esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Bluetooth Low Energy: A Systematic Review
## 3 BLE Performance
### Latency
In _connections_, latency can be measured either as the _one-way_ delay time, that is the time a packet spends from the source to the destination, or as the _round-trip_ delay time, i.e., the sum of the _one-way_ latency from the source to the destination and the _one-way_ latency in the opposite way for the response. [PERSON] et al. [50] also simulated the latency during connections between a _master_ and a _slave_, in one-way and round-trip ATT communications, in relation to _connInterval_ and BER values, as shown in Figure 25.  
Figure 24: Mean discovery latency with various parameter settings. (**a**) The mean discovery latency as the number of _scanners_ is increased (\(\tau_{SI}\) = 10,240, \(\tau_{SW}\) = 2560, \(\tau_{WA}\) = 10, \(\tau_{AI}\) = 1280); (**b**) the mean discovery latency as the number of _advertisers_ increases (\(\tau_{SI}\) = 10,240, \(\tau_{SW}\) = 2560, \(\tau_{WA}\) = 10, \(\tau_{AI}\) = 1280); (**c**) the mean discovery latency as _ScanWindow_ (\(\tau_{SW}\)) is varied (\(\tau_{SI}\) = 10,240, \(\tau_{WA}\) = 10, \(\tau_{AI}\) = 1280, M = 5, N = 5); (**d**) the mean discovery latency as \(\tau_{WA}\) is varied (\(\tau_{SI}\) = 10,240, \(\tau_{SW}\) = 2560, \(\tau_{AI}\) = 1280, M = 5, N = 5); (**e**) the mean discovery latency as _AdvInterval_ (\(\tau_{AI}\)) is varied (\(\tau_{SI}\) = 10,240, \(\tau_{SW}\) = 2560, \(\tau_{WA}\) = 10, M = 5, N = 5). \(\tau_{SI}\) is the _scanInterval_; \(\tau_{SW}\) is the _ScanWindow_; \(\tau_{AI}\) is the _advInterval_; M is the number of _advertiser_; N is the number of _scanners_. Adapted from [88]. The figure shows how the latency, with a reasonable BER (included between \(10^{-6}\) and \(10^{-4}\)), is included in a range of values from 0.6 ms to 10 ms, both in _one-way_ and _round-trip_ communications. In [92], the authors developed a new connection establishment method for Mobile Ad Hoc Networks (MANET). These networks usually use a method to establish the connection based on Classic Bluetooth. On the other side, this paper shows that the new modality proposed, firstly based on BLE and then switching on Classic Bluetooth, is almost 75% shorter with an energy consumption 18% lower.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 2 Methodology of the MPM  
The MPM, as a meshless numerical computation method, employs a dual description with Lagrangian material points and Eulerian background grids, thereby avoiding the mesh distortion issues encountered in traditional Lagrangian solutions and overcoming the challenges of non-advective term treatment in Eulerian methods. It is precisely because the MPM combines the strengths of both approaches that it can effectively address problems such as moving material interfaces, multi-material coupling, and large-amplitude flow deformations that arise in grid-based methods. The fundamental concept of the MPM is to discretize a continuum into a set of material points carrying all material information and to establish background grids in the regions where these particles move. The motion of the material points represents the deformation of the object, while the background grids carry no material information and are solely used to solve spatial derivatives and momentum equations. Shape functions serve as the bridge between material points and background grids. In a single time-step calculation, material point information is first mapped onto the background grid through shape functions. Then, spatial derivatives and momentum equations are solved at the background grid nodes. The solved physical quantities are mapped back to the material points through shape functions, and finally, the deformed grid is discarded. In the next time step, a new background grid is used, as shown in Figure 1.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 2 Imaging Model of Non-Orthogonal Waveform MIMO Radar
### MIMO Radar Echo Signal Model
Let \(\boldsymbol{T}=[T_{0},\boldsymbol{T}_{1},\cdots,\boldsymbol{T}_{K-1}]\in \mathbb{C}^{(K+L)\times LK}\) and define \(\boldsymbol{T}\odot\boldsymbol{S}\triangleq[\boldsymbol{T}_{0}\boldsymbol{S },\boldsymbol{T}_{1}\boldsymbol{S},\cdots,\boldsymbol{T}_{K-1}\boldsymbol{S }]\in\mathbb{C}^{(K+L)\times MK}\), so the echo signal of the whole imaging scene received at \(R_{n}\) can be expressed as  
\[\boldsymbol{y}_{n}=\boldsymbol{T}\odot\boldsymbol{S}\cdot\boldsymbol{\theta} _{n}+\varepsilon \tag{3}\]  
where \(\boldsymbol{\theta}_{n}=\left[\left(\boldsymbol{\theta}_{n}^{0}\right)^{T}, \left(\boldsymbol{\theta}_{n}^{1}\right)^{T},\cdots,\left(\boldsymbol{\theta} _{n}^{K-1}\right)^{T}\right]^{T}\in\mathbb{C}^{MK\times 1}\) is the corresponding target complex range image at \(R_{n}\), and \(\varepsilon\) represents the received noise.
