esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Maintenance Strategy Optimization for Railway
## 3 Optimization Model of the Joint Maintenance Strategy
### Joint Maintenance Probability Model
#### 3.2.2 The Risk of Wheel Flange Failure  
The wheel flange thickness exceeding the limit during vehicle operation may cause serious accidents. The risk of failure is introduced to control the risk. Failure risk refers to the conditional probability of failure in the future under the current normal working conditions. It reflects the possibility of failure in real time, which is helpful for users to adjust the usage plan according to the actual usage needs. The failure risk of the wheel flange at any time \(s\) refers to the probability that, under the condition of normal operation at time \(s\), the flange thickness is over the limit after continuous operation time \(t\). It is defined by  
\[Risk(s)=\frac{\Pr(\text{The wheel flange thickness over the limit during the time period }(s,s+t])}{\Pr(\text{Normal operation to time }s)}\]  
where \(t\) is the time that the user wants the wheelset to continue working, which is often related to the task time. RAMS includes reliability, availability, maintainability and safety. For the urban rail transit system, the availability of the system is guaranteed by ensuring the reliability of the system and by improving the maintainability of the system. Combined with the key prevention of failures that may endanger safety, the safety of the system is guaranteed. The failure risk controls the failure probability of the flange thickness exceeding the limit to ensure the safety of wheelset operation.  
1. Event \(D(t^{m},\ i\Delta t)\) is denoted as the \(m\)-th imperfect maintenance of the wheel at time \(t^{m}\), where \(t^{m}\in((k\ -\ 1)\ \Delta t,\ k\Delta t])\), and it works to time \(i\Delta t\), normally. Considering the relationship between maintenance time \(k\Delta t\) and time \(i\Delta t\), the following cases are discussed.  
1. If \(i=k\), it means that the reproriling is in the same cycle with periodic inspection, and the wheel flange thickness deterioration does not exceed the limit \(Ls\).  
\[D(t^{m},i\Delta t) =\big{\{}X(i\Delta t)<Ls,Q(t^{m},i\Delta t),k=i\] \[=\big{\{}X_{T_{m-1},T_{m-1}+i\Delta t-t^{m}}<Ls,Q(T_{m-1},T_{m-1} +i\Delta t-t^{m})\]  
\[E(C)=C_{i}E(t)+C_{m}E(t)/\Delta t+\sum\limits_{k=1}^{\infty}\sum\limits_{j=1}^{N} \left(P(A(k^{j}\Delta t))\cdot C_{p}+\int_{(k-1)\Delta t}^{\Delta t}dP(B(t^{j}) )\cdot C_{c}+P(C(k^{j}\Delta t))\cdot C_{i}\right)+C_{r} \tag{23}\]  
The minimum cost rate within the life cycle of the wheelset is taken as the objective, and the optimization model is established.  
\[\left\{\begin{array}{c}\min C(t|\Delta t,Sd_{L},Sd_{H})\\ C(t|\Delta t)=\frac{E(C)}{E(t)}\\ Risk(i\Delta t)<\alpha\end{array}\right. \tag{24}\]  
where \(\alpha\) is the maximum allowable value of risk failure.
