esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Applications for Railway Safety
## 7 Station Safety
### Simulation and Scheduling  
Transportation modeling is difficult because it is a dynamic and complex system with interdependent factors, such as humans, equipment, and their temporal attributes. Recently, a deep learning approach that can extract complex high-level representations through hierarchical learning processes was applied to transportation modeling. [PERSON] et al. [91] proposed CLF-Net, a deep learning model that combines 3D-CNN, LSTM, and fully connected neural networks to handle complex variables in dynamic systems. The proposed model separately processes data with different attributes for better predictive performance, uses spatio-temporal variables to capture space-time dependencies, and receives variables to learn the potential effects of static factors.  
With the development of cities, short-term traffic prediction has become the core of the intelligence transportation system (ITS). Accurate short-term traffic forecasting can provide technical support to monitor train passenger flow and warn of excessive traffic congestion. [PERSON] et al. [92] proposed a spatio-temporal long-term network (ST-LSTM) that captures spatio-temporal features from railway traffic data. Their proposed model improved the original LSTM structure, focusing on temporal rather than spatial features.  
Predicting train delays can improve the quality of train operation, which helps to estimate train operation and more accurately make reasonable operational decisions. A train delay is affected by many factors, such as passenger flow, failure, extreme weather, and dispatch strategies. Considering such temporal and spatial factors between multiple trains and routes is challenging, which makes it difficult to accurately predict train delays. [PERSON] et al. [93] focused on predicting the cumulative effects of train delays over a certain period of time, represented by the total number of arrival delays in one station, rather than predicting each specific delay time of a single train. A deep learning framework based on  
Figure 10: Diagram of the air quality monitoring and supervisory control process. Reproduced with permission from IEEE [90].  
the spatio-temporal attention mechanism and spatio-temporal convolution was proposed. Their model receives recent input of daily and weekly time series data and each component includes a spatio-temporal attention mechanism and spatio-temporal convolution, which can effectively capture spatio-temporal characteristics. Experiments on train operation data in the railway passenger ticket system of China demonstrated that the proposed model clearly outperforms existing performance criteria in train delay prediction.
## 5 Security Analysis of Strike  
### Statistical Tests and Balance  
Testing the stream cipher with statistical tests is critical to ensure that the cipher is secure against statistical attacks [34; 35]. Our Strike stream cipher is tested against the standard statistical test suite NIST [36]. The NIST statistical test is composed of 15 tests that calculate 188 results values [35]. The test is carried out on 1500 sequences of keystream (1.5 GB of data), which represent the sample size used in this test. The size of each sequence is 1-MB resulting from 64 keystreams. The results presented in Table 4 show that the Strike cipher successfully passed all the NIST tests.  
In addition to NIST tests, we carried out a balance test to examine whether the generated keystream has an equal number of zeros and ones. The test examined 300 different keystreams. The number of zeros and ones is counted in each keystream. The test shows that no biased is detected in the generated keystream. The number of zeros and ones was almost equal in all keystreams, with a minor difference that does not exceed 2 bits. Figure 7 visualises the balance test. These results indicate that Strike is able to generate balanced keystreams that are resistant to several statistical attacks.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Test** & \(\mathbf{p}\)**-Value** & **Passing Rate** & **Decision** \\ \hline Frequency & 0.098956 & 0.994 & passed \\ Serial & 0.589774 & 0.991 & passed \\ Block frequency & 0.333145 & 0.985 & passed \\ Random excursion & 0.228764 & 0.991 & passed \\ Approximate entropy & 0.839894 & 0.982 & passed \\ Universal & 0.598752 & 0.991 & passed \\ FFT & 0.021888 & 0.981 & passed \\ Longest run & 0.129204 & 0.982 & passed \\ Linear complexity & 0.456098 & 0.988 & passed \\ Rank & 0.098780 & 0.986 & passed \\ Random excursion variant & 0.333248 & 0.992 & passed \\ Overlapping templates & 0.506503 & 0.988 & passed \\ Non-overlapping templates & 0.229941 & 0.981 & passed \\ Cumulative sums & 0.377852 & 0.983 & passed \\ Runs & 0.187012 & 0.991 & passed \\ \hline \hline \end{tabular}
\end{table}
Table 4: NIST statistical tests results.  
