esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment
### Other Experimental Studies  
Due to space limitations in this paper, we chose to present data from Zurich as a representative example to complete the model ablation experiment. This is because the data from Zurich has good representativeness, which can effectively demonstrate the performance of our proposed method in dealing with different parking lot networks. In addition, Zurich's high data quality and wide coverage provide a sufficient testing basis for our model. We let DSTAGCRN denote the complete network; W/o Trend represents the removal of the trend term, W/o Attention represents the removal of the multihead attention mechanism, W/o Embeddings represents the removal of weekly and daily embeddings, and W/o Dynamic represents the use of only static graphs without dynamics. The evaluation results for the Zurich parking data using these modules are shown in Table 3.  
Throughout the entire ablation experiment, we applied the same dataset as Zurich in Table 2, and the specific implementation and result calculation were completed in our research environment, which used Python and related machine learning libraries as well as the PyTorch framework. Overall, the performance using only static graphs was the worst, demonstrating the limitations of considering spatial correlations between different parking lots on the road network solely from the perspective of available parking bay data. The performance of using weekly and daily information embeddings and trend terms was better than that of using only dynamic graphs, indicating that VPS data have periodic features. In addition, the DSTAGCRN model achieved the best performance, indicating that the weight design changed by adding the multihead attention mechanism would greatly affect the graph convolution and that this method could better learn the spatio-temporal correlation between different parking lots.  
Figure 10 gives the results of our model with different embedding dimensions on the Zurich parking dataset. DSTAGCRN achieved a relatively good performance on all tested embedding dimensions, which demonstrates the robustness of our method. Furthermore, DSTAGCRN achieved the best performance when the embedding dimension was set to 10. Node embedding dimensions that are too small or too large will lead to poor performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **MAE** & **RMSE** & **MAPE** \\ \hline DSTAGCRN & 3.61 & 8.66 & 13.54\% \\ W/o Attention & 3.64 & 8.8 & 13.80\% \\ W/o Trend & 3.63 & 8.67 & 14.33\% \\ W/o Embeddings & 3.69 & 8.73 & 15.42\% \\ W/o Dynamic & 4.36 & 10.09 & 16.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment based on the Zurich parking dataset.  
Figure 9: Changes in the spatial weights of two parking lots in one day.  
Appropriate embedding dimensions can derive more accurate spatial correlations and prevent overfitting. Figure 11 shows the impact of dimensions and layers in the PLGCRM module on the model. When the hyperparameters are different, the results of the model are different. This article selects the best parameters.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 3 Methods
### BC Index Development  
Following the sensitivity and separability analysis, the rationale for developing a bare coal index can be summarized in three aspects: (1) BC exhibits lower reflectance in the NIR, SWIR1, and SWIR2 bands compared to other land cover types, except for water areas; (2) the NIR band is highly sensitive to BC and can serve as a reliable indicator for distinguishing BC from other land covers; and (3) BC exhibits higher reflectance in the SWIR2 band compared to the SWIR1 band. The difference between SWIR2 and SWIR1 reflectance is highly sensitive to the BC fraction, making SWIR2-SWIR1 the optimal band combination for distinguishing BC from other land covers. Additionally, since the reflectivity of water in the NIR band is lower than in the red band, using the NIR-Red band combination can lead to significant confusion between bare coal and water. Thus, the unique spectral characteristics of bare coal can be summarized as follows:  
\[BCI=\frac{R_{Swir2}-R_{Swir1}}{R_{Nir}\times(R_{Nir}+R_{Swir1})} \tag{2}\]  
In this equation, \(R_{Nir}\), \(R_{Swir1}\) and \(R_{Swir2}\) are the reflectance of the NIR, SWIR1, and SWIR2 bands, respectively.
## 4 Calculation Results and Discussion
### Comparison of Forecasting Models
The evaluation metric statistics of the 106-day power generation forecast results in the testing set of HC city and GL city are shown in Tables 4 and 5, and the comparison between the forecasted values and the true values of each model is shown in Figure 8. It is easy to see that the _AC_, _MAPE_, _RMSE_, and \(R^{2}\) of the proposed multimodal deep learning model based on the fusion of a CNN and an MLP are significantly better than those of the SVR, GBRT, RF, LSTM, and separate MLP and CNN models. In particular, the percentage of days with absolute percentage error (_APE_) less than 10% or 5% is much higher than other models. This proves that the proposed model is more effective and universal, and it is more suitable for forecasting the daily power generation of practical small hydropower stations.
