esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Real-World Application: Circulating Fluidized Bed Boiler  
In this section, early abnormal detection times are compared to evaluating the applicability of the proposed method to a shutdown case of a CFBC boiler. CFBC is a power generation system that utilizes biomass, including methane, ethanol, and hydrogen, along with low-quality fuels like solid refuse fuel. As shown in the CFBC power generation schematic in Figure 9, the use of fluidized media and limestone improves combustion efficiency, reduces fossil fuel consumption, and lowers pollutant emissions [39]. However, the use of fluidized media, such as sand, results in the attachment of alkali salts from exhaust gases to the media and heat pipes, leading to erosion, corrosion, and agglomeration. As the flow medium passes through the rough areas of the inner surface of the pipe, faults such as deposit formation, recirculation blockage, and tube leakage may occur [40]. In the event of an actual fault, the process of identifying the cause, repairing the fault, verifying operational status, and restarting the plant takes at least one week. Therefore, a fault detection technique for CFBC based on data from the distributed control system (DCS) is essential. The generator capacity, voltage, and rotational speed of the CFBC are 9.1 MW, 6.6 kV, and 1800 RPM, respectively. Additionally, the steam turbine's maximum capacity, maximum inlet steam pressure, and steam temperature are 72 t/h, 41 ata, and 420 \({}^{\circ}\)C.  
The CFBC shutdown occurred on 9 September 2020, at 14:35, when the operator responded to an emergency alarm, unexpectedly shutdown the boiler for inspection, and confirmed the failure. As shown in Figure 10, the failure was caused by tube rupture in the superheater and desuperheater, as well as by deposit formation in the furnace and cyclone. Notably, the failure was characterized by a gradual increase in size after the tubes ruptured, affecting other equipment. Additionally, deposits in the furnace and cyclone were dislodged, leading to nozzle blockages. Data for fault detection were collected from the DCS from 2 September 2020, until the boiler shutdown, with 10,000 training and 7284 validation data samples used. Figure 9: Diagram of power generation system in CFBC boiler [41]. The input variables for fault detection consisted of 109 boiler- and steam-related variables, as shown in Table 2. The parameter settings for each detection method were as follows: PCA reduced the 109 input variables to 13 dimensions, with a cumulative percentage variance of 90%. The number of nearest neighbors was set to 20 for LOF and 35 for kNN. The bandwidth parameters for AAKR, AASKR-NP, and AASKR were set to 0.37, 0.37, and 1, respectively. Fault detection thresholds were set using KDE (\(\alpha\) = 0.01).
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 3 Materials and Methods
### Statistical Test Suite  
The National Institute of Standards and Technology (NIST) Statistical Test Suite (STS) comprises an assemblage of open-source cryptographic software and algorithms [39]. Encompassing both encryption and digital signature schemes, the STS serves diverse purposes, including secure communication and data protection. Engineered for broad applicability, it is well-suited for deployment in both commercial and research contexts. The STS provides not only cryptographic tools but also incorporates test materials and benchmark results, enabling a comprehensive evaluation of the performance and security attributes of cryptographic systems. Consequently, the STS stands as an invaluable resource for  
Figure 2: Graph adjacency matrix.  
developers, researchers, and organizations engaged in the formulation and implementation of cryptographic systems.  
The NIST STS includes a variety of tests designed to assess the randomness and statistical properties of cryptographic algorithms and random number generators. Some of the key tests commonly employed in the NIST STS include the following:  
1. Frequency (Monobit) Test--assesses whether the number of ones and zeros in a sequence are approximately equal;
2. Block Frequency Test--examines the frequency of fixed-size blocks in the sequence;
3. Runs Test--analyzes the number of runs of ones and zeros in the sequence;
4. Longest Run of Ones in a Block Test--investigates the length of the longest run of ones in a sequence;
5. Binary Matrix Rank Test--assesses the rank of disjoint submatrices of the entire sequence;
6. Discrete Fourier Transform (Spectral) Test--utilizes the discrete Fourier transform to analyze the frequency content of the sequence;
7. Non-overlapping Template Matching Test--checks for the presence of specific fixed-size patterns in the sequence;
8. Overlapping Template Matching Test--similar to the non-overlapping template matching test but allows for overlapping patterns;
