esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design for an Internet Router with a Digital Optical Data Plane
## 1 Introduction
### The Need for Optical Routing  
A high-bandwidth Internet requires per-packet channel sharing and optical transmission. Optical circuit switching is already widely used but limited to traffic that is predictable over long timescales and can be very inefficient for typical Internet traffic. High-efficiency capacity sharingrequires packet-level processing. Packets are variable-length messages and packet-level statistical multiplexing results in an effective capacity gain of 3-10 times larger, i.e., a 10 Gb/s link shared using packet multiplexing can support tens to hundreds of users with 1 Gb/s data flows, because Internet traffic is bursty and packet-based sharing is efficient. This efficiency occurs when multiplexing at the packet level and is undermined by multiplexing where shares are allocated longer timescales, e.g., for packet bursts or persistent channels.  
High-bandwidth, long-distance data transmission necessarily requires optical signals. Electronic transmission is limited to roughly 10 Gb/s over 10 m. Electronic signals must be slowed down to traverse longer distances, or can conversely support higher rates over shorter distances. Optical transmission is required for higher bandwidth and distance combinations and is typical for long-haul Internet connections in the network core. As transmission bandwidths increase, the use of optics is expanding from the core to metropolitan aggregation sites and ultimately to end users.  
An Internet router is a device with multiple inputs and outputs that shares channel capacity on a per-packet basis [2]. Assuming its inputs and outputs are already often optical, having an optical router would avoid complex and costly optical-electronic-optical (OEO) conversion. Routers are composed of a control plane and a data plane; the control plane exchanges information with other routers to ensure that packets make progress toward their destination and the data plane is a packet switch that directs incoming packets as indicated by their header information and control plane configuration (a process known as 'forwarding' when it applies to Internet protocol (IP) datagrams). In this paper, we focus on developing an optical data plane to natively switch optically encoded packets and we assume a conventional electronic control plane for management operations.
## 3 Results  
### Continuum Models  
The continuum isotropic model converged to an adapted solution after 25 iterations. The Young's modulus distribution of the converged isotropic model is shown in Figure 3a-c. Regions with high stiffness were found along the arcuate line, anterior superior sacrum, superior pubis ramus, acetabulum and near the greater sciatic notch. Conversely, large parts of the ilium, inferior pubis ramus and inferior sacrum were found to have a low stiffness.  
The continuum orthotropic model of the pelvis was found to converge after 60 iterations. The distributions of the mean and dominant Young's moduli across the model are shown in Figure 3d-i. The mean Young's modulus for each element was calculated as the average of the three directional Young's moduli, whereas the dominant Young's modulus was selected as the maximum value of the three directional moduli. Regions with high mean stiffness were found around the sacroiliac joint, along the arcuate line, around the top of the sacrum, along the superior pubis ramus, in the acetabulum and between the posterior inferior iliac spine and greater sciatic notch. Low mean stiffnesses were found at the iliac fossa, ischial tuberosity and inferior sacrum. Both models produced trabecular distributions which were broadly consistent with previous findings [9].  
Visually, the distributions of isotropic Young's modulus and orthotropic dominant Young's modulus share more similarities than with the distribution of mean orthotropic Young's modulus. Although the CT scan derived model has generally a higher stiffness across the whole surface compared to the adaptive continuum models, an interesting aspect to note is a region at the top of the ilium medically where the dominant Young's modulus values of the orthotropic model were similar to the same region in the CT scan derived model. In addition, the same region appeared to have a lower stiffness in the case of the isotropic model.  
A frequency distribution of Young's modulus values for the orthotropic model, isotropic model and CT scan derived model suggests that both predictive models have similar distributions of elastic moduli, with over 75% of elements having a stiffness lower than 2 GPa. On the other hand, the CT derived model had less than 50% of elements with a stiffness lower than 2 GPa (Table 2).  
Figure 2: Distribution of [PERSON]’s modulus (MPa) across the CT scan derived model - shown for (**a**) frontal, (**b**) lateral, and (**c**) medial views.
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 4 Validation  
### La Biche Shale  
#### 4.1.1 Project Case  
Northern Alberta, Canada, is home to vast reserves of shale oil and gas, the development and utilization of which can significantly support the energy market. According to a geological investigation by [PERSON] and [PERSON] [104], the surface bedrock consists of various Cretaceous sandstones and shale formations, unconformably overlying a Devonian erosional surface. Among these formations, the La Biche Formation, while being the weakest, possesses considerable potential for development. Obtaining the stress-strain relationships and expansion characteristics of the shale is crucial for assessing the commercial viability of shale gas extraction in the La Biche Formation. The construction of a pipeline near the Athabasca River in Alberta provided an excellent opportunity to explore the geotechnical properties of the clay shale bedrock within the La Biche Formation [18; 105]. Shale cores for experimental studies were extracted from a drilled borehole (15-68-23-W4W) located northwest of the Athabasca River, Alberta. Core sampling was conducted using a conventional rotary core barrel with an inside diameter of 75 mm. To preserve their original moisture content, the cores recovered at the surface were immediately sealed with waxed cloth. The core recovery rate during drilling was excellent, ranging from 90 to 100%. The borehole reached a depth of 120.2 m below the surface [18]. Subsequently, [PERSON] [18] conducted a series of triaxial tests on the La Biche shale samples, obtaining relationships between stress-strain, volumetric strain, and axial strain. These valuable data were also utilized to validate various constitutive models [106].
