esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MGD Characterization with the CATANA Pulsed Proton Beams  
The MGD was irradiated with a pulsed frequency of 1 Hz, the pulse width was 500 us, and there was a fixed number of protons per second (corresponding to \(\sim\)10 nA). The bias voltage was changed between 3 and 2100 V and, for each voltage value, the detector was irradiated with the same number of particles. The integral MUs measured by the three ICs and compared with the integral charge measured by a Faraday cup placed just after the MGD and readout with an acquisition time interval of 200 ms. The fluctuations in the number of counts within each pulse are mainly due to the pulse-to-pulse fluctuations of the delivered protons. The measurements of the Faraday cup were used to correct for these fluctuations by applying to the readout at each pulse a correction factor obtained as: \(\frac{\langle\mbox{FC}\rangle}{\mbox{FC}_{i}}\), where \(FC_{i}\) represents the Faraday cup readout for the actual pulse and \(<\)\(FC\)\(>\) is the average Faraday cup readout value with the given beam conditions. Figure 10 shows the average MUs measured per pulse by the three chambers as a function of the bias voltage.  
Figure 9: Calibration curves measured for chamber 1 as a function of Q\({}^{\prime}_{\rm n}\)d\({}_{\rm 1}\)/Q\({}^{\prime}_{\rm 1}\)d\({}_{\rm n}\). Fit A describes the results achieved using data from chamber 1 and chamber 2, while fit B describes the data from chamber 1 and chamber 3. The color indicates the beam intensity.  
Figure 10b shows the counts per unit of thickness as a function of V/d\({}^{2}\). The fit shown in the figure was calculated using Equation (5) and the Boag's function for pulsed beam (Equation (2)) to compute the efficiency \(f\). The large charge recombination observed in chamber 3 with the CATANA pulsed beam lead to a few good points to reconstruct the calibration curve; therefore, \(f_{1}\) was evaluated using data from IC1 and IC2 only, as shown in Figure 11. The fit to the data with Equation (6) is overlapped to the points in Figure 11, and the parameters are listed in Table 2.
## 4 Proposed COA Protocol  
The COA-based energy-aware CH selection protocol proposed in this paper significantly enhances network lifetime and improves throughput in WSNs. The COA is responsible for two processes: CH selection and cluster formation. The CH is selected by executing the COA algorithm, and a cluster is then constructed based on the neighboring nodes of the CH. Thus, the overall lifetime of the network increases.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 7 Discussion
### Goal Evaluation and Efficiency Overview  
The implementation presented in this paper successfully fulfills the primary goals outlined in Section 3.3, particularly in terms of security, consistency, and automation. Modular Bicep templates, environment-specific parameterization, and GitHub Actions pipelines contribute to a reproducible and secure infrastructure deployment model.  
From a performance standpoint, automated deployments consistently completed in under 15 min across all tested environments, reducing manual setup time by over 80%. Error rates decreased from approximately 30% in manual runs to less than 1% post-automation. These empirical outcomes, detailed in Section 6, demonstrate measurable gains in reliability and operational speed.  
While cost optimization was not quantitatively assessed in this study, the architecture supports ephemeral environments and automated teardown, enabling organizations to minimize idle resource expenditure. Future work will incorporate detailed cost-performance benchmarking based on Azure usage analytics.
## 4 Analysis of Test Results  
### Dynamic Stress  
#### 4.1.1 Variation of Vertical Dynamic Stress  
Figure 5 displays the variation of the dynamic stress along the depth direction under dynamic load excitation with specific loads and different speeds. To align with practical engineering applications, a straight line representing 0.2 times the self-gravity-stress relationship is also included in the figure. The depth of the working area of the roadbed is indicated by the longitudinal coordinates where the self-gravitational stress line intersects with the dynamic stress change line.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Excitation** & **Simulated** & **Simulated** & **Cumulative** & **Simulated** \\  & **Fre-** & **Axle Load/t** & **Vehicle** & **Number of** & **Operating** \\  & **quency/Hz** & **Speed/km-h\({}^{-1}\)** & **Vibrations** & **Hours/Day** \\ \hline
1 & 9.5 & 35 & 105 & \(4.0\times 10^{3}\) & 3.8 \\
2 & 9 & 30 & 100 & \(7.0\times 10^{3}\) & 6.6 \\
3 & 8.5 & 30 & 90 & \(1\times 10^{4}\) & 9.5 \\
4 & 9.33 & 4  
The results uncover a pattern of dynamic stress variation characterized by a gradual decrease with depth, displaying a typical exponential decline. The loaded road layer and subgrade exhibit a notably higher rate of decay in the initial and middle stages compared to the later stages, reflecting the dissipation of a substantial amount of dynamic loading energy. In the measurement data obtained under the 30/40 t load, it is observed that the decay rate of dynamic stress in the upper layer of the roadbed is greater than that in the middle and lower layers. Furthermore, an increase in vehicle speed results in an increase in surface dynamic stress, but this does not necessarily correspond to a proportional increase in dynamic stress in the middle and lower layers.  