9. Maurer's Universal Statistical Test--measures the compressibility of a sequence;
10. Random Excursions Test--analyzes the number of cycles having specified numbers of visits in a random walk;
11. Random Excursions Variant Test--examines a variant of the random excursions test.  
These tests collectively provide a comprehensive evaluation of the randomness and statistical properties of sequences, helping to ensure the robustness of cryptographic systems. The NIST STS is widely used in cryptographic research and standardization efforts.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 5 Case Study
### Analysis of Testing on Different Lines  
To verify the transferability of the proposed algorithm in this paper, a test was conducted on Shenzhen Metro Line 1. The line involved the operation of 16 trains across 30 stations, with the sections between stations constructed based on the actual layout of the line. The train headway was set at 340 s. In Scenario 1, as depicted in Figure 12(a), the 2 nd train encountered a delay of 300 s at the 19 th physical station. In Scenario 2, illustrated  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**No Adjustment**} & \multirow{2}{*}{**Manual Adjustment**} & \multirow{2}{*}{**FIFO**} & \multicolumn{2}{c}{**Traditional Q-Learning**} & \multirow{2}{*}{**The Proposed Method**} \\  & & & & **(\(\varepsilon\) = 0.6)** & \\ \hline \multicolumn{5}{c}{Scenario 1} \\ Total delay time (s) & 885 & 510 & 400 & 256 & 256 \\ Affected trains & 5 & 5 & 3 & 2 & 2 \\ Affected physical stations & 5 & 6 & 3 & 2 & 2 \\ Average computation time (min) & 8 & 5.1 & 0.1 & 2.5 & 1.1 \\ Average Convergence Iterations & - & - & - & 634 & 235 \\ \multicolumn{5}{c}{Scenario 2} \\ Total delay time (s) & 1750 & 970 & 830 & 480 & 480 \\ Affected trains & 10 & 11 & 6 & 4 & 4 \\ Affected physical stations & 13 & 12 & 6 & 4 & 4 \\ Average computation time (min) & 16 & 12.3 & 0.1 & 5.2 & 2.3 \\ Average Convergence Iterations & - & - & - & 763 & 312 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the effectiveness of the proposed algorithm with other methods.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**The Proposed Method**} & \multicolumn{4}{c}{**Traditional Q-Learning**} \\  & \(p\) = 0.002 & \(p\) = 0.005 & \(p\) = 0.01 & \(p\) = 0.02 & \(\varepsilon\) = 0.6 & \(\varepsilon\) = 0.4 \\ \hline Delay scenario 1 & 256 (591) * & 256 (363) & 256 (251) & 256 (235) & 256 (634) & 256 (661) \\ Delay scenario 2 & 480 (640) & 480 (484) & 480 (334) & 480 (248) & 480 (763) & 480 (782) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization results of train schedules with different \(p\) or \(\varepsilon\) values in two delay scenarios.  
in Figure 13b, the 9 th train experienced a 300 s delay at the 18 th physical station, and the 11 th train faced a 200 s delay at the 16 th physical station. In both scenarios, the algorithm demonstrated the capability to swiftly restore normal operation. This case confirms that the proposed algorithm can effectively handle delays and recover the system's functionality across different metro lines.