## 4 Results and Discussion  
This paper evaluates the application of the league championship optimization for hybridizing ANFIS in order to enhance its prediction capability. The results are presented in this part of the study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{6}{c}{**Descriptive index**} \\ \cline{2-10}  & **Mean** & \begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & **Median** & **Mode** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Sample** &
\begin{tabular}{c} **Skewness** \\ **Variance** \\ \end{tabular} & **Minimum** & **Maximum** \\ \hline Depth of sample (m) & 17.93 & 0.45 & 17.80 & 7.80 & 10.08 & 101.66 & 0.40 & 1.80 & 52.80 \\ Sand (\%) & 33.44 & 0.45 & 30.30 & 33.10 & 10.07 & 101.39 & 1.87 & 17.90 & 71.00 \\ Loam (\%) & 43.23 & 0.25 & 44.80 & 44.50 & 5.61 & 31.44 & \(-1.68\) & 22.10 & 51.40 \\ Clay (\%) & 23.20 & 0.25 & 24.25 & 23.50 & 5.54 & 30.73 & \(-1.33\) & 5.20 & 37.50 \\ Moisture content (\%) & 41.45 & 0.36 & 42.05 & 39.40 & 8.11 & 65.84 & \(-0.20\) & 20.30 & 66.70 \\ Wet density (g/cm\({}^{3}\)) & 1.75 & 0.00 & 1.74 & 1.75 & 0.08 & 0.01 & 0.87 & 1.55 & 2.05 \\ Dry density (g/cm\({}^{3}\)) & 1.24 & 0.01 & 1.22 & 1.21 & 0.13 & 0.02 & 0.81 & 0.93 & 1.67 \\ Void Ratio & 1.15 & 0.01 & 1.16 & 1.09 & 0.20 & 0.04 & \(-0.22\) & 0.58 & 1.83 \\ Liquid limit (\%) & 44.04 & 0.27 & 44.60 & 44.20 & 5.92 & 35.03 & \(-0.60\) & 23.20 & 58.30 \\ Plastic limit (\%) & 30.37 & 0.23 & 30.06 & 32.60 & 5.07 & 25.73 & \(-0.21\) & 16.80 & 43.60 \\ Plastic Index (\%) & 13.67 & 0.12 & 14.10 & 14.10 & 2.64 & 6.95 & \(-1.40\) & 5.20 & 19.10 \\ Liquidity index & 0.81 & 0.01 & 0.76 & 0.94 & 0.29 & 0.09 & 0.83 & 0.11 & 1.99 \\ Coefficient of compression (cm\({}^{2}\)/kg) & 0.06 & 0.00 & 0.06 & 0.08 & 0.02 & 0.00 & 0.42 & 0.01 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the used dataset.  
Figure 5: Distribution of the SCC versus soil parameters.  
Firstly, the optimization procedure is explained and second, the accuracy enhancement is evaluated by comparing the results of the improved and typical ANFIS.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Saliency-Based Methods  
Salience is a technique for drawing attention from the bottom up in the human visual system, which is typically drawn to things or pixels that are more significant than their close vicinity. The saliency-based fusion method can preserve the integrity of the salient target area, improving the visual quality of the fused image in accordance with the workings of the human visual system. First, the source image is divided into significance, detail, and basic layers using significance detection and Gaussian smoothing filters. The target in the image is then highlighted using the highlight layer after the weight coefficient is computed using the nonlinear function. Then, the fusion rule based on phase consistency is used to fuse the detail layer, which makes the preservation of the detail layer better than the traditional maximum-absolute fusion rule. A multi-scale fusion approach based on visual significance graphs (VSM) and weighted least squares (WLS) optimization was put forth by [PERSON] et al. [47] Multi-scale decomposition (MSD) was introduced, and a scroll-oriented filter (RGF) and Gaussian filter were used to decompose the input image into a basic layer and a detail layer. Also suggested is an improved welding method based on VSM. To integrate the detail layer, a new WLS optimization approach is suggested. The merged image can contain more visible features and less noise or unimportant infrared details. In order to better retain the crucial details of the infrared and visible images in the fused image, [PERSON] et al. [48] developed the important mapping of source images in the fusion process. First, using the framework of the joint sparse representation model, the global and local significance maps of the source images are obtained. The local significance map and the global significance map are then combined to create a full significance map using a significance detection model that is then provided. In order to implement the fusion process, a weighted fusion algorithm based on an integrated importance graph is proposed. Decomposing the input image into low-frequency and high-frequency coefficients was done first by transformation, according to [PERSON] et al. [49] The significance-based technique is then utilized to fuse the low-frequency coefficient. Finally, the high-frequency coefficient fusion is performed using the max-abs technique, and the calculation time requirements of various transformations are provided.
