esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Critical Safety Distance in Different Critical Conditions
### Design of Platoon Lane Changing Process  
The design of the entire platoon lane-changing process is shown in Figure 4. There is a platoon intending to change lanes, maybe because of an off-ramp or road construction. The CAVs system is an approach to realize real-time location awareness and information communication between vehicles. Therefore, the existence, motion state, and spatial positions of vehicles in the target lane and the platoon can be accessed in real-time. Based on the received information of vehicles in the target lane, the platoon determines the available gap and tracks it. In addition, the required distance for lane changing is composed of the platoon length; the distances between intra-platoon vehicles; and the distances between \(FV\), \(PV\), \(TV\), \(TPV\), and the platoon. The latter two are calculated using field theory. Among them, the distances inside the platoon are determined using Equation (3), and those outside the platoon are obtained using Equation (2). It should be noted that if a non-platoon vehicle does not exist, the critical distance between it and the platoon is zero. This means that the proposed method would also be valid when \(FV\), \(PV\), \(TFV\), and \(TPV\) are optional. Then, whether the tracking gap is suitable for lane changing is judged. If the answer is \"N,\" the platoon will keep the car-following state until it finds a suitable gap. Once the gap in the target lane meets the critical gap criteria, the platoon will prepare for lane changing with \(LV\) first. At this time, the motion information of vehicles around \(LV\) will be used as the input to calculate the safe distance thresholds between \(LV\) and other vehicles. It will be judged whether all distances between \(LV\) and the surrounding vehicles meet the critical thresholds at that moment. Only when all the thresholds are satisfied separately will \(LV\) be permitted to change lanes. If they are not satisfied, \(LV\) in the platoon will adjust its movement state to meet them. The detailed safety distance thresholds for intra-platoon vehicles are introduced in Sections 4.2 and 4.3. After \(LV\) completes a lane change, the following vehicle will judge, adjust, and accomplish lane changing in the same way until all platoon vehicles have finished the changing lane task.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 2 PFAS Applications and Related Concerns
### Analytical Techniques for PFAS Detection in Water  
Qualitative and quantitative analysis of PFAS in water systems is extremely important, not only to assess the scale of contamination but also to inform and direct the development of remediation methods. Recent discussions [33] in the field of PFAS analysis highlighted the need to reach a consensus on standard methods for several analytes (short-chainPFAS in particular) and to validate methodologies beyond those for potable water. These objectives will require efforts regarding multi-laboratory validation for analytical methods.  
Indeed, numerous analytical techniques for PFAS have been developed over time, in line with the observation of emerging contamination [33]. The most used methods in water systems to date are presented here.
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 4 Experimental Results and Analysis
### Verification with Experimental Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Precision/\%** & **Recall/\%** & **Accuracy/\%** & **Number of Parameters/10\({}^{8}\)** & **Model Size/MB** & **Time for 100 Epochs/S** \\ \hline Original Model & 99.9 & 99.6 & 99.8 & 111.81 & 42.65 & 10,633 \\ Improved Model & 99.3 & 99.7 & 99.5 & 7.15 & 2.73 & 10,341 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of model results and parameters before and after improvement. The results show that the model exhibits significant fluctuations in accuracy and loss during the first 300 epochs, which may be attributed to noise in the measured data, environmental interference, and the impact of newly introduced radionuclides. However, after training for 300 epochs, the model gradually converges, reaching an accuracy of 92.6% at 500 epochs, with the loss stabilizing around 0.05, indicating good convergence and demonstrating the model's strong predictive capability. Further analysis of the confusion matrix reveals that the model performs well overall. For known radionuclides, the model shows high stability and accuracy in recognition, with the number of correct identifications for all known radionuclides closely matching the actual samples in the test data. For the mixed data, the model is able to correctly classify it as the \"unknown\" category. Of the 5200 test samples, 1800 contain newly introduced data from three categories. Statistical analysis shows that 1842 samples were marked as the \"unknown\" category, resulting in a recognition error rate of 2.33% for the new radionuclides. Overall, although the accuracy of the measured data is slightly lower than that of the simulated data, this difference can be attributed to the complex noise and environmental interference present in the measured data, such as instrument errors and inconsistencies in the radiation source characteristics. Nevertheless, the model still demonstrates a high accuracy of 92.6% and is able to identify unknown radionuclides, proving its robustness and generalization capability in practical applications. In the future, further improvement in the model's accuracy for unseen categories can be achieved by incorporating more radionuclide samples into the training process.
