esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modelling of the Structure and Experimental Set-Up
#### 2.2.1 Structural Design and Numerical Model of Analyzed Bridge  
To present the application of the proposed model of mining-induced kinematic excitation, a five-span bridge (160 m long) was taken into consideration as a multiple-support structure. The analyzed bridge (Figure 2), erected in 2013, is located in Chrzanow, Southern Poland, on the border of the USCB mining activity region. The bridge under consideration has five spans. Two continuous girders integrated with the concrete slab form the bridge's primary structural system. The girder beams are prestressed by steel tendons. Additionally, the central (longest) span is suspended on two arches. The geometry of the bridge is presented in Figures 3 and 4. The girders rest on abutments and intermediate pillars, supported by a system of pot bearings. A set of fixed and transversally guided sliding bearings is used over the piers P3 (see Figure 3), whereas multidirectional and longitudinally guided sliding bearings are applied at other supports (abutments and piers).  
Structural elements' material specification is as follows: concrete classes C50/60 (arches), C40/50 (girders), C30/37 (pillars, abutments), and steel grades S460 (hangers), Y1860 (prestressing tendons).  
The finite element analysis of the bridge was performed in Abaqus FEA [43]. Most of the structural components of the bridge (girders, crossbars, pillars, archers) were modeled with brick elements. Continuum shell elements were used for the slab and truss elements for the hangers. To model the bearing behavior, connector elements allowing for different sliding directions were used. To account for the girders' precompression, their Young's modulus was determined as the weighted average, proportionally to the steel and concrete volume fraction [44].
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
## 2 The State of the Art
### Electromagnetic Isolation  
The EM isolation between transmitter (RFID reader) and receiver (RFID tag) is a critical problem in a pulse-modulated RFID system, where transmitter and receiver use identical frequencies [21]. Metals can create blind spots in RFID reading zones by completely reflecting high-frequency EM waves [22]. These blind spots cause signal interruption and receiving signal strength indicator (RSSI) variations [23]. RSSI assesses the attenuation of a received signal with respect to the source. The value of this parameter is always negative, and numbers closer to zero indicate better power and quality of the network [24]. Therefore, embedding the passive RFID tags in the analog devices will be affected by the composed material of the devices, especially when the cover is made of metallic components. The isolation of passive RFID tags from EM waves transmitted from the RFID reader is not always a disadvantage, especially when the physical parameters required are not continuously varied. So, the metallic interaction could play an essential role in RFID isolation from the reader whenever needed in an automatic mode, and the passive RFID tags could be detected whenever the metallic cover is (re)moved. This dynamic scenario supported by the facts mentioned in the paragraphs above shall guide us to the main research questions:  
* Can RFID technology digitize the measurement process of analogic devices?
* Are passive RFID tags appropriate to be embedded in the analog systems collecting the data wirelessly?  
The following sections will clearly answer and explain how RFID technology can support the digitization of the measurement process of analogic devices.
## 2 Multi-Chip Interconnection Topology Design  
Designing the wireless multi-chip interconnection topology essentially involves determining the intra-chip interconnection architecture for each chip as well as wireless interface (WI) placement for intra-chip or chip-to-chip communication. We first discuss the approaches for designing the intra-chip interconnections followed by the approaches for the inter-chip wireless communication.
## 4 Grid Planning and Design
### Alternative Development of the Grid
#### 4.1.3 Lines and Substations  
Relating to new designs of lines and substations, [PERSON] and [PERSON] [240] propose the application of an imperialist competitive algorithm (ICA - a genetic algorithm), which optimizes the expansion of a distribution network.  
[PERSON] et al. [241] present a method based on voltage drop to evaluate the performance of power lines.  
[PERSON] and [PERSON] [242] present a new active powerline conditioners control design based on ANNs for new lines.  
It is necessary to replicate the voltage instability, and [PERSON] and [PERSON] [243] propose an analysis to reconstruct said phenomena.  
Based on a mixed integer linear programming problem it is possible to propose a substation planning model, as done by [PERSON] et al. [244].  
It is also necessary to optimally organize transformers in low voltage, [PERSON] et al. [245] propose a novel system to do so.  
Through the new model of [PERSON] et al. [246], it is possible to minimize the investment and operating costs of the electric substations.  
[PERSON] and [PERSON] [247] develop a simple, systematic and logical scientific approach to evaluating power substation location based on fuzzy process, similar to [PERSON] et al. [248].  
[PERSON] et al. [249] present a method to classify the substations into several classes, and for which it classifies the characteristics of the load from a self-organization map (SOM).  
