P3, located 4.5 km downstream of the study site and after the confluence with the Coxim River), the drainage area is 27,600 km\({}^{2}\) and the annual average discharge is 350 m\({}^{3}\)/s. Floods normally occur during the summer (December-March), with maximum discharges higher than 1600 m\({}^{3}\)/s at extreme events. During the drought season (July-September), flow is near 250 m\({}^{3}\)/s. The annual average rainfall in the region is 1230 mm/year, and the monthly average rainfall during the summer is 200 mm/month.  
At the monitored cross-section (P1), during the field campaign, in a flood season, the river width was approximately 110 m, mean water depth was near 2.0 m, mean flow velocity was 1.2 m/s and the water level slope was 15-25 cm/km. The riverbed is mobile, and bedload is formed by fine and medium sand (125 \(\upmu\)m \(<\) d \(<\) 500 \(\upmu\)m). Along the 1.0 km surveyed reach, upstream of the confluence of the Taquari and Coxim Rivers, the morphology is dynamic, with the interaction of bars, ripples and dunes of varying sizes.  
Figure 1: (**a**) Upper Paraguay Basin, Pantanal wetland and study site at Taquari River in Coxim, Brazil; (**b**) points of measurement in Coxim: P1 and P3 at Taquari River, P2 at Coxim River.
###### Abstract  
When South Korea develops a new city, the government has made a preliminary decision on one of two heating systems, an individual heating system (IHS) or a district heating system (DHS). However, it is still unclear which system is desirable in terms of maximizing the national economic effect. Thus, this article aims to derive quantitative information about the economic effects of the same amount of production or investment in the two systems through an input-output (IO) analysis using the recently published 2017 IO table. More specifically, the production-inducing effects, value-added creation effects, and wage-inducing effects are systematically analyzed focusing on the IHS and DHS sectors. The results show that one dollar of production or investment in IHS or DHS causes about 1.073 and 1.388 dollars of production, about 0.228 and 0.658 dollars of value-added, and about 0.051 and 0.108 dollars in wages, respectively, throughout the national economy. Overall, the economic effects of the DHS sector are greater than those of the IHS sector. That is, when the same amount of investment or production is made in the two sectors, DHS produces more economic effects than IHS.  
individual heating system; district heating system; economic effect; input-output analysis Article
# Preliminary Study on the Damping Effect of a Lateral Damping Buffer under a Debris Flow Load
## 4 Numerical Simulation
### The Cushion Phase  
In this section, the buffer action of the device is the main consideration and the calculation of peak acceleration is the major subject. The structure is simulated as an entirety with infinite rigidity of the beam assumption and the steel ball is simplified to a particle. In addition, the buffer is simulated by a contact spring. The mechanical model is shown in Figure 7. The equations of motion can be established on the basis of the _D'alembert principle._  
In the system shown in Figure 7, \(m_{0}\) and \(M\) represent the impact ball and the primary structure respectively, \(k_{0},\ k_{1},\ \text{and}\ k_{\phi}\) denote the stiffness of the contact spring, the lateral bracing rigidity and the anti-rotation stiffness of the structure respectively, \(H,\ h_{g},\ h\) stand for the total height of the structure, the height of the center of gravity from the bottom of the structure and the difference of height between the center of gravity and the contact spring respectively, and \(J\) means inertia rotation.  
In the process of the impact, the displacement of the steel ball \(m_{0}\) is represented by \(x_{0}\), the displacement of the primary structure is represented by \(X\), the rotational angle at the bottom of the structure is represented by \(\theta\), and the bottom lateral translation of the structure is small enough to be neglected. Meanwhile, the influence of the damping is not considered because the peak value appears at the very beginning of the process of the response and the damping has not come into play to reduce the vibration. The equation of motion is given as:  
Figure 7: Mechanical model: (**a**) Geometric parameters; (**b**) Motion parameters.  
\[\begin{array}{c}m_{0}\tilde{x}_{0}+k_{0}[x_{0}-(x_{1}-h\Theta)]=0\\ M\tilde{X}+k_{0}[x_{0}-(x_{1}-h\Theta)]=0\\ J\tilde{\theta}+k_{\varphi}\varphi+k_{0}h[x_{0}-(x_{1}-h\Theta)]=0\end{array} \tag{1}\]  
The velocities \(v_{0}\) of the steel ball in different loading grades can be calculated by the theorem of kinetic energy (shown in Table 2). Therefore, the equation of motion can be solved and the simulated peak values of the acceleration response of the structure in different loading grades can be obtained.