## 3 Experiments and Results
### Mask Generator
#### 3.1.1 Dataset, Evaluation Metrics, and Hardware  
The AVSbench dataset, specifically designed for fine-grained audio-visual segmentation tasks, was employed in the experiments for the mask generator due to its compact size and ease of use. Each video in the dataset was trimmed to a 5 s duration, with the last frame from every 1 s interval extracted as an image frame. The test subsets included masks for all image frames to streamline testing and validation [1].  
Since the proposed method focuses exclusively on single-object segmentation, only the single-source subset (S4) of AVSbench was utilized. Furthermore, as the Wav2 CLIP model relies on frame-level audio embedding, the dataset's audio log MEL spectrogram was not used. Instead, segments extracted directly from the original audio files served as the audio input.  
The evaluation metrics included the Jaccard Index, mean Intersection over Union (mIoU), and F-Score, which are widely accepted as critical measures in audio-visual segmentation. The experiments were conducted on a laptop equipped with an Intel i9-12900H CPU, 32 GB RAM, and an NVIDIA RTX 3080 GPU with 16 GB VRAM. The software environment consisted of Ubuntu 20.04, Python 3.8.16, and PyTorch 2.4.1.
## 3 Results
### Experimental Results
#### 3.4.2 Robustness Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Model** & **5\%** & **10\%** & **15\%** & **20\%** \\ \hline \multirow{5}{*}{Qijiang} & SVR & 0.611 (0.361) & 0.599 (0.233) & 0.437 (0.119) & 0.441 (0.012) \\  & RNN & 0.709 (0.412) & 0.656 (0.246) & 0.525 (0.145) & 0.538 (0.023) \\ \cline{1-1}  & LSTM & 0.712 (0.698) & 0.684 (0.259) & 0.570 (0.189) & 0.564 (0.012) \\ \cline{1-1}  & STALSTM & 0.807 (0.634) & 0.791 (0.312) & 0.634 (0.259) & 0.608 (0.031) \\ \cline{1-1}  & AGCLSTM & 0.813 (0.287) & 0.792 (0.189) & 0.673 (0.012) & 0.632 (0.022) \\ \cline{1-1}  & DK-HTAN & 0.824 (0.264) & 0.735 (0.174) & 0.711 (0.014) & 0.683 (0.027) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PICP performance of various models under different proportions of noisy data. outliers. Taking the Qijiang River Basin as an example, the PICP value of DK is 14%-28% higher than that of STALSTM. This may be because the data preprocessing method of tensor factor decomposition is adopted in this paper, which reduces the impact of outliers on the prediction accuracy of the model. The robustness of the model to outliers is improved. However, it should be noted that when the proportion of outliers is high, the PICP performance of DK also shows a trend of weakening. This may be because the input matrix of the model deviates from the normal range with the increase in outliers, leading to the decline of the prediction performance of the model.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 3 Related Work
### BPM Meets IoT  
The introduction of IoT technology into the BPM domain has been vibrantly discussed in recent years. Related work addresses a broad range of aspects to augment the modeling [24; 25; 26; 27], execution [17; 28; 29; 30], monitoring [14; 31; 32; 33], and mining [2; 4; 11] of business processes with data and concepts from the IoT and CPS. Our work can be considered as a pre-processing stage of process mining where process-awareness of IoT data is increased and subsequently used for _event extraction_, _event abstraction_ and _event correlation_[34].  
The _BPM-IoT Manifesto_ discusses the benefits and challenges of bringing the worlds of BPM and IoT together [1]. Sensors and actuators are new sources of real-time data that provide opportunities to develop advanced monitoring approaches, e.g., for condition monitoring, predictive maintenance [35] or verification of process outcomes [36]. With our work we aim at contributing a solution to the challenge _Bridging the Gap Between Event-Based and Process-Based Systems_ as we raise the abstraction from IoT sensor and actuator events to the level of business processes. From these insights, we may then also contribute to the challenge _Detecting New Processes from Data_.  
