esa-sceva/satcom-chunk-collection huggingface dataset:
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 3 Proposed Method  
Inevitably, the road surface temperature of bridges is significantly affected by shadows because of the lack of geothermal heat [21]. Additionally, major accidents may occur if black ice forms on the downhill and uphill road segments or sharp turns, due to the difficulty of stopping vehicles in such conditions. Consequently, this study proposes a method that adds a hillshade simulation with 3D spatial information and a slope-and-curvature analysis with road structure information to the existing black ice risk estimation with meteorological information, as shown in Figure 1.  
Figure 1: The overall concept of the proposed black ice risk estimation system.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.4 Comparison of PUTPOSH with Stationary Spectrum Access and Hybrid Approaches  
Channel Utilization  
The PUTPOSH scheme is compared with the stationary spectrum access method in terms of channel consumption (see Figure 9). A fixed spectrum access approach is categorized by the spectrum access method where only licensed operators are permitted to exploit the channel. CRU cannot utilize the spectrum resourcefully, even when PU is lacking. The PUTPOSH scheme, being a lively spectrum access pattern, depicts extraordinary results in terms of channel consumption because it permits CRU to exploit the vacant spectrum in the absence of PU [2; 18]. The solitary PU is permitted to exploit the band in the static spectrum access, and is effected by the underutilization of the channel. The comparison (in Figure 9) reveals that the PUTPOSH scheme produces 20% better results than the static spectrum access (for both reactive and proactive) approaches.
## 2 Related Research  
### Definition and Features of Radical Innovations  
According to the degree of technological change and impact on the market, innovations are traditionally divided into incremental innovation and radical innovation (RI) [26; 27]. Incremental innovation is continuous and small changes on the original technological trajectory (S-curve) [28; 29] which is the continuous exploration and improvement of the original technological potential. As shown in Figure 1, incremental innovation occurs from point a to b or from point b to c on the same S-curve. Most companies, especially those large and established ones, prefer to engage in incremental innovation because of its low risk. RI is to replace the old technological trajectory with a new one, which can greatly improve the product performance or significantly reduce the cost of the product [30; 31]. RI occurs in the replacement phase of S-curve from point c to d as shown in Figure 1. When the performance of current technology has developed to the limit, engineers or designers need to find a new technology to replace the current one [32; 33]. The discontinuity of the S-curve is the most obvious technical manifestation of RI [34].  
RI is characterized by divergence and nonlinearity, showing a leap-type and discontinuous development [35]. The radical performance of RI means that a new product or technology is significantly different from the current product or technology in one of the following aspects: (a) the new product offers an entirely new set of performance features, (b) five-times or greater improvement in known performance features, (c) a significant (30%or great) reduction in cost [36; 37]. These performances provide operational opportunities to implement RI.  
Features of RI related to the technological trajectory and radical performance are the new technical principles inspired by cross-domain knowledge. However, how to accurately and effectively find cross-domain knowledge suitable for the development of radical idea is still a problem. The technological trajectory discontinuity of RI makes it difficult for engineers to predict the next-generation technology and obtain cross-domain knowledge for RCG. And RI has high requirements on firms' knowledge reserves [38], strategic positioning [39], absorptive capacity [21; 22] and other aspects. It is difficult for firms to find appropriate cross-domain knowledge to form radical concepts and ideas with low cost and risk, especially small and medium-sized ones [40]. Therefore, it is essential to have an operable process for RCG to improve the success rate of RIs.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 3 Methodology
### Input Data  
The prediction of traffic speed on a single location usually requires the series of the speed values associating with '\(n\)' historical time steps for input data. We have the input data with \(p\) locations \([l_{1},\ l_{2},\ \cdots,l_{n}]\), where each location \(l_{i}\)\([t_{1},\ t_{2},\ \cdots,\ t]\) represents the traffic speed data at \(p\) location, with '\(t\)' historical time steps (frames) from the sensor location \(l_{i}\), that can be illustrated with the vector as shown in the following equation.  
\[Y_{T}=\left[Y_{(i,t-1)},Y_{(i,t-2)},\ldots,Y_{(i,t-n)}\right] \tag{1}\]  
where \(Y_{i,\ t-1}\) indicates the speed at \(i\)th location and \(t-1\) time. When traffic jams or traffic congestion propagate in a traffic network, it may affect the speed at nearby as well as faraway locations. Thus, considering these influences, network-wide speed data are taken as input data for the proposed and compared models. We aim to envisage traffic speed at '\(T\)' time using '\(t\)' historical time frames from the traffic network consisting of '\(P\)' locations. The speed data matrix can be characterized as input.  