## 3 Materials and Methods
### WGAN-GP Model Development  
Once the data are preprocessed, they can be fed into the GAN model. GANs consist of two neural networks: the generator and the discriminator. As described by [PERSON] et al. (2016), the discriminator's objective is to distinguish between the generator's output and the real training data, while the generator aims to produce samples that closely resemble the training data. To address the learning instabilities associated with GANs, the WGAN was introduced.  
While the original GAN focuses on minimizing the Jensen-Shannon (JS) divergence between the real distribution \(P_{r}\) and the generator distribution \(P_{g}\), the WGAN optimizes the Wasserstein distance between \(P_{r}\) and \(P_{g}\). Also called the Earth mover's distance (\(EM(p,q)\)), this metric represents the minimum cost required to move elements of one distribution (\(q\)) to match another distribution (\(p\)), where the cost is calculated as the product of the mass and the transport distance ([PERSON] and [PERSON], 2017). To further enhance the stability of the WGAN, the gradient penalty (GP) was introduced. The WGAN-GP adds a gradient penalty term to the objective function, which helps enforce the Lipschitz constraint required for the Wasserstein distance. This penalty term ensures that the gradients of the discriminator (D) are well behaved, leading to improved gradient quality during training. This approach promotes more stable learning and better convergence ([PERSON] and [PERSON], 2017).  
The objective function of WGAN-GP is given in Equation (3):  
\[L=E_{\widetilde{x}\in P_{g}}[D(\widetilde{x})]-E_{x\in P_{r}}[D(x)]+\lambda E_{ \widetilde{x}\in P_{g}}[(||\
abla_{\widetilde{x}}D(\widetilde{x})||_{2}-1)^{2}], \tag{3}\]  
where \(\lambda\) is the gradient penalty coefficient, and \(\hat{x}\) is generated by interpolating between real data \(x\) and the generated data \(\widetilde{x}\), as shown in Equation (4).  
\[\widehat{x}=\varepsilon x+(1-\varepsilon)\widetilde{x},\varepsilon\in uniform \left[0,1\right],x\in P_{r},\widetilde{x}\in P_{g}. \tag{4}\]  
This interpolation helps enforce the Lipschitz continuity requirement by computing gradients at these points.
## 4 Methods
### Mapping Artificial Areas from OBI4  
When high resolution is used (_i.e._, when the pixels are significantly smaller than the object concerned, [12]), the spectral information in an image is spatially heterogeneous and the pixel no longer corresponds to the object. This was the case in our study: artificial areas in HSR images are composed of pixels with different spectral behaviors (roofs, roads, parks, _etc._) distributed in a disorganized way throughout the area. Many researchers have pointed to the limited accuracy of this method of classification, the \"salt and pepper\" effect being responsible for this drawback (e.g., [6, 13, 14, 15, 16, 17, 18, 19]). Object-based image analysis (OBIA) has proven to be an effective way of solving this problem. First, a segmentation process identifies and builds up homogeneous regions (segments or image objects) to delineate the objects to be taken into account. Next, the objects are classified using spectral as well as  
Figure 2: Order of steps in the fine characterization of artificial sprawl.  
spatial information such as texture, shape and context features to more clearly distinguish spectrally similar land cover types. Most comparative studies that have compared OBIA and pixel classification approaches reported that OBIA provides the most accurate results (e.g., [19, 20, 21, 22, 23]). Recent advances in OBIA have revolutionized the processing of high to very high spatial resolution remote sensing data by providing effective computer-assisted classification techniques whose results are close to the quality of manual photo-interpretation, while also being much faster, cheaper and reproducible over large areas (e.g., [6, 7]).  
