esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sulfur Analysis
#### 3.1.3 Prediction of Unknown Samples  
Prediction plots can be created to evaluate the prediction ability for the unknown samples on the basis of the PLSR results, these are shown in Figure 8. The UO1 and UO2 samples were regarded as an unknown sample even though the sulfur concentration was known to be 0.8800% by air-dried analysis. The UO1 produced a concentration of 0.8878%. This value is calculated as the specifically predicted value from multivariate statistical analysis. There is only 0.0078% difference between the reference value and predicted value in absolute error terms. The relative error value is as small as 0.8864%. In the case of the UO2 sample, the absolute error and relative error values were 0.0998 and 11.341%, respectively. Furthermore, the predictability of unknown blended coal samples was tested. The unknown original sample and another coal sample were mixed to make five blended samples named from UB1 to UB5. Sulfur concentrations in the unknown blended samples are listed in Table 2. The absolute errors and relative errors were calculated by  
Figure 6: Relative error between measured and predicted sulfur concentration for the PLSR of the original data.  
Figure 7: Relative error between measured and predicted sulfur concentration for the PLSR of the SG smoothing-processed data.  
the PLSR model and are indicated in Table 2. Compared to the original unknown sample prediction, the prediction of the blended unknown samples produced similar error values. The produced absolute errors varied from 0.0999 to 0.1492% and the relative errors ranged from 8.8619 to 11.4769%.
## 7 Risk Analysis
### WiFi
#### 7.5.1 Tampering  
1. **Communications** An attacker can gain unauthorized access to the communication between the vehicle's WiFi module sending data to the TCU, and thus tamper with the messages. Exploitation of this vulnerability can lead to the compromise of the vehicle software. Altered data reaching the WiFi module could significantly affect the operations: vehicle systems, performance, and operational decisions. Tampering with data exchange could expose or manipulate sensitive information, violating user privacy and security.  
_Security measures_: Measures that can be implemented are data integrity checks and proper penetration testing. For data integrity, checks can use cryptographic hashing or digital signatures in order to verify the integrity of the data during transmission.
# A Review of Deep Learning Applications for Railway Safety
## 7 Station Safety
### Air Quality Control  
Air quality measurement sensors are installed in railway stations for air quality control. However, the measurement sensors often fail due to being in the wrong location for measurement, expired sensor equipment, malfunctioning electrical equipment, etc. Since  
Figure 9: Proposed network structure for managing accident records. Reproduced with permission from IEEE [89].  
air quality data are collected from several sensors, it is difficult to identify normal data by models having a linear or fixed structure because the variance of the data is significant, and values that do not follow a normal distribution are included. [PERSON] et al. [90] proposed a machine-learning-based soft sensor verification technique for detecting, diagnosing, identifying, and reconstructing abnormal measurements of multivariate air quality data. Figure 10 presents a diagram of the air quality monitoring and supervisory control process. Normal and abnormal values were extracted from the collected air quality data. A memory-gated current network auto encoder (MG-RNN-AE) algorithm based on an auto-encoder was developed to process air quality data. Furthermore, experimental results showed that the proposed method has a sustainable balance between power consumption and air quality levels, effectively performing air quality management within the station.
## 4 Severity Curves Derived from the Calculated Damage Deflections
### Tests Performed Involving Laboratory Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Beam nr.**} & \multicolumn{5}{c}{**Measured Natural Frequencies [Hz]**} \\ \cline{2-7}  & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** \\ \hline Beam 1 & 4.060 & 25.439 & 71.426 & 139.902 & 231.038 & 344.750 \\ Beam 2 & 4.052 & 25.448 & 71.213 & 139.342 & 230.295 & 343.254 \\ Beam 3 & 4.011 & 25.237 & 71.102 & 138.575 & 229.421 & 342.904 \\ Beam 4 & 4.044 & 25.482 & 71.287 & 139.420 & 228.528 & 344.177 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Obtained natural frequencies for the undamaged test beams. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Damage Scenario**} & \multirow{2}{*}{\(a\)} & \multirow{2}{*}{\(x\)} & \multirow{2}{*}{\(w\)} & \multirow{2}{*}{\(\gamma\)**(a)**} & \multicolumn{5}{c}{**Measured Natural Frequencies [Hz]**} \\ \cline{5-10}  & & & & & & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** \\ \hline Beam 1 & 0.8 & 310 & 2 & 0.00450323 & 4.054 & 25.425 & 71.286 & 139.832 & 230.933 & 343.984 \\ Beam 2 & 1.2 & 587 & 2 & 0.009104778 & 4.051 & 25.356 & 71.071 & 139.131 & 229.403 & 343.204 \\ Beam 3 & 1.2 & 395 & 2 The results prove the accuracy of the applied method for determining the position and severity of transverse cracks.