## 2 TFR Techniques
### Uncertainty Principle  
The significant issue in signal processing is the uncertainty principle, also named the Heisenberg-Gabor limit, which clarifies that one cannot simultaneously localize a signal with an arbitrary precision both in the time and frequency domains. There is a trade-off between the time resolution and frequency resolution depending on a key control parameter, e.g., the length of window function \(g(t)\) in STFT. In other words, it is difficult to identify the frequency in a concise time interval because the frequency resolution is relatively low under these conditions.  
Many time-frequency analysis tools are proposed to address this trade-off problem, such as quadratic representations using the Wigner-Ville distribution (WVD) [37] or other autoregressive methods [38]. The frequency resolution is improved using these methods but may produce undesirable artifacts.
## 5 Discussion
### Comparison with Alternative Methods
This accentuates the potential superiority of our suggested methodology for precise and straightforward gravitational assessments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Sensor** & **Accuracy** & **Uncertainty** & **References** \\ \hline Free fall and acoustical & Microphone with a & 99.08\% & 99.00 \(\pm\) 0.2 m/s\({}^{2}\) & [20] \\ Doppler effect & personal computer & & & \\ \hline Free fall & Smartphone acceleration & 98.06\% & 10.00 \(\pm\) 0.2 m/s\({}^{2}\) & [21] \\ \hline Free fall & Smartphone’s onboard & 99.99\%, 97.45\%, and 99.59\% & 9.82, 10.06, and 9.77 m/s\({}^{2}\) & [22] \\ \hline Free fall & IR transceiver & 99.8063\% & 9.8092 \(\pm\) 0.0384 m/s\({}^{2}\) & [23] \\ \hline Free fall & Ticker tape timer & 90–95\% & 9.82 \(\pm\) 0.04 m/s\({}^{2}\) & [24] \\ \hline Pendulum & Ultrasonic sensor & 99.60\% & 9.82 \(\pm\) 0.10 m/s\({}^{2}\) & [25] \\  & and arduino & & & \\ \hline Pendulum & Smartphone’s proximity & 99.99–99.46\% & 9.75 \(\pm\) 0.02 m/s\({}^{2}\) & [26] \\  & sensor & & & \\ \hline Pendulum & Smartphone magnetometer & 98.98\% & 9.8 \(\pm\) 0.1 m/s\({}^{2}\) & [27] \\ \hline Pendulum & Piezo buzzer and & & & \\ \hline Pendulum & smartphone’s onboard & 99.39\% & 9.72 \(\pm\) 0.15 m/s\({}^{2}\) & [28] \\  & microphone & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons used to determine gravitational acceleration (g).
## 5 Simulation and Analysis
### Results of Simulation  
Trajectory A: the initial position of the underwater vehicle was 122.07\({}^{\circ}\) E, 37.585\({}^{\circ}\) N. The initial location of INS was 300 m to the east, and 300 m to the north; the initial course was 10\({}^{\circ}\) north by east, and the vehicle traveled straight at a constant speed. Figure 4 shows the trajectories of the two matching algorithms. Figure 5 shows the longitude and latitude error curves of trajectory A.  
In Figure 4, the matching accuracy of F-WAPSO is significantly better than that of TERCOM. In Figure 5, in the longitude error curve, the error of TERCOM oscillates around 0 m in the early stage and gradually increases in the late stage, whereas that of F-WAPSO oscillates consistently around 0 m. In the latitude error curve, the error of TERCOM is between \(-\)100 and \(-\)200 m, and that of F-WAPSO is between \(-\)100 and 50 m. Table 3 lists the statistical results of trajectory A. The matching error is expressed by the mean value of the root mean square of the longitude and latitude errors of the whole orbit sequence.  
Figure 4: Trajectory A matching results of the two algorithms.  
Figure 5: Longitude and latitude error curves of trajectory A.  