\[Y_{P,T}=\begin{bmatrix}Y_{T-n}^{1}&Y_{T-n+1}^{1}&\cdots&Y_{T-2}^{1}&Y_{T-1}^{1 }\\ Y_{T-n}^{2}&Y_{T-n+
# Speaker Diarization: A Review of Objectives and Methods
## 11 Discussion  
This paper has reviewed the technical problems and methods associated with speaker diarization. It first discussed the acoustical nature of speech signals, from the point of view of which aspects or features may be employed to distinguish speakers. Notably, most speech is voiced (quasi-periodic) with resonances and F0 that mostly change slowly. Approximately eight times per second, speech transitions between phonemes, which results in spectral changes that are sometimes abrupt and sometimes smooth. Otherwise, i.e., during approximately 85-90% of speech frames, there is significant spectral continuity in the acoustic signal of a speaker.  
Individual phonemes have patterns of two types: (voiced) sonorants or noisy consonants. The former have slowly changing resonances (on average, one per kHz in typical 17 cm vocal tracts), with slowly changing harmonics. The latter have mostly brief high-pass noise patterns. As the second category has much less speaker information than the first category, SD should likely pay far more attention to sonorants.  
However, SD rarely exploits any of this phonological/acoustic knowledge and treats speech as a general acoustic signal. Modern SD methods do not explicitly take advantage of the smooth movements of formants and F0 in individual speakers' speech. The widely used Mel spectrogram patterns contain these data, but in a highly coded form, which is not evident to exploit in neural networks, especially when using general loss functions. In addition, SD generally assumes access to entire recordings before processing, which hinders streaming, on-line use.  
Applications for SD extend widely to interviews, ASR, transcription, multimedia content indexing, content retrieval, meeting analysis, and call-center transcription. This paper examined audio-only SD, but multi-media applications can also do audiovisual speaker diarization, where visual information can help significantly in determining the source of people talking and the timing of interventions.  
In general, accurate ASR and SD in real-world multi-speaker conversational scenarios requires more research to achieve excellent performance. The major difficulties include high rates of overlapping speech, background noise and reverberation, unknown numbers of speakers, and varied conversation styles. There are recent efforts to combine speaker diarization and speech separation, which integrates speaker counting, diarization, and separation in a multi-tasking framework. For example, the NOTSOFAR-1 (Natural Office Talkers in Settings Of Far-field Audio Recordings) Challenge examines distant (far-field) speaker diarization and automatic speech recognition using a single recording device.  
Future SD must be able to handle diverse acoustic situations (e.g., speakers at varying distances and volumes), overlapping speech (e.g., with interruptions and rapid speaker changes), presence of various noises, environments (rooms of various dimensions and acoustic characteristics), and various numbers of speakers.
## 1 Introduction  
Bisphosphonates (BPs) have seen wide use in the treatment of various bone diseases [1, 2]. Zoledronic acid (ZA) is considered one of the most intensive and effective BPs, and is widely used in clinical practice for the treatment of various bone diseases.  
Despite the many beneficial effects of BPs, bisphosphonate-related osteonecrosis of the jaw (BRONJ) is a problem, and the number of identified BRONJ cases appears to be increasing rapidly. BRONJ is often accompanied by pain and leads to reduced quality of life in patients [3]. Recently, the term \"medication-related osteonecrosis of the jaw\" (MRONJ) has been used to include cases of osteonecrosis of the jaw showing associations with other antiresorptive and antiangiogenic therapies [4].  
Diverse hypotheses regarding the pathogenesis of MRONJ have been advocated, such as over-suppression of both bone resorption by osteoclasts and bone turnover, antiangiogenic activity, and toxicity against the oral epithelium [5]. Several reports have demonstrated that diabetes mellitus, severe periodontitis and traumatic tooth extraction represent risk factors for the progression of MRONJ [6, 7, 8]. Furthermore, compressive force as seen in traumatic occlusion, bruxism and bite force from ill-fitting dentures may also contribute to the pathogenesis of MRONJ. However, the effects of compressive force and ZA on osteoblast proliferation and differentiation remain uncertain. In the present study, we investigated the effects of ZA and compressive force on osteoblast functions, to clarify the possible pathogenesis of MRONJ.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 2 Intelligent Systems at Home
### Bluetooth Multi-Hop Networking
On the one hand, when any node operates in sniff mode, the inactivity intervals (\(T_{\mathit{sniff}}\)) or periods of absence in the piconet are configured such that the slave node agrees with the master to periodically listen to its transmissions. The sniff low power mode is configured at the HCI level and is defined by some parameters: \(T_{\mathit{sniff}}\), \(N_{\mathit{sniff\_attempt}}\) and \(N_{\mathit{sniff\_timeout}}\). A device in sniff mode listens to incoming data periodically with an interval of \(T_{\mathit{sniff}}\), a value between the \(\mathit{Sniff\_Max\_Interval}\) and \(\mathit{Sniff\_Min\_Interval}\) parameters, and with the duration specified by the other two parameters. \(N_{\mathit{sniff\_attempt}}\) defines the amount of receive timeslots the device will listen to, and \(N_{\mathit{sniff\_timeout}}\) describes how many receive timeslots it will listen to after the latest received packet. On the other hand, when the nodes operate in park mode, some beacons are listened by the slave node every \(\mathit{Beacon\_interval}\). Meanwhile, the node can be low powered or dedicated to other tasks out of the radio tasks in that piconet. Then, the inactivity interval in park mode (T\({}_{\text{park}}\)) was defined by the value for the \(\mathit{Beacon\_interval}\) between the \(\mathit{Beacon\_Max\_Interval}\) and \(\mathit{Beacon\_Min\_Interval}\).