## 1 Introduction
They are able to recognize time-series patterns and non-linear characteristics, yielding a better accuracy than other models; consequently, ANNs are frequently used for prediction purposes, such as fluctuations in the monthly mean level of the surface of the Caspian Sea in the short term [29], data mining and modeling of river systems [30], estimating the daily water discharge with two input variables from rainfall stations at Langat River in Malaysia [31], predicting the daily returns of crude oil prices [32], analyzing the Singapore housing market and future condominium price index [33], measuring electrical load accurately to help power companies with better scheduling and efficient management [34], predicting human mortality rates [35], implementing preventive healthcare interventions and epidemic control [36], predicting monthly tourism demand for ten European countries [37], and tracking COVID-19 cases [38], identifying 6 mA modifications for a deep understanding of cellular processes [39] and sites of Histone lysine crotonylation (Kcr) in histone proteins [40]. In addition, [PERSON] et al. [41] carried out a bibliographic review of works related to the prediction of time series using artificial neural networks for the period 2006-2016, observing that the models proposed in the selected studies do not fully satisfy a procedure for building an ANN model. Meanwhile, [PERSON] et al. [42] carried out a bibliographic review of the use of neural networks to forecast air temperature for 2005-2020 and concluded that the ANN methods are viable for short-term forecasts. Artificial neural networks have been applied in different fields, such as (a) for solving the facial recognition problem. [PERSON] et al. [43] used an approach using a basic function of radial classification in the training of a neural network and the results highlighted the excellent performance in terms of error with regard to classifying and efficient learning. (b) They have been applied to provide effective real-time traffic signal control for a large complex traffic network; [PERSON] et al. [44] used a hybrid NN-based multiagent system. Various neural network models are used for price forecasting in the market using different indexes, such as the New York Stock Exchange (NYSE) [45], NASDAQ stock exchange [46], price of gold stock in the NYSE [47], real estate price in Hong Kong [48], and Apple stock in NASDAQ [49]. Despite the predictive power, the widespread use of ANNs, and the relevance of having price forecasts for decision making, no information is currently available on the application of this tool for the price forecasting of fisheries' resources. For this reason, the objectives of this work are to (1) predict Mexican lobster prices using ANNs and (2) to compare the performance of ANNs with ARIMAX models commonly used for the price forecasting of fishery products.
# Endophytic Entomopathogenice Fungi: A Valuable Biological Control Tool against Plant Pests
## 4 Endophytic EPF as Biocontrol Agents  
EPF endophytes have been recorded to provide effective protection to host plants against pest infestation and have been evaluated as biocontrol agents in numerous studies (Appendix A Table 12). Many commonly applied EPF species, such as _Beauveria bassiana_, _Isaria fumosroscea_ and _Metarhizium anisopliae_, naturally occurring as endophytes, have been recorded to provide effective protection against herbivores [16; 17; 57; 58; 59; 60; 62; 63; 64; 65; 66; 67; 68; 69; 71; 72; 73; 74; 75; 76; 77; 78; 79; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106].  
The best-studied EPF, _B. bassiana_, was reported to colonize numerous crops, such as corn, wheat, cotton, soybean, tomato, grapevine, sorghum, and citrus, and to reduce infestation by serious pests, mainly moth larvae and aphids. The presence of endophytic EPF resulted in the significant reduction of both pest population and plant damage in most cases. However, there have been reports of poor performance, indicating the complexity of these EPF insect-plant interactions [17; 56; 88; 91].  
