esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response Analysis of Parallel Twin Tunnels Under Different Train Loads
###### Abstract  
Due to the spatial constraints of underground environments, the spacing between dual-line tunnels in urban metro systems is often limited, leading to potential mutual interference during the operation of trains in closely spaced parallel tunnels. In this study, a twin-tunnel model was developed using PFC2D to simulate the variations in displacement, velocity, porosity, and strain of the T2 structure and its surroundings under eight conditions (\(F_{i}=62.4\)-131.5 kN, \(i=1,\,2,\,3.\,\cdot\,,\,8\)), elucidating the static and dynamic responses of the adjacent tunnel structure and its surroundings. The results indicate that the vertical response of T2 sleepers is significantly larger than the horizontal response under the same load. Increasing train loads induce non-uniform deformation in T2 liners, and excessive overloading may result in microcracks or structural failure. The velocity and displacement at the ground surface are substantially more significant than those in the surrounding areas closer to the vibration source, primarily due to the surface amplification effect. The surroundings of the adjacent tunnel experience uneven compressive forces, potentially causing liner separation. Under the A\({}_{7}\) condition, the static and dynamic responses of the tunnel structure and its surroundings sharply decreased due to the combined effects of pressure and train load dynamics. This phenomenon is attributed to the interplay between the pressure effect and the dynamic amplification effect of the train load. It is recommended that the operational train load in practical engineering should not exceed the A\({}_{4}\) condition (92.0 kN). This study can provide a reference for analyzing the static and dynamic responses of twin-tunnel structures under metro overloading conditions.  
parallel twin tunnels; subway train; overload; particle flow method (PFC); dynamic response 2025  
155565
## 3 Short-Reach Systems Classification and Enabling Technologies
### Short-Reach Systems' Overview and Classification
#### 3.1.3 Extended Reach Inter-Data Center, Access, and Metro Links  
As these links can be between 20 km and 80 km, optical amplification can be employed in certain scenarios. In spite of this, direct detection receivers are still the preferred candidates for these applications when compared with the full CO-Rxs. Therefore, to enhance the bit rate, polarization-multiplexed direct detection configurations such as the SV-DD receivers have been receiving considerable attention. Alternatively, there are various intensive efforts towards enhancing the bit rate, as well as the transmission distance of \(O\)-band systems without the need for optical amplification. Due to the considerable advances in digital COH systems, full COH transceivers are becoming more economical, power efficient, and compact. In this regard, full COH transceivers are anticipated to be adopted for the extended reach inter-data center links [35]. In the following subsection, we focus on the DCNs and the related concepts.
# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 3 Neural Networks for Graph Embedding
### General Neural Network Structure  
The general setup of the neural networks is composed of four main components that are put together in successive order (see Figure 3).  
These components are the _embedder_, the _propagation layer_, the _aggregator_, and the _graph similarity_. The embedder transforms the raw graph input data into an initial vector representation, resulting in a single embedding vector for each node and edge, respectively. During propagation, the vector-based node information is iteratively merged according to the edge structure of the graphs. Thereby, the node embedding vectors are updated via element-wise summations according to the information of all incoming edges and the nodes that are connected via these edges. This captures information about the local neighborhood of each node in its embedding vector. After propagating information for a certain number of rounds, the aggregator combines the embeddings of all nodes from each graph to form a single whole-graph embedding. The final component computes a single scalar similarity value with the embedding vectors of both graphs by utilizing a vector similarity measure such as the cosine similarity. The difference between both models comes from a different propagation strategy. The GEM uses an isolated propagation strategy that only propagates information within a single graph. In contrast, the GMN uses an attention-based cross-graph matching component that propagates information across both graphs in an early state of similarity assessment. In the following, all four components are described more formally and in more detail. The inputs of each neural network are two graph structures \(G_{1}=(N_{1},E_{1})\) and \(G_{2}=(N_{2},E_{2})\), although a general graph \(G=(N,E)\) with nodes \(N\) and edges \(E\subseteq N\times N\) is used as a placeholder for components in the Siamese architecture that operate on a single graph.  