## 1 Introduction
### Analysis of Simulation Results of Shafting Over-Critical  
The mid-point of support I and support II was selected as the measuring point, and fixed control forces of 100 N, 200 N, 300 N, 425 N, 800 N, and 1000 N were applied respectively to simulate the shaft system passing through the critical point, and the displacement response at the measuring point was  
Figure 5: Realization of the Smart Spring function.  
Figure 6: Joint simulation model of open-loop control.  
obtained. Due to the isotropy of the rotor, the shafting had similar displacement response curves in X and Y directions. Therefore, the displacement response curves in the Y direction were selected for analysis.  
Figure 7 shows the over-critical bending vibration response of a double-disk three-support shafting with Smart Spring support under different fixed control forces. It can be seen from Figure 7 that the application of fixed control force can significantly suppress the critical response peak value and the critical speed shifts to the right. When the fixed control force was less than 200 N, the vibration response decreased in the whole speed range. When t < 6 s, i.e., the speed was lower than 3600 \(\upmu\)min, the vibration could be attenuated by applying 0-1000 N fixed control force. However, when t > 7 s, i.e., the rotating speed was greater than 4200 r/min, and the fixed control force was greater than 200 N, the vibration was increased by applying the fixed control force.  
Figure 7: Response of the shaft system under (**a–f**) fixed control forces, respectively 100 N, 200 N, 300 N, 425 N, 800 N and 1000 N of the Smart Spring.  
Figure 8 shows the relationship between the critical response peak value and the fixed control force. The results showed that with the increase of the fixed control force, the peak value of the over-critical response first decreased and then increased. When the fixed control force was 0 N, the peak value was 0.722 mm; when the fixed control force was 425 N, the peak value was the minimum, which was 0.205 mm. At this time, the vibration reduction rate was the largest, reaching 71.6%. Therefore, the control force of the Smart Spring was not better when greater, but there was an optimal fixed control force. Figure 9 shows that with the increase of the fixed control force, the first-order critical speed of the double-disk shaft system gradually increased, and tended to be stable when the control force was greater than 700 N, which was close to 4350 r/min, that is, the damping control gradually transited to the stiffness control.
# _Applied_
## 7 Evaluation of Deep Learning for ESR: A Convolutional Neural Network Approach
### State-of-the-Art CNNs for ESR  
Several DL approaches for ESR have been proposed in recent years. The performance of the proposed method is compared against these state-of-the-art solutions for the same dataset. Since most recent work has been evaluated on the ESC-10 dataset, this comparison includes results obtained for this dataset. Nonetheless, the results for the BDLib and the UrbanSound datasets are also included in the following section.  
The classification accuracy of our proposed CNN model compared with the best recent solutions from the literature is shown in Table 12. The highest accuracy is achieved by the model proposed in [44], which presents a CNN without max-pooling function and using Log-Mel audio feature extraction (CNN-Model-2 (No-maxpooling) + Log-Mel + augmentation). In [47], the application of the Masked Conditional Neural Networks for sound classification (MCLNN) to the problem of music genre classification, and ESR is presented. Both proposed models achieve high accuracy, but they are large models demanding prohibitive memory requirements for embedded platforms. Their inference time is also expected to be higher than the proposed solution. Slightly smaller models are proposed in [8], where the authors present a compact and effective model capable of characterizing different urban sounds based on deep and handcrafted features combining two models.  
The proposed model improves the classification accuracy to 90.71% with only 51 K parameters, around 60 times lower than the number of parameters other methods. Such a model is expected to be not only significantly faster than the state-of-the-art solutions but also more suitable for embedded devices due to its lower memory demand.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Type of Layer** & **Output Shape** & **Number of Parameters** \\ \hline conv1d (Conv1D) & (None, 166, 12) & 72 \\ conv1d\_1 (Conv1D) & (None, 162, 28) & 1708 \\ dense (Dense) & (None, 162, 30) & 870 \\ dropout (Dropout) & (None, 162, 30) & 0 \\ flatten (Flatten) & (None, 4860) & 0 \\ dense\_1 (Dense) & (None, 10) & 48,610 \\ \hline Total : & & 51,260 \\ Trainable : & & 51,260 \\ Non-trainable : & & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Architecture of our proposed CNN model and parameter information used in each layer.
# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
## 4 Formulation of Markov Decision Process Problem
### State Space  
In the multi-agent scenario, we consider each edge server as an agent. These agents collaborate to explore the optimal edge caching strategy. For a given edge server \(i\), we consider both the currently requested content and the currently cached content for the state space. Furthermore, we consider that each agent \(i\) communicates with its neighboring agents \(\mathcal{E}_{nb}\). Hence, in the state space \(\mathcal{S}\), it is important to include the cache states of the neighboring servers as well. Thus, every state \(\mathbf{s}_{t}\) at the \(t\)th decision epoch can be represented by \((R_{i,t},C_{i,t},C_{j,t}y\in\mathcal{E}_{nb})\), where \(t=\{1,\cdots,T\}\). However, in this case, the state space would be significantly large, which would make the learning model difficult to converge. Hence, we resort to an alternative way of representing the state vector by employing numerical feature vectors to encode each state at time \(t\) by concatenating the feature vectors of the current request and the cached contents of the considered agent and neighboring servers. Then, \(\mathbf{s}_{t}\) can be written as  
\[\mathbf{s}_{t} = \{\mathbf{f}_{0},\mathbf{f}_{1,i},\cdots,\mathbf{f}_{k,i},\cdots, \mathbf{f}_{C,i},\mathbf{f}_{1,j},\cdots,\mathbf{f}_{k,j},\cdots\cdots, \mathbf{f}_{C,j}\forall j\qquad\in\qquad\mathcal{E}_{nb}\}, \tag{1}\]  
where \(\mathbf{f}_{0}\), \(\mathbf{f}_{k,i}\), and \(\mathbf{f}_{k,j}\) are the feature vectors of the current request, the feature vector of the \(k\)th cached content (\(k=\{1,\cdots,C\}\)) of the considered edge server \(i\), and the feature vector of the \(j\)th neighboring server, respectively. In (1), \(\mathbf{f}_{0}\), \(\mathbf{f}_{k,i}\), and \(\mathbf{f}_{k,j}\) can be defined as  
\[\mathbf{f}_{x}=\{f_{x}^{s},f_{x}^{m},f_{x}^{l}\}, \tag{2}\]  
where \(x\in\{0,\{k,i\},\{k,j\}\}\). Moreover, the elements in (2), \(f_{x}^{s}\), \(f_{x}^{m}\), and \(f_{x}^{m}\), represent the short-term, middle-term, and long-term recent access times, respectively. The use of access time frequencies also serves the purpose of implementing the LFU policy. Maintaining perpetual access records necessitates a memory cost of O(N), which is notably prohibitive. Consequently, to circumvent this high memory expenditure, a strategic approach involves retaining only a limited subset of historical data. This subset is then subjected to regression and exploration through the utilization of a neural network, facilitating the extraction of latent information. By implementing this methodology, a commendable level of efficacy is achieved while concurrently mitigating the burdensome memory costs. In our empirical investigations, we consider varying historical windows, namely the past 10, 100, and 1000 accesses, to assess the performance and effectiveness of this approach.
## 1 Introduction
### Single Modal Feature Construction Method
#### 2.1.2 Voice Signal Feature Extraction Based on MFCCs  
The Mel frequency scale [35] characterizes the nonlinear perception of speech frequencies by the human ear. This is reflected in the Mel filter bank, where filters are denser at lower frequencies and sparser at higher frequencies, placing greater emphasis on the resolution of low-frequency signals. This allows for a more refined representation of both high- and low-frequency spectral features. As a result, MFCCs are particularly effective at extracting low-frequency acoustic features and are widely used in sound signal analysis, such as feature extraction, speech recognition, and voiceprint identification.  