## 3 Experimental Program
### Testing Procedure  
The Consolidated Drained triaxial tests were carried out on six specimens, with the same dimensions, in Water Centre-Laboratory, Warsaw University of Life Sciences, Poland. When specimens were completely prepared, standard initial steps of triaxial tests were performed, i.e., flushing, saturation and consolidation. The first three specimens, of pure RCA, were consolidated under different mean effective stress (\(p^{\prime}\)), by the following scheme: \(p^{\prime}\) for specimen No. 1 was equal to 45, for No. 290 and for No. 3180 kPa. Then, the same testing program was applied to three mixtures of RCA with rubber chips. After the consolidation stage finished, BEs tests started.  
Figure 6: Cross-correlation method. An example of test results from pure RCA specimen No. 2, \(T=0.16\) ms, \(p^{\prime}=90\) kPa.  
BEs were installed in the triaxial apparatus, in a top cap and pedestal (Figure 8). Those BEs are made from piezoelectric ceramic bimorphs. A wave was produced by a displacement in the source transducer due to applied excitation voltage equal to 10 V. A wave transmission created a displacement in the receiver, which in turn resulted in a voltage that could be measured. The frequency of the input signal was variable and decreased from the value \(f=10\) to \(f=3.57\) kHz (for lower pressure) or to \(f=5.56\) kHz (for higher pressure). This frequency range was used in order to reduce the near field effect [54]. According to [PERSON] et al. [60], the undesirable near field effect can be minimized by an appropriate selection of frequency (or period). Properly high frequency of the shear wave increases the ratio between the travel distance (\(L_{TT}\)) and the wavelength (\(\lambda\)). Different researchers have given different values of this ratio [61], suggesting keeping it, e.g., between 2.0 and 9.0 [60]. The authors, following recommendations of [PERSON] et al. [62], found it satisfactory to provide \(L_{TT}/\lambda\) ratio >3.0 in order to keep off the near field effect.  
Four time domain methods were employed here to compute shear wave velocity (\(V_{S}\)) for all tested specimens, namely: peak-to-peak, zero crossing, first bump and cross-correlation technique. The method peak-to-peak was used twice, the first time in the software during the tests, and again, in the analysis of all the data in the spreadsheet. Additionally, one frequency domain method was applied as well, i.e., cross-power spectrum technique. However, due to the high interference in the signal's beginning, the values of shear wave velocity by first bump method were very inconsistent. Therefore, the authors missed them in the presentation of the results in the next section.
## Materials and Methods
### Porous Structures Design by Triply Periodic Minimal Surfaces (TPMS)  
To design 3D porous structures, a mathematical approach has been used, starting from triply periodic minimal surface (TPMS) equations. TPMS are minimal surfaces periodic in three independent directions, extending infinitely, and in the absence of self-intersections, partitioning the space into two labyrinths. Wolfram Mathematica software was used to generate CAD-files that describe the surfaces of Gyroid (G), Diamond (D), and Schwarz (S) architectures at different porosity. The following trigonometric equations were used with boundary condition x, y, z = [\(-\)3\(\pi\); 3\(\pi\)]:  
\[\mathrm{G:}\ \sin(y)+\cos(y)\cdot\sin(z)+\cos(z)\cdot\sin(x)=C \tag{1}\]  
\[\mathrm{D:}\ \sin(x)\cdot\sin(y)\cdot\sin(z)+\sin(x)\cdot\cos(y)\cdot\cos(z)+ \cos(x)\cdot\sin(y)\cdot \tag{2}\]  
\[\mathrm{\cos(z)+\cos(x)\cdot\cos(y)\cdot\sin(z)=C} \tag{3}\]  
\[\mathrm{S:}\ \mathrm{G}\ \cos(x)+\cos(y)+\cos(z)=C \tag{4}\]  
In these equations, the C parameter is the offset which controls the porosity of the structures. An accurate study has been conducted in order to understand the correlation between percentage of porosity and offset value C and it has been reported in Section 3.1. Three different porosity values (40%, 60%, and 80%) have been set for each geometry in order to study the effect of porosity on thermal and electrical conductivity. Rhinoceros software was used to scale the CAD-files to the required dimensions in order to obtain a 10 \(\times\) 10 \(\times\) 10 mm\({}^{3}\) sized cube. In the following, Gyroid, Diamond, and Schwarz-based porous structures are labelled as GX, DX, and SX, respectively, where G, D, or S represents the geometry and X represents the % of porosity. As an example, G20 stands for Gyroid-based architecture with 20% porosity.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Experimental Setup  
#### 3.2.1 Evaluation Metrics  
To validate the performance of the proposed model, we employed the mean Dice and mean IoU metrics, which are widely used in medical image segmentation tasks. Furthermore, we evaluated three additional metrics (i.e., \(S_{m}\), \(E_{m}\), and MAE) that are commonly used in salient object detection tasks [15; 37; 43]. The S-measure [44], which evaluates structural similarities, is calculated as follows: \(S_{m}=\alpha\times S_{o}+(1-\alpha)\times S_{r}\). \(S_{o}\) and \(S_{r}\) indicate the object- and region-aware structural similarity, respectively, and we set \(\alpha=0.5\). The E-measure [45] considers the difference between the prediction and ground truth in terms of both the global and pixel levels. The MAE is computed by the average of the pixel-wise absolute values.