To summarize, Table 10 shows all the work described in this section. In the rows are shown different areas (cables and HTS, FACTS, lines and substations) while in the columns is shown the application rate on electrical equipment (AI and other techniques). As can be seen in the previous table, the development of new electricity infrastructure scenarios is necessary; in this sense, improved substations, new cables will be developed. Again, these developments are being addressed through AI and other simpler techniques.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 4 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of Popular Video
### Linear Program Formulation for an Unknown Time Horizon  
The main change in the new LP model with regard to the fixed time horizon model is the periodicity constraint. This guarantees the playback property when repeating the optimal schedule. The LP formulation is stated as follows:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(N\) & \(B^{*}\) & \(t\) (s) & \(N\) & \(B^{*  
\[\begin{split}&\text{Minimize $B$}\\ &\text{Subject to :}\\ &\sum_{j=1}^{N}x_{ji}\leq B,\,t\in\{1,2,\ldots,C\}\\ &\sum_{k=t}^{t+j-1}x_{jk}\geq 1,\\ &\quad j\in\{1,2,\ldots,N\}\text{ if }t\leq C-j+1\\ &\quad\sum_{k=t}^{C}x_{jk}+\sum_{k=1}^{j-(C-t+1)}x_{jk}\geq 1,\\ &\quad\text{if }C-j+1\leq t\leq C\\ &\quad\sum_{i=1}^{N}\frac{1}{i}\leq B\leq N\\ & x_{j,t}\in\{0,1\},j\in\{1,2,\ldots,N\}\\ &\quad\text{and }t\in\{1,2,\ldots,C\}\\ & B\in\mathbb{N}^{*}\\ \end{split} \tag{37}\]  
All the constraints are the same as the previous LP, except constraints (37) and (39), where we consider circular scheduling. They guarantee that a segment \(i\) is scheduled at least once in any successive \(i\) slots from slot \(j\) to slot \(C\) and from slot \(1\) to slot \(i-(C-j+1)\). Note here that the minimum time horizon \(C\) for a circular schedule is equal to the video size \(N\).  
Solving the periodic LP for \(N=C=10\) using a Gurobi solver [53] gives the optimal circular schedule portrayed by Figure 10.  
Table 3 summarizes the solving results of the periodic LP for \(N\in\{2,3,\ldots,48\}\).  
Using the circular optimal schedule, the storage node allocates one more time slot to stream the segments indicated by the optimal schedule on the broadcasting channels for any newly arriving SN request where the distance between the newly allocated slot and the arrival slot is equal to \(N\). As shown in Figure 10, a request arriving at slot \(1\) induces allocating slot \(11\) to broadcast segments \(1\), \(2\), \(6\) and \(10\).  
The plots in Figure 11 show that fixed time schedules could be naturally periodic (circular). The optimal bandwidth value \(B^{*}\) is the same for many configurations considered in fixed time and circular schedules. This proves that at least one optimal schedule exists for a fixed time horizon version that is naturally periodic.  
Through Figure 11 plots, we can observe that in the time horizon value \(C=20\), the optimal bandwidth value \(B^{*}\) decreases by \(1\). Thus, we can ask the following open question: what are the time horizon values of \(C\) that minimize the bandwidth \(B\) for a given video size \(N\)?  
Figure 10: An example of optimal circular scheduling for \(N=C=10\). \(B^{*}=4\).
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 3 Proposed Method
### Hypergraph Convolution Operator  
First, the degree of a hypergraph vertex is defined as \(N_{d}\), which represents the number of hyperedges passing through the vertex. The degree of a vertex is defined as follows:  
\[N_{d}=\sum_{i=1}^{j}W_{ii}H_{ei} \tag{4}\]  
In Equation (4), \(W_{ii}\) represents the weight of the edge connected to vertex i, indicating the weight of the edge in the degree of the vertex. \(H_{ei}\) represents whether the i-th vertex is connected to the edge; if connected \(H_{ei}=1\), otherwise \(H_{ei}=0\).  
The degree of a vertex's edge represents how many vertices a hyperedge connects to, and it is defined as follows:  
\[\mathrm{S}_{d}=\sum_{j=1}^{k}H_{ej} \tag{5}\]  
The diagonal matrices \(\mathrm{N}\in R^{k\times k}\) and \(\mathrm{S}\in R^{l\times j}\) are, respectively, the degree matrices of vertices and hyperedges in the hypergraph.  
In the hypergraph, convolutional network operators are used to estimate the probabilities of transitions between vertices. The embedding representation of each vertex can be transmitted throughout the entire hypergraph. This hypergraph convolutional network operator is defined as follows:  
\[x^{l+1}=\sigma(N^{-\frac{1}{2}}HWS^{-1}H^{T}N^{-\frac{1}{2}}X^{l}P) \tag{6}\]  
In Equation (6), \(X^{l}\) represents the vertex features input at layer \(l\), \(X^{l+1}\) represents the vertex features input at layer \(l+1\), \(P\) represents the trainable weight matrix between layers \(1\) and \(1+1\), \(\sigma\) represents an activation function, \(N\) represents the degree matrix of vertices in the hypergraph, and \(\mathrm{S}\) represents the degree matrix of hyperedges in the hypergraph. \(H\) is an incidence matrix with vertices as rows and hyperedges as columns in the hypergraph. Conversely, if a message-passing path from vertices to hyperedges is specified, vertices are considered as columns and hyperedges as rows. The specific operational steps are as follows:  
Figure 2: Diagram of the Course Hypergraph Incidence Matrix.  