Figure 3: Graph Embedding Model (GEM; **left branch**) and Graph Matching Network (GMN; **right branch**) (derived from [23]).
## 2 Network Architecture and Training
### Network Architecture Overview
Figure 2 illustrates the structure of a depth-wise separable module that integrates the attention mechanism within the network. This module first performs global context awareness on the input feature maps and significantly improves the network's focus on key information through feature recalibration. Figure 1: The architecture of the U-Net network. The attention module adopts a spatial attention mechanism. For the input feature map, average pooling and max pooling are, respectively, performed along the channel dimension to obtain two single-channel feature maps. These two feature maps are concatenated along the channel dimension to form a 2-channel feature map. Then, a convolutional layer (with kernel_size = 7 and padding = 3), a batch normalization layer, and a Sigmoid activation function are applied successively to obtain a single-channel attention map. Finally, the attention map and the input feature map are element-wise multiplied to enhance the feature responses of important regions in the feature map. For the design of the residual block structure, a variety of architectural elements are incorporated to create network units with both efficient feature extraction and adaptive optimization capabilities (as shown in Figure 3). The architecture features a multi-branch residual design, in which two parallel branches each contain a 3 \(\times\) 3 convolution (Conv), batch normalization (BN), and ReLU activation function. Feature fusion is achieved by element-wise addition, an idea inspired by residual connections, which effectively alleviates the vanishing gradient problem in deep network training, facilitates cross-layer feature reuse and supports the construction of deeper networks. In the feature optimization stage, a channel attention mechanism (SE module, i.e., Gating (se)) is introduced to apply channel-wise attention to the outputs of the residual branches. The SE module learns inter-channel dependencies, adaptively highlights key feature channels, and suppresses redundant information, thereby enabling the network to focus on more discriminative features. Additionally, the module combines 1 \(\times\) 1 convolutions with shortcut connections: the 1 \(\times\) 1 convolution flexibly adjusts feature channels  
Figure 3: Residual block structure. Figure 2: Depth-wise separable convolution [34]. and dimensions, while the shortcut conveys the original features directly. The two are fused again by element-wise addition and, after final ReLU activation, further integrate the features and introduce nonlinearity, thus refining the feature transformation process.
## 3 Methodology
### The ReMAHA Algorithm
#### 3.2.1 Feature Weight Calculation  
In this study, we first employed the relief-F algorithm for feature selection and obtained a weight matrix used to assess feature importance. The choice of using the relief-F algorithm is driven by the following reasons: due to the sparsity of features and the large volume of traffic accident data, both of which result in issues with the covariance matrix being non-invertible, using the relief-F algorithm to filter out these overly sparse and uninformative features can alleviate such problems.  
Relief-F is an algorithm designed for multi-class problems to calculate feature weights based on the correlation between features and class labels. Its primary objective is to calculate the weights between features, thus determining which features are more influential for subsequent classification tasks [35]. Given a feature \(A\), its weight at the \(t\)-\(th\) iteration is denoted as \(W_{t}(A)\). In any \(t+1\) iteration, the relief-F algorithm updates the weighting of the feature vector as follows:  
\[W_{t+1}(A)=W_{t}(A)-\frac{1}{T}\frac{\sum\limits_{j=1}^{k}diff(A,X_{i},NH_{i} )}{k}+\frac{1}{T}\left[\frac{\sum\limits_{\begin{subarray}{c}\mathcal{C}\in Class (X_{i})\end{subarray}}\frac{p(c)}{1-p(Class(X_{i}))}\times\sum\limits_{i=1}^{ k}diff(A,X_{i},NM_{i})}{k}\right] \tag{6}\]  
Figure 2: Flowchart of the ReMAHA algorithm.  
where \(T\) represents the number of iterations, \(k\) is the number of nearest neighbors chosen, \(p\)(\(c\)) represents the prior probability of class \(c\), and Class(\(X_{i}\)) indicates the class to which \(X_{i}\) belongs.  