Human frequency perception is nonlinear, with individuals being more sensitive to low-frequency signals than high-frequency ones. The relationship between the Mel scale and the actual signal frequency \(F\) in the frequency domain is given by the following equation:  
\[F_{mel}=2595 lg(1+f/700) \tag{1}\]  
In the equation, \(F_{mel}\) represents the perceived frequency in _Mel_ units, while \(f\) denotes the actual frequency in hertz (Hz).  
After preprocessing, each frame of the sound signal undergoes a fast Fourier transform to obtain \(Y_{i}(k)\), and the spectral energy \(E_{i}(k)\) of each frame of the sound signal is calculated.  
\[Y_{i}(k)=\sum_{n=0}^{N-1}y_{i}(n)e^{-|2\pi mk/N} \tag{2}\]  
Figure 2: Gramian Angle Field conversion process.  
\[\mathrm{E_{i}(k)}=[Y_{i}(k)]^{2} \tag{3}\]  
\(\mathrm{E_{i}(k)}\) is passed through Mel filter banks, and the logarithmic energy \(\mathrm{S_{i}(m)}\) of the output of the filter banks is calculated as follows, realizing the nonlinear Mel mapping of the frequency dimension and enhancing the features of low-frequency signals:  
\[\mathrm{S_{i}(m)}=\mathrm{lg}\left[\sum_{n=0}^{N-1}\mathrm{E_{i}(k)H_{i}(k)} \right],0\leq m\leq M \tag{4}\]  
where \(\mathrm{H_{i}(k)}\) represents the response of the m-th filter in the Mel filter bank, and M denotes the total number of filters in the bank.  
The l-th-order MFCC \(\mathrm{M(l)}\) can be calculated from \(\mathrm{S_{i}(m)}\) using a Discrete Cosine Transform (DCT).  
\[\mathrm{M(i,l)}=\sqrt{\frac{2}{M}}\sum_{n=0}^{N-1}\mathrm{S_{i}(m)}\cos(\frac {\pi\mathrm{l}(2m-1)}{2M}),0\leq 1\leq L \tag{5}\]  
where \(\mathrm{M(i,l)}\) represents the i-th frame of l-th MFCC for sound signal.  
In the case of most rotating machinery sound signals, the frequency range typically spans from a few hertz to several thousand hertz. The information carried by higher-order MFCCs is often negligible. Therefore, this paper uses a maximum Mel frequency of 8000 Hz and selects the first 13 MFCCs as feature extraction parameters for sound data.
## 5 Discussion
### Limitation of Motion Compensated HR Measurement Using ToF Camera  
The results of the pilot human study suggest that the magnitude of motion contributes the most to the failure of the HR measurements. When the motion score is relatively small (<0.3 cm), the success rate is 84% while such success rate falls quickly as the motion score increases. The adverse effects of larger motion to the HR measurements seem intuitive as it both makes the ROIs harder to track as well as creates larger intensity artifacts for the ToF camera to compensate. However, there are also some less noticeable factors that can also contribute to the erroneous reading even when the motion artifacts are small.  
To obtain a stable signal from a ROI, the ROIs needs to be stably and accurately tracked in every frame. In some cases, reflective spots on the face as well as the changing head pose/facial expression could introduce artifacts to the tracked ROI. For example, we find that the light reflection from the participants' eyes combined with the blinking motion can sometimes cause the positions of the ROI to jitter, even though the participants are not moving at that time. If such artifacts are present during measurement, it can mislead the system to take the blinking frequency as HR.  
In terms of the limitation of the depth-based motion compensation, other than the obvious cases where significant motion overwhelms the underlying HR signal, we also find the system performance degrading when local motions, such as involuntary muscle twitches or facial expression changes [6; 10], are present. Since the human face is not a rigid body, these local motions can cause intensity artifacts without inducing a change in depth signal at the same time, making them \"invisible\" to the depth-based motion compensation.  
Therefore, the depth signal-based method is more effective toward motion artifacts that induce depth variations that are large enough to be captured by the ToF camera. As an example, global motions, especially scaling and translational motion, can be easily captured by the ToF camera. Even though these larger motions cause large grayscale intensity change on the ToF camera, they can be effectively removed to prevent them from affecting HR measurement.
