esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanical Model Establishment and Equations Derivation
### Prediction Model of Deformation of Enclosure Structure  
The design theory of controlling deformation has been widely applied in foundation pit engineering. The deformation of enclosure structure is taken as the control parameter to control the effect on the adjacent shield tunnel because of the foundation pit excavation. This section establishes a prediction model for the deformation of the enclosure structure suitable for the internal bracing system.  
Figure 2 means the deformation diagram of the enclosure structure. The enclosure structure is deformed due to foundation pit excavation, in which: \(H\) is the height of the enclosure structure. The depth of excavation is \(d\). \(\eta\) is the embedded depth at any point on the sidewall of the retaining structure. Set the displacement of any point on the enclosure structure toward the foundation pit as \(\
u(\eta)\).  
[PERSON] et al. [19] fit the deformation increment of the enclosure structure with piecewise cosine function. According to the statistics of existing engineering, the deformation increment of the internal bracing enclosure structure reaches the maximum close to the excavation surface. The increment of the enclosure structure deformation is:  
\[\delta_{i}(\eta,d_{i}) =\frac{\delta_{\max}}{2}\Big{[}1-\cos\Big{(}\frac{\pi\eta}{d_{i} }\Big{)}\Big{]} (0\leq\eta\leq d_{i})\] (1) \[\delta_{i}(\eta,d_{i}) =\frac{\delta_{\max}}{2}\Big{\{}1-\cos\Big{\{}\frac{\pi[\eta+(H-2 d_{i})]}{H-d_{i}}\Big{\}}\Big{\}} (d_{i}\leq  
The maximum cumulative deformation is commonly taken as the control required for the deformation of the enclosure structure in engineering applications. Referring to the research of [PERSON] et al. [20], the ratio of the maximum deformation of the enclosure structure to the depth of excavation is selected as the control parameter in the calculation process in this study. Assume that the cumulative deformation of each layer meets the control value, so the maximum increment of deformation due to the \(i\)-th layer excavation is:  
\[\delta_{\text{max}i} =\frac{v_{\text{max}}}{d}\cdot d_{i} (i=1)\] \[\delta_{\text{max}i} =\frac{v_{\text{max}}}{d}\cdot d_{i}-\sum\limits_{j=1}^{i-1}\delta _{j}(d_{i}) (i\geq 2) \tag{2}\]  
when the excavation depth reaches \(d\) after \(n\)-th layers of excavation, the deformation at any point on the enclosure structure is:  
\[v(\eta)=\sum\limits_{i=1}^{n}\delta_{i}(\eta,d_{i}) \tag{3}\]  
where the depth of excavation is the sum of the excavation thickness of each previous layer: \(d=d_{1}+\sum\limits_{i=2}^{n}\left(d_{i}-d_{i-1}\right)\).
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 4 Performance Evaluation
### GPU Utilization  
In this subsection, we present experimental results for a comparison between the proposed scheduling and other existing scheduling policies in terms of cluster GPU utilization. The scheduling policies compared to our algorithm include First-Fit, Round-Robin, and the Tetris algorithm, which schedules tasks based on a bin-packing approach. Figure 10 displays a graph showing the GPU utilization of the cluster over the course of a day, with jobs scheduled based on time of day. The \(x\)-axis represents the number of jobs processed up to that point, and the \(y\)-axis shows the GPU utilization of the cluster after scheduling the corresponding number of jobs.  
From the graph, it is evident that the Round-Robin scheduling algorithm, which is widely used in task scheduling, is not suitable for maximizing GPU utilization when considering the diversity of ML workloads and the heterogeneity of GPU resources in the cluster. Note that Round-Robin scheduling assigns tasks in the queue to each GPU machine in turn without considering the load of the tasks and the capacity of GPUs, resulting in some GPUs being overloaded, while others are underutilized.  
Figure 9: Fitness values of proposed genetic algorithm as generation evolves.  
Tetris and First-Fit exhibit similar GPU utilization patterns, with each algorithm alternately gaining and losing advantage over time. Note that the Tetris algorithm schedules tasks by projecting each task's resource demands into Euclidean space and selecting the task-machine pair with the highest dot product, proving suboptimal for maximizing GPU utilization. As shown in the figure, however, the proposed algorithm consistently achieves the best results in terms of GPU utilization across the cluster. This is due to the optimized selection of task-machine pairs in terms of GPU utilization, leading to the minimized fragmentation of GPU resources across clusters. The improvement of the proposed algorithm against Round-Robin, First-Fit, and Tetris in terms of GPU utilization is 12.8%, 6.5%, and 6.6%, respectively.