In a given dataset, the relief-F algorithm operates as follows: first, a sample \(X_{i}\) is randomly selected, and then the nearest same-class sample point \(NH_{i}\) and the nearest different-class sample point \(\text{NM}_{i}\) are found in the dataset. Here, the function _diff_(\(A\), \(X_{i}\), \(X_{j}\)) is used to calculate the distance between sample points \(X_{i}\) and \(X_{j}\) in feature \(A\), where \(\text{max}(A)\) and \(\text{min}(A)\) denote the maximum and minimum values of feature \(A\).  
For numerical features, when feature \(A\) is numerical:  
\[diff(A,X_{i},NH_{i})=\frac{|X_{i},NH_{i}|}{|\text{max}(A)-\text{min}(A)|} \tag{7}\]  
For discrete features, when feature \(A\) is discrete:  
\[diff(A,X_{i},NH_{i})=\begin{cases}0&X_{i}=NH_{i}\\ 1&X_{i}\
eq NH_{i}\end{cases} \tag{8}\]  
After \(T\) iterations, each feature will obtain a feature weight \(w_{i}\), forming a feature weight matrix \(W\) = [\(w_{1}\), \(w_{2}\),..., \(w_{m}\)]. The larger \(w_{i}\) is, the better the feature is for classification.
# PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control
## Materials and Methods
### PISIoT: Architecture and Functionality  
PISIoT is based on a layered architecture, which provides a clear definition and description of the activities and functions of each module. This facilitates maintenance and allows for high scalability. Figure 1 presents the PISIoT architecture, which is made up of five layers: the presentation layer, monitoring network layer, integration layer, data management layer, and IoT-based services layer. Each layer is composed of various components with a specific functionality and relationship. A general description of the layers is presented below.  
* Presentation layer. Through this layer, communication is made between the patient and the platform, thus facilitating a web application and a mobile application through which patients are able to view and track their biomedical variables, available IoT-based services, medical history, and recommendations. In addition, the platform allows manual input of water and food consumed during the day. When patients enter food or drinks consumed using a smartphone or computer with Internet access linked to the wearable device, calories, carbohydrates, fat, and proteins are automatically obtained from the database of the device provider. Nevertheless, in PISIoT, it is possible to enter food or a beverage not included in the provider's database, in addition to any physical activity performed by the patient.
* Monitoring network layer. This layer is made up of different IoT-based devices such as wearable and smart devices linked to telecommunication equipment, which have communication interfaces that facilitate the exchange of information. These devices make it possible to collect information on the patient's biomedical variables (heart rate, calories burned, sleep, minutes of physical activity and weight), based on the activities performed during a day. All the information collected is sent to the data management layer for further processing and analysis.
* Integration layer. This layer is made up of the wearable and smart device providers and receives the patient's data and consultations to generate the requested answers. In addition, this layer is responsible for requesting services from the IoT-based services layer based on the PISIoT recommendations.
* Data analysis layer. This layer is responsible for identifying critical variables and generates medical recommendations. In addition, this layer protects useful information for the PISIoT and patients' medical history.
* IoT-based services layer. This layer is responsible for linking, invoking, selecting and confirming the availability of IoT-based services.
* Data management layer. This layer is responsible for the storage and backup of the patient's medical history and the data collected by the devices used.  
The following subsections describe the most important aspects of the PISIoT, as a fundamental part of this work.
## 2 Materials and Methods
### Characterization  
The synthesized CQDs underwent comprehensive characterization using various techniques. UV-vis absorption spectroscopy was carried out in the range of 200-700 nm with a Jasco V-570 instrument (The Jasco Corporation, Tokyo, Japan). To assess their emission behavior, photoluminescence studies were performed using fluorescence spectroscopy with a Fluorog 3 (HORIBA New Jersey Optical Spectroscopy Center, Piscataway, NJ, USA), both  
Figure 1: Schematic of the microplasma system used to synthesize the CQDs and its compact antibacterial activity.  
