esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Analysis  
### Simulation Setup  
In the simulation, the number \(M\) of microphones was set to 10, the spacing element \(d\) was set to 0.02 m and the acoustic speed was 340 m/s. A TIMIT corpus [27] was used to generate the observed signal through a microphone array signal simulator given in [28]. A total of 200 utterances was randomly selected for the target speech source and 200 other utterances were randomly selected for the interference speech source. The signal-to-noise ratio (SNR) of white noise was set to 0 dB, 5 dB, 10 dB, 15 dB, 20 dB and 25 dB, respectively. The signal-to-interference ratio (SIR) was set to 0 dB. The DOAs of the target source and the interference source were randomly generated from \(0^{\circ}\) to \(180^{\circ}\) with a minimum interval of \(25^{\circ}\). The sampling rate of the signal was 8 kHz, the frame length was set to 256 samples and a 256-point fast Fourier transform (FFT) was used. The Blackman-Harris window and Hamming window, both with a 50% overlap, were used for signal analysis and signal synthesis, respectively. The length of these two windows encompassed all 256 samples.  
The signal-to-interference-plus-noise ratio (SINR) [5], perceptual evaluation of speech quality (PESQ) [29], short-time objective intelligibility (STOI) [30] and speech distortion index (SDI) [2] were used as the evaluation measures. Among them, the SINR indicates the capability of the interference suppression and noise reduction, the PESQ and STOI indicate the speech quality and intelligibility and the SDI indicates the target speech distortion. Hence, the larger the SINR, PESQ and STOI, the better the performance of the algorithm. The smaller the SDI, the better the performance of the algorithm. We compared the proposed method with the CMB of Equation (4), ideal MVDR beamformer (IMB) of Equation (2), HKB, LNR, SMF, BPR, GLC, NRP-TMMSE, PFLI and IDL-SMI.
## 4 Results and Discussion
### Blade Vibration under Different Normal Loads
At this time, the relative motion of the contact surface consumes the vibration energy, so the amplitude will be slightly reduced. With an increase in the normal force, the relative motion of the model blade contact surface weakens or even ceases to slip. So, the contact surface develops fretting slip or even becomes viscous, and the vibration characteristics are also similar to linear vibration characteristics. The various curves can be seen more clearly by drawing them in two concentrated figures (Figures 16 and 17). In the numerical analysis for a certain value of blade exciting force, the response amplitude for the test blade increases with the normal force as the interface increases gradually. However, under 1 N conditions, the contact state of the model interface of the outer holding ring becomes viscous under a 20 N normal load, and the vibration amplitude is almost unchanged as the normal force increases. Under an excitation force of 2 N and normal loads of 10 and 20 N, the resonance amplitude is smaller than at normal loads of 30 N and 40 N, which indicates that the test blade model contact interface appears, to a certain extent, to possess nonlinear characteristics. Because the relative motion of the test model contact interface consumes the vibration energy, test blade amplitude decreases. Figure 16: Response curve under different normal loads with 1 N exciting force. Figure 17: Response curve under different normal loads with 2 N exciting force.
## 4 Performance Evaluation
### Results on the Network Connectivity  
Figure 6(a) shows the results of the connectivity probability \(p_{c}\) under different transmission range \(r\) varying from 1,000 m to 5,000 m with \(\rho\) being either \(1\times 10^{-7}\) or \(2\times 10^{-7}\). Again, it demonstrates that the analytic and simulation results match closely. Moreover, it can be seen that a larger \(\rho\) results in a greater connectivity. This is intuitive because a larger \(\rho\) will increase the probability of having one-hop neighbors of any sensor node, thus rending a higher connectivity.  
Figure 6(b) shows the connectivity with the optimal transmission range under different node density values. It can be seen that for a certain \(a\) and \(\rho\), the optimal transmission range that achieves minimum average energy consumption might not lead to an satisfactory connectivity. For example, when \(a=5,000\) m and \(\rho=1\times 10^{-7}\), the optimum transmission range is 2,852 m, resulting an average energy consumption of 0.00317 J/m but a connectivity of 0.4317, which does not meet the practical connectivity requirement for UWSNs. Thus, the selection of the transmission range needs to take into consideration the tradeoff between the energy consumption and the connectivity. In particular, a targeted network connectivity requirement can be fulfilled by increasing either the node density or the transmission range, each of which incurs a cost. Increasing node density introduces additional hardware  
Figure 6: The results of the network connectivity with different transmission ranges (**a**) and with optimal transmission ranges (**b**).  
cost while increasing transmission range causes higher operational energy and transmission interference. Our formulation enables network designers to determine the tradeoff and thus derive an adequate setup to meet the requirements.