In conclusion, under the same conditions, when the initial position error is small, both TERCOM and F-WAPSO have a good matching effect, but when the initial position error is large, F-WAPSO has a better matching effect than TERCOM. As shown in Tables 3-6, when the initial position error is (300 m, 300 m), compared with TERCOM, the matching error of F-WAPSO decreases by up to 80.1%, the longitude error decreases by up to 96.8%, the latitude error decreases by up to 88.6%, and the matching time decreases by up to 13.3%. When the initial position error is (700 m, 700 m), the matching error, longitude error, and latitude error of F-WAPSO are reduced by 63.3%, 92.4%, and 66.5% compared with TERCOM, and the matching time is shortened by 15.4%. When the initial position error is (1000 m, 1000 m), the matching error, longitude error, and latitude error of F-WAPSO are reduced by 57.1%, 94.3%, and 82.8% compared with TERCOM, and the matching time is shortened by 13.8%. With the increase of the initial position error, the matching error of the two algorithms increases and the matching time becomes longer. However, regardless of the size of the initial position error, the matching accuracy of F-WAPSO is obviously better than that of TERCOM, and the error variance of F-WAPSO is smaller in the four cases, indicating that F-WAPSO is more stable.
## 3 Results
### Test-Retest Reliability  
In this study, the correlation coefficient between Visit 1 and 2 trials was calculated to verify Co-Wis test-retest reliability. The test-retest was done in a time interval ranging between two weeks and three months. Most items showed significant test-retest reliability, with scores ranging from 0.529 to 0.872. Trail making (A type)'s test-retest low reliability results 0.251 were shown (Table 4).
## Materials and Methods
### Performance Measurement
#### 2.2.3 Hyperparameter Tuning  
The tuning process is done independently for ND, FD, and SD signals, to observe which one provides better performance results. The type of signal is not specified through this section, but it is repeated similarly for the three of them. The only difference is on the model hyperparameters and their performance results.  
The goal of tuning is determining the most optimal set of values to model our data. This work achieves these results by first applying a Random Search, as it has been proven a suitable technique that requires less computational time [40]. The exhaustive Grid Search is performed afterward, as represented in Figure 4. The whole tuning process is done based on the data in one of the visits, which behaves as the enrolment. In this case, it is the data in R1, which is expected to be the most stable set because the user's relaxed and sitting down. This process is repeated individually for ND, FD, and SD, obtaining 3 different configurations, one per signal.  
As for testing the model after the tuning process, the test data needs to be new, we need to split R1 into two parts: one is used for training (development matrix), and the other one for validating (validation matrix) the trained models. We decided to set the train size to 50% to obtain the same sizes for training and validating, which results in 125 QRS complexes per user in both matrixes.  
The division is done using a method called Stratified Shuffle Split, which selects the samples randomly but keeping the class proportion to avoid having an unbalanced model. The random shuffling is replicable, so results are not biased by different shuffling orders.  
Figure 4: Hyperparameter tuning scheme.  
The Random Search method sets a number of random hyperparameter combinations, training a new model for each one of them. The activation function can be all four types in Table 1. The rest of the hyperparameters are numerical, and the selected range of values tries to represent extreme values, with equidistant middle values to observe if performances are correlated to little changes in any of them, as seen in Table 4. Tolerance is still relatively low to try to achieve more accurate results.  
In this case, we obtain 50 random value combinations by training with the development set. The MLP training is done using cross-validation to observe how well the model performs under different train and test sets. In this case, the cross-validation process is done 5 times, dividing the development set into 80% training and 20% test. This division is also done by using Stratified Shuffle Split. The final performance results for every combination contains the mean EER through all the splits. Table 5 shows the best 3 results for ND, FD, and SD in descending order.
## 3 A Security-Enhanced Authenticated Key-Agreement Protocol: SE-UAVAKA
### SE-UAVAKA Authentication Key-Agreement Phase
#### 3.4.2 Explanation of Key Processes in the Authentication Key-Agreement Phase  
The authentication key-agreement phase is the core step of the SE-UAVAKA protocol, which supports secure two-way authentication and session key establishment between the UAV and the ground station.  
In this phase, the pre-shared long-term keys (\(y_{a}\), \(y_{b}\)) and identities (\(ID_{A}\), \(ID_{B}\)) are extracted from secure storage as the starting point. Each entity generates fresh temporary session parameters (such as random numbers \(r_{a}\), \(r_{b}\)) to ensure session uniqueness and forward security; public parameters (\(N_{a}\), \(N_{b}\)) are derived using elliptic-curve scalar multiplication while minimizing computational overhead.  