The IoT introduces new data sources for monitoring activities, behavior and daily routines, e.g., using multi-modal motion and vision sensors to detect objects or humans [37]. Many approaches specialize in the detection of specific activities from one or more sensors [38; 39]. The automated correlation of detected activities with each other in the form of daily routines and habits [40; 41] as well as business processes [4] for process mining [42] is also receiving increasing attention. We aim at providing a general method for correlating data from arbitrary types of IoT sensors and actuators with the execution of arbitrary types of process activities executed in the physical world.
# Analysis of Collapse Mechanism and Treatment Evaluation of a Deeply Buried Hard Rock Tunnel
## 3 Mechanism Analysis of Tunnel Collapse
### Mechanism Analysis of Tunnel Collapse
#### 3.4.2 Analysis of the Mobility of Block Cone on Free Face  
In the calculation of the block cone, the free face should be added into the calculation. In this paper, the tunnel vault is the free face, and the tunnel contour is considered to be a circular arch. The unit normal vector of the top surface is:  
\[\vec{n_{4}}=(0,0,1) \tag{17}\]  
The edge vector of the intersection between the free surface and other structural surfaces is:  
\[\left[\begin{array}{c}\vec{I_{14}}\\ \vec{I_{24}}\\ \vec{I_{34}}\end{array}\right]=\left[\begin{array}{ccc}-0.1170&0.3214&0.0000 \\ -0.9623&0.0842&0.0000\\ 0.2988&-0.6409&0.0000\end{array}\right] \tag{18}\]  
The direction parameter matrix is:  
\[[I]=\left[\begin{array}{cccc}0&0&1&1\\ 0&-1&0&-1\\ 1&0&0&1\\ 0&-1&1&0\\ 1&0&-1&0\\ -1&1&0&0\end{array}\right] \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block Number** & **Crevice Cone** & **Block Cone** & **Movable or Not** \\ \hline
000 & Nonempty set & Nonempty set & No \\
001 & Nonempty set & Nonempty set & No \\
010 & Nonempty set & Nonempty set & No \\
100 & Nonempty set & Nonempty set & No \\
011 & Nonempty set & Nonempty set & No \\
110 & Nonempty set & Nonempty set & No \\
101 & Nonempty set & Nonempty set & No \\
111 & Nonempty set & Empty set & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of movable block of tunnel vault.  
Similarly, No. 101 block is used as an example:  
\[[D]=\left[\begin{array}{cccc}-1&0&0&0\\ 0&1&0&0\\ 0&0&-1&0\\ 0&0&0&1\end{array}\right] \tag{20}\]  
So there are:  
\[[T]=[I][D]=\left[\begin{array}{cccc}0&0&1&1\\ 0&-1&0&-1\\ 1&0&0&1\\ 0&-1&1&0\\ 1&0&-1&0\\ -1&1&0&0\end{array}\right]\left[\begin{array}{cccc}-1&0&0&0\\ 0&1&0&0\\ 0&0&-1&0\\ 0&0&0&1\end{array}\right]=\left[\begin{array}{cccc}0&0&-1&1\\ 0&-1&0&-1\\ -1&0&0&1\\ 0&-1&-1&0\\ -1&0&1&0\\ 1&1&0&0\end{array}\right] \tag{21}\]  
According to the discriminant matrix and block finite discriminant method, the block cone is a non-empty set, so the block is an immovable block.  
Accordingly, the same calculation is carried out for other blocks, and the results are shown in Table 2.  
It can be seen that at the vault of the tunnel, No. 111 block is movable, and may collapse in the construction process. The rest blocks are immovable. Therefore, it is further indicated that the collapse of the vault is caused by the unstable partial and small-scale collapse of the key block.
## Questions concerning the Relationship between CLIL and Digital/Computer Technology
### How Is CLIL Teaching Being Practiced Using Online Learning Environments?
#### 2.4.3 The Gap between CLIL Online Class and TELL  
As [PERSON] [32] mentions, CLIL is an underexplored educational approach in terms of the learning efficacy of distance education. This is demonstrated in the fact that there is insufficient literature available to investigate the integration of TELL and CLIL online teaching, while literature on CALL and CLIL integrated learning is more widely available.  
Figure 1: Multimodalities—Entektualization Cycle (MEC). Reprinted with permission from Ref. [21]. 2016, [PERSON], [PERSON].  