Additionally, under varying working conditions involving different axle loads and vehicle speeds, the depth of the roadbed's working area follows an inconsistent pattern of change. Specifically, an increase in vehicle speed leads to an expansion of the depth of the working area, while an increase in the axle load may cause a reduction in the depth of the roadbed's working area.  
The variation of dynamic stresses along the depth for different axle-load conditions at 90/100 km/h vehicle speeds is presented in Figure 6.  
For different axle-load conditions at the same vehicle speed, the impacts of dynamic stresses are primarily concentrated in the surface layer. Larger axle loads result in higher dynamic stress responses, but there is also a greater degree of attenuation in the pavement layer. At higher vehicle speeds, its influence on the dynamic parameters of the lower layer is nearly negligible. For instance, in the test results obtained at 100 km/h with a 30-45 t load, the depth of the working zone remains primarily around 2.3 m.  
Figure 5: Variation of dynamic stress with depth under different vehicle speeds for 30/40 t loads: (a) 30 t; (b) 40 t.  
Figure 6: Variation of dynamic stress with depth under different axle loads at 90/100 km: (a) 90 km/h; (b) 100 km/h.
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 2 Materials and Methods
### Cell Culture  
Rat bone marrow (RBM) cells were isolated from the femurs of 8-week Sprague-Dawley rats. This study was approved by the Guidelines for Animal Experimentation of Osaka Dental University (Approval No. 18-03007). The rats were euthanized by 4% isoflurane inhalation and their bones were aseptically excised from their hind limbs. The proximal ends of the femurs and the distal ends of the tibae were clipped and flushed with Eagle's minimal essential medium (MEM; Wako Pure Chemical Industries Ltd., Osaka, Japan). A 21-gauge needle (Terumo, Tokyo, Japan) was used to aspirate the bone marrow. The marrow pellets were dispersed by trituration. Cell suspensions from all bones were combined in one centrifuge tube. The RBM cells were cultured in 75-cm\({}^{2}\) Falcon culture flasks (BD Biosciences, Franklin Lakes, NJ, USA) at 37 \({}^{\circ}\)C under a 5% CO\({}_{2}\) atmosphere in a growth medium containing MEM supplemented with 10% fetal bovine serum (FBS; Invitrogen/Life Technologies, Carlsbad, CA, USA), penicillin (500 U mL\({}^{-1}\); Cambrex Bio Science, Walkersville, MD, USA), and Fungizone (1.25 \(\mu\)g mL\({}^{-1}\); Cambrex Bio Science, Walkersville, MD, USA). When the cells reached confluence, they were removed from the flask by trypsinization, washed twice with phosphate-buffered saline (PBS), resuspended in culture medium, and seeded at a density of \(4\times 10^{4}\) cm\({}^{-2}\) in 24-well Falcon tissue culture plates (BD Biosciences, Franklin Lakes, NJ, USA) containing either a test or a control titanium disk.
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
## 3 Controller Design
### IMCPIDNN Controller Design  
In order to compensate the nonlinearity of the steless flow control system, the neural network algorithm based on the IMCPID control scheme is proposed [30]. The block diagram of a MIMO system with a backpropagation (BP) neural network structure is as shown in Figure 5.  
Figure 5: The block diagram of a MIMO nonlinear system with BP neural networks.  