Figure 7: Balance analysis.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Challenges in IoT
#### 2.1.3 Video Streaming in the IoT  
Technologies and mechanisms have been presented in the literature to cope with devices constrained by low power, limited storage, low processing capabilities, loss and delay in data forwarding. For resource-constrained devices, 6 LowPAN [18] has been designed, which is suitable for use in IoT [18] and is cheap, scalable to many devices, can function over low bandwidth, and has support for topologies such as a star and a mesh. The use of the Hypertext Transfer Protocol (HTTP) over the Web for video streaming is also a popular practice. Dynamic Adaptive Streaming over HTTP (DASH) is another development in the area of video processing. DASH helps to adapt video streaming to changing network conditions, such as varying bandwidth, so as to improve a user's streaming experience. The authors of Reference [19] also suggested the use of compression standards such as H.264/Advanced Video Coding (AVC), which is suitable for video streaming withlow bitrates. The remaining video streaming challenges faced by IoT are highlighted in Reference [20], where possible solutions to those problems were presented.  
As has been already mentioned, the use of videos in the IoT is rapidly increasing and with this increase, ways of processing videos on resource-constrained devices has been practiced by researchers and practitioners alike. For research and experimental purposes, tiny processing devices are used as an IoT device. The most popular of these devices are the Raspberry Pi and Arduino. They have storage and processing capabilities, interfaces for user interaction, internet and peripheral connectivity, video processing, as well as many other functions. In Reference [21] research was reported on low-latency video streaming on a Raspberry Pi. Video encoded with H.264/AVC standard was sent over a Peer-to-Peer (P2P) network with low latency using that resource-constrained IoT device. In general, for video and image processing [22], these small devices can help in their cost effectiveness, real-time operation, good quality visual output, and the ability to transmit over low bandwidth links. A Raspberry Pi based video surveillance system was developed and tested in Reference [23]. The setup helped in security monitoring, indicating if motion was detected, based on frame differencing. A general surveillance system on a small device can be developed easily with such small devices. Thus, in Reference [24], a similar home-based security system using a Raspberry Pi was designed.
## 3 Results
### Comparison between MACS-Countes to FACS Methods
#### 3.3.1 Generating STCs between Countess (MACS) and FACS  
In all, 38 samples with different CD4+ T cell concentrations were analyzed using the FACS system and the optimized MACS-separation procedure (50 uL blood input, 25 uL NP-CD4). The regression plot of CD4+ T cell counts from the two methods was generated using Microsoft Excel. We focused on the CD4+ T cell concentration from 600 to 100 cells/ uL, as this is the reported cell concentration range in blood samples of patients with immunodeficiency. We also tested the MACS separation in blood samples with cell concentrations as low as \(<\)100 cells/ uL to determine if MACS-Countess can return good results. Thus, we divided 38 samples into 4 groups based on the frequencies of CD4+ T cell counts found in HIV-infected patients: group I contained 4 samples with CD4+ T cell concentrations \(>\)600 cells/ uL (undiluted blood), group II contained 14 samples with cell concentration 600-200 cells/ uL (2-fold diluted blood), group III contained 15 samples with cell concentration 200-100 cells/ uL (6-fold diluted blood), and group IV contained 5 samples with cell concentration \(<\)100 cells/ uL (12-fold diluted blood). Thus, Figure 3a represents a linear regression equation for all samples from the four groups, which is \(\mathrm{y=1.22x+1.9}\), where x is the number of CD4+ T cell counts in samples after MACS separation and y is that with FACS. Upon further analysis for particular groups as shown in Figure 3b,c, two STCs were obtained: Groups I and II shared the same STC (I + II), which was \(\mathrm{y=1.15x+35.87}\) (Figure 3b), whereas the STC (III) for group III was \(\mathrm{y=0.4x+90.27}\) (Figure 3c).  
Unfortunately, for group IV with extremely low CD4+ T cell counts, the error of MACS-Counters compared with FACS was very large because of the diversified plot (highest error of 72%, data not shown); therefore, it does not ensure the accuracy of cell counting at this threshold. Therefore, we concluded that the developed MACS-Countess system has a limit of detection (LOD) of 100 cells/ uL.  
Finally, considering all the data shown in Figure 2, we demonstrate the operation scheme of the MACS-Coutness system, as illustrated in Figure 4.