In addition, timestamps (\(TS_{a}\), \(TS_{b}\)) and hash-based checksums (\(H_{a}\), \(H_{b}\)) are used in this paper to resist replay attacks and verify message integrity. Crucially, the session keys (\(SSK_{AB}/SSK_{BA}\)) in this phase are dynamically generated through temporary random numbers, long-term identities, and exchange parameters to ensure that the current session still has specific security even if the previous keys are leaked.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 4 Numerical Evaluations
### Decoding Time  
Next, we evaluate the decoding time. We apply the protocol in Section 3.4 and make the same assumptions used for Figures 4 and 5. Suppose a node can recover the data when the total rank reaches \(F=1000\). The number of timeslots required for decoding is plotted in Figures 8 and 9, respectively, for \(M=4\) and \(8\). We use the same legend as that described in the last subsection, except there is no curve for the \"capacity\".  
We can see that two-phase RAP (RAP2) and the heuristic (heur.) give almost the same decoding time, which again confirms the accuracy of our heuristic. The gain of SAP in decoding time becomes smaller in general when the loss rate \(p\) is larger. With a large \(p\)SAP might give a longer decoding time than that without using systematic recoding. The reason is that we have allocated too many resources to the last phase of SAP, which leads to a poorly optimized strategy. When there are more phases, less resources are allowed to the last phase; thus, we can still observe an improvement in decoding time for three-phase SAP. For RAP, the difference between decoding time for two and three phases is smaller when \(M\) becomes larger. When \(M\) is larger, more idling timeslots are induced by deferring recoding after notifying by the previous node that all the packets of the batch for the phase have been transmitted. We cannot conclude whether using more phases is better or not when \(M\) becomes larger. However, this can be partially compensated by SAP. We can see that three-phase SAP still works well when \(p=30\%\). Yet, this result suggests that instead of focusing on optimizing the throughput solely, we should also consider the decoding time and the behavior of the protocol in future works of BNC.  
We want to point out that there could be many possible solutions for RAP achieving almost the same throughput. Two different solutions that have almost no difference in throughput may result in a difference in the decoding time. In this case, we suggest to choose the solution that allocates more resources to early phases so that early phases have more chances to send more innovate packets. The optimal strategy is left as a future research direction.
## 1 Introduction
### Main Contributions  
In the face of new requirements, typical MES functions still remain necessary in the I4.0 era as well, even though in a renewed architecture. Just as the traditional automation pyramid is evolving, the MES should also be changed accordingly, towards providing services. Figure 1 shows how the development process of the MESA automation pyramid is extended with the current proposal in this paper.  
Following the above considerations, research presented in this paper targeted the design and realization of a novel concept of _Manufacturing Execution System as a Service_ (MESS in the following). This is a new attempt to model, integrate, and orchestrate essential CPS services that make up a modern digitized manufacturing facility. The architecture of MESS facilitates a novel, generic, and simple way for the collaboration of distributed, autonomous manufacturing entities. The focus of the paper is on the core functionality of MESS and the standardized integration of CPS services.  
Major points of this research work can be summarized as follows:  
1. Proposition of a simplified CPS conceptual and information model, whose elements represent the basis for the definition of a common vocabulary in service integration and interoperability semantics (see Section 3.2);
2. Realization of such service integration model as the core of a new, minimalistic concept of MES, which is based on as few assumptions as possible. It privileges standardized industrial technology service and data publishing via Open Platform Communications Unified Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT) (see Section 3.3);
3. Exploitation of such universal service integration model to a variety of heterogeneous manufacturing cells through by means of a so-called Production Administration Shell (PAS) (see Section 4.3);
4. Demonstration of the presented approach in a research lab for manufacturing. This is a learning factory and an open ecosystem which offers students, researchers, as well as industrial partners the opportunity to perform research on CPPS related topics. An ideal place to study the challenges and to understand the benefits of elevating the CPS to a mature level of interoperability in a production context (see Section 5).