###### Abstract  
Optical fiber sensing is a promising detection method for spacecraft health monitoring, since optical fiber sensors are lightweight, small in size, easy to integrate and immune to electromagnetic interference. As a significant optical sensor, fiber Bragg gratings (FBG) are widely used for force sensing because of their axial strain characteristics. However, it is necessary to detect not only one-dimensional strain but also plane strain and its deformation in order to comprehensively evaluate the condition of the structure. Therefore, it is very important to analyze the reflection spectrum of FBG under lateral stress. When FBG are subjected to lateral stress, the refractive index of the waveguide in the x and y directions changes, resulting in a birefringence phenomenon. This result causes the reflection spectrum of FBG to split into two peaks. In this paper, a transverse stress detection method based on spectral split sensing for the fiber Bragg grating is proposed, intended for monitoring spacecraft-small particle collisions. The FBG local lateral stress detection system is designed and verified by experiments. The wavelength pressure correlation is established in the experiment by adjusting the number of weights to change the lateral pressure on the FBG. The loading range of FBG lateral pressure is 4.0-7.0 N, the step size is 0.5 N, and round-trip measurement is carried out four times. The wavelengths of the peak and split point of the FBG reflection spectrum are recorded. The experimental results show that FBG's split point and right peak pressure sensitivities are 16.57 pm/N and 45.14 pm/N, respectively. The spectral splitting phenomenon can be applied in spacecraft structure health monitoring systems and has certain reference value for the simplification of sensor systems.  
fiber Bragg grating: lateral stress; spectral splitting; spacecraft structural health monitoring
## 3 Case study  
The methodology explained in the previous section was applied to the instantaneous consumption data per second of different household appliances, extracted from [43], namely lighting, refrigerator, HVAC, dryer, washing machine and dishwasher.  
The input data are the consumption of different household appliances in a house located in Vancouver. The powers were measured for 63 days, from 6 March 2016 to 7 May 2016. The dwelling consists of two floors, with a total of 199 m\({}^{2}\) and three people living in it. Before starting the statistical evaluation, the consumption was grouped according to a 10-min period of power and then separated into three groups, i.e., working days, Saturdays and Sundays. The last step beforehand was to distinguish between continuous and discontinuous consumption, given that the treatment of the latter is more complex.  
The reason for evaluating the 10-min periods is a matter of trade-off in accuracy and computational cost. On one hand, in a 10-min period, an appliance is not very likely to experience a large number of consumption changes. If it were 20 min, there is a risk of adding a larger error, as it could cover more than two phases of operation, e.g., a dishwasher. On the other hand, if the period is reduced to 5 min, the accuracy would increase, but the computational cost would be doubled, as twice as many periods would have to be modeled. Thus, a period of 10 min shows an adequate balance. The refrigerator, lighting and HVAC consume a considerable minimum at all times, while the dryer, dishwasher and washing machine only consume a considerable amount when they are running, thus they are considered point loads, or discontinuous consumptions.  
A summary of the type of data can be observed in Table 3, with its type of consumptions, days and amount.
# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
###### Abstract  
This article investigates the efficiency of a doParallel algorithm and a formal concept analysis (FCA) network graph applied to recommendation systems. It is the first article using the FCA method to create a network graph and apply this graph to improve the accuracy of a recommendation system. According to the fundamental knowledge about users who have similar feature information, they may like the same items. This idea was used to create an FCA network graph. In the proposed process, the \(k\)-clique method was used to divide this network graph into various communities. A combination of the \(k\)-nearest neighbor and the betweenness centrality methods was used to find a suitable community for a new user based on the feature information similarities between the new user and an existing user in each community. Finally, a data mining method created a list of items from suitable communities and recommended them to the new user. In essence, the execution in this article uses a doParallel algorithm as a mechanism in parallel processing technology. The result of the implementation is satisfactory. It proved that the proposed method could resolve the cold-start problem in a recommendation system and may overcome the vast time consumption when a huge dataset is involved.  
Keywords:betweenness centrality; data mining; formal concept analysis; \(k\)-clique; \(k\)-nearest neighbor; parallel processing; recommendation system +
Footnote †: journal: Applied
## 2 Materials and Methods
### Approaches
#### 2.2.1 Single Differences  
Since TMI operated for more than 17 years, the observed Tb stability is an important metric to be evaluated. Therefore, the SD between the observed and simulated Tb over more than 15 years are analyzed as an indicator of its long-term radiometric stability. The simulated Tb was obtained using the XCAL ocean RTM. In this paper, the Remote Sensing Systems (RSS) surface emissivity model [15; 16], the Rosencrantz atmospheric water vapor absorption model [17; 18] and the Liebe atmospheric oxygen absorption model were combined to form the XCAL RTM [15; 16; 17; 18; 19]. The radiometer parameter inputs were: the channel frequency, the earth incidence angle (EIA), and the polarization (V- and H-pol). The environmental dataset was from the Global Data Assimilation System (GDAS), which uses NOAA's Global Forecast System (GFS) model (NCEP 2000) to provide outputs at 0000, 0600, 1200, and 1800 Greenwich Mean Time (GMT), on a \(1^{\circ}\times 1^{\circ}\) latitude (lat)/longitude (Ing) grid [20].  