## 3 Performance Evaluation
### Triaxial Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Particles**} & \multicolumn{3}{c}{**Calculate Contact Forces between Particles**} & \multicolumn{3}{c}{**Update Kinematic State of Each Particle**} \\ \cline{2-7}  & **CPU Sequential** & **GPU Parallel** & **Speedup** & **CPU Sequential** & **GPU Parallel** & **Speedup** \\ \hline
3920 & 482 & 18.5 & 26 & 136 & 0.5 & 272 \\ \hline
7840 & 1900 & 47 & 40 & 348 & 1.8 & 193 \\ \hline
15,680 & 5478 & 73 & 75 & 594 & 3.3 & 180 \\ \hline
31,360 & 11,764 & 144 & 82 & 1283 & 5.9 & 217 \\ \hline
100,880 & 36,404 & 455 & 80 & 3971 & 20 & 198 \\ \hline
250,880 & 107,312 & 1369 & 78 & 8281 & 40 & 210 \\ \hline
501,760 & 316,020 & 3784 & 83 & 17,580 & 95 & 185 \\ \hline
1,003,520 & 627,622 & 8070 & 78 & 38,676 & 258 & 150 \\ \hline
2,007,040 & 1,326,887 & 16,291 & 81 & 72,339 & 425 & 170 \\ \hline
4,997,600 & 3,273,441 & 39,280 & 83 & 191,309 & 1007 & 190 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Speedups of GPU parallel calculations for the procedures in triaxial test cases. Figure 7: Speedups of the CPU and GPU parallelisation for the triaxial test. (**a**) CPU parallelization. (**b**) GPU parallelisation.
## 3 Results and Discussion
### Optimization of \(P_{m\,est}\)

Table 9: Upper and lower end of the ranges over which the operational variables maximized \(P_{m\,est}\). As can be seen, the conditions maximizing \(P_{m\,est}\) were different from those maximizing \((r_{A})_{global\,est}\). In fact, \(P_{m\,est}\) and \((r_{A})_{global\,est}\) are related by the mean volume of medium, \(V_{m\,est}\), in Equation (19). Since the bioreactors worked in a semi-continuous mode, the mean volume was dependent on the particular operating conditions and those resulting in the maximum possible values of \(P_{m\,est}\) and \((r_{A})_{global\,est}\) need not coincide (see \(V_{m\,est}\) values in Tables 6 and 12). \[(r_{A})_{global\,est}=\frac{P_{m\,est}}{V_{m\,est}}, \tag{19}\]  
As stated above for \((r_{A})_{global\,est}\), an increased mean availability of ethanol and a decreased mean acidity boosted acetic acid production \((P_{m})\). However, ethanol and acetic acid concentrations above certain levels have an inhibitory effect whereas too low levels can even boost bacterial activity. These effects have been extensively examined in modelling studies [8; 9; 10; 22; 23; 31; 32]. For example, ethanol concentrations above 6% \((v/v)\) and acetic acid concentrations above 6% \((w/v)\) have been found to have substantial inhibitory effects [8; 9; 10]. This knowledge, and the mean acidity and ethanol values of Tables 6 and 12, allow the \((r_{A})_{global\,est}\) and \(P_{m\,est}\) results to be more easily understood. One other major inference is that, based on Table 12, maximizing \(P_{m\,est}\) leaves a substantial amount of unavailable ethanol in the second bioreactor [\(E_{u2}\) = 4.3% \((v/v)\)], which is, in general, industrially unacceptable. It is, therefore, interesting to identify the specific conditions that will maximize \(P_{m\,est}\) while ensuring that the substrate is depleted to an acceptable degree. The procedure followed for this purpose is described in the following section.
## 3 Results  
### Descriptive Statistics of the Soil Properties  
The descriptive statistics of the soil properties in the Haihun River sub-watershed are provided in Table 2. Across the sample points, the SOC content ranged from 0.10 to 2.82 g\(\cdot\)kg\({}^{-1}\) with a mean of 1.27 g\(\cdot\)kg\({}^{-1}\).The STP content varied between 161.00 and 1103.00 mg\(\cdot\)kg\({}^{-1}\) with a mean of 505.53 mg\(\cdot\)kg\({}^{-1}\). The SAP content fell into the range of 0.12-59.30 mg\(\cdot\)kg\({}^{-1}\) with a mean of 10.30 mg\(\cdot\)kg\({}^{-1}\). Based on their coefficients of variation, the SOC and STP contents showed moderate spatial variability, and the SAP content showed strong spatial variability. The data of all three soil properties basically obeyed normal distribution in terms of skewness and kurtosis (Supplementary Tables S1-S3).  
