esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To independently control the chatter signal amplitude, [PERSON] et al. connected a driving circuit of a proportional valve with a compensation network [21]. To reserve the zero-average oscillations of the valve core and reduce power consumption, [PERSON] et al. proposed that the proportional valve is driven by modulating the frequency of the PWM signal [22]. [PERSON] et al. proposed a double-voltage control mode, where high voltage is used to improve the opening speed and low voltage is used to reduce the power dissipation and closing time [23]. Even though various digital driving circuits have been developed, the fast and accurate realization of the coil current is still a tough task. To improve the steady-state precision and response speed of proportional valve coil current, this paper proposes a new capacitive reservoir-based PWM digital circuit (CRPDC), which comprises three key parts: A MOSFET driving circuit, a capacitive reservoir circuit, and a current sampling circuit. While the MOSFET driving circuit is in a turn-on state, the proportional valve coil is actuated by the power supply. Inversely, the capacitive reservoir circuit provides electricity to the valve coil. The cooperation between the MOSFET driving circuit and capacitive-energy-storage power circuit results in a faster current response and lower current ripple. Mathematical models of the CRPDC are established to explore the dynamic characteristics of the coil current. Simulations based on mathematical models and Multisim models are carried out, which shows that the CRPDC is applicable to the coil current control of the electro-hydraulic proportional valve. The organizational structure of this paper is as follows. The principle design of the CRPDC is introduced in Section 2. The mathematic models of the CRPDC are established in Section 3. The CRPDC is simulated and analyzed in Section 4. A comparison of CRPDC and other PWM driving circuits is performed in Section 5. Finally, several conclusions are drawn in Section 6.
## 4 Simulation Model Description
### Triage Process  
The modelling procedure for the triage process is similar to that described above, with the operating time being dictated by a lognormal distribution (regarding the probabilistic fitting illustrated in Figure 8). In the triage process the patient is assigned a priority (in accordance to the percentage distribution found in the database). For the present study only orange, yellow, green and blue patients were considered since they represent 99.8% of the cases. As mentioned previously, blue patients were aggregated to the green ones as they share the same medical resources. For the triage process the modelled resources were nursing staff. The redesigned emergency department is planned to have three triage workstations, so the simulation model is flexible to accommodate any number of nursing staff for triage purposes.  
For this process, the most important performance indicators to analyze are the resource utilization (nursing staff) and the process queue (both time and number in queue). The simulation model also records the time when the triage process finishes which will allow for computing the triage process cycle time and, consequently, estimate the takt time for each treatment room. When the triage process ends, the simulation model also starts monitoring the waiting time of the patient until the first medical intervention. This waiting time is one of the most important performance indicators of the study as it will reveal whether the emergency department is complying with the Manchester protocol time or not.
# A Review of Deep Learning Applications for Railway Safety
## 2 Overview of Deep Learning Approaches
### Data Types  
#### 2.1.1 Image Data  
Image data are represented in a two-dimensional structure in the form of a numerical matrix consisting of points called pixels. Pixels represent the contrast of colors in numbers ranging from 0 to 255, where 0 is black and 255 is white. In the case of a color image, the color of the image is expressed using the light and shade of red, green, and blue with three channels of RGB. Before processing images using a deep learning model, image preprocessing, such as image alignment, cropping, and adjustment (e.g., brightness and contrast), can be conducted.  
A convolutional neural network (CNN) is a representative deep neural network used in image data processing. CNN can extract image patterns with spatial structures because filters composed of multiple weight values move spatially. The following equation represents the convolution in CNN:  
\[\mathbf{U}_{i,j}=(\mathbf{W}\times\mathbf{X})_{i,j}=\sum_{m}\sum_{n}\mathbf{ X}_{m,n}\mathbf{W}_{i-m,j-n} \tag{3}\]  
\[\mathbf{Z}=f(\mathbf{U}) \tag{4}\]  
where \(i,j\) are the index of the output matrix, and \(m,n\) are the index of the input matrix. \(*\) is convolution. \(\mathbf{X}\) is the input matrix. \(\mathbf{X}_{m,n}\) is the value of row \(m\) and column \(n\) of \(\mathbf{X}\). \(\mathbf{Z}\) is the output matrix of the convolution layer.  
A CNN performs convolutional operations commonly used in image or signal processing. The CNN moves a mask, also called a kernel, filter, or window, and performs convolutional operations with input data to extract data features. Because this approach allows the detection of relevance between one element and neighboring elements, CNNs are suitable for data processing with grid structures, such as images. CNNs have shown superior performance to humans in some complex image processing problems and have also contributed significantly to image retrieval services, autonomous vehicles, and image automatic classification systems.