The combination of endophytic EPF with other biocontrol agents, such as predators and parasitoids, has been proposed in previous studies [16; 57; 65; 66]. For instance, a study was recently carried out to explore the effectiveness of the combined use of _B. bassiana_, _M. brunneum_, and the aphid endoparasitoid _Aphidius colemani_ [PERSON] (Hymenoptera: Braconidae) against the green peach aphid _Myzus persicae_ [PERSON] (Homoptera: Aphididae) in sweet pepper [53]. Similar research has documented successful combinations of endophytic EPF species with entomophagous insects against leafminers in beans [16].
## 8 Validation Using HFSS  
A finite element method (FEM)-based high frequency structure simulator (HFSS) is used to simulate the PMA unit cell to justify the CST result of absorbance. The HFSS result shows good agreement with the CST result shown as in Figure 13. It also exhibits the dual band resonance with a little change of frequencies with nearly same absorbance.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 4 Results  
### Research Experiments  
To investigate the Incremental Information Function (\(IIF\)), this study conducted experiments using video steganography as an example of an iterative steganography. A simple LSB (Least Significant Bit) [13] technique was employed to encode a two-dimensional black and white image in the spatial domain. The details of the method are as follows:  
* The covers consist of video files treated as sequences of consecutive frames, each being an image;
* Successive iterations in which the message is encoded occur every third frame of the video files;
* The message is encoded using a steganographic method operating in the spatial domain, modifying the color values of the pixels in the video frames;
* The decoding function assumes that, from the first iteration, all bits of the message are initialized to zero. Subsequent iterations incrementally build information by setting 1's and clearing 0's in the message;
* The message is encoded in the form of a version 1 QR code with error correction level H, enabling the encoding of up to 72 bits in a 21 \(\times\) 21 module matrix with error correction up to 30% [20], facilitating the use of automatic tools for reading the decoding message.  
The steganography technique employed in the experiments encodes the subsequent bits of the message as follows:  
* Each video frame is divided into pixel blocks, with the total number corresponding to the length of the message, and each block represents a single bit of the message.
* For consecutive pairs of video frames, the difference in color values between corresponding pixels is calculated;
* Encoding a bit value of 1 in a pixel block involves increasing the value of the R (red) color channel and the B (blue) color channel while simultaneously decreasing the value of the G (green) channel. Conversely, encoding a bit value of 0 involves increasing the G (green) color channel value while decreasing the R (red) and B (blue) color channel values;
* The technique adaptively selects pairs of pixels for encoding, focusing on those with minimal initial color differences between frames to maintain imperceptibility, particularly pixels where the color difference between consecutive video frames is zero;
* The values of the respective color channels are modified based on a parameter of the encoding algorithm, called the encoding level, which ranges from 1 to 5. At level 1, the adjustment affects only the least significant bit (00000001b), while at level 5, it modifies the three least significant bits (00000101b).
## 3 Methodology
### Shapley Additive Explanations for Model Interpretation  
The AI-based methods are often considered unreliable in many fields due to their opacity and insufficient explanation for the predictions rendered. The internal workings of these models are complex and difficult to comprehend using human logic, which has earned them the label of 'black box' models [79]. To address this issue and enhance transparency, explainable AI (XAI) tools are employed to interpret these models and provide insights into the complex relationships between input features and output predictions [80]. These XAI tools include SHAP and local interpretable model-agnostic explanations (LIME). In this study, SHAP is used to interpret the models because of the following reasons: (1) it provides a global understanding of the dataset, providing insights into the overall contribution of each feature [81]; (2) it has great visualizations that make it easier to understand the model's behavior; and (3) it handles feature interactions, showing the impact of two or more features when combined [82].  