The distribution of the soil properties under different types of land use is shown in Figure 4. There were minor differences in the distribution of the SOC content among various types of land use, with generally higher values in woodland, paddy fields, and orchards compared to dry land. The median of the STP content under different land use types was ranked in descending order, as follows: dry land \(>\) paddy field \(>\) orchard  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Property \({}^{*}\)** & **Min** & **Max** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} & **Skewness** & **Kurtosis** \\ \hline SOC (g\(\cdot\)kg\({}^{-1}\)) & 0.10 & 2.82 & 1.27 & 0.56 & 44.09 & 0.16 & 2.64 \\ STP (mg\(\cdot\)kg\({}^{-1}\)) & 161.00 & 1103.00 & 505.53 & 192.17 & 38.01 & 0.42 & 2.73 \\ SAP (mg\(\cdot\)kg\({}^{-1}\)) & 0.12 & 59.30 & 10.30 & 11.95 & 116.02 & 0.32 & 3.84 \\ \hline \hline \end{tabular}  
* SOC, soil organic carbon; STP, soil total phosphorus; and SAP, soil available phosphorus.  
\end{table}
Table 2: Descriptive statistics of the soil properties at the sample points.  
woodland. The values of the SAP content were mainly distributed at low and medium levels, with a higher median in the dry land compared to other land use types.
## 2 Materials and Methods
### Dataset Production  
To accurately evaluate the performance of the improved object detection algorithm in handling panoramic images, this study conducted a comprehensive annotation of these images. Figure 2 demonstrates the dark label annotation tool's utilization to precisely annotate pedestrian targets in each frame and assign a unique target ID. This step was pivotal to the experiment's success, as it provided the necessary real-label information for training deep learning models and ensured the accuracy and reliability of the experimental outcomes.  
After the annotation process, a dataset in the Multi-Object Tracking (MOT) format, consisting of 'det' and 'gt' directories, was generated. The 'det' directory contains a single file named 'det.txt', where each line represents a detected object. The fields in 'det.txt' are as follows:  
The first field indicates the frame number;  
The second field represents the trajectory ID, which is always \(-1\) in this file;  
The next four fields, prefixed with 'bb', denote the coordinates of the top-left corner of the bounding box and its width and height;  
The 'conf' field indicates the confidence level of the detection;  
The last three fields are used in MOT3D and are always set to \(-1\) for 2D detections;  
In the 'gt' directory, there is a 'gt.txt' file, where:  
The first field indicates the frame number,  
The second field is the ID of the object's motion trajectory;  
The third to sixth fields represent the coordinates of the top-left corner of the bounding box and its dimensions;  
The seventh field indicates whether the object trajectory is within the consideration range, with 0 meaning ignore and 1 meaning active;  
The eighth field denotes the category of the object corresponding to the trajectory (the category label ID correspondence is provided in a table below);  
The ninth field is the visibility ratio of the box, reflecting the extent to which the moving target is included/covered by other object boxes or the situations where the edges of the boxes between targets are clipped.  
Figure 2: Dataset labeling method.  
This structured format facilitates the use of the dataset for training and evaluating object detection and tracking algorithms, providing a standardized way to represent each object's spatial and temporal information within the scenes.  
Through this series of meticulous data preparation efforts, this study has laid a solid foundation for exploring the application of panoramic images in object detection tasks. This broadens and deepens our research and provides valuable experience and data resources for future researchers.
## 7 Performance Evaluation
### Simulation Results and Discussions
#### 7.3.2 DRRF and GWF  
Figures 5a, 5b, and 6a show that the throughput follows the GWF criterion with weights of 0, 1, and 0.5. Figure 5a shows that all mobile stations achieve byte fairness with the weight set to 0. Figure 5b shows slot fairness with weight 1. In other words, the mobile station throughput is proportional to its channel condition or modulation and coding scheme (MCS). Figure 6a shows the effect of GWF with weighted fairness of 0.5.  
Notice that the throughput varies because of Downlink Channel Descriptor (DCD), Uplink Channel Descriptor (UCD), and other management messages being transmitted as well. In addition, since the packing feature is disabled, the anticipated number of a MAC header for each allocation is not precise;  
Figure 5: DRRF Throughput. a. (left) 4 flows: with GWF weight 0; b. (right) 4 flows: with GWF weight 1 [11].  