# Optimized Implementation and Analysis of CHAM in Quantum Computing
## 3 Quantum Implementation of CHAM
### Binary Matrix in Key Schedule  
The round key \(RK\) matrix of CHAM is generated using Equation (1) of Section 2.1. The \(i\) range of CHAM 64-128 is \((0\leq i<r)\), so CHAM 64-128 exists from \(RK_{0}\) to \(RK_{15}\). For \(RK\), indices 0 to 7 use the first formula in Equation (1) and 8 to 15 use the second formula, resulting in a total of two \(RK\) matrices. In the case of CHAM-64/128, since the word size \(w\) is 16, the matrix obtained through the above process has a size of 16 \(\times\) 16. The following is the \(RK_{0\sim 7}\) matrix of CAHM 64-128:\[\begin{bmatrix}\mathbf{1}&0&0&0&0&0&0&0&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}\\ \mathbf{1}&\mathbf{1}&0&0&0&0&0&0&0&\mathbf{1}&0&0&0&0&0\\ 0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&0&\mathbf{1}&0&0&0&0\\ 0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}&0&0&0&0\\ 0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}&0&0&0\\ 0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}&0&0\\ 0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}&0\\ 0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0&\mathbf{1}\\ \mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0&0\\ 0&\mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0&0\\ 0&0&\mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0&0&0\\ 0&0&0&0&\mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}&0&0\\ 0&0&0&0&0&\mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}&0\\ 0&0&0&0&0&0&\mathbf{1}&0&0&0&0&0&\mathbf{1}&\mathbf{1}\\ \end{bmatrix} \tag{9}\]  
The row of the matrix \(RK\) shows the result of each rotation operation when \(i\) is applied to the key-schedule equation. For example, the second row of \(RK_{0\sim 7}\) is composed of \((1,1,0,0,0,0,0,0,0,1,0,0,0,0,0)\), according to Equation (9). When \(K_{i}\) is in column 2, 1 in column 1 is a value of \(RoL_{1}\), and 1 in column 1 is a value of \(ROL_{8}\). The matrices of the remaining parameters including CHAM-64/128 are generated using sage math tool.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Grey Relational Analysis  
Unlike statistical correlation measuring the relationship between random variables, GRA explores relationships between data sequences [27; 29] by treating one of these sequences as the goal [28]. Assume that \(n\) denotes the number of attributes. Let \(\mathbf{x}_{j}=(x_{j1},x_{j2},\ldots,x_{jn})\) (\(1\leq j\leq m\)) be a comparative sequence and \(\mathbf{x}_{i}=(x_{i1},x_{i2},\ldots,x_{in})\) (\(1\leq i\leq m\)) be a reference sequence. The grey relational coefficient \(\xi_{k}(\mathbf{x}_{i},\mathbf{x}_{j})\) can be used to measure the relationship between these two sequences on attribute \(k\) (\(1\leq k\leq n\)) [37]:  
\[\xi_{k}\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}\ =\ \frac{\Delta_{\min}+ \rho\Delta_{\max}}{\Delta_{ijk}+\rho\Delta_{\max}} \tag{17}\]  
where  
\[\Delta_{\min}=\min_{s}\min_{l}\Big{|}x_{jl}-x_{sl}\Big{|},\ 1\ \leq s\leq m,\ 1\ \leq l\leq n \tag{18}\]  
\[\Delta_{\max}=\max_{s}\max_{l}\Big{|}x_{jl}-x_{sl}\Big{|},\ 1\ \leq s\leq m,\ 1\ \leq l\leq n \tag{19}\]  
\[\Delta_{ijk}=\ |\ x_{ik}-x_{jk}\| \tag{20}\]  
where \(\rho\) is a discriminative coefficient, commonly specified as 0.5 [29], but this is apparently not an optimal setting. \(\xi_{k}(\mathbf{x}_{i},\mathbf{x}_{j})\) falls somewhere between zero and one.  
The grey relational grade (GRG) \(Y(\mathbf{x}_{i},\mathbf{x}_{j})\) can be used to measure the overall relationship between \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\):  
\[(\mathbf{x}_{i},\mathbf{x}_{j})=\sum_{k=1}^{n}w_{k}\xi_{k}(\mathbf{x}_{i}, \mathbf{x}_{j}) \tag{21}\]  
where \(Y(\mathbf{x}_{i},\mathbf{x}_{j})\in[0,1]\). \(w_{k}\) denotes the relative importance of attribute \(k\), and \(w_{1}\), \(w_{2}\), \(\ldots\), \(w_{n}\) satisfy  
\[\sum_{j=1}^{n}w_{j}=\ 1 \tag{22}\]