## 5 Conclusions  
To achieve transformer core loosening diagnosis without relying on manual feature extraction and improve diagnostic accuracy, this paper proposes a transformer vibration-signal core loosening diagnosis method based on the Gram angle field and multi-head attention mechanism. The following conclusions are drawn:  
1. By using GAF to convert vibration signals into images and directly inputting the images into a deep residual network for learning, the feature extraction ability of convolutional neural networks can be fully utilized, without the need for the manual extraction of vibration-signal feature quantities.
2. Applying the multi-head attention mechanism to the residual network can enhance the feature representation ability of the model and improve its accuracy.
3. The use of Ensemble Empirical Mode Decomposition (EEMD) for signal decomposition and reconstruction can effectively eliminate the noise components of vibration signals in complex environments and enhance robustness, improving the applicability of the proposed method.  
The model proposed in this paper has opened up a new way to effectively identify transformer core loosening faults and has good practical application potential. However, it is worth noting that all testing and validation procedures were conducted in an ideal laboratory environment. Therefore, in the future, we plan to conduct field tests in different environments to verify the model's generalization ability and robustness. This includes testing on various types of transformers and under different operating conditions to ensure that the model can accurately diagnose various iron-core loosening faults. In addition, in practical applications, besides vibration signals, there may be other factors (such as temperature, humidity, load, etc.) that can affect the operating status of transformers.  
Figure 10: Fault recognition accuracy of different models.  
Therefore, we plan to incorporate these variables into the model in future research to more comprehensively evaluate the health status of transformers.  
[PERSON] proposed the topic of the review; [PERSON] surveyed the literature and composed the manuscript; [PERSON] conducted the literature review; [PERSON], [PERSON], and [PERSON] discussed and revised the manuscript; [PERSON] and [PERSON] supervised this project; [PERSON] and [PERSON] contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 52077161.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical.  
The authors declare no conflicts of interest.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.3 Comparison of Unbiased Altimeters under the Same and Adjacent Orbit  
To verify the pros and cons of the navigation and positioning algorithms under the same orbit and adjacent orbits after combining the altimeter, we compare the two algorithms by simulation. Figure 8 shows the comparison of the effect of INS + LEO2 switching + unbiased altimeter positioning algorithms under the same orbit and adjacent orbits.  
From Figure 8 we can see that whether it is position error, or velocity error and attitude error, the effect of INS + LEO2 switching + unbiased altimeter positioning algorithm in adjacent orbits is better than that of the INS + LEO2 switching + unbiased altimeter in the same orbit positioning algorithm; this is also true in terms of trajectory error. We take the statistical results of Figures 5 and 7 as an example: when the switching time is 60 s, compared to the INS + LEO2 switching + unbiased altimeter positioning algorithm under  
Figure 8: Comparison of the effect of INS + LEO2 switching + unbiased altimeter positioning algorithm under the same and adjacent orbits.  
Figure 7: Under the adjacent orbit, the navigation and positioning error statistics of Table 2. Switching + unbiased altimeter algorithm and the INS + LEO2 switching algorithm when the switching times are 60 and 90 s, respectively.  
the same orbit, in terms of mean statistics, the INS + LEO2 switching + unbiased altimeter positioning algorithm in the errors of longitude, latitude and altitude have improved by 80.38%, 49.43% and 27.52%, respectively, and the corresponding standard deviations have improved by 57.38%, 54.65 and 8.28%, respectively. When the switching time is 90 s, in terms of mean value statistics, the INS + LEO2 switching + unbiased altimeter positioning algorithm under adjacent orbits, the errors of longitude, latitude and altitude have improved by 40.42%, 40.07% and 2.57%, respectively, and the corresponding standard deviations have improved by 56.53, 41.99% and 20.49%, respectively. It can be seen that the use of adjacent orbits is still better than the same orbit, there are similar conclusions at other switching times; due to space limitations, we will not make too many arguments here.