We developed two specific OBIA procedures to map the artificial areas for each period using eCognition Developer 8 software (see Sections 3.1.1 and 3.1.2). With this software, fuzzy classification can be used to extract objects of interest at the scale desired. To this end, a hierarchical image object network able to simultaneously segment images at fine and coarse scales is created from the multi-resolution algorithm, and a hierarchical classification scheme is built to allow semantic organization of the classes. A complete description of the multi-resolution algorithm (region-growing algorithm) can be found in [24, 25, 26]. Fuzzy classification improves class description, using understandable linguistic concepts built from expert knowledge, and facilitates fusion of heterogeneous multi-source information including non-image data [25].  
The classification process we developed will be easy to transfer to other areas since: (i) the fuzzy classification introduces flexibility in defining classification rules; and (ii) the expert knowledge classification method allows thresholds to be changed between each image, and classes to be added if the context is different [7, 27].  
The fuzzy classification method combined with expert knowledge classification means the definition of classes can be adapted to each individual context
## 3 Framework Design and Deployment
### Proposed Framework
#### 3.4.1 Model Initialization on Blockchain  
The model initialization process establishes the baseline for collaborative learning by creating and storing initial machine learning model parameters, denoted as \(M_{0}\), on the blockchain. Specifically, this step initiates the model as \(M_{0}=init()\to BChain\), where the blockchain (referred to as \(BChain\)) securely records the model's parameters. By record  
Figure 3: Workflow of the proposed framework for blockchain-assisted FL with digital twins in IIoT, incorporating HE for privacy-preserving local training and blockchain for secure model validation and deployment.  
ing the model on the blockchain from the outset, the framework ensures an immutable and transparent history of the model's evolution, facilitating security and trust in the collaborative learning process.  
This initialization serves as a foundation for training across a federated network of edge devices. By establishing \(M_{0}\) on the blockchain, the framework provides a verified starting point that enhances accountability and prevents unauthorized modifications, aligning with the principles of decentralized trust and data integrity.
# Buckling of Rectangular Composite Pipes under Torsion
## 1 Introduction  
The Mars exploration airplane project [1, 2] is a joint project of JAXA (Japan Aerospace Exploration Agency) and Japanese universities. It is intended to be the first extraterrestrial atmospheric flight exploration. Aerial exploration can avoid complex terrain where a rover cannot explore and can also explore where a satellite cannot observe, like the sides of a valley. An image of the Mars exploration airplane in flight is shown in Figure 1. To build the airplane, the authors designed a wing to be tested at high altitude, where the density of the air is close to the density of the Martian atmosphere. A rectangular pipe, which can easily align the attack angle of the wing, was used. In addition, because the density of Martian atmosphere is 1/100 th that of the Earth, the airplane must be ultra-lightweight; thus, the rectangular pipe was designed using thin carbon fiber reinforced plastics (CFRP). Thin structures usually fail not for lack of material strength or delamination but through buckling load because the buckling load of thin structure lower than material failure load. The buckling load can be increased by careful design of the layup sequence of the composite layers; however, no available simple design equation or chart were found in spite of there being many studies on composite structures [3, 4, 5, 6, 7, 8, 9, 10]. The buckling load under shear force has been studied; however, those studies were not for composite rectangular tubes, but rather for long anisotropic plates [11, 12]. An approximate expression for the torsional buckling load was derived by [PERSON] and [PERSON] [13]; however, the solution is foran isotropic tube. [PERSON] and [PERSON] investigated postbuckling behavior of composite box sections [14]. [PERSON] analyzed buckling of composite stiffened box sections under compression and bending by finite strip method [15]. [PERSON] and [PERSON] conducted numerical analysis for flexural-torsional buckling of thin-walled composite box beams by finite element method [16]. Moreover, while a comprehensive design handbook for the buckling of composite structures [17] has been published, it does not contain a method for the torsional buckling load of composite rectangular tubes. Given this state of affairs, it was decided that a numerical analysis of the torsional buckling load of cantilever rectangular orthotropic pipes should be conducted by using the energy method including a consideration of layup anisotropy. In addition, to easily use this analysis for design, a simple closed-form polynomial equation for the buckling load factor was derived by minimizing the weighted residuals of the safe and non-safe side errors of the derived numerical results. Although a numerical analysis like a finite element analysis (FEA) can determine the buckling load of anisotropic structures, a simple closed-form design equation is especially useful for the initial design and optimum design of the whole system that require a huge number of iterations for analysis of not only the structure but also its aerodynamics, guidance, and control.