However, the studies on CALL and CLIL-integrated classes generally discuss the role and impact of various technologies in the classroom. Little literature connects technology as a language-use environment, which is proposed by [PERSON] and [PERSON] and [PERSON] (2012) as the main characteristic of TELL. Furthermore, while there is a partial discussion of the literature regarding CLIL's online classes, the authors do not contribute to TELL research by correlating the technological environment of CLIL's online classes with the term TELL. For example, T-CLIL is a new word proposed by [PERSON] (2013) that blends technology-enhanced content and language-integrated learning instruction. Although T-CLIL appears to be related to TELL and CLIL, the authors did not elaborate on it further in the paper. As a result, this research highlights this gap in sufficient elaboration. In order to contribute to addressing this gap, a study was undertaken into the learning results and challenges encountered in CLIL online classes that integrate TELL's delivery platform and classroom activities.
## 4 Future Directions
### Edge Computing and Real-Time Industrial Analytics  
The convergence of low-code with edge computing is critical for latency-sensitive applications, such as autonomous vehicle telemetry and energy grid monitoring. Platforms like Siemens MindSphere now support edge-native (designed to run directly on edge devices with minimal reliance on cloud resources) low-code apps, enabling real-time fault detection in automotive production lines. A recent study by [PERSON] et al. [63] demonstrates how low-code apps deployed on edge devices reduced computational overhead in smart grid simulations, achieving energy efficiency improvements.  
Future research should prioritize adaptive resource allocation algorithms to optimize low-code app performance in resource-constrained environments, such as remote oil rigs or satellite systems. For example, [PERSON] et al. [28] propose a dynamic compilation framework that adjusts code generation based on hardware specifications, ensuring compatibility with heterogeneous IoT ecosystems.  
Figure 2: Timeline of Al/ML adoption in low-code by industry.
## 2 Literature Review
### SNSAs Use in Relation to Students' Life Satisfaction  
Life satisfaction has been defined as an intrapersonal feeling of well-being and a general evaluation of one's life in relation to self and current status [25]. In the online community, there appears to be a positive association between frequent online communication and social networks on the one hand, and an individual's life satisfaction on the other hand [26; 27]. Furthermore, using SNSAs for sharing information and providing encouragement produces different findings associated with varying social support domains [28]. For example, [PERSON], [PERSON], and [PERSON] [29] found that using SNSAs stimulated an increased number of connections with friends, which led to the reception of positive feedback, thereby, enhancing life satisfaction of the adolescent participants.  
Similarly, the range of applications and tools on SNSAs can support different interactions, which positively influence life satisfaction and increasing use of SNSAs [28]. SNSAs appear to generate supportive emotions, such as enjoyment, which have been associated with life satisfaction [30]. In a USA study by [PERSON] et al. [31], life satisfaction was found to be associated with the personality of individuals on SNSAs with two indicators: emotional stability and extraversion. They reported a strong positive correlation between being emotionally stable and extraverted, and life satisfaction. A high level of extraversion was correlated with a high level of using SNSAs, suggesting that frequent users who are extraverted and emotionally stable tend to be satisfied with their lives [31].  
[PERSON] et al.'s [16] study with 286 undergraduate students at Michigan State University found that the intensity of using SNSAs, such as Facebook, correlated with a high life satisfaction and more social connections with friends. Similar outcomes were reported by [PERSON] et al. [32] in relation to virtual communities conducted on social networking platforms. [PERSON] et al. [15] study of 2603 college students found that intensity of using SNSAs was associated positively with students' life satisfaction.  
Previous studies have demonstrated a positive relationship between the use of SNSAs and students' life satisfaction while other research found this relationship could be reciprocal between their levels [16]. A German study by [PERSON] et al. [33] suggests that feelings of envy through SNSAs use can impact negatively on the life satisfaction of individuals. Context is important and particularly pertinent to the Saudi context, where aspects such as trust and civic and political engagement have slightly different connotations.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 2 Network Model and Spectral Efficiency  
The model of the user-centric cell-free massive MIMO system is shown in Figure 1. This paper considers L APs equipped with N antennas and K single antenna user equipments (UEs). All APs are connected to the CPU through the fronthaul, and it is assumed that APs and UEs operate in time division duplexing (TDD) as a massive MIMO [1]. Each AP does not serve all users but only a few users. In this network model, each coherence block is represented by \(\tau_{c}\) and is divided into three phases  
\[g_{kl}\sim\mathcal{N}_{\mathbb{C}}(0,1). \tag{1}\]  
In the \(h_{kl}\), \(\sqrt{\beta_{mk}}\) represents the large scale fading that consists of pathloss and shadow fading, while \(g_{mk}\) represents the small-scale fading. For all \(l=1\),..., L, \(k=1\),..., K, \(g_{mk}\) is assumed to be independent and identically distributed (i.i.d.) random variables [23; 24]. This paper defines \(M=LN\); then, the collective channel of \(k\) can be represented as \(h_{kl}=[h_{k\mathbb{L}}^{\top},h_{k\mathbb{Z}}^{\top},\ldots,h_{kl}^{\top}]^{ \top}\in\mathbb{C}^{M}\). In this paper, \(q_{k}\) denotes the symbol for \(k\) user, and it satisfies \(\{\left|q_{k}\right|^{2}\}=1\). All of the symbols are mutually independent, and all channel coefficients and noise are independent of the symbols. In addition, the matrix \(\alpha\), which indicates the connection between APs and UEs, is defined. If AP \(l\) and user \(k\) are connected, \(\alpha_{kl}=1\); otherwise, it has a value of 0. This matrix makes the difference between the original cell-free system and the user-centric system.