## 6 Conclusions  
In this paper, the FSCs control and NOTC of a class of nonlinear systems have been studied. Through two coordinate transformations, the system is transformed into a system whose subsystems are linear from the order 1 to the order \(n-1\). Based on backstepping, by designing the controller, a suitable CLS can be obtained; thus, the expression of tracking error can be obtained, and then the conditions of NOTC can be analyzed. The algorithm in this paper can not only make all the signals of the closed-loop system bounded, but can also make the overshoot of the system zero for each condition with non-overshooting. According to the simulation section, the algorithm in this paper is effective. The algorithm in this paper obtains more conditions with non-overshooting. Therefore, compared with the existing results, the algorithm in this paper reduces the conservatism.  
The method proposed in this paper primarily focuses on the research findings for SISO systems. Compared to such systems, MIMO systems are more aligned with practical requirements and represent a more generalized scenario. Therefore, future work will investigate the non-overshooting control problem for multi-input-multi-output systems.  
Conceptualization, X.-Q.X. and C.Z.; Methodology, X.-Q.X.; Formal analysis, C.Z.; Investigation, C.Z.; Resources, C.Z.; Data curation, X.-Q.X.; Writing--original draft, X.-Q.X. and C.Z.; Visualization, X.-Q.X.; Supervision, C.Z.; Project administration, C.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the Key Education Teaching Management Project of Anhui Province (2023)XGL039) and the \"101 Plan\" Project of Anhui Province (2023 YLYJH038).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
During the preparation of this manuscript, the authors used the Deespseek for the purposes of grammar checking and correcting typos. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
NOTC Non-Overshooting Tracking Control  
FSCs Full-State Constraints  
CLS Closed-Loop System  
BLF Barrier Lyapunov Function
###### Abstract
Such a multi-scale spatial structure necessitates models that can effectively capture both global and local features. The Transformer model is effective in capturing long range dependencies and is suitable for extracting global spatial features, which helps reveal the relationship between large-scale environmental factors and the distribution of SOM [24; 25; 26]. Convolutional neural networks (CNNs) are more capable of capturing local spatial features and can effectively detect small-scale variations at the micro-scale [27; 28]. According to [PERSON]'s first law of geography, everything in space is related, and things that are closer in space are more strongly related. The positional encoding used in conventional Transformer models does not fully incorporate actual geographic location information [29], which limits their capacity and generalization in geospatial prediction tasks. Incorporating geographic location information into the model can make better use of spatial context and improve prediction accuracy [30]. To address the above limitations, this study proposes a geographic positional encoding mechanism that embeds geographic location information into the feature representation of the Transformer model and modifies its encoder structure to build an enhanced model with spatial awareness named Geo-Positional Transformer (GPTransformer). Based on this, a dual-branch neural network model called Geo-Positional Transformer-CNN (GPTransCNN) is further developed by integrating the improved geographic positional encoding with a multi-scale feature extraction mechanism. This model combines the strength of the GPTransformer branch in capturing large-scale global features with the advantage of the one-dimensional convolutional neural network (1D-CNN) branch in extracting fine-grained local features. It is designed to improve the prediction accuracy of SOM spatial distribution and provide stronger technical support for sustainable land management and responses to global climate change.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 4 Direction for Future Work
### Combating Anti sAnalysis Techniques  
Malware developers perform various anti-analysis techniques to suppress the detection and analysis of their released malware. Obfuscating a sample, compressing/packing the binary/exe, and encrypting the file, all are tools to make it difficult to detect and analyze the malware. Future work can be to mitigate all these anti-analysis tools to eradicate the possibility of the destructive threat posed by malware.  
In short, the aggressively dynamic nature of the cyber world demands researchers to take care of the following points while conducting their research in this domain of malware detection.  
* Since malware easily changes its shape due to sophisticated techniques used by malware writers so research in the future should be conducted with the motive of dealing with metamorphic, polymorphic, and obfuscated malware.
* The day-by-day increase in malware is the prime reason for the increasing no. of malware families and with the passage of a certain period various new forms of malware keep on showing up on the surface of the cyber world. Future research should focus on developing a generic model that should be capable of detecting zero day malware.
* To implement the real time solution, a model should be reliable enough to handle any kind of unseen malware as well.  
Deep learning based research has proved to be fruitful by producing quotable results in the detection of malware. To further improve the solution, meta learning based algorithms can be exploited in conjunction with deep learning. Meta learning based algorithms help in producing generic models. These generic models are trained for self-learning. Through self-learning, the strategies of learning the properties of even unseen types of malware can be learned easily. More specifically few shot learning has proved itself worthy of being explored in the future due to its effectiveness, efficiency, and robustness.