analyses employing quartz cells with a 1 cm path length. Particle size and uniformity were analyzed using a transmission electron microscope (TEM) and high-resolution transmission electron microscopy (HRTEM) (JEOL 2100, JEOL Ltd., Tokyo, Japan), providing insights into their structural features. TEM samples were prepared by dispersing the powdered sample in water, then drip-drying on a copper grid coated with a carbon film. The functional groups of carbon quantum dots (CODs), including hydroxyl (OH-) and carbonyl (C=O), were examined using Fourier-transform infrared spectroscopy (FTIR) with an FTIR Cary 630 (Agilent Technologies, Santa Clara, CA, USA) over a wavenumber range from 400 to 4000 cm\({}^{-1}\). The shifting of functional group peak readings was observed, and FTIR measurements were conducted by preparing a solution of the sample and placing a drop of the solution onto the measurement disk for analysis. All these measurements were conducted at room temperature to ensure consistency in the characterization conditions.
## 3 Results and Discussion
### Stress-Strain Curve of Concrete
#### 3.3.1 Peak Stress  
The peak stresses of seven groups of concrete after subjecting to the compressive load test are shown in Figure 6. The peak stress of NAC was 44.7% higher than that of RAC-non. After using the RCA treatment techniques, the peak stresses of the RAC were increased. The increment by using the pressurized carbonation was higher than that of flow-through carbonation and wet carbonation. That is because when using the pressurized carbonation, the carbonation degree of RCA was higher than using other two carbonation techniques, leading to a better enhancement of RCA. The NS pre-spraying technique caused a slightly better improvement in peak stress than using the three types of carbonation techniques. That is because the ITZ between RCA and new mortar was enhanced after using NS pre-spraying, which may be more efficient to improve peak stress than the enhancement of RCA. When using the combined pressurized carbonation with NS pre-spraying technique, the peak stress exhibited the highest increase (17.1%) than the RAC-non because both the new ITZ and the RCA were enhanced. However, it was still significantly lower than that of NAC.  
Figure 5: An example of stress–strain curve of concrete.
## 4 Comparison Results
### Analyzing Local Metrics on Real-World Datasets
#### 4.3.2 Smoothness  
The gradient map \(G\), as defined in Equation (5), captures the smoothness of decision maps. Figure 6 shows this map for the three studied decision-map techniques. We can see that each technique created its own unique type of gradient. The DV technique shows high gradient values close to the projected points, which indicates a high degree of data compression during the projection \(P\) in these areas. Additionally, the DV technique shows some high gradient 'lines' connecting the clusters of the projected points, indicating discontinuities. The remaining areas have uniformly low gradient values. In contrast, the SDBM shows low gradient values almost everywhere, with slightly higher values in the gap areas between point clusters. Both the above patterns for DV and the SDBM are similar to what we observed on the blob dataset in Figure 4. Finally, the DBM's gradient map presents a more complex pattern, unlike what we observed on the easily classifiable blob dataset (Figure 4). As stated in Section 4.2, we expected that the gradient maps would not show peaks and would have low values in areas close to the decision boundaries. However, _no_ method simultaneously exhibited these two properties. Notably, the SDBM's relatively high gradient values in the gap areas between point clusters were still low in absolute terms, making the SDBM the method that best satisfied the two mentioned gradient-map properties. The SDBM's high smoothness can be attributed to the joint training of \(P\) and \(P^{-1}\). DV was clearly the least smooth method, as indicated by the number of discontinuities reflected in the 'peaks' in its gradient map. The DBM fell in between, exhibiting a complex pattern that displayed less smoothness but still maintained continuity.
## 1 Introduction
### Research Gap  
Most of the work on resource allocation (i.e., VNF placement, SFC chaining and routing) mentioned in Section 1.1 does not consider mobile networks and, even when they do, they ignore the wireless AN segment. The last remark motivates our paper, which also studies joint VNF placement and routing in a MEC/cloud-enabled heterogeneous mobile network consisting of macro BSs and SCs. We explicitly include the AN, as well as potential wireless X-haul links, while accounting for the inherent wireless channel fluctuations. Hence, this paper extends the concrete and detailed communication model of [25] by adding all necessary controls for the computational resources and by modeling the associated delay and capacity constraints. In addition, the extensive technical documentation released by Standards Developing Organizations (SDOs) (such as ETSI) regarding the E2E network efficiency of mobile networks in conjunction with the NFV-supporting infrastructures [26; 27], indicate that solutions targeting E2E network energy efficiency are of prime importance.  