The procedure used to calculate the single differences between TMI observed and simulated Tb is shown in Figure 8. The collocation process for TMI Tb's and the environment parameters of GDAS was performed in \(1^{\circ}\times 1^{\circ}\) latitude (lat)/longitude (Ing) boxes. Within each \(1^{\circ}\) box, the Tb's were combined, and the means and standard deviations were calculated. Afterwards the observed mean Tb's were compared with the simulated Tb for that box. Since the GDAS model output is available every 6 h, a \(\pm\) 2 h window for collocations that occurred 4 times per day was used.  
Figure 8: Flow chart for Single Differences procedure, between observed and modeled brightness temperature.  
The SD procedure seeks to estimate long-term Tb biases between radiometers, therefore, it is important that the collocations be carefully filtered to provide only high quality data for comparisons. The ideal conditions are homogeneous clear-sky ocean conditions, where there is the greatest confidence in the simulated Tb's. As a result, the match-up dataset was subject to conservative filters, which typically reduce SD comparisons to <50% of the possible collocations. An effective filter used was the standard deviation test, which removes non-homogeneous environmental conditions that include heavy clouds, rain, and mixed ocean/land scenes [21]. Cloud liquid water from GDAS was also used to as a filter to ensure cloud-free ocean scene. Finally, a filter based upon a threshold of the maximum observed ocean Tb by channel was also used.  
Then, the SD's for each channel were obtained by:  
\[\mathrm{SD_{TMI}}=\mathrm{T_{b-obs}}-\mathrm{T_{b-sim}} \tag{1}\]
# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 5 Experimental Results and Analyses
### Dynamic Positioning Experiment  
For better dynamic positioning testing, vehicle tracking was used for the experiment in this paper. The parameters of the three algorithms were the same as those in the previous static experiment. The environment of the dynamic positioning experiment is exhibited in Figure 9. The black line was paved according to the preset trajectory, starting at (50, 50) and ending at (150, 249.7). The tag nodes were placed on a 4 cm high vehicle. Then, the actual measured distance between the UWB tag and UWB was calculated as \(d=\sqrt{d_{c}^{2}-(4)^{2}}\), where \(d_{c}\) is the measured distance. Next, the vehicle was located at (50, 50), and the primary base station node was connected with the hose computer. In the end, two iron plate obstacles were placed at (11, 35) and (64, 240), respectively, and the wooden board obstacle was at (155, 75).  
The vehicle moved at a constant speed along the black line in the experimental field, during which the system performed positioning once every 0.5 s, and data collection was stopped when the vehicle arrived at the endpoint. The dynamic positioning data were then analyzed to obtain the positioning trajectories of the three algorithms (Figure 10). The RMSE curves of the positioning trajectories are given in Figure 11, and the RMSEs of the three algorithms in X direction, Y direction and overall positioning are listed in Table 3.  
According to Figures 10 and 11, the positioning trajectory of the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was closer to the target trajectory in the second half, and its positioning RMSE was lower than that of the other two algorithms at most moments. These findings revealed that the combination of historical information and characteristic information of the CIR signals could better mitigate the NLOS errors.  
It was manifested in Table 2 that the positioning accuracy of the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was improved to varying degrees in comparison with that of the other two algorithms. In contrast with those of the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 48.83% in the X direction,  
Figure 8: CDF diagram of the three positioning algorithms.  
38.37% in the Y direction and 43.31% in overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in the X direction, Y direction and overall positioning were lowered by 24.54%, 12.89% and 12.89%, respectively.  
Figure 10: Positioning trajectories of the three algorithms.  