## 3 Objective Measures Overview
### Sib  
SIIB [44] estimates the amount of information shared between a talker and a listener in bits per second. This measure is motivated by information theory, and suggests that the speech process can be understood as the transmission of a message from a talker to a listener. The message \(\{M\}\) can be thought of as a sequence of sentences or phonemes. The talker encodes the message into a speech signal \(\{X\}\) and sends the signal through a communication channel that may distort it and create a degraded speech signal \(\{Y\}\). So, the whole communication process is described by a Markov chain:  
\[\{M\}\rightarrow\{X\}\rightarrow\{Y\} \tag{1}\]  
where \(\{M\}\rightarrow\{X\}\) is the speech production channel and \(\{X\}\rightarrow\{Y\}\) in the environmental channel.  
This metric is based on the assumption that intelligibility is a function of the mutual information rate between the message \(\{M\}\) and the degraded speech \(\{Y\}\), so it needs the signal before the distortion as reference. The authors state that this measure works better in general conditions than other measures designed with a heuristic motivation and with an specific distortion or dataset in mind. SIIB estimates the intelligibility in an open scale, where an optimal signal obtains a score between 150 and 180 b/s.
## 2 System Model and Configuration
### Link Availability Analysis  
The atmospheric attenuation represents the combined effect of rain, gas, clouds and scintillation, and it is a function of the location, elevation angle and link availability of G/S [31; 32; 33]. This paper calculates the atmospheric attenuation according to the latest updates of P Series radiowave propagation recommendations issued by the International Telecommunication Union (ITU). Rain attenuation and scintillation attenuation are calculated according to ITU-R P.618-13 [31]. Cloud attenuation is calculated according to ITU-R P.840-8 [32]. Gaseous attenuation is calculated according to ITU-R P.676-12 [33].  
For a specific G/S of LEO EO satellites, the link condition is worst at the minimum 5\({}^{\circ}\) elevation angle, whose link availability directly determines the reliable data transmission capacity of the full transmission arc segment. When the location and elevation of G/S are determined, the link availability corresponding to the maximum allowable atmospheric attenuation value can be calculated according to the aforementioned three ITU recommendations. The link availability, link unavailability and outage period of Kashgar G/S, Beijing G/S and Sanya G/S are shown in Table 4. For a specific MCS and G/S, the summation  
Figure 4: Comparison between receiving value in clear sky and threshold of \(E_{\mathrm{S}}/N_{0}\).  
of the link availability and the link unavailability is 100%, and the outage period is the product of link unavailability and 365 days (1 year).  
As clearly shown in Table 4:  
1. For a specific G/S, with the increase of MCS, the link availability decreases, but the link unavailability and the outage period increase because of the smaller allowable atmospheric attenuation for the larger MCS.
2. For a specific MCS, since Kashgar G/S is in the driest location while Sanya G/S is in the wettest location, Kashgar G/S provides the most link availability, the least link unavailability and outage period. However, the link availability of Sanya G/S decreases significantly, and the link unavailability and the outage period of Sanya G/S increase significantly.
3. For different G/S, with the increase of MCS, the absolute values of link availability decreased have apparent difference. Kashgar G/S with little rain changes slightly (only 0.165%), and Beijing G/S with moderate rainfall changes moderately (3.689%), but Sanya G/S with abundant rainfall changes greatly (19.392%).
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 5 Results
### Hypothyroid Data  
In this second data analysis example, we consider the hypothyroid dataset [24] available from the UCI Machine Learning Data Repository [23]. The dataset comprises information on 2012 patients, with 122 diagnosed as suffering from hypothyroidism. Age of patients and measures related to functioning of the thyroid gland, such as TSH, T3, TT4, T4U, and FTI, are used as features for classification.  
Approximately 2/3 of the patients were randomly allocated to the training set, leaving the remaining 668 patients for the test set. The minority class proportion in the training set is equal to 0.0565, resulting in an imbalance ratio of IR = 16.68. In contrast, the test set exhibits a slightly higher proportion of the minority class at 0.0689.  