## 3 Materials and Method
### Functionality Verification Phase
#### 3.3.2 ISO 10328 Cyclic Tests  
The re-adapted 2D FE model of the foot prosthesis was used to simulate the cyclic tests proposed in the ISO 10328 standard. Constraints, contacts, mesh modeling, joints, materials and width (dimension in the transverse direction) were the same for the foot prosthesis, while the load conditions were changed. The foot was loaded at the heel and forefoot with two platforms (Figure 18). The two platforms compressed the foot prosthesis with two forces (Figure 19), simulating the ground reaction forces during the gait cycle. The heel and the forefoot platforms were inclined by \(-15^{\circ}\) and \(20^{\circ}\), respectively. An initial space between the forefoot platform and the foot prosthesis was given (Figure 18). The forefoot touched the front platform when the forefoot force started to increase from 0 N (around 22% of the gait cycle, Figure 19). The first contact between the forefoot and forefoot platform simulated the toe strike. By considering the values of the _ground reaction forces_ specified in Section 2 and the 60 kg weight category of users, the heel force peak was 764 N, while the forefoot force was 635 N. The same virtual markers used in Section 3.1.1 were used to calculate the rotation of the ankle-foot. For the dynamic analysis, an entire gait cycle of 1 s was considered.  
Figure 17: The width of each part in the 2D FE model was constant and the holes were not considered.  
Figure 18: ISO 10328 cyclic test configuration: the heel platform was inclined at \(-15^{\circ}\), while the forefoot platform had an inclination of \(20^{\circ}\). The heel and the forefoot forces followed the paths given in Figure 19.
# A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks
## 2 Literature Review
### Dataset  
As described in [PERSON] et al. (2018), the Camelyon dataset was sampled from 399 patients from two hospitals in the Netherlands. The dataset was annotated with the help of experienced pathologists from the Netherlands. In particular, the dataset labels were manually annotated by two students and, subsequently, were checked in deep detail by two experienced pathologists. To check the performance of the pathologists on this dataset, two sets of experiments were performed.  
The first experiment was performed without any time constrain, and 11 experienced pathologists were asked to annotate a first subset of the images. In the second experiment, a two-hour time limit was given to the experts for annotating a second subset of images. The challenge organizers chose the AUC of the ROC curve as an evaluation criterion for this competition, thus every score is presented in terms of AUC of the ROC curve. The AUC score achieved by the first experiment was 96.6%, and the score of the second experiment was 81%.  
The PatchCamelyon dataset (PatchCamelyon, 2018; 2018) is an extension of the Camelyon dataset, which contains 277,000 histopathology images with an image size of 96 \(\times\) 96 pixels at 10\(\times\) magnification. A total of 220,000 images are annotated with 60% positive cases and 40% negative cases. A total of 57,000 images are un-annotated images to test the classifier, and there are no duplicates in the PatchCamelyon dataset. The resulting model is subsequently used to predict the labels of the test set and finally uploaded to the Kaggle platform to obtain the model AUC. This process was necessary since the test labels are available only to the owner of the data. To increase the size of the dataset and to make the model more robust to overfitting, the following augmentation techniques were applied: horizontal flip, vertical flip, rotation range, zoom range, width shift range, height shift range, shear range, and channel shift range. Figure 3 shows two images of the considered dataset.  
Figure 2: Different designs tested: (**a**) shows the first design, the proposed architecture with the normalization layer placed before the activation function, and two dropout layers are placed in the classifier block; (**b**) shows the second design, which is similar to the first design but without any dropout layers; (**c**) shows the third design, where the normalization layers are placed after the activation function, and two dropout layers are placed in the classifier block; and (**d**) shows the fourth design, which is similar to the third design but without any dropout layers.
## 3 Classification
### Role of VRU Devices  
The mechanism how VRU devices participate in V2P communication may be categorized in two categories, namely, _active_ and _passive_. Table 1 shows the existing efforts that are also classified by the VRU device role.