Here, \(r\) and \(y\) are set-point value and output value, respectively. \(W_{\text{if}}\) is the network weight between the \(j_{\text{th}}\) node of the input layer and the \(i_{\text{th}}\) node of the hidden layer and \(W_{i}\) is the network weight between the \(i_{\text{th}}\) node of the hidden layer and the output layer node.  
From Equation (26), the discrete PID gains can be written as:  
\[\left\{\begin{array}{l}K_{p}(k)=\dfrac{2t+\tau}{k(2\lambda(k)\gamma+\tau)}\\ K_{i}(k)=\dfrac{2}{k(2\lambda(k)\gamma+\tau)}\\ K_{d}(k)=\dfrac{t\tau}{k(2\lambda(k)\gamma+\tau)}\end{array}\right., \tag{27}\]  
where \(\gamma\) is the tuning coefficient to determine the flexibility of the control process.  
Then, according to the incremental PID principle [31], the output of IMCPID controller can be written as:  
\[\begin{array}{l}u(k)=u(k-1)+[K_{P}(k)+K_{I}(k)+K_{D}(k)]e(k)\\ -[K_{P}(k)+2K_{D}(k)]e(k-1)+K_{D}(k)e(k-2)\end{array} \tag{28}\]  
In this article, a three-layer BP neural network is proposed as show in Figure 5. There are five input nodes, six hidden nodes and one output node in the neural network structure.  
The input of the neural network is defined as:  
\[I_{j}(k)=[r(k),y(k),e(k-1),u(k-1)](j=1,2,3,4). \tag{29}\]  
The input of the hidden layer is defined as:  
\[net_{i}(k)=\sum_{j=0}^{4}W_{ij}\cdot I_{j}(k)(i=1,2,...,6). \tag{30}\]  
The output of the hidden layer is defined as:  
\[O_{i}(k)=f[net_{i}(k)], \tag{31}\]  
where \(f(x)\) is the sigmoid function:  
\[f(x)=\dfrac{1}{1+e^{-x}}. \tag{32}\]  
The input of the output layer is defined as:  
\[net(k)=\sum_{j=0}^{6}W_{i}\cdot O_{i}. \tag{33}\]  
The output of the output layer is defined as:  
\[\lambda(k)=g[net(k)], \tag{34}\]  
where \(g(x)\) is a non-negative sigmoid function to ensure the outputs to be positive as shown below:  
\[g(x)=\dfrac{e^{x}}{1+e^{-x}}. \tag{35}\]  
The central ideal of the learning algorithm for the IMCPIDNN is to obtain a gradient vector recursively. In this study, we define energy function as:  
\[J(k)=\dfrac{1}{2}[r(k)-y(k)]^{2}. \tag{36}\]  
Through the above derivation, we obtain the weight coefficient learning algorithm for the neural network, and only the parameter \(\lambda\) can be adjusted in real time according to the above derivation. The parameters of the IMCPIDNN controller are shown in Table 2.
# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
## 2 Human-Voice Interface System
### Automatic Speech Recognition (ASR)
#### 2.3.3. Feed-Forward Module  
As proposed in reference [29], the feed-forward module of the transformer architecture appears after the multi-head self-attention layer. Although it is based on the feed-forward module, it has a nonlinear activation function called Swish activation [30] between the two linear transitions. A dropout is also added to normalize the network. The module follows the feed-forward module in the transformer architecture and uses the pre-norm residual unit as shown in Figure 12.  
Figure 11: Convolution module.  
Figure 12: Feed-forward module.  
Figure 9: Multi-head self-attention module.  
The conformer training data set for this paper is the entire training set of LibriSpeech, which is approximately 1000 h long. The audio is extracted one frame every 25 ms, with each frameshift at 10 ms. The 80-D audio spectrum is obtained by 80-channel filter banks and the speech signal is enhanced by using Spec Augment [31; 32].  