## 2 Materials and Methods  
### Experimental Set Up  
Thirty-two outdoor flow-through marine mesocosms were set up at the Queen's University Marine Laboratory in Portaferry, Northern Ireland to identify biofilm response to exposure from both a grazer (_Littorina littora_, common periwinkle sea snail) and a rhamnoid biosurfactant (JBR 425, Jeneil Biosurfactant Company, Saukville, WI, USA). _L. littora_ was the grazer chosen for this experiment, as it is often used in marine toxicology experiments and can be found along the Strangford Lough coastline. Due to space limitations, only one biosurfactant was used in order to maximize the number of replicates and robustness within the study. Rhammolipids were chosen as our biosurfactant due to their potential application as an alternative biofilm control agent to chemical surfactants. Mesocosms were contained within a shallow table receiving a constant flow of sand-filtered seawater pumped directly from the adjacent Strangford Lough. Each mesocosm was one 20 L bucket, with a hose for seawater inflow and a hole approximately 11 cm above the base for water outflow. In total, four variables were tested (control with no grazers or surfactant, grazer exposure only, surfactant exposure only, and combined) with eight replicates per variable.  
_L. littora_ (_n_ = 80) were collected by hand from the Strangford Lough coastline and transported in sea water to the laboratory within one hour after collection. Only snails with an observable length > 2 cm were chosen, as smaller snails may not have an observable effect on biofilm biomass over a two-week period. A 150 ppm solution of rhamnoid was created in a large bucket. Thirty-two clay tiles (L10 \(\times\) W10 \(\times\) H0.7 cm) were dosed in the solution for five minutes before being allowed to thoroughly air dry on a sterile table. Afterwards, two dosed clay tiles were placed flat on the bottom of sixteen of the mesocosms within a mesh bag. The remaining sixteen mesocosms received two tiles without chemical additions within a mesh bag. Tiles were used as substrata for marine biofilm growth during the experiment. Eight mesocosms containing rhamnoid-dosed tiles and eight mesocosms without chemically dosed tiles received snails (_n_ = 5) within their mesh bags. Bags were tightened to prevent snail loss, and snails were initially placed directly on the tiles. Water quality parameters (pH, dissolved oxygen (DO) (mg/L) and water temperature (\({}^{\circ}\)C) were measured daily, and snail loss was monitored. The experiment began on 28 July 2020 and lasted a period of 14 days.
## 5 Conclusions  
In this paper, a novel design of polarization-converted RMS is proposed, which utilizes an applied DC bias voltage to regulate the relative permittivity of LCs, thereby modulating the MS properties. The design enables the realization of a dual-LCPC function across multiple frequency bands, where the reflected EM wave can be either LHCP or RHCP, and the incident EM wave can be either \(x\)- or \(y\)-polarized. This approach enhances the potential for \(x\)-band applications to a certain extent. The proposed MS not only overcomes the limitation of conventional designs, which typically operate in a single frequency band or a single LP wave, but also adapts to the diverse communication requirements across different regions and standards. Consequently, it allows the communication system to support more simultaneous user access, significantly improving both the stability and flexibility of its performance. The proposed MS expands the wide range of application prospects in wireless communication, radar, remote sensing, and other fields.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Unit Cell** & **Substrate** & **No. of** & **3 dB AR** & **Polarization** & **LTC PCR** & **No. of Bands** \\  & **P (mm)** & **Thickness** & **Substrate** & **FBW (\%)** & **Conversion** & **Mode** & **(\%)** & **No. of Bands** \\ \hline
[23] & 6.0 & 1.6 & 1 & 35.23 and & 26.62 & \(y\)-CP & 99 & 2 \\ \hline
[33] & 6.0 & 1.6 & 1 & 50.8 & \(y\)-CP & 98 & 1 \\ \hline
[26] & 8.0 & 0.5 & 2 & 26.2 & \(x\)-CP & N.A. & 1 \\ \hline
[27] & 8.0 & 3.2 & 3 & 5 & \(x\)-CP and & N.A. & 1 \\ \hline
[28] & 13.0 & 1.575 & 1 & N.A. & \(y\)-CP & N.A. &
\begin{tabular}{c} single \\ frequency \\ (12.4 GHz) \\ \end{tabular} \\ \hline \hline \end{tabular} N.A. represents not applicable.  