Figure 9: Environment of the dynamic positioning experiment.
###### Abstract  
This paper studies a novel recurrent neural network (RNN) with hyperbolic secant (sech) in the gate for a specific medical application task of Parkinson's disease (PD) detection. In detail, it focuses on the fact that patients with PD have motor speech disorders, by converting the voice data into black-and-white images of a recurrence plot (RP) at specific time intervals and constructing the detection model that combines RNN and convolutional neural network (CNN); the study evaluates the performance of the RNN with sech gate compared with long short-term memory (LSTM) and gated recurrent unit (GRU) with conventional gates. As a result, the proposed model obtained similar results to LSTM and GRU (an average accuracy of about 70%) with less hyperparameters, resulting in faster learning. In addition, in the framework of the RNN with sech in gate, the accuracy obtained by using tanh as the output activation function is higher than using the relu function. The proposed method will see more improvement by increasing the data in the future. More analysis on the input sound type, the RP image size, and the deep learning structures will be included in our future work for further improving the performance of PD detection from voice.  
recurrent neural network (RNN); hyperbolic secant (sech) function; recurrence plot; Parkinson's disease +
Footnote †: journal: Applied Sciences  
Article  
Performance Evaluation of RNN with Hyperbolic Secant in Gate Structure through Application of Parkinson's Disease Detection
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 4 Modeling and Control
### Navigation Modes  
The prototype has two different navigation modes: Manual, through a teleoperation device (remote control) by the user. In addition, with an algorithm that calculates forthcoming actions, based on the obstacles present in its trajectory, while trying to keep a constant surge speed and heading selected by the user.
## 5 Experiments and Results
### Evaluation Metrics of Models  
#### 5.3.1 Running Performance  
To validate the computational complexity of deep learning models, five evaluation metrics in this experiment were firstly used to assess the algorithm's running performance: the number of parameters, video memory usage, training duration, memory consumption, and frame rate (_FPS_). It is important to note that the _FPS_ measures the number of images processed per second and serves as a significant indicator of prediction speed.
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 5 Grocery Shop Example
### Results  
Figure 5 shows the accuracy of the inventory process with an MSL threshold of 80% versus the response time (waiting time in queue plus interrogation time) for the checking-out traffic. At each point, the \(\Delta\) parameter is changed with the values indicated in the figure. Three primary outcomes are clear from these results:  
* A very significant reduction of the response time can be achieved with a negligible loss in the inventory accuracy by selecting \(\Delta=50\).  
Figure 4: Configuration scheme of the readers and checkouts in the supermarket. Antennas are at 1-m and 1.3-m height at checkouts, and 5-m height above the floor of supermarket shelves. Checkouts are RFID portals of 2-m width to let carts pass through. Each stock inventory reader comprises a reader and 4 shelves of 0.9 m \(\times\) 3 m \(\times\) 1.8 m (W \(\times\) L \(\times\) H) placed back-to-back pairwise.  
* Higher \(\Delta\) values can trade-off even less response time for minor losses in the accuracy. The reason for this minimal accuracy loss is that the inventory is a slow-varying process. A significant number of items have to be incorporated or removed to have a noticeable accuracy change, and the readers will be most likely able to correct the dataset, even with low-priority access to the channel.
* Pareto-front shows an exponential shape, and we can effectively select a response-time goal (only limited by the interrogation time, which does not depend on the \(\Delta\) parameter).  
On the other hand, Figure 6 provides additional insight in LBTM operation. It shows the time evolution of the response time (left column), the number of customers in the exit queue (middle column), and the inventory ratio (right column) for a simulation. Each row represents the results for a given \(\Delta\) parameter. The first (upper-most) row corresponds to the standard LBT behavior. The queue backlogs and the response times have a higher variance for the lower \(\Delta\) values, as the checking-out readers have to struggle with the inventory process ones more often. As the \(\Delta\) values increase, the contention for the channel is usually constrained to checking-out readers, which can serve the traffic quickly and avoid customers having to wait in the exit queue, as can be seen from the reduced backlogs.