In this paper, the conformer used character error Rate (CER) and word error rate (WER) [33] for testing. We focused mainly on the WER, in which the lower the WER, the better the training of the model. The Equation for the calculation of the WER is shown in Equation (10):  
\[WER=\frac{(S+D+I)}{N}\times 100\%=\frac{S+D+I}{S+D+C}\times 100\% \tag{10}\]  
where S denotes the number of substituted words, D is number of deleted words, I is the number of inserted words, N is the total words in original text, and C is the correct number of words.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
###### Abstract  
Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.  
blatt conveyor; conveyor belt; damage detection; deep learning; lightweight CNNs; smart mine +
Footnote †: journal: Applied Sciences  
Article  
Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt [PERSON] [PERSON] and [PERSON]
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Measure of Sieve**} & \multicolumn{3}{c}{**Mark of**} & \multicolumn{3}{c}{**Fractions Representation of Birch Wood [\%]**} \\ \cline{3-5}
**Mesh, [mm]** & **Fraction** & **Untreated** & **Modified by Mode I** & **Modified by Mode II** \\ \hline
2.000 & coarse & \(69.43\pm 3.47\) & \(66.25\pm 3.31\) & \(63.72\pm 3.19\) \\
1.000 & coarse & \(6.28\pm 0.31\) & \(9.12\pm 0.46\) & \(8.67\pm 0.33\) \\ \hline
0.500 & medium & \(10.14\pm 0.51\) & \(11.47\pm 0.57\) & \(10.23\pm 0.51\) \\
0.250 & coarse & \(9.13\pm 0.46\) & \(9.50\pm 0.48\) & \(8.71\pm 0.44\) \\
0.125 & coarse & \(3.16\pm 0.16\) & \(2.02\pm 0.29\) & \(5.75\pm 0.29\) \\ \hline
0.063 & & \(1.67\pm 0.08\) & \(1.52\pm 0.08\) & \(2.63\pm 0.13\) \\
0.032 & fine & \(0.19\pm 0.01\) & \(0.12\pm 0.01\) & \(0.29\pm 0.01\) \\ \(<\)0.032 & & \(0.00\pm 0.00\) & \(0.00\pm 0.00\) & \(0.00\pm 0.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Chips from the CNC machining centre SCM Tech Z5, average values with standard deviations. Criterion for establishing a separation technique requirement for the separation of extracted bulk solids, including dust chips from conveyed air in exhaust systems is the limit of separation of separation technology. By comparing the smallest chips in the conveyed bulk material from individual machines \(a=32\)\(\upmu\)m with the separability limit of the separators in Figure 7, the possibility of meeting environmental criteria is demonstrated. Separation limits of selected filters and separators are shown in Table 7.  
Figure 6: Comparison of sawdust and chips fractions from CNC machining centres (**a**) coarse fraction (**b**) medium coarse fraction (**c**) fine fraction. Based on data in Table 4 and Figure 6, it follows that for separating chips with dimensions above and \(\geq\)32 \(\upmu\)m, suitable separation techniques are: wet scrubbers, fabric filters, and electro-scrubbers. For the purpose of separating loose wood from transport air in wood processing plants, the best available technology are filters with G4 fabric having a separation limit value of \(a_{\text{SL}}=10\)\(\upmu\)m, thus meeting the requirements of EN 779/2012 [40]. Fabric filters are fully sufficient to catch the chips extracted from the sawing and milling process native birch wood, and thermally modified wood with saturated water vapor [38; 39; 40; 41]. Analyses of the grain size of chips formed in the processes of sawing dried birch wood on PRW-15M frame saws and milling on CNC machining centers showed that heat treatment of birch wood with saturated steam did not affect the grain size of the chips. Based on this fact, it can be concluded that, just as chips from raw birch wood are used for the production of composite materials, chips from heat treatment birch wood are also a suitable raw material for the production of composite materials. The use of a given chip to produce agglomerated materials prolongs the life cycle of wood and thus contributes to increasing the degree of sustainability.
## 5 Conclusions  
In the present work a model able to predict the microalgae growth in reactors with different geometries was developed. We combined a Monod-like model for the specific growth rate with the Lambert-Beer law for non-homogeneous light distribution in thick photobioreactors. Kinetic parameters relating to the cultivation of the microalga _Nannochloropsis gaditana_ were obtained, for the first time, through batch cultivation under different photon flux densities inside a quasi-isoatinic photobioreactor in order to obtain a practically homogeneous light distribution. The maximum specific growth rate and saturation constant resulted, respectively, as \(\mu_{max}\) = 0.0256 h\({}^{-1}\) and \(I_{k}\) = 15.28 \(\mu\)E s\({}^{-1}\)m\({}^{-2}\).  