On the other hand, the multiplicity of technology-specific orchestration platforms presents interoperability challenges, which motivates current research in 5G and B5G networks towards delivery of E2E Network Slicing solutions that support the orchestration and management of heterogeneous resources in distributed edge to cloud infrastructuresand across the different network segments [28], i.e., radio, core, and transport. In addition, although there are solutions that optimize the hardware (optimized design of dedicated solutions) or the job allocation in the computing nodes, they provide local optimization benefits, while overlooking the holistic network optimization. To this end, so far, there has been no complete solution for a unified Orchestration Framework having a holistic view of the entire network (potentially by integrating existing technology-specific platforms such as ETSI MANO and O-RAN) and using it for the management and orchestration of Vertical-driven services within E2E Network Slices provisioned and configured over multi-technology components. The search for such a solution provides additional motivation for our paper.
## 2 Results and Discussion
### Volatile Compounds

Table 2: Codes and relative retention times (Rt) of the volatile compounds quantified in the hams, mean concentration and standard deviation of volatiles determined in non Iberian and Iberian hams, and \(p\) values of each volatile compound classifying the hams by their breeds (non Iberian _vs_. Iberian). Odor threshold values (OT) in mg/kg and sensory descriptions obtained by GC-olfactometry (GC-O) are displayed as well. In terms of total concentrations of the most abundant series (alcohols, aldehydes, hydrocarbons and ketones) quantified in Iberian hams, the highest concentrations corresponded to alcohols followed by ketones, aldehydes and hydrocarbons [17], while ketones showed the highest concentrations in non Iberian hams, followed by alcohols, aldehydes and hydrocarbons [18]. The concentration of these compounds depends on the location where they are quantified. Thus, Table 3 shows the concentration of the volatile compounds quantified in the four ham locations displayed in chemical series. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chemical series** & **Volatile compounds** & **BF**\({}^{\ast}\) & **SF**\({}^{\ast}\) & **SM**\({}^{\ast}\) & **ST**\({}^{\ast}\) \\ \hline Hexane & 0.40 (0.03–1.77) & 0.27 (0.04–0.66) & 0.37 (0.03–0.97) & 0.38 (0.04–1.25) \\  & Heptane & 0.16 (0.03–1.13) & 0.26 (0.05–0.70) & 0.29 (0.03–2.99) & 0.15 (0.02–0.58) \\  & Octane & 1.66 (0.10–7.91) & 3.22 (0.52–10.81) & 2.12 (0.39–5.30) & 1.37 (0.40–3.81) \\ Hydrocarbons & Methyl benzene & 0.14 (0.04–0.37) & 0.15 (0.02–0.44) & 0.19 (0.07–0.42) & 0.13 (0.06–0.24) \\  & Ethyl benzene & 0.12 (tr-0.35) & 0.20 (tr-0.73) & 0.17 (0.01–0.74) & 0.14 (0.01–0.71) \\  & Limonene & 0.40 (0.01–4.71) & 0.51 (tr-3.16) & 1.42 (tr-14.60) & 0.56 (tr-5.14) \\  & \(\alpha\)-Pinene & 0.19 (tr-1.61) & 0.10 (tr-0.82) & 0.28 (tr-1.38) & 0.22 (0.02–1.56) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile compounds (mg/kg) quantified in four locations of dry cured ham samples (SF, subcutaneous fat, BF, _biceps femoris_, SM, _semimembranosus_, ST _semitendinosus_ muscles) and distributed in chemical series. Presented data are mean values from all samples (non Iberian and Iberian), while figures within brackets are minimum and maximum concentrations. Figure 1: Chromatograms of volatiles of the subcutaneous fat of two non Iberian hams from different geographical origins: (**a**) Iberian hams from PDO “Jamon de Huelva”; (**b**) Non Iberian ham from PDO “Jamon de Teruel”; and (**c**) Non Iberian ham from Parlan, Auvergne (France). Note: codes are described in Table 1. Chromatographic method is described in Experimental section. **Table 3.**_Cont._