SHAP is a cumulative feature attribution algorithm that assigns a relevance score to each input feature. It leverages concepts from game theory to calculate feature importance and satisfies key properties of feature attribution, including accuracy, missingness, and consistency [83]. Given a dataset \(D\), the Shapley value estimation of an \(m\)-th feature with \(i\) combinations of features, target feature \(y\), and predictive model \(f\) is computed using Equation (13).  
\[\phi_{im}=\hat{f}(y+m)-\hat{f}(y-m) \tag{13}\]  
where \(\phi_{im}\) is the mean Shapley value of the \(m\)-th feature with the \(i\)-th feature. \(\hat{f}(y+m)\) is the prediction for target feature \(y\) with a random number of feature values, which also includes the \(m\)-th feature. \(\hat{f}=(y-m)\) is the prediction without the \(m\)-th feature. Generally, Equation (14) is used to compute the Shapley value of the \(m\)-th feature.  
\[\phi_{m}(y)=\frac{1}{n}\sum_{i=1}^{n}(\phi_{im}) \tag{14}\]
# CNN Based Image Classification of Malicious UAVs
## 2 Materials and Methods
### Model Training and Validation  
The 3 \(\times\) 101 training images were then used to train each of the deep-learning CNN image classification models specified in Table 1. This number of training samples is not sufficient to train the model from an initial (random) state. Instead, the models were pretrained with the ImageNet database, i.e., pre-loaded with weights corresponding to the result of training on ImageNet [20], then the last model layer was replaced so as to classify just three objects (i.e., the three UAVs use in this study), and the 3 \(\times\) 101 training images were employed to further optimize all the weights to apply to the UAV images under consideration. This is known as _transfer learning_[6] and it is a standard technique employed in image classification for relatively small image datasets. As part of the training, we employed data augmentations of a random horizontal flip and a random resized crop to the model input image resolution (see Table 1). Such data augmentations are useful for generalizing the applicability of the trained model to new and previously unseen data.  
Table 2 shows the hyperparameters used in the training for all models. The number of epochs (25) was sufficient to train the model to a converged final accuracy in all cases.  
When training is complete, the accuracy of the model in correctly predicting the UAV type in each image of the test set is given by:  
\[Accuracy=\frac{n_{correct}}{n_{total}} \tag{1}\]  
\(n_{correct}\) is the number of test images for which the trained model correctly predicted the UAV type, and \(n_{total}\) is the total number of test images.
## 3 Example Application  
### Project Examples  
The Tianjin-Qinhuangdao High-Speed Railway construction project, a ballasted track, prestressed concrete, two-row continuous girder bridge, is the engineering example chosen to illustrate a bridge using the cantilever casting method of construction. The total length of the beam is 221.5 m, the calculated span is \(60+100+60\) m, the height of the beam at the centre pivot point is 7.85 m, the height of the beam in the 10 m straight section in the middle of the span and the 15.75 m straight section in the side span is 4.85 m, and the bottom edge of the beam is varied according to a quadratic parabola. The schematic diagram is shown in Figure 3.  
The girder is a single-box, single-compartment, variable-height, variable-section structure. The top width of the box girder is 12.0 m; the bottom width of the box girder is 6.7 m; the thickness of the top plate is 40 cm, except near the end of the girder; the thickness of the bottom plate is from 40 to 120 cm and varies linearly according to a straight line; and the thickness of the web plate is from 60 cm to 80 cm, while the width of the web plate varies from 80 cm to 100 cm according to a folding line, as shown in Figure 4. There are five transverse partitions at the end pivot, mid-span pivot, and centre pivot of the whole girder, and the transverse partitions are equipped with hollows for inspectors to pass through.  
Figure 2: Flowchart of GM-BP model calculation.  
Since the bridge is completely symmetrical, with the mid-span section as the centre, this paper only takes the linear control of cantilever casting towards the mid-span portion of Pier 54 as its example, and its cantilever casting towards the mid-span side is divided into 13 sections, as shown in Figure 5.  
Before using the mathematical model to predict the pre-camber, in order to ensure the smooth progress of the monitoring process, the deformation of the structure needs to be calculated and verified to obtain a theoretical value of the bridge deformation, which, in this paper, is modelled using MIDAS Civil 2021 software. The nodes were divided according to  
Figure 4: Bridge bearings and mid-span cross-sections.  
Figure 5: Bridge segmentation chart.  