## 4 Coherence Loss Measures
As shown in Figure 4, the functions \(E(t)\), \(E_{AB}\), and \(E_{M\text{s}}\) are plotted at a dipole-dipole coupling \(J=30\lambda\) and the other parameters are the same as in Figure 3a,b. The entropy functions \(E_{AB}\) and \(E_{M\text{s}}\) show rapid fluctuations with the interference between the patterns most of the time. For the small decay rate value \(\gamma=0.01\lambda\), the growth of the entire entropy \(E(t)\) is less than that of the subentropies compared to \(J/\lambda=0\) in Figure 3b. We deduce that the dipole-dipole rate increases the chaotic oscillatory behavior of the entropy functions, and the generated mixedness of the TLAS and EMF states is more than that of the TLAS-EMF state. Figure 5a shows the dynamics of the mixedness of the system and its subsystems for the case \((k,\alpha)=(1,5)\). By comparing this case with the case \((k,\alpha)=(\frac{1}{4},5)\) of Figure 3a, we find that the mixedness of the entropies is generated regular with \(\pi\)-period, where the system and its subsystems are in initial pure states at the times \(\lambda t=n\pi(n=1,2,3,...)\). The effects of the initial coherent field intensity \(\alpha\) on the dynamics of the mixedness are shown in Figure 5a for the case \((k,\alpha)=(\frac{1}{4},1)\). The small coherent field intensity leads to enhancing the generated mixedness and weakening the effect of the decoherence term.
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 4 MAG Algorithms
### Degree  
The definition of degree in a traditional graph stems from the number of edges incident to a given vertex. This concept can be generalized for MAGs, so that degrees can be defined for composite vertices, sub-determinations, or elements of a given aspect. Further, since MAG edges are considered to be directed, the degrees are also divided into out-degree and in-degree. In this section, we present algorithms for calculating these distinct degree definitions.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 4 Experimental Results and Analysis
### Comparison with Other Algorithms/Heuristics  
In this section, we elaborate and compare the results of six other well-known algorithms/heuristics with the 2-Opt++ algorithm and highlight the key findings. In the original comparative study [10], the authors selected some parameters, such as the number of iterations and number of experiments with each algorithm. The minimum number of iterations was selected as 10,000. Each algorithm continued its execution until it reached the already published results or completed the defined number of iterations.  
In Table 3, we present the resultant error margins of the six selected algorithms along with the results of the 2-Opt++ algorithm. Each problem was evaluated with 2000 iterations of the 2-Opt++ algorithm. Table 3 provides the error percentages of all the algorithms using a simple, basic formula for the error percentage, i.e., \(((best-opt)/opt)\times 100\). It is clearly evident that the results of the 2-Opt++ algorithm are superior and it is more effective than the other algorithms in terms of the error percentage.  
Out of the 14 problems, our proposed algorithm yielded the best results for 11 problems, and, even for the other three problems, the difference was minimal. In problem rat99, Tabu search provided a better solution, with an error percentage of 2.68%, while the 2-Opt++ algorithm yielded a 2.79% error rate. However, when we regenerated our results with the graph compression technique using only 40 active edges, our algorithm produced a much improved error rate of 2.26%, even outperforming the initially best-performing Tabu search. For the problem eil101, Tabu search provided a 6.14% error rate, while the 2-Opt++ algorithm showed a 6.27% error rate. However, again, when we applied the graph compression technique, the 2-Opt++ algorithm was able to surpass Tabu search, with an error rate of 5.63%. For the problem d198, Tabu search showed an error margin of 1.92% and the 2-Opt++ algorithm produced a 2.74% error. Again, after reproducing the results with graph compression, i.e., setting the active edges to 40, we were able to produce a 1.49% error rate.  
Figure 5: Time comparison of \(R\&R\) and 2-Opt++ algorithms with and without graph compression in small TSP instances.  