\end{table}
Table 2: Comparison of polarization conversion with the previously published works.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
###### Abstract  
Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results.  
M2M communications; machine-type communications (MTC); Internet of Things (IoT); cellular network; random access channel (RACH); congestion +
Footnote †: journal: Sensors
# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 2 Proposed Approach for Identification of Image Operations  
The design of the feature set plays an important part in classification problems. Fortunately, we can borrow some powerful features from other research fields, such as image classification, computer vision, and image steganalysis. An example is the modern universal steganalytic features, such as SRM [23], which consists of statistics derived from a number of image residuals. In such features, different high-pass filters are used to suppress the image content in different ways, and thus the obtained features can represent different local properties. In this paper, we propose the SDNR by constructing residual values from locally supported filters in the spatial domain. Then, to make use of the extracted SDNR features, we propose applying a deep learning method of a five-layer CNN to the extracted SDNR feature to produce the detection and classification results. The framework of the proposed approach is shown in Figure 1. In Figure 1, it can be seen that there are two major steps: feature extraction with the proposed SDNR method, and feature learning with a five-layer CNN model. In the proposed approach, firstly, we apply the variety of postprocessing operations, including spatial enhancement, spatial filtering, frequency filtering, and lossy compression, to the original images. In this way, the original images are trained and the corresponding postprocessed images are thus produced. Then, the SDNR is employed to the generated images to extract the feature sets. Next, we employ a five-layer CNN model to extract the CNN features from the extracted SDNR feature sets. A patch-sized sliding window is used to scan the extracted SDNR feature sets, and feature fusion is adopted to aggregate the CNN features and thus to obtain the discriminated feature from the image. Afterward, the softmax classifier of the CNN is trained for the detection and identification of the various operations.
## 3 Results and Discussion
### MALDI-TOF Analysis
Table 1 shows the mass possibilities as a function of the oligomer's size in the photo resin composition: \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Oligomer** & ED0 & ED1 & ED2 & ED3 & ED4 & ED5 & ED6 & ED7 & ED8 & ED9 & ED10 & ED11 & ED12 \\ \hline Monomer & 228.29 & 284.36 & 340.32 & - & - & - & - & - & - & - & - & - \\ Dimer & 468.59 & 524.66 & 580.72 & 636.79 & 692.85 & - & - & - & - & - & - & - \\ Trimer & 708.89 & 764.96 & 821.02 & 877.09 & 933.15 & 989.22 & 1045.28 & - & - & - & - & - \\ Tetramer & 949.20 & 1005.26 & 106.312 & 1117.39 & 1173.45 & 1229.52 & 1285.58 & 1341.65 & 1397.71 & - & - & - \\ Pentamer & 1213.49 & 1343.62 & 169.55 & 1399.68 & 1325.62 & 1485.75 & 1318.68 & 1511.81 & 1437.74 & 1567.87 & 1493.81 & - & - \\ Hexamer & 1429.80 & 1485.86 & 1541.93 & 1597.99 & 1654.06 & 1701.21 & 1766.19 & 1822.25 & 1878.31 & 1937.38 & 1988.43 & 2046.51 & 2102.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mass peaks as a function of the oligomer size versus epoxidation degree. Figure 10: Mass variation in function of the epoxidation degree. Figure 9: Region of 450–1200 Da in the MALDI-TOF of the photo resin solution samples. To evaluate the spectra, it is necessary to take into account the influence of the matrix dissociation during the analysis method. The MALDI-TOF has the advantage of avoiding the fragmentation of analyte molecules when a light-absorbing matrix is used. Thus, only intact molecules are observed in the mass spectrometer. For polymer samples, it is possible to identify all the absolute sizes in the distribution after the polymerization step. However, it is common that the matrix has an interaction with the sample, leading to the formation of adducts [35; 40; 41; 42; 43; 44]. causing some differences in the Dalton values expected. As mentioned in the methods section, in this work DHB and sodium chloride were used as matrices. Therefore, it could be possible that the compounds have increased Dalton values. As an example, the main possible adducts for the monomer are presented in Figure 11.  