Figure 6: DRRF Throughput. a. (left) 4 flows: with GWF weight 0.5; b. (right) 5 flows: \(MS_{I}\) with 2 Mbps \(r_{min}\)[10, 11].  
the overhead here includes both the MAC header (6 bytes) and the fragmentation subheader (2 bytes). In these and subsequent figures, the variability is low and so the confidence intervals are expected to be small.
## 2 Background
### Statistical Model Checking
As a result, SMC has found widespread use in many areas, ranging from computer networks to safety-critical systems, where it provides an indispensable tool for verifying the correctness and performance of stochastic systems.
# Audio Time Stretching Using Fuzzy Classification of Spectral Bins
###### Abstract  
A novel method for audio time stretching has been developed. In time stretching, the audio signal's duration is expanded, whereas its frequency content remains unchanged. The proposed time stretching method employs the new concept of fuzzy classification of time-frequency points, or bins, in the spectrogram of the signal. Each time-frequency bin is assigned, using a continuous membership function, to three signal classes: tonalness, noisiness, and transientness. The method does not require the signal to be explicitly decomposed into different components, but instead, the computing of phase propagation, which is required for time stretching, is handled differently in each time-frequency point according to the fuzzy membership values. The new method is compared with three previous time-stretching methods by means of a listening test. The test results show that the proposed method yields slightly better sound quality for large stretching factors as compared to a state-of-the-art algorithm, and practically the same quality as a commercial algorithm. The sound quality of all tested methods is dependent on the audio signal type. According to this study, the proposed method performs well on music signals consisting of mixed tonal, noisy, and transient components, such as singing, techno music, and a jazz recording containing vocals. It performs less well on music containing only noisy and transient sounds, such as a drum solo. The proposed method is applicable to the high-quality time stretching of a wide variety of music signals.  
audio systems; digital signal processing; music; spectral analysis; spectrogram Article
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
###### Abstract  
The paper presents the design and implementation of an electromagnetic field monitoring sensor for the measurement of the Terrestrial Truncked Radio (TETRA) signals using low-cost software defined radio (SDR) platforms. The sensor includes: an SDR platform, a Global Positioning System (GPS) module and a hardware control module. Several SDR platforms having different resolutions of the analog-digital converters were tested in the first phase. The control module was implemented in two variants: a fixed one, using a laptop, and a mobile one, using a Raspberry Pi. The tests demonstrate the following achieved performances: instantaneous acquisition band of 5.12 MHz; dynamic range of the input signal level of (\(-\)100 to \(-\)30) dBm; frequency resolution of 2.5 kHz; portability and flexibility for use in outdoor environments. The sensor allows complete reporting through amplitude-time-frequency-location descriptors, and in the case of the mobile version, the system performs correctly even at a maximum speed of displacement of 120 km/h. The price of the mobile sensor system variant is approximately EUR 320.  
electromagnetic field monitoring; real-time sensor; software defined radio; low-cost; TETRA signals +
Footnote †: journal: Sensors  
0000-0002-2136-1130][PERSON]
## 7 Discussion
### System and Technical Challenges
#### 7.1.1 Data Quality and Integration  
The success of DT systems in transportation heavily relies on high-quality data and seamless integration throughout the infrastructure's lifecycle. DTs depend on vast amounts of data from diverse sources, such as infrastructure-embedded sensors, IoT devices, traffic cameras, and GPS systems. However, integrating this data poses significant challenges due to differences in formats, protocols, and update frequencies across various systems. These discrepancies can hinder the creation of a unified and coherent DT model [37].  
Ensuring data quality is equally critical across all lifecycle stages. During the planning and design phase, data quality issues can lead to flawed simulations and suboptimal designs. In the construction phase, poor data integration can result in delays and cost overruns. During the operation and maintenance phase, noisy or inconsistent data can degrade the performance of predictive models, leading to inefficient maintenance schedules and increased risks. Advanced data processing techniques, such as machine learning algorithms, are increasingly being used to filter noise and detect errors in real-time data streams. Additionally, the development of standardized data formats and communication protocols can streamline data integration [27]. Despite these advancements, maintaining consistently high-quality data inputs remains a persistent challenge, particularly in large and dynamic urban environments.