1. Hyperedge Feature Construction: Construct the feature representation of hyperedges by aggregating the features of vertices connected to each hyperedge.
2. Vertex Feature Classification and Aggregation: Classify and aggregate the features of all hyperedges related to a specific vertex to generate an enhanced vertex feature representation.
3. Model Training and Nonlinear Activation: Train the model using a nonlinear activation function to capture higher-order dependencies and complex structural features in the hypergraph.  
In summary, this hypergraph convolutional network operator effectively captures higher-order correlations in the hypergraph, thereby significantly enhancing the model's expressive power and generalization performance.
## 4 Results and Discussion
### Effects of Stratification on SSFI with Various Structures  
Figure 12 compares the roof motion's peak acceleration for S1, S2, and S3 (PGA\({}_{\text{RM}}\)) obtained on layered profiles to those on uniform ground for different levels of input earthquake. The layered ground leads to an increased PGA\({}_{\text{RM}}\) for the S2 structure, which followed a similar trend to the S1 structure. Furthermore, the increment for the L1 profile was more significant than for the L2 profile, where the greatest changes were about 22.6% and 17.4%, respectively. However, the layered ground led to a decrease in response for the S3 structure where the values of PGA\({}_{\text{RM}}\) on the layered profiles were smaller than those on uniform ground because (1) the S3 structure had a high fixed-base frequency of 7.05 Hz compared to site-fundamental frequency (f\({}_{\text{G}}\) of approximately 2 Hz); and (2) the width of the S3 foundation (B\({}_{\text{S3}}\) = 1.2 m) was much smaller than the thickness of the soft, upper layer and. Therefore, the effect of the layered ground was reduced [15; 16]. The trend of a slight reduction in response for the S3 structure continued for PGA\({}_{\text{FFM}}\), shown in Figure 5a, revealing that any amplification by the soft upper layer had been eliminated.  
Figure 11: Comparison of the maximum of (**a**) acceleration of RM, (**b**) relative displacement of the roof, and (**c**) bending moment at the bottom (Max.MB) for the S1 structure on L1 and L2 profiles with those on Uni profile.  
A similar procedure to define the transfer function and incoherence parameters for the S2 and S3 structures on all three soil profiles was carried out. Figure 10c,d shows the variation of the k\({}_{\text{t}}\) parameter for the S2 and S3 structures on all three soil profiles with increasing PGA\({}_{\text{BM}}\). For the S2 structure, the k\({}_{\text{t}}\) parameter increased on layered profiles, which was similar to the S1 structure, as shown in Figure 10b, because of the variation in the wave path. However, the k\({}_{\text{t}}\) parameter observed for the S3 structure on layered profiles was smaller than on Uni profiles. This inverse trend could be attributed to a reduction in soil amplification in layered profiles that cause a decrease in wave reflection, while the effects of variation of wave path in layered profiles, which was observed in the S1 and S2 structures, reduced with the width of the foundation. The numerical simulation results indicate a similar trend with increment in k\({}_{\text{t}}\) of the S2 structure and reduction in the S3 structure in the layered profile compared to those in uniform ground.
## 5 Conclusions  
In this paper, a lightweight dual mutual-feedback network (DMFN) is proposed for use in artificial intelligence in medical image super-resolution. It contains two back-projection units working in a dual mutual-feedback manner. We propose a contrast-enhanced residual block (CRB), which is used in the back-projection units as each cell block. The CRB uses the contrast-enhanced channel and spatial attention within residual learning to enhance its ability to express details. We used the concat function for SR image reconstruction. Finally, a unity feedback method was designed to supervise the process of SR, which down-sampled the SR result to LR images as the inverse process of SR. As illustrated in the experimental results, our DMFN outperformed the other methods with very little computational cost. Accordingly, our method can help doctors to make accurate diagnoses by improving the resolution of medical images. The DMFN introduces a feedback mechanism into medical image SR and was shown to exhibit good performance on synthetic datasets. However, we are not sure whether it will perform well in real-world medical image SR, as the degradation of real-world images is complicated. In the future, we will focus our attention on real-world medical image SR, as it is possible that the feedback mechanism can also be used to improve performance in this scenario.  
Figure 10: Comparison of visualization results on ADNI100 [36] and OASIS100 [37] datasets. Images on the right are the recovery details of the red box.  