It is clearly evident that the 2-Opt++ algorithm performs well for more than 80% of the cases in normal execution, and, even for the rest of the cases, our algorithm is able to outperform the others in terms of the error percentage with the introduction of the graph compression technique.  
Figure 6 depicts a graphical comparison of the error margins of the seven different algorithms. The graph clearly shows the impressive performance of the 2-Opt++ algorithm.
## 4 Discussion
### Limitations  
While this study provides valuable insights, several limitations should be acknowledged when interpreting the findings. Firstly, the relatively small sample size (30 participants) may limit the generalizability of the results, suggesting the need for larger, more representative samples in future research. Secondly, only 4 out of the 30 participants were female, resulting in a gender imbalance that might affect the applicability of our conclusions to female skiers. Future investigations should aim for more balanced cohorts or conduct subgroup analyses to explore potential sex-specific responses.  
Additionally, the accuracy of wearable technology, which can be influenced by device calibration, user error, and external conditions, may have affected the precision of our measurements. Furthermore, external factors, such as snow conditions, slope variability, and skier fatigue, were not fully controlled for, potentially introducing additional variance into the on-snow performance measures. Finally, the possibility of uncontrolled confounding variables--such as prior training experience, nutritional habits, or physiological characteristics--could have influenced the outcomes. These considerations should guide subsequent research efforts to refine study designs and enhance the robustness of the findings in this domain.
## 3 Methods
### Channel Attention Module  
After obtaining the high-dimensional features of each point, we focus on the influence of the information on each feature channel on the whole point clouds information and realize the point clouds data channel attention mechanism by obtaining the correlation between features and information on each channel (shown in Figure 4). Highlight the information on the more critical channel in each point feature. First, we obtain a feature that reflects the global point clouds, then on this basis, we learn the attention weight of each channel to obtain the point clouds features after processing it by the feature channel attention module.  
Figure 3: The sampling method of Pointnet++ with the adaptive radius sampling method. The green blob represents the point clouds data within the initial radius, the yellow blob represents those with the maximum value selected for replication, and the red blob and blue blob represent the initial radius as the learned point clouds features.  
Unlike 2D image data, 3D point clouds data have the characteristics of unstructured and disordered. The features of the point clouds can be expressed explicitly in the neural network as \(F=\mathbb{R}^{\mathbb{B}\times\mathbb{N}\times 1\times\mathbb{C}}\) where B, N, and C are the Batch Size, the number of point clouds and the number of feature channels. In this paper, Pooling is along with the C dimension of the number of characteristic channels, as shown in Equation (1),  
\[F_{c}=F\otimes M_{c}(F) \tag{1}\]  
where \(M_{c}\in\mathbb{R}^{\mathbb{B}\times\mathbb{N}\times 1\times\mathbb{C}}\) is the point clouds channel attention mechanism, \(F_{c}\) is the output feature of point clouds channel attention, and \(\otimes\) is the matrix fork multiplication. After obtaining the point clouds features, to generate the point clouds channel attention mechanism \(M_{c}\), along the feature channel number N dimension, using average pooling can effectively obtain the holistic features of the point clouds, and using maximum pooling can effectively obtain more distinguishable point clouds features. Two types of pooling are used in the channel attention mechanism, and feature aggregation is performed on the point clouds data input features \(F\) to generate two different point clouds global features \(F_{avg}\) and \(F_{max}\). In this paper, MLP is used to extract the point clouds features, and the features are trained using MLP with shared parameters, reduce the point clouds channel C first and then revert to the original, where the reduction factor is \(k\). Finally, the required channel attention mechanism weights are obtained by Sigmoid.  
The point clouds channel attention mechanism \(\text{M}_{c}\) can be expressed as Equation (2),  
\[M_{c}=s\{\text{MLP}(Avg(F))+\text{MLP}(Max(F))\} \tag{2}\]  
where \(s\) is the sigmoid activation function.
## 5 Experimental Results
### Software Used
#### 5.3.2 Network Device State Diagram (Figure 13)  
The network device starts its configuration when the event Boot.booted in TinyOS is reported. When receiving a beacon, it transmits an associating request to the Coordinator. When the Coordinator answers the request, two processes initiate:  
1. The ADC is configured to do a periodic selection of 333 ECG samples.
2. A periodic Timer is configured every 30 s for signal transmission (ecgTimer).  
When the signal sampling ends (ecgDataready) the Pan Tompkins algorithm starts, this algorithm was implemented by posting tasks for each processing phase. If the algorithm detects an irregularity, it sets a flag to TRUE (troubleDetected) and when receiving the following beacon, it transfers the report and the original ECG signal. When the ecgTimer overflows, it sets a flag (dataReady) to do a routine transmission of the ECG signal at the next reception of a beacon.  
Figure 12: Main Node State Diagram.