## 2 Application of T-Invariants to the Verification of Petri Net-Based Concurrent Systems
### Definitions and Notations
: _A matrix \(A\) is in the reduced row echelon form (RREF) [44], if (1) any rows consisting entirely of zeros are grouped together at the bottom; otherwise, the first non-zero number in the row is 1 (leading one); (2) each column containing a leading one has zeros everywhere else; (3) in any two successive non-zero rows, the leading one in the lower row occurs farther to the right than the leading one in the higher row._  
An exemplary Petri net \(N_{1}\) is shown in Figure 1. There are six places: \(P=\left\{p_{1},\ldots,p_{6}\right\}\), and five transitions: \(T=\left\{t_{1},\ldots,t_{5}\right\}\) in the Petri net. The initial marking consists of places: \(p_{2}\) (formally \(M_{0}(p_{2})=1\)). Firing of transition \(t_{2}\) consumes a token in place \(p_{2}\) and moves it to places \(p_{3}\), \(p_{4}\), and \(p_{5}\) Such a firing changes the state (marking) of the system in which places \(p_{3}\), \(p_{4}\), and \(p_{5}\) are marked. Moving on to the properties of the system represented by Petri net \(N_{1}\), there are two t-invariants in the system: \(\overrightarrow{y}_{1}=\left[\ 11101\right]\) (with a support \(\left\{t_{1},t_{2},t_{3},t_{5}\right\}\)), \(\overrightarrow{y}_{2}=\left[10011\right]\) (with a support \(\left\{t_{1},t_{4},t_{5}\right\}\)). Note that these two minimal t-invariants determine two cycles in the Petri net, i.e., exactly two different possible scenarios of transition firings from the initial state back to the initially marked places. Since each transition belongs to at least one support, the Petri net is covered by transition invariants. Hence, each transition is a part of a cycle of firings back to the same state. Furthermore, atany marking at least one transition is enabled, which means that the system is deadlock-free. What is more, it is possible to fire any transition at any state of the system (through a sequence of firings), and therefore the system is live. Finally, there is no more than one token in any place at any reachable marking. This means that the analysed Petri net is safe (and consequently bounded).
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 5 Evaluation of IFC Schema Extension Cases
### Analysis of Extension Cases by Product and Process Representations
#### 5.3.1. Detailed Analysis of Extension Approaches in the Architecture Domain
These cases did not create new entities but focused on extending the properties or defining additional enumeration types of existing entities in the IFC schema. In summary, the majority of the cases in the architecture domain were developed for process representations. These extensions mostly utilized 'entity and property extension' approaches to represent concepts and processes for the analysis or management of data in specific fields, such as structural analysis, MEP and O&M procedures, and module design. A few select cases for cost control and code compliance exclusively used property extensions to define the required values and attributes of existing entities. The rest of the cases did extend to product representations but were used for integrating new technologies or systems. The focus on processes in the architecture domain is understandable, as most building elements had already been defined and formalized in earlier versions of the IFC schema.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 3 Improved Hilbert Huang Transform  
### Wavelet Packet Transform  
In order to deal with the above-mentioned problem, the inspected signal can be pre-processed into several narrow band signals first and then those narrowband signals can be decomposed by EMD. Therefore, the processed IMF will be in narrow frequency range and their instantaneous frequencies computed using Equation (5) will be in narrow range. There are several methods available to act as a pre-processor for decomposing the original signal into various narrow band signals. In this paper, WPT is adopted as a proper pre-processor due to its computational efficiency and well-recognised properties of being orthogonal, complete, and local [36]. Figure 4 exhibits WPT decomposition tree (three levels) of a time series signal \(f(t)\).  
Figure 3: Instantaneous frequencies of the multicomponent nonstationary signal.  