In Figure 4, the results obtained from fitting the models outlined in Section 2.2 and addressing data imbalance as discussed in Section 4 on the test set are presented. The EDDA model, devoid of any threshold-based or sample-based adjustments, exhibits the lowest sensitivity/recall (around 50%), resulting in both a less than satisfactory F2 score and balanced accuracy. In contrast, the MclustDA model, even without any correction for class imbalance, demonstrates noteworthy performance. Fine-tuning the threshold, particularly using \(\zeta_{\text{CV}}=0.01\) selected by cross-validation, or employing a sampling-based approach in the EDDA model, enhances the classification of the minority class. In the case of the MclustDA model, various adjustments result in marginal improvements, except for upsampling and SMOTE, which, conversely, lead to diminished performance compared to the unadjusted MclustDA model. This latter observation underlines that sampling-based approaches do not necessarily enhance classification accuracy.  
Figure 3: Performance outcomes for various models and methods addressing class imbalance on the wine quality dataset.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 1 Introduction
By using this method, we draw the conclusion that an attack on eNodeB of the LTE-R base station affects the time synchronization process the most. This result can provide a certain reference basis for the evolution of the GSM-R time synchronization network to LTE-R.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
## 1 Introduction
### State-of-the-Art
#### 1.1.2 the Aspect of Storing Data in Decentralized Systems  
The field of storing and retrieving big data is already a mature domain, and represents a step forward from the classical SQL database solutions. In order to store and process ever larger amounts of data, the first step was to build clusters, such as the MySQL cluster [22], and some subsequent steps were the NoSQL databases [8], one of which is based on the Google Map-Reduce model [23]. Other popular solutions based on the Map-Reduce model have been implemented in the Apache projects Hadoop [21] and, more recently, Spark [20]. At almost the same time with the evolution of these solutions, distributed filesystems have evolved, such as NFS (Network Filesystem) [24], CFS [1] (the peer-to-peer Chord [9]-based distributed filesystem) and later also Blobseer [25; 26], aiming to store the files in such a manner that makes them _persistent_, _coherent_ and _more efficiently to be retrieved_ in a large-scale distributed system. The NoSQL storage solutions, including the peer-to-peer-based distributed filesystems, have been used for applications that run on top of high and medium-volatile infrastructure. A common characteristic of most of these solutions is the fact that they are not based on the client-server model, but they are mostly _decentralized_.  
Aside from using the novel peer-to-peer distributed hash table (DHT) techniques, in order to benefit from their flexibility and elegance of data retrieval, an interesting idea is to also solve the scheduling of resource allocation [27]. More recently, in the field of data mining, there are solutions for bringing the data needed by a node closer to it, so as to mask the data transfer latency, in a manner similar to how it is performed by the hardware cache, standing between the processor (i.e., in a distributed system, a processing node) and the main memory (i.e., in a distributed system, a data source node) [28; 29; 30; 31].  
To summarize, work in the field of large-scale distributed systems data storage and manipulation ranges from:  
1. Storing the data in distributed filesystems [24; 25; 32], in classical SQL databases [22] and more recently in No-SQL distributed databases [8];2. Smart distribution and retrieval techniques for geographically distributed data, a field in which the P2P DHTs [9] have been of great help and have become very popular;
3. Search engines and large scale distributed applications for collecting and mining huge amounts of data [20; 21; 23].
## 4 Discussion
### Integration of Stem Cells with GTR Membranes  
The integration of stem cells with GTR membranes represents a significant advancement in regenerative medicine [36]. Stem cells, such as OMSFCs and DPSCs, possess self-renewal and multipotency, enabling them to differentiate into multiple cell types and regenerate complex tissues [37]. [PERSON] et al. [27] demonstrated that OMSFCs cultured using the neurosphere technique could regenerate cement-like structures and ectopic bone in non-hard tissue regions. This aligns with previous findings that stem cells, when embedded in bioengineered constructs, adapt to their microenvironment and respond to regenerative cues, even in compromised conditions [38].  