These parameters were applied to the model to obtain microalgae growth curves in reactors with three different geometries: two flat-plate photobioreactors (FP-PBR) with different thicknesses (15 mm and 30 mm), and one annular column photobioreactor (AC-PBR). Simulation results showed good agreement with experimental growth curves of _Nannochloropsis gaditana_ obtained in the three photobioreactors.  
All the cultures cultivated in this work were also employed to perform biochemical analysis of the content of proteins, carbohydrates and lipids, and for the characterization of the fatty acid profile and the carotenoids content. The results show that the carbohydrate and protein content do not vary significantly with changing average light intensity, while the lipid content does. In particular, lipid content increases with increasing average photon flux density. The fatty acid profile also shows a marked shift when the average light intensity varies: the PUFA content decreases and the SFA content increases when the average light intensity increases. The carotenoids content is markedly shifted towards an accumulation at lower photon flux densities. These last responses indicate a strong low-light adaption of _Nannochloropsis gaditana_ grown under low average light intensities.  
Figure 10: Carotenoid analysis of the biomass obtained at the end of the batch cultivations grown under different conditions: (**a**,**c**), quasi-isoactinic reactor with different photon flux densities; (**b**,**d**), reactors with different configurations at incident light intensity \(I_{0}\) = 210 \(\mu\)E s\({}^{-1}\)m\({}^{-2}\). DW: dry weight.  
**Author Contributions:** Conceptualization: F.S. and S.L.; methodology: S.L., F.S. and A.B., software: F.S., S.L. and L.S.; validation, S.L. and L.S.; formal analysis, S.L. and L.S.; investigation, S.L.; resources, S.L. and G.C.; data curation, F.S. and S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L., F.S., G.C. and L.S.; visualization, S.L. and F.S.; supervision, F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Advanced Financial Fraud Malware Detection Method
## 6 Conclusions  
This study proposed a new framework for detecting malware apps used to commit financial fraud in Android systems. We utilized the datasets of benign and malware apps analyzed by Bank A in South Korea using the FDS system from April 2023 to September 2023. As the data on benign and malware apps were unbalanced, undersampling was performed to adjust the proportion of benign apps in the training dataset. Moreover, 92 datasets were constructed through daily training to select the optimal model. To test the proposed approach, we used five ML algorithms: logistic regression, random forest, LightGBM, XGBoost, and CatBoost. Upon calculating the optimal hyperparameter values for each algorithm using a grid search, the models were evaluated using the following common evaluation metrics: accuracy, precision, recall, F1-score, AUROC, and AUPRC. We selected the LightGBM model for our evaluation because it achieved the best performance with an accuracy of 99.99% and an F1-Score of 96.86%. Upon selecting 0.5 as the optimal decision threshold to determine whether an app was malicious, the LightGBM model was re-evaluated and yielded an accuracy of 99.99% and an F1-Score of 97.04%. Additionally, the importance of the features in the final model was evaluated. Age, app source, app size, the elapsed time after app installation, package installation request permission, and second- and third-string statistics of the package name were the most influential variables. The major achievement of this study is that we proposed a malware detection model that can be used in real operational environments by adding derived variables \"User activity\", \"User information\", and \"App package name statics\" to the \"App information\", \"App permissions\", and \"App service\" feature sets, which are commonly used in existing studies on static analysis-based malware detection. The proposed model will be highly effective in detecting financial fraud malware if applied to an FDS that detects abnormal transactions in the financial sector or financial apps.  
Conceptualization, J.S.; methodology, J.S.; validation, D.K.; formal analysis, J.S.; investigation, D.K.; data curation, D.K.; writing--original draft preparation, J.S.; writing--review and editing, J.S.; visualization, D.K.; supervision, K.L.; project administration, K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The dataset used in this study cannot be publicly shared due to security policies and confidentiality restrictions imposed by Bank A.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANOVA & Analysis of variance \\ APK & Android package kit \\ AUPRC & Area under the precision-recall curve \\ AUROC & Area under the ROC curve \\ BiLSTM & Bidirectional long short-term memory \\ \end{tabular}