**Author Contributions:** B.W.: Conceptualization, Methodology, Software, Writing--Original draft; B.Y.: Supervision, Validation; G.J.: Supervision, Formal analysis; C.L.: Data Curation, Resources; X.Y.: Writing--Review, Editing, Funding acquisition; Z.Z.: Investigation, Visualization. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Sichuan University and Yibin Municipal People's Government University and City strategic cooperation special fund project (Grant No. 2020 CDYB-29); Science and Technology plan transfer payment project of Sichuan province (2021 ZYSF007); The Key Research and Development Program of Science and Technology Department of Sichuan Province (No. 2020 YFS0575, No.2021 KJT0012-2021 YFS0067); The funding from Science Foundation of Sichuan Science and Technology Department (2021 YFH0119); The funding from Sichuan University (Grant No. 2020 SCUNG205) and National Natural Science Foundation of China under Grant No. 62201370.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.4 3 GPP Release 16  
Release 16 was introduced in 2021. It starts to specify the V2X relevant core network elements--namely the V2X router functionality--and the new radio (NR)-based sidelink communication [64]. Within the core network, requirements were developed in relation to the vehicular communication quality of service support. The main features of Release 16 and later releases are summarized in Figure 3. The work concluded that a standardized interface is needed in order to negotiate the QoS requirements and changes in the QoS among the client and the V2X application servers. Architecture enhancements for V2X services were also introduced. This included interworking between EPS V2X and 5 GS V2X, the PC5 and Uu reference points, their various configuration parameters, and alternative service requirements. The V2X application-specific layer and the V2X application support layer were defined in the application layer. The latter contains the Service Enabler Architecture Layer (Service Enabler Architecture Layer--SEAL) and the V2X Application Enabler Layer (V2X application enabler--VAE). The 5G V2X with an NR sidelink is a close relative of the LTE-based V2X sidelink communication; however, it is not backward compatible with the legacy system [57]. The 5G introduces two resource allocation modes, Mode 1 and Mode 2 (similarly to the LTE V2X's Mode 3 and Mode 4). In Mode 1, the resource allocation is performed by gNB and in Mode 2 the UE selects the resources autonomously [66]. The time synchronization issue is solved via GNSS signals, just like in the predecessor standards [60; 68].  
Apart from the dedicated interface designed for V2X communication, there were other efforts as well in the scope of the 5G standards, which could be important from the V2X perspective. Such efforts are MEC, URLLC, and uM2M communication:  
* Multi-access mobile edge computing is expected to enable low-latency and highly reliable service provisioning since it is able to deploy services near the vehicles themselves.
* Ultra-reliable low-latency communication (or ultra-reliable machine-type communication) drives mission-critical information exchange. One of its use cases is remote driving, but, apart from that, the URLLC combined with the MEC could serve as a foundation for many centralized V2X applications.
## 4 Application Scenarios of the Multipath Error Mitigation Algorithm
### Precise Positioning of Urban Areas  
With the increase in urbanization, the demand for location information services, such as mobile phones and vehicles, in urban areas is increasing. According to the 2015 \"GNSS Market Report\" of the European Union, the two major application markets, location service (LBS) and road navigation, have already occupied a dominant position (91%) in GNSS revenue. GNSS technology in the urban area environment has also received extensive research attention and been widely applied. There is a complex geographical and architectural environment in urban areas, which is mainly due to its large number of buildings and the green environment being scattered in the city. In the application of GNSS positioning techniques within urban environments, the dense positioning of buildings introduces a significant challenge, namely, the occurrence of pronounced reflections of satellite signals during signal propagation. This phenomenon results in a formidable multipath effect, substantially impairing the accuracy and reliability of positioning in urban settings [110]. [PERSON] et al. [111] proposed a multipath estimation, detection, and mitigation method for carrier-phase observations. The experimental results in urban environments showed that the robustness and accuracy of navigation positioning were improved. [PERSON] et al. [112] analyzed and verified the weakening effect of MHM and T-MHM on the PPP multipath effect of the BDS-3 system in a complex urban environment.  
The multipath errors in the urban area are mainly divided into non-line-of-sight signal (NLOS) and reflected multipath effects. These two types of errors are essentially caused by the coherent signal of the extra path, and the difference lies in the participation of the direct signal or not. They often co-exist in urban environments, such as dense buildings and elevated areas, causing complex and obvious positioning errors. Due to the difficulty of eliminating these errors through baseline difference technology and the difficulty of obtaining a unified parameterized expression, it has a serious impact on the precise positioning of the urban environment [113]. Therefore, it is of great application value and significance to study the error correction methods of NLOS signals and reflection multipath effects for the achievement of GNSS high-precision positioning in the disciplines of crowd vehicle route navigation and driverless vehicles driving in urban areas [114].