The adduct formation can occur with all molecules in the photoresist. In the spectra, it will be possible to observe a minimum of three different mass peaks for the same structure (the pure oligomer, the adducts with sodium, and the adducts with DHB). Although adducts with DHB do not occur typically, the mass of these adducts was also calculated. Not all the peaks will always be observed because the interaction with the matrix is a random process. In Table 2, the mass peaks that could be observed in the different monomers are listed. Dimers, trimers, tetramers, pentamers, and hexamers were identified in the samples mainly with the sodium adduct. Correlating the data of Tables 1 and 2, it is possible to identify the main oligomer fractions in the photoresists. The most important mass peaks are 491, 533, 715, 1067, 1364, and 1419 Da, representing sodium adducts coming from the matrix solution, as Figure 12 shows. Figure 11: (**a**) Dihydroxybenzoic acid (DHB) adduct with the monomer, (**b**) Sodium adduct with the monomer.
## 5 Conclusions  
The integration of reversible heat pumps in hybrid renewable trigeneration systems serving low-energy district heating and cooling microgrids was proposed and investigated in this paper. Traditional thermal back-up units, i.e., natural gas boiler and non-reversible electric chiller, have been replaced by an electrically driven reversible heat pump as single thermal back-up unit. The energy system also includes an internal combustion engine, an absorption chiller, photovoltaic panels, and a wind turbine.  
The proposed configuration was compared to the benchmark configuration of hybrid trigeneration system with conventional back-up units. A case study was considered, and the integrated optimal sizing and operation were identified by means of a genetic algorithm-based procedure. The proposed configuration achieved the best economic performance, with a 7% saving in the equivalent annual cost. Moreover, with the integration of the reversible heat pump, a 23% higher capacity of renewables was installed in the optimal design of the system. As a result, annual carbon dioxide emissions were lowered by 11% compared to the traditional configuration.  
Overall, the integration of electrically driven reversible heat pumps in trigeneration systems was found to be an economically viable solution capable of increasing the share of renewables, reducing the use of fossil fuels, and lowering greenhouse gas emissions.  
Conceptualization: L.U., D.T. and J.C.B.; Methodology: L.U. and D.T.; Software: L.U.; Data curation: L.U.; Writing-original draft: L.U.; Writing-review & editing: L.U., D.T. and J.C.B.; Supervision: D.T. and J.C.B.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 11: Optimal operation: cooling demand and load shares in a typical week.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results
### SHAP-Based Feature Importance
This effect can be attributed to the combined application of batch normalization, dropout, and L2 regularization, along with the architectural inclusion of GRU units, which collectively help to stabilize learning and guide attention toward more meaningful features. Importantly, the most contributory EEG channels recognized by SHAP in the optimized model were also predominantly situated in the fronto-central and frontal areas (e.g., F3, Fz, and FCz), which are inherently implicated in executive function, response inhibition, and sustained attention as cognitive processes that are typically disrupted in patients with ADHD. Interestingly, higher SHAP values were quantified in task periods  
Figure 23: SHAP-based interpretation of EEG feature importance in the regularized hybrid CNN-GRU-LSTM model. demanding response inhibition, which further confirmed the clinical validity of the model in recognizing neural processes. The results not only confirm previous neurophysiological findings but also demonstrate the potential of SHAP-based interpretability for providing model predictions as clinically relevant information, thereby enhancing transparency and trust in AI-supported EEG diagnosis.