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 4 Evaluation and Analysis
### Experimental Setup
#### 4.1.3 Evaluation Index  
We select two commonly used indicators for evaluating recommendation algorithms: normalized discounted cumulative gain (NDCG@K) and recall (Recall@K), where @K represents K different items in the recommended list returned by the model when evaluating the results. Generally, K is between 10 and 20. In these experiments, we set K = 10.  
NDCG@K evaluates the ranking of the recommendation list obtained by the model. The following is the calculation rule: in the recommendation list, the higher the correlation between candidate items and the current user, the higher the evaluation score. The calculation method is shown in Equations (29) and (30).  
\[\text{DCG@K}=\sum_{i=1}^{\text{K}}\frac{r(i)}{\log_{2}(i+1)} \tag{29}\]  
\[\text{NDCG@K}=\frac{\text{DCG@K}}{\text{IDCG@K}} \tag{30}\]  
where \(i\) is the position number in the recommendation list. \(r(i)\) represents the score of the item in the \(i\)-th position. DCG@K is the total score of the candidate items in the current recommendation list considering the sorting order factor. IDCG@K is the normalization parameter, representing the maximum value that DCG@K can achieve.  
Recall@K is the recall index of a recommendation list, which represents the correct proportion of model predictions in real-world interactions. The calculation method is shown in Equation (31):  
\[\text{Recall@K}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{31}\]  
where TP represents the number of positive samples predicted as positive samples in the recommendation model, and FN represents the number of positive samples predicted as negative samples in the recommendation model.
## 3 Results
### Machine Learning Regression  
As mentioned in Section 2.3, we employed a _Gradient Boosting_ machine learning model for the regression task, using a _maximum number_ of \(N=30\)_trees_, each with a maximum depth of 5. We arrived at this configuration using a trial-and-error approach, in which these hyperparameters achieved excellent accuracy without an enormous amount of complexity. The computations were carried out on a PC with an AMD Ryzen 12-Core Processor utilizing 16 GB of RAM. The model required approximately 0.11 seconds of training time. We trained the model using a so-called 75-25 split, i.e., 75% of the dataset was randomly selected for training and the remaining 25% was used to gauge the model's performance on truly unseen data. We used the well-known regression metrics of _Mean Absolute Error (MAE)_, _Root Mean Squared Error (RMSE)_, and _Coefficient of Determination \(R^{2}\)_. Their corresponding formulae are shown in Table 1. Table 2 summarizes their computed values on both the training and testing datasets in the log-transformed space as well as the original space by using the inverse exponential transformation.  
It can be readily observed that the trained ML model achieved excellent performance in both the log-transformed and original spaces, with values of \(R^{2}\) equal to 0.99 and 0.98, respectively. In addition, the performance was similar between the training and test dataset in both cases, indicating that the model did not overfit. Finally, the values of MAE and RMSE were low and relatively close to each other, indicating that the trained model did not have very many large values in its errors. Such errors are accentuated when squared in the calculation of RMSE; thus, this effect manifests in a large discrepancy between RMSE and MAE, which is not present in our case.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 5 Simulation Results and Findings
### V2I Data Transfer Capability  
Figure 5 delivers a powerful illustration of how the proposed QMIX scheme transcends existing approaches, particularly the baseline MARL scheme, in ensuring seamless V2I performance, especially as V2V data sizes inflate. Unlike the diminishing V2I transmission rates exhibited by other methods as data loads increased, QMIX maintained an impressive consistency, thus showcasing remarkably high rates over its competitors. From Figure 5, we can observe that when the payload size was 1 \(\times\) 1060 bytes, the corresponding improvement of the QMIX was 24.91% as compared with the benchmark MARL technique. As we increased the payload size, the efficiency of the proposed QMIX technique remained improved at 27.7%, 33.46%, 38.67%, 42.86%, and 47.17%, respectively. Figure 5 shows that the average improvement of the proposed technique over MARL was 35.795%. This unwaring performance signifies QMIX's superior adaptability and effectiveness in handling diverse data demands within V2X communication scenarios.  
Furthermore, QMIX established itself as the undisputed winner across the spectrum of V2V data sizes. Regardless of the payload size, it consistently outperformed the baseline scheme, thus achieving the highest V2I transmission rates. This consistent excellence emphasizes QMIX's robustness and its ability to excel in various communication scenarios, thereby solidifying its potential for real-world applications.  
By effectively addressing challenges faced by existing approaches, QMIX emerges as a frontrunner in ensuring reliable and efficient V2X communication, thus paving the way for a future of seamless data exchange and improved network performance.