## 3 DL Models for Ocean Studies
### Convolution-Based Architecture  
#### 3.2.1 Vanilla CNNs  
Vanilla CNNs are a kind of DL model specifically designed for image processing tasks, making them highly effective for ocean remote sensing applications, such as sea ice classification, sea surface wind estimation, and chlorophyll concentration estimation [82]. A typical CNN architecture includes convolutional layers, activation functions, pooling layers, and fully connected layers. Convolutional kernels slide over the input data, such as satellite or underwater camera images, extracting spatial features like edges and textures, while activation functions like ReLU and Leaky ReLU introduce nonlinearity. Pooling layers, including max pooling and average pooling, reduce the spatial dimension of feature maps to alleviate computational overhead. Batch normalization and dropout further stabilize the training process and prevent overfitting [83].
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Resource Placement  
The geographical distribution of resources is one of the most important components in edge computing as it supports the mobility of edge computing applications. Mobile ends collect data and transfer it to the nearest edge computing nodes. The cloud center supports application mobility by anchoring the server's position and transferring data to the server through the network.  
In the area of resource placement, some works focus on the placement of edge nodes. To calculate the cost and average latency of the edge computing network, factors such as the location and the number of edge nodes must be considered. The study [63] proposed the Cost Aware Cloudlet Placement in Mobile Edge Computing (CAPABLE) strategy, considering both the Cloudlet cost and average end-to-end delay. This strategy also includes a Lagrange-based heuristic algorithm and a workload allocation scheme to minimize the mobility delay. The results show that CAPABLE minimizes the average end-to-end delay between users and their Cloudlets while also reducing the cost of the Cloudlets. In another study [64], researchers presented a framework to address the edge server placement problem. The goal of this framework is to optimize server placement strategies and reduce the costs of deploying edge computing networks.  
Nowadays, data-intensive tasks that need large storage space and rich calculation resources have become common in edge computing. Current research usually focuses on transmitting data from end devices to edge servers but ignores the data storage on lightweight edge servers with limited resources. [PERSON] et al. [65] presented an efficient graph-based iterative algorithm for edge-side data placement problems. Compared to the traditional storage strategy, the proposed algorithm increases the cache hit rate significantly.  
Geographical mobility leads to another challenge in effectively managing hardware resources. Virtualization is one of the fundamental edge computing technologies to address this issue. Virtualization technology, usually in the form of virtual machines (VMs) or containers, provides flexible and reliable management for edge computing. In [66], the authors designed a complementary algorithm for dynamic VM placement. The proposed algorithm decides whether there is a more suitable place for the VM allocation before the offloaded task is processed at the VM. The algorithm is based on the predicted mobility of users and the load of the base stations' communication and computation resources.
## 2 FSO Channel Model  
\(M\) transceivers at the ground terminal and \(N\) transceivers at the UAV terminal are considered for the MIMO based vertical FSO link.
# Classification of Heart Sounds Using Convolutional Neural Network
## 2 Materials and Methods  
### Dataset  
In this study, we used a dataset from PhysioNet/Computing in Cardiology Challenge2016 [28], which aims to facilitate research on novel methods for heart sound classification. The dataset, which contains a total of 3153 PCG recordings, is composed of six subsets that were provided by different groups around the world. All of these PCGs recorded the heart sounds of adults and children using clinical and nonclinical methods. Their sampling frequency is 2000 Hz. The length of the recordings varies from 5 s to over 120 s. They are all in the wav format. The dataset includes 2488 normal samples and 665 abnormal samples manually labelled with \(-\)1 and 1, respectively [29]. For convenience, the labels \(-\)1 and 1 were replaced with 1 and 0, respectively, in this study. The normal recordings are from healthy subjects and the abnormal recording are from patients with a confirmed cardiac diagnosis, which is not specified [30]. For more details, please refer to Reference [29].  
Normal heart sounds usually include a first (S1) and a second (S2) heart sound. S1 occurs at the beginning of the isovolumetric ventricular contraction, when the mitral and tricuspid valves close due to the rapid increase in pressure within the ventricles. S2 occurs at the beginning of diastole with the closure of the aortic and pulmonic valves. Between S1 and S2 or starting at or after S2 and ending before or at S1 separately, the cardiac cycle also contains systole (Sys) and diastole (Dia). So, one PCG recording can be divided into S1, systole, S2 and diastole [31].  
The proposed method includes two major steps--feature extraction and feature selection and classification based on the designed CNN. The workflow of this approach is shown in Figure 1. We describe each step in the following subsections.