Figure 3: Schematic diagram of bridge cantilever construction.  
the construction sections of the design drawings, so 16 construction phases were set up, with a total of 86 nodes in the whole bridge, and a rigid connection was adopted between the abutment and Block 0. The established finite element model is shown in Figure 6.  
The data were collected during the construction monitoring process and selected as the original data according to their influence on the actual vertical mould's pre-camber. The original data included the temperature (T, \({}^{\circ}\)C), cantilever gravity (G, KN), distance from the tension section to the bearing (L, m), height of the beam cross-section (h, m), the theoretical vertical mould's pre-camber (f1), and the actual vertical mould's pre-camber (f2). These original data are presented in Table 1.
# Frequency Response of Optically Pumped Magnetometer with Nonlinear Zeeman Effect
## 3 Theory
The oscillating part, which is expressed as Equation (18), is the OPM's response to the magnetic-field modulation, oscillating at the magnetic modulation frequency \(\omega_{\mathrm{S}}\) with an amplitude proportional to the magnetic modulation \(B_{\mathrm{S}}\). It is also a sum of the response from the eight individual Lorentz peaks. If we have \(\delta\omega_{\alpha\;\alpha-1}=0\) for some \(\alpha\), then its corresponding contribution on \(Out_{\mathrm{In}}^{(1)}(t)\) is  
\[ \begin{split} Out_{\mathrm{In}}^{(1)}(t)_{\alpha}|_{\delta\omega _{\alpha\;\alpha-1}=0}=&\frac{1}{2\mathrm{i}}\left(\bar{\rho}_{ \alpha\;\alpha-1}^{F_{1}(1)}-\bar{\rho}_{\alpha-1\;\alpha}^{F_{1}(1)}\right) \times C_{\alpha}|_{\delta\omega_{\alpha\;\alpha-1}=0}\\ =&-\frac{C_{\alpha}P_{\alpha\;\alpha-1}A^{(-1)} \gamma B_{\mathrm{S}}e^{\mathrm{i}\omega_{\mathrm{S}}t}}{\Gamma_{\alpha\; \alpha-1}^{2}}\times\frac{\Gamma_{\alpha\;\alpha-1}}{\mathrm{i}\omega_{ \mathrm{S}}+\Gamma_{\alpha\;\alpha-1}},\end{split}  \tag{19}\]  
in which the magnetic-field modulation frequency \(\omega_{\mathrm{S}}\) related term \(\Gamma_{\alpha\;\alpha-1}/(\mathrm{i}\omega_{\mathrm{S}}+\Gamma_{\alpha\; \alpha-1})\) is exactly a first-order Butterworth low-pass filter with bandwidth \(\Gamma_{\alpha\;\alpha-1}\), coincident with the common sense about OPM's frequency response. Typically, when we operate the OPM in the open-loop mode, we will make sure that \(\gamma B_{0}\approx\omega_{\mathrm{M}}\). Under this condition, we can get the frequency response \(FR(\omega_{\mathrm{S}})\) of the OPM by normalizing the oscillating amplitude of \(Out_{\mathrm{In}}^{(1)}(t)\) with the oscillating amplitude at the limit that \(\omega_{\mathrm{S}}\) approaches 0 as  
\[FR(\omega_{\mathrm{S}})= \frac{Out_{\mathrm{In}}^{1}(t)e^{-\mathrm{i}\omega_{\mathrm{S}}t }|_{\gamma B_{0}=\omega_{\mathrm{M}}}}{Out_{\mathrm{In}}^{1}(t)e^{-\mathrm{i} \omega_{\mathrm{S}}t}|_{\gamma B_{0}=\omega_{\mathrm{M}},\;\omega_{\mathrm{S} }\to 0}}. \tag{20}\]  
To predict the frequency response of the OPM, we could first fit the magnetic resonance curves with Equation (17), which gives parameters \(C_{\alpha}P_{\alpha\;\alpha-1}A^{(-1)}\) and \(\Gamma_{\alpha\;\alpha-1}\) for each \(\alpha\) ranging from \(-3\) to \(4\). Afterward, we can use these parameters to calculate the frequency response of the OPM according to Equation (18) and Equation (20).