## 3 Results
### Accuracy and Uncertainty Analysis
#### 3.1.2 Uncertainty of Interference Filters  
The interference filter used in two-color thermometry is a narrowband filter with a bandwidth of 10 nm, the uncertainty of each wavelength is half bandwidth, \(\Delta\lambda_{1}=\Delta\lambda_{2}=5\) nm.  
\[U_{\lambda_{1},\lambda_{2}}=\Delta\lambda_{1}\bigg{|}-\frac{5}{F\lambda_{1}}+ \frac{1}{\lambda_{1}-\lambda_{2}}-\frac{1}{\lambda_{1}}\bigg{|}+\Delta\lambda _{2}\bigg{|}\frac{5}{F\lambda_{2}}-\frac{1}{\lambda_{1}-\lambda_{2}}+\frac{1}{ \lambda_{2}}\bigg{|}, \tag{11}\]
# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 5 Discussion
### Computing Cost and Economic Effectiveness  
In the current study, the computing cost and economic effectiveness of ANFIS were not evaluated. However, this is an important aspect to consider when implementing ANFIS in real-world applications. ANFIS models can be computationally intensive, particularly for large and complex datasets. This may result in high computing costs and may limit the feasibility of using ANFIS in certain organisations, particularly those with limited computing resources. Additionally, the cost effectiveness of ANFIS should also be considered in terms of the benefits it provides in terms of decision-making, policy analysis, and resource allocation. While ANFIS can provide valuable insights and improve the accuracy of predictions, its implementation may require significant time and resources, which could impact its overall economic effectiveness. Therefore, it is important to carefully evaluate the computing cost and economic effectiveness of ANFIS when considering its implementation in real-world applications. This may involve conducting cost-benefit analyses, comparing ANFIS to alternative modelling techniques, and considering the trade-offs between the computational requirements and the benefits provided by ANFIS. In conclusion, ANFIS has the potential to provide valuable insights for decision-making in the public sector, but its implementation requires careful consideration of the computing costs and economic effectiveness. Factors such as the size and complexity of the data, the available computing resources, and the specific requirements of the application should be taken into account when evaluating the feasibility of using ANFIS. Future research could focus on developing strategies to reduce the computational requirements of ANFIS and on comparing the performance and cost effectiveness of ANFIS with other modelling techniques. Furthermore, the limitations of ANFIS should be carefully considered and addressed in future studies to improve its accuracy and usefulness in real-world applications.  
Ultimately, the implementation of ANFIS in the public sector will require a comprehensive and interdisciplinary approach, involving experts from various fields, including computer science, operations research, and public administration. By considering the technical and economic feasibility of ANFIS and by addressing its limitations through further research, the public sector can harness the potential of this powerful tool to support effective decision-making, policy analysis, and resource allocation.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 4 Evaluation Methods  
### Evaluation Algorithm  
The impact of node selfishness is examined by running a simple User Data Protocol (UDP) application on the Transport Layer. The application is regarded as UDP network traffic. The sender node generates ping requests to the destination node and calculates the packet loss and round-trip times of the replies. It works exactly like the 'ping' command in most operating systems. Every ping request is sent out with a sequence number, and replies are expected to arrive in the same order. Whenever there is a jump in the responses'  
Figure 2: An example of single node energy variation.  
sequence number, then the missing pings are counted as lost. Then if it still arrives later, it will be counted as out-of-sequence arrival, and at the same time the number of losses is decremented [8].  
As illustrated in Figure 3, the sender (source) node and receiver (destination) node are fixed at left and right side of the constraint area, respectively. Both the sender and receiver are out of the transmission range of each other, which means they need at least one intermediate node to communicate with each other. All other nodes are randomly distributed between the sender and receiver node and arbitrarily move in the constraint area. It means that all nodes except for the sender and receiver are intermediate nodes bearing forwarding tasks.  
In this evaluation algorithm, selfish behaviors of nodes are more noticeable because the only communication task the nodes perform is data forwarding. In order to estimate the overall communication performance effected by selfish nodes, more general evaluation algorithms in which more senders and receivers are deployed are planed in our future work.