First, the time series signal \(f(t)\) is passed through couple of low-pass filter (LF) and high-pass filter (HF) in order to divide \(f(t)\) into low frequency and high frequency components known as approximation coefficient (A1,0) and detail coefficient (D1,1) respectively. The approximation coefficient (A1,0) and the detail coefficient (D1,1) will split themselves again into a second level approximation and detail coefficients and the process is repeated. WPT can be expressed as Equation (10).  
\[\varphi_{j,k}^{i}(t)=2^{\frac{i}{2}}\varphi^{i}\left(2^{i}t-k\right) \tag{10}\]  
where \(\varphi_{j,k}^{i}\) is a wavelet packet function with three indices \(i\), \(j\), \(k\) corresponding to modulation, scale, and translation parameters, respectively. Wavelet function \(\varphi^{i}\) is derived from the following recursive equations:  
\[\varphi^{2i}(t)=\sqrt{2}\sum_{k=-\infty}^{\infty}h(k)\ \varphi^{i}(2t-k) \tag{11}\]  
\[\varphi^{2i+1}(t)=\sqrt{2}\sum_{k=-\infty}^{\infty}g(k)\ \varphi^{i}(2t-k) \tag{12}\]  
where \(h(k)\) and \(g(k)\) are quadrature mirror filters related to the scaling function and mother wavelet function, respectively. Wavelet packet coefficients \(C_{j,k}^{i}\) of signal \(f(t)\) are calculated using the following expression.  
\[C_{j,k}^{i}(t)=\int_{-\infty}^{\infty}f(t)\ \varphi_{j,k}^{i}(t)dt \tag{13}\]  
Each wavelet packet component is reconstructed individually to make a part of the signal using inverse wavelet transform. The reconstructed signal from wavelet packet \((j,i)\) is as follows:  
\[f_{j}^{i}(t)=\sum_{k=-\infty}^{\infty}C_{j,k}^{i}(t)\varphi_{j,k}^{i}(t) \tag{14}\]  
The original raw signal \(f(t)\) is obtained by summing the reconstructed signals from packets of \(j\)th level decomposition as follows:  
\[f(t)=\sum_{i=1}^{2^{j}}f_{j}^{i}(t)dt \tag{15}\]  
Figure 4: Wavelet Packet Transform decomposition tree (three levels).  
The frequency band of each packet at \(j\)th level decomposition, \(F_{j}\) is computed as follows:  
\[F_{j}=\frac{F_{s}}{2^{j+1}} \tag{16}\]  
where \(F_{s}\) is the sampling frequency.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.3 Accounting Management  
This measures how network services are used and determines the associated costs for providers, as well as charges for customers. It also assists in calculating appropriate pricing for these services.
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Feedback  
The design of the feedback module must be carefully chosen to avoid difficult user experiences. Feedback is defined by [PERSON] et al. [10] as \"the means used by the system to convey information to the blind and visually impaired people.\" To be efficient, the navigation instructions must be delivered quickly and clearly. They must be adapted to the difficulty of the task being performed (for example, scene description delivers more complex  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Smartphone Position** & **\% of Papers** & **Ref. to Papers** \\ \hline in hand & 20 & [34; 35; 36; 40; 41; 42] \\ worn & 13.3 & [18; 37; 38; 39] \\ \hline \hline \end{tabular}
\end{table}
Table 4: How the smartphone is held by the users. Not all systems have been implemented on a smartphone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acquisition Sensors** & **\% of Papers** & **Ref. to Papers** \\ \hline monocular camera(s) & 33.3 & [18; 28; 36; 40; 42; 45; 46] \\ stereo vision (two cameras) & 10 & [26; 31; 47] \\ \hline RBG-D camera & 43.3 & [19; 21; 22; 23; 24; 25; 27; 29; 30; 33; 41; 43; 44] \\ wide angle camera & 6.7 & [19; 33] \\ \hline GPS & 10 & [21; 29; 47] \\ \hline position sensors & 33.3 & [21; 27; 28; 34; 32; 33; 34; 42; 44; 47] \\ IR/laser & 20 & [19; 23; 31; 23; 44; 45] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data-acquisition tools. By “position sensors”, we consider techniques and sensors such as inertial measurement units, odometry, or accelerometers/gyroscopes/magnetometers from a smartphone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Device** & **\% of Papers** & **Ref. to Papers** \\ \hline smartphone & 23.3 & [29; 34; 40; 41; 42] [46] * \\ tablet, laptop, etc. & 53.3 & [19; 21; 22; 23; 24; 25; 26; 27; 30; 33; 36; 38; 39; 43; 44] [32] * \\ remote server & 23.3 & [16; 28; 31; 32; 45; 46; 47] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Devices used for data processing. In some papers (mentioned with a “*”), a hybrid client–server architecture was used (smartphone/laptop + remote server).  