Similarly, [PERSON] et al. [28] reported that DPSCs embedded in CS/Gel scaffolds pre-treated with BMP-2 significantly enhanced osteogenesis and mineralized matrix deposition. The role of BMP-2 in upregulating osteogenic pathways has been extensively documented, showing promise in promoting bone repair, particularly in diabetic patients where chronic hyperglycemia disrupts osteoblast function [39]. In cancer patients, stem cell therapies embedded within GTR membranes could mitigate treatment-induced bone loss and accelerate healing following surgical resections or radiotherapy [40].  
Collectively, these studies highlight that stem cell integration is not merely additive but foundational for the success of advanced GTR therapies. By embedding stem cells within bioengineered scaffolds and enhancing their activity through growth factors, GTR membranes can overcome significant regenerative deficits observed in immunocompromised populations [41].
## 2 Materials and Methods
### Feature Selection Based on mRMR Technique
\[\max_{S}(W_{S}/V_{S}), \tag{9}\]  
The computational burden of obtaining the optimal feature subset is huge. Thus, mRMR technology uses the incremental search algorithm to sort all feature groups and then select the optimal feature subset. The detailed process is as follows. 1. Define the set of selected feature groups as \(S\). 2. Calculate the correlation between each group of input features \(x_{1}\) and the target \(y\), and then select the group of input features \(x^{(1)}\) that is most relevant to the target according to Equation (10). The selected group of input features \(x^{(1)}\) is added to the set \(S\) as the first input feature group. \[\max_{X^{(1)}\in D}I\big{(}x^{(1)};y\big{)},\] (10)
3. Select the next group of input features \(x^{(j)}\) according to Equation (11), using the previously recorded features \(x^{(i)}\) in \(S\). \[\max_{x^{(j)}\in D-S}\left[I\big{(}x^{(j)};y\big{)}/\left(\frac{1}{|S|}\sum_{ x^{(j)}\in S}I\big{(}x^{(j)};x^{(i)}\big{)}\right)\right],\] (11)
4. Add the selected feature group \(x^{(j)}\) in Step 3 to the set \(S\), and then repeat Step 3 until all input features are sorted. The final sorting result indicates that if a subset of \(N_{1}(N_{1}\leq N)\) feature groups are selected as the input of the learning machine, the first \(N_{1}\) feature groups in the set \(S\) will be the optimal subset, which shows a stronger correlation with the target and less interior redundancy. Since the bus \(V\) has a total of 39 groups of features, and the generator \(P_{e}\) had only 10 groups of features, the bus \(V\) seems to make a greater contribution to the transient stability prediction in mRMR feature selection. On the other hand, the transient stability prediction results in Section 3.2 show that the classification accuracy of the generator \(P_{e}\) is higher than that of the bus \(V\). However, both types of features have better prediction results than the electromechanical variables of \(\delta\) and \(\omega\). Section 2.4 will aim to combine these two types of superior features \(V\) and \(P_{e}\), and jointly form an improved transient stability prediction model.
# PC5-Based Cellular-V2X Evolution and Deployment
## 4 C-V2X Deployment in Global  
Even 802.11p based V2X technology was early adopted by some global automotive manufacturers as an electronic toll collection technology because C-V2X demonstrates a forward evolutionary path towards 5G. 5 GAA [10] started to develop C-V2X technology. Moreover, FCC (Federal Communications Commission) officially announced to allocate 5.9 GHz ITS spectrum for C-V2X in December 2019 [21]. Finally, FCC decided to retain 30 megahertz of spectrum in the 5.895-5.925 GHz for the ITS radio services using C-V2X technology in November 2020 [2]. Meanwhile, Europe is developing a new EN (European Standard) to define the use of C-V2X as an access layer technology for C-ITS (Cooperative-Intelligent Transport System), which is approved by ETSI. Australia originally started on-road tests of C-V2X technology in Victoria, late 2018. In China [10; 14], first C-V2X trials were launched in 2016 by using Triple-level CATT(Datang), Huawei Hisilicon and Qualcomm chipset. Interoperability testing of PC5-based LTE-V2X applications with Multi-vendor Interoperability had finished in Shanghai in November 2018, and C-V2X \"Four Layers\" Interoperability Application Demonstration focusing on security mechanism was organized in Shanghai in October 2019. Japan C-V2X trial began in 2018, and the use cases include V2V, V2P, V2I, as well as V2N operations over cellular network-based wide area communications with cloud access. South Korea had successfully demonstrated 5G C-V2X communications between autonomous test vehicles (AVs) in 2019.  