## 2 Experimental Section  
### Test Variables  
Experiments were conducted on seven beams. One beam was a control beam with no end anchorages, three others had different types of conventional anchorages, and the remaining three had the proposed end anchorage. For all of the pre-stressed beams, the bending moments, by applying the prestress force, were identical at the mid-span of the beams. This approach allowed us to make a direct comparison of the prestressed strengthened beams. Because the newly proposed anchorage also uses a lifting hole, the prestressing force applied to the steel rebar was regarded as a test variable. Table 1 outlines the test variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\begin{tabular}{c} **Concrete** \\ **Strength** \\ \end{tabular} } & \multicolumn{3}{c}{**Prestressing Tendon**} & \multicolumn{2}{c}{**Steel**} & \multirow{2}{*}{\begin{tabular}{c} **Rebar** \\ **Anchorage Shape** \\ \end{tabular} } \\  & & **Eccentricity** & \begin{tabular}{c} **Prefress** \\ **Force for** \\ **two Tendons** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **Layout** \\ **Two Tendons** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Prestress** \\ **Force** \\ \end{tabular} } \\  & & **(MPa)** & **(mm)** & **(\&N)** & **(-)** & **(\&N)** & **(-)** \\ \hline Control Beam & CON & & - & - & - & - & - \\ \hline  & LHS & & 140 & 190 & Draped & - & \\ \cline{2-6}  & & & & & & & Type II \\ \cline{2-6} Conventional Anchorage & LHT & & 280 & 95 & Straight & - & \\ \cline{2-6}  & & 30 & & & & Type III \\ \cline{2-6}  & & LHU & & 280 & 95 & Straight & - & \\ \hline  & & JBM & 280 & 95 & Straight & - & \\ \cline{2-6} Newly Proposed Anchorage & JBM5 & & 280 & 95 & Straight & 50 \\ \cline{2-6}  & & JBM10 & & 280 & 95 & Straight & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables for tested beams. CON: the control beam; LHS: the beam applied with the conventional anchorage Type I; LHT: the beam applied with the conventional anchorage Type II; LHU: the beam applied with the conventional anchorage Type III; JBM, JBM5, JBM10: the beams applied with the newly proposed anchorage as well as used 0, 5 and 10 kN prestressed steel rebars.
## 2 Method and Validation
### Experimental Validation  
In order to verify the accuracy of the numerical results, the opening time and closing time of the valve are compared with the data in the literature [41]. According to results obtained by [PERSON], the duration of opening stage is 66.2 ms, the duration of closing stage is 35.9 ms. In this paper, the duration of opening stage is 67.7 ms, the duration of closing stage is 37.4 ms. The relative error of the duration of opening stage is 2.3% and the relative error of the duration of closing stage is 4.2%. It can be found that the relative errors between the numerical results and reference values in the literature are very small, which verifies the accuracy of the numerical method.
# Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
###### Abstract  
Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.  
corn damage; peeling damage; recognition method; RGB image; corn ear harvest Article
# A Practical and Adaptive Approach to Predicting Indoor CO2
## 1 Introduction
As a result, the model is constantly updated and the performance is improved as  
Figure 1: A typical data collection scenario for retail stores. soon as possible. Once the window achieves the optimal size to guarantee high prediction accuracy, it can be moved at a certain rate over time to update the model, effectively becoming a mobile window. As the training can be directly performed on site, the result is a potential zero-touch approach for predicting indoor CO2, enabling the edge device to work independently. Starting from a prediction error between 40 and 50 ppm after one day of data collection, the proposed approach improves the accuracy day by day. In particular, after just a week of data for training the model, the prediction error is more than halved, making it around 15 ppm, and predictions could be effectively used to regulate HVAC systems accurately. The performance improves until a window of past 30 days is used: in this case, an error of predictions of around 10 ppm is achieved. Further increasing the mobile window does not improve the accuracy and requires more computational resources. Combining the practical constraints of real-world deployments with the requirement of accurate CO2 predictions, this work makes the following contributions:  
* A deep learning solution for indoor CO2 prediction that can be trained with just a small amount of recent environmental data (i.e., temperature, humidity and CO2) collected over a short time frame, thereby guaranteeing a high prediction accuracy after few days from the beginning of the data collection with no model pre-training. * An updating mechanism based on a mobile window that keeps the CO2 predictions consistent with any environmental changes. As a result, the CO2 predictions can be effectively used to regulate HVAC systems, guaranteeing IAQ comfort to occupants. The rest of the paper is organized as follows. Section 2 provides a detailed overview of the relevant literature. Section 3 introduces the dataset used for the experimental evaluation. Section 4 describes the proposed methodology and the AI architecture. Section 5 illustrates the simulation setup. Section 6 describes and discusses the results of the proposed approach. Finally, Section 7 concludes the paper and provides future directions for predicting IAQ.