information than instructions to turn left or right), but also to the type of environment encountered (level of noise). This module must respect user knowledge (choice of symbolic representations that will be easy to understand) and avoid sensory overload (information prioritization). Developers must also ensure that the interface delivering the feedback will not have a negative impact on other senses and communication capabilities (for example, by favoring bone-conducting headphones rather than traditional ones).
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
## 4 Problem Formulation and Optimum Solution
\]  
Equation (1) is the objective function that minimizes the number of visual sensor nodes that can be used (activated) in the network. Equation (2) is the capacity constraint that makes sure that flows are routed with respect to the residual capacity of the cliques. Equation (3) ensures the flow conservation rules. Equations (4) and (6) ensure that a flow should not loop back to its source and leave its destination. Equations (5) and (7) force the source and the destination to be used. Equation (8) shows the single path constraint and that a flow should not split into several paths. Equation (9) represents the capacity reserved on a link as the sum of the capacity reserved by each flow on that link. Equation (10) puts a limit on the path length of each flow. Each flow path should not be longer than \(\mathsf{P}_{m}\) hops so that the delay is limited. Finally, Equation (11) ensures a node is used if it receives or sendstraffic. As can be seen from the Equation (11), the constraint is not linear. Since most of the linear programming solvers require linear formulations of the objective functions and constraints, it is therefore necessary that the non-linear constraint should be converted to the equivalent linear constraints. Using the techniques as in [25] and a constant wireless capacity assumption, the linearization of the constraint in Equation (11) is the following:  
\[C*x_{i}-\sum_{(i,j)\in E}d_{i,j}\geq 0,\ \forall i\in V \tag{12}\]  
\[C*x_{i}-\sum_{(j,j)\in E}d_{j,i}\geq 0,\ \forall i\in V \tag{13}\]  
\[\sum_{(i,j)\in E}\bigl{(}d_{i,j}+d_{j,i}\bigr{)}-B\cdot x_{i}\geq 0,\ \forall i\in V \tag{14}\]  
Constraints (12) make sure that a node is used (\(x_{i}=1\)) if there is a video stream going out of the node. Constraints (13) also makes sure that a node is used (\(x_{i}=1\)) if there is a video stream going into the node. Constraints (14) ensures the node is not used (\(x_{i}=0\)) if there is no video stream passing through the node. In order to solve the optimization problem, we use the branch-and-bound method [24].
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 5 Evaluations and Results  
We designed three sets of experiments to verify the performance of SACA: convergence time, success ratio, large-scale network environment, and power loss. We observed that the active sensor quickly converged to the target value, avoiding the waste of power resources. In Section 5.2, we compare the convergence capabilities of the SACA, Gur Game, and ACK algorithms. We analyzed the performance of the three algorithms in terms of convergence time and success ratio to reach the target value. In Section 5.3, we apply the three algorithms to a severe network environment to verify self-organization ability. In Section 5.4, the power-saving capabilities of the three algorithms are compared. We also provide suggestions for improving SACA's performance.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 5 Conclusions and Next Steps  
This paper presents the main challenges for CCAM services in a cross-border and multi-MNO environment as they have been studied and identified by the 5 GCroCo project. ToD, HD mapping, and ACCA have been used as examples of use cases to describe in detail the automotive and communication requirements as well as the role of 5G in order to address the identified challenges. Service continuity, low latency, high reliability, high data rate, and guaranteed QoS during the transition from one operator to the other are some of the key technical issues in the context of a cross-border and multi-domain environment. Enhancement of roaming schemes to reduce roaming latency, inter-MEC communication, multi-domain orchestration, enhancements on network slices selection as well as QoS prediction are identified solutions discussed in this paper that could be used to address the cross-border challenges.  