Considering the current 3 GPP releases and supply chain readiness, 5 GAA even developed a long-term blueprint of traffic efficiency and basic safety C-V2X use cases worldwide in September of 2020 [22] as shown in Figure 5.  
As a result, C-V2X has gained momentum in the United States, Europe, Australia, China, Japan, Korea, and other markets. C-V2X is dominating globally, and Taiwan is one of the countries at the forefront of C-V2X development supported by the government in the intelligent transportation systems programs [23].
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 3 Sea Surface Cross-Section
### Sea Spray Signatures
In contrast to the NSCAT4 GMF, the observation data demonstrate a NRCS saturation tendency, starting at \(U>25\) m s\({}^{-1}\). The KaDPMod GMF does not reproduce these high wind observations well, because its wind dependence is fitted by a power function, \(\sigma\propto U^{n}\), based on weaker wind data, \(U<18\) m s\({}^{-1}\). Alternatively, the observed NRCS at \(\theta=45^{\circ}\) is fit with a second-order polynomial function, \(\sigma_{\rm dB}=A\cdot U_{\rm dB}^{2}+B\cdot U_{\rm dB}+C\), where \(\sigma_{\rm dB}\) and wind speed, \(U_{\rm dB}\), are in dB-units, \(X_{\rm dB}=10\log_{10}X\) (see Figure 5c,f, green dashed lines).
## 1 Introduction
It is capable of modeling the abundant semantics of multi-type components of Android applications as a heterogeneous graph and summarizing it into numerical representations to identify whether an Android application is malicious or not. Specifically, the first step in this method is to extract the entities in Android applications, including API  
Figure 1: Example illustrating the differences among three structured approaches. NN: neural network. Android application in blue, red and black: benign application, malicious application and uncategorized application, respectively. Nodes and relations in multiple colors: multiple types of nodes and relations. calls, permissions, shared object files, intents, Apps, and features, and the relations among them in Android application packages as a structured heterogeneous graph. HertDroid employs a module called Android APK (Android Application Package) Transformer to differentiate between benign and malicious applications. To address the issue of the redundancy of APIs, the Android APK Transformer performs a targeted API dropout on the heterogeneous graph based on a series of centralities, which not only improves the performance of detection but also reduces resource consumption. The processed heterogeneous graph is fed into a multi-component self-attention module to embed the nodes in the graph into the same embedding space. Finally, a Multilayer Perceptron (MLP) will be trained to detect malicious Android applications deploying these node embeddings. The contributions we make to Android malware detection in this paper can be encapsulated as follows:  
Efficient method. The method we proposed outperforms the baselines by 9.25% on our dataset dated from 2018 to 2022. User-friendly method. The method we proposed can identify malicious behavior based on the heterogeneous graph without manually designing meta paths. Lightweight method. The method we propose has a relatively small number of parameters, making it capable of handling larger-scale data under limited resources.
## 2 Materials and Methods
### Data Analysis
#### 2.2.1 Linear Interpolation  
The linear interpolation method is often used to estimate the unknown values or missing data through the known values or available data.  
\[Yl_{r}p_{r}=y1+((x-x1)/(x2-x1))\times(y2-y1) \tag{1}\]  
where \(Yl_{r}p_{r}\) represents the unknown values or missing data for literacy and poverty rates, \(x\) represents the known values of literacy and poverty rates, \(x1\) and \(y1\) are the coordinates that are below the known \(x\) value, and \(x2\) and \(y2\) are the coordinates that are above the known \(x\) value.  
Once linear interpolation was finalized, a complete data set for poverty and literacy rates was obtained from 1991 to 2016. Data for these two variables were mainly missing from 1991 to 1994 for most of the regions. It is important to note that this was performed for each region, country and a regional average for poverty and literacy rate (%) was computed (see online database).  
The climate data were equally downloaded in the format of monthly precipitation and temperature for each country. A regional average was computed from these monthly and country-specific precipitation (mm) and temperature (\"t) data. Since the goal is to assess readiness for climate change adaptation, it becomes necessary to normalize the regional monthly temperature and precipitation data to ease comparison.