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
## 3 Analysis of Test Results
### Local Wind Force  
Wind force is composed by drag force, lift force and torque. For square or rectangular tall buildings, wind-induced torque is usually very small. Therefore, only the drag force and lift force are discussed in this paper. The mean and RMS drag force coefficients are obtained with Equation (5) and the RMS lift force coefficients are determined by Equation (6):  
\[C_{D}(z_{i})=\frac{\overline{F_{D}(z_{i})}}{A(z_{i})q_{H}},\ C_{D}^{\prime}(z_{ i})=\frac{\sigma_{D}(z_{i})}{A(z_{i})q_{H}} \tag{5}\]  
\[C_{L}^{\prime}(z_{i})=\frac{\sigma_{L}(z_{i})}{A(z_{i})q_{H}} \tag{6}\]  
where, \(C_{D}(z_{i})\) is the mean drag coefficient of the \(i\) th measurement layer, \(C\prime_{D}(z_{i})\) and \(C\prime_{L}(z_{i})\) are the RMS drag coefficient and RMS lift coefficient of the \(i\) th measurement layer, respectively, \(F_{D}(z_{i})\) is the mean drag force integrated by the simulated measured wind pressures from pressure taps and the associated area on the \(i\) th measurement layer, \(\sigma_{D}(z_{i})\) and \(\sigma_{L}(z_{i})\) are the corresponding RMS drag force and RMS lift force, \(A(z_{i})\) is building frontal area of the \(i\) th measurement layer, and \(q_{H}\) is the incoming wind pressure at the top of the model.  
Figure 8 illustrates the definition of wind force and wind attack angle. Since the unfavorable wind direction for wind resistant design of an isolated rectangular tall building  
Figure 7: The comparisons of wind pressure coefficients at the height of 2/3H for corner set-back models. (**a**) Mean wind pressure coefficients at the height of 2/3H for models. (**b**) RMS wind pressure coefficients at the height of 2/3H for models.  
is 0\({}^{\circ}\)[18], and the mean lift force and the mean torsional component under this attack angle are close to zero, hereby only the mean drag force, RMS drag force and RMS lift force of the five models are discussed in this study.
# Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon
## 3 Results
### Statistical Exploration
The observed relationships between the output variables \(\rho\), \(U\), and \(H\) and the input parameters \(T\), \(P\), and \(R_{c}\) provides insights into the equations of state, which describe how thermodynamic properties evolve under different conditions. Each data point in the scatter plots represents a specific thermodynamic state of the a-Si, offering a detailed visualization of how structural and energetic properties respond to changes in the cooling rate, temperature, and pressure. These plots serve as a crucial tool for identifying trends, verifying consistency with known physical principles, and providing insights into the interplay between atomic-scale interactions and macroscopic material behavior. Moreover, understanding these dependencies is essential for tailoring processing conditions to achieve desired material properties, particularly in applications where the density, internal energy, or enthalpy play a critical role. However, while the scatter plots effectively capture qualitative trends, obtaining a precise mathematical representation of these relationships requires predictive modeling. The next section presents mathematical models to quantitatively describe these thermodynamic equations of state, enabling more accurate predictions of a-Si behavior under varying conditions.
## 3 Results, Case Studies, and Applications  
In the domain of the public sector and e-governance, the applicability of the LLMs' capabilities is extremely important and beneficial. In our framework, we present the advantages of the above by highlighting an advanced practical example of how single or incorporated sources could provide accurate and sufficient information to the user. We will focus on a Q&A on various sources and the inclusion of all of these to provide a comprehensive answer. Each RAG pipeline from each source could be used individually and together, leveraging agents and conversational pipelines.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Analysis of Our Proposed Model  
To deeply investigate the clustering characteristics and cluster formation behavior of our model, we derive formulations using the stochastic properties of our model for the number of CHs in terms of the SD, and the COV in the selection of CHs. This formulation is very helpful in designing and selection of optimal CHs for our proposed model.