The 5 GCroCo project aims at validating 5G technologies in the Metz-Merzig-Luxembourg cross-border corridor, for three key CCAM services (ToD, HD mapping, and ACCA) at the borders between France, Germany and Luxembourg. While the actual trials and validations in 5 GCroCo will be focused on these particular use cases with envisioned high potential market opportunities, the activities of 5 GCroCo aim at deriving recommendations and insights that can be valid for a wider set of CCAM use cases.  
[PERSON] contributed to Sections 1, 2, 4 and 5, [PERSON] contributed to Sections 3.1 and 4.1, [PERSON] contributed to Sections 3.2 and 4.2, [PERSON] contributed to Sections 3.3 and 4.3, [PERSON], [PERSON] and F.M. contributed to Section 2, [PERSON] contributed to Section 4.1, F.V.-G., [PERSON] ([PERSON]), [PERSON] and R.S. ([PERSON]) contributed to Sections 3.3 and 4.3, [PERSON] contributed to Section 2, [PERSON] contributed to the introductory part of Section 4 and also to Section 4.2, [PERSON] contributed to Sections 3.1 and 4.1, [PERSON] contributed to Sections 3.3 and 4.3, [PERSON]-Z. contributed to Sections 1 and 2. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is part of 5 GCroCo project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No825050. CTTC also acknowledges the financial support from TEC2017-87456-P and 2017 SGR_891.  
**Acknowledgments:** EURECOM acknowledges the support of its industrial members, namely, BMW Group, IABG, Monaco Telecom, Orange, SAP and Symantec.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Fusion of events by mobile edge computing (MEC) (**a**) simple/flooding scheme; (**b**) smart fusion enabled to reduce redundant transmissions.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.5 New Revenue Streams  
IoT and Distributed Computing Systems enable innovative business models and services that generate additional income. [PERSON] et al. [69] found that IoT devices can transform traditional products into smart, connected products that offer additional value to customers. For example, a manufacturer of home appliances can integrate IoT sensors into their products, allowing customers to monitor and control them remotely through a mobile app [70]. This can lead to the introduction of subscription-based services for remote monitoring and maintenance, generating recurring revenue [71; 72]. Distributed systems facilitate these new business models by providing the infrastructure to support large-scale IoT deployments and data processing [73]. They enable businesses to offer services such aspredictive maintenance, where customers pay for monitoring and maintenance services based on real-time data from their equipment. In addition, the data collected by IoT devices can be monetized through data analytics services, where businesses analyze and sell insights derived from the data to other companies or stakeholders.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 4 Experimental Validation and Analysis
### Ablation Experiments  
In order to validate the effectiveness of the improved coal and gauge detection model based on YOLOv5s and ensure the validity of the training results, various ablation experiments were conducted for performance analysis. The results of the ablation experiments are shown in Table 1.  
Figure 8: Diagram of model pruning channel number change.  
Figure 7: Comparison of detection accuracy and border loss. (**a**) Comparison chart for model accuracy. (**b**) Comparison diagram of model losses.  
Experiment A represents the original YOLOv5s algorithm, with a model accuracy of 91.3%, model weight of 14.4 MB, and computational complexity of 16.0 G. Experiment B introduces the ConvNext Block to YOLOv5s, resulting in a 1.7% increase in model recall and a 0.5% increase in accuracy, effectively reducing the false-negative rate for small targets. In Experiment C, after improving BiFPN, the model's accuracy and recall increased by 1.1% and 1.5%, respectively. In Experiment D, by changing the loss function to ESIOU, the model's accuracy increased by 2%. In Experiments E and F, the improvements were combined, resulting in a slight increase in model weight. However, accuracy, recall rate, and mAP all showed improvement. In Experiment G, compared to the original YOLOv5s model, the accuracy was improved and the model weight was reduced by 38.8%, with a decrease in parameter count by 39.3%. Pruning redundant channels in the model ensured performance while reducing its size. The experiments demonstrated that the improved coal gangue detection model based on YOLOv5s proposed in this paper effectively enhances the detection results of coal gangue in harsh environments.