esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Profit-oriented Local Search Algorithm
#### 3.5.2 Repair Operation  
The repair operation is executed to insert the unscheduled missions into the current observation sequence, which consists of direct insertion, shifting insertion, backtracking insertion, and deletion insertion. Each insertion operation corresponds to an insertion execution probability, i,e, \(P_{I1}\), \(P_{I2}\), \(P_{I3}\), and \(P_{I4}\). The insertion operations are executed if the repair probability \(P_{R}\) exceeds its insertion execution probability. The above four insertion operations attempt to insert the unscheduled missions in descending order of priority and allocate the earliest feasible observation time window (OTW) for each unscheduled mission:  
(1) Direct insertion: Direct insertion attempts to insert the unscheduled mission directly into the current observation sequence if there is enough idle time on the orbit.  
(2) Shifting insertion: For each unscheduled mission, the shifting insertion first moves the OTW of the scheduled mission before the insertion position forward and moves the OTW of the scheduled mission behind the insertion position backward to free up more idle time without violating the observation constraints. Then, the shifting insertion attempts to insert the unscheduled mission at the insertion position directly, and all insertion locations are traversed to try to insert the unscheduled mission.  
(3) Backtracking insertion: For each mission waiting to be inserted, the backtracking insertion first selects the conflict mission set, which consists of scheduled missions whose VTW overlaps with the VTW of the current waiting mission. Then, one scheduled mission in the conflict mission set is temporarily removed, and the waiting missions and the removed missions are attempted to be observed in turn. The backtracking insertion is successful only when both the waiting mission and the removed mission can be scheduled simultaneously. An example of backtracking insertion is shown in Figure 4. When attempting to insert the waiting mission \(t_{i}\), we first remove the mission \(t_{2}\) from the current observation sequence and then try to perform the mission \(t_{i}\) at the idle time. If the mission \(t_{i}\) is scheduled successfully, the removed mission \(t_{2}\) is then reinserted in another location in the observation sequence.  
(4) Deletion insertion: In the deletion insertion, the mission with less profit than the unscheduled mission is first deleted, and we then try to insert the unscheduled mission directly into the current observation plans. We attempt to insert the current waiting mission by deleting scheduled missions in descending order of profit until successful.
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 2 Materials and Methods
### Systematic Procedure for Developing the Virtual Reality-Based System  
For developing the virtual reality-based welding system to simulate the SMAW, MIG, and TIG welding processes, the systematic procedure illustrating the sequence of steps for developing the VR system is shown in Figure 2.  
In the first step, a module for controlling the infrared radiation source and the stepper motor was developed. This was module #1 of the system with the physical devices, including the MCU#1; IR emitters attached on the SMAW, MIG, and TIG welding torches; stepper motor; and power source converters.  
MCU#1 was designed and fabricated with the aim of controlling the infrared radiation source and the stepper motor, receiving and sending data to a computer via the RS485 port. Algorithm for controlling the IR source and the stepper motor was proposed and  
Figure 2: Systematic procedure for developing the virtual reality-based system.  
implemented. The connections of MCU#1 with the VR module, IR emitters, and stepper motor were established.  
In the second step, a module for receiving the IR signals was developed and named module #2 in the system. This module #2 with several physical devices consisted of MCU#2, two circuit boards attached to IR sensors for receiving the IR signals from the IR emitter attached on the welding torch, and power source converters.  
MCU#2 was used for receiving the infrared radiation source and sending data to the computer via the RS485 port. MCU#2 and two circuit boards were designed and fabricated. The algorithm for receiving the IR signals and processing these data was proposed and implemented. The connections of MCU#2 with the VR module and two circuit boards were established.  
In the third step, the VR module was programmed and connected with modules #1, #2, and #4. The VR module was module #3 of the system. To develop this module, the SMAW, MIG, and TIG welding processes were analyzed to have the 3D components as well as determine the input parameters, output parameters, and their allowed values for each welding process. These 3D components were generated by using the 3D scanner for scanning real welding devices. Then, these models and scenes were inputted to the Unity3D platform for generating the virtual reality environment. Once the system had all the integrated modules, the functionality of the system was tested and evaluated.
## 4 Results and Discussion  
The product of the pre-close wind volume coefficient and the aluminum extraction speed comprise the pre-close wind volume, as shown in Equation (14). In this study, the UKF algorithm was used to accurately and rapidly calculate the aluminum extraction speed, and a self-correction mechanism for the pre-close wind volume coefficient was introduced. The AF algorithm was used to precisely calculate the coefficient. In a MATLAB simulation, comparing it with the KF algorithm, the UKF algorithm improved the estimation accuracy of the aluminum extraction speed by 98.5%, while significantly reducing the computation time by approximately 50%. However, the UKF algorithm also had some shortcomings, such as a higher dependence on the accuracy of the system model and initial parameters, especially when the electrolytic cell operating conditions change drastically, leading to a potential decrease in estimation accuracy. Additionally, the computational complexity of the UKF was still higher than that of the KF, which may lead to challenges regarding the system load in real-time industrial environments with limited hardware resources.  
To further improve the accuracy of aluminum extraction, this study proposes a method for calculating the pre-close wind volume coefficient that considers aluminum extraction deviation. By introducing the self-correction mechanism for the pre-close wind volume coefficient, the aluminum extraction deviation, i.e., the difference between the planned and actual aluminum extraction, was calculated after each extraction. The optimal pre-close wind volume coefficient was determined using Equation (16), and this coefficient was used for the next pre-close wind volume prediction calculation. Several optimal coefficients were processed with the AF algorithm to obtain the final pre-close wind volume coefficient. This method can significantly improve computational accuracy and filter the noise caused by the system's weighing process. However, some limitations remain, such as the real-time performance of the AF algorithm, which is constrained by its reliance on historical data for smoothing. In complex, nonlinear conditions, the linear smoothing characteristic of the AF algorithm may not capture the dynamic features of the system, limiting the further improvement of the compensation accuracy.
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 3 Machine Learning Techniques in Cybersecurity
### Supervised Learning
The model was used to prevent advanced persistent threat (APT) attacks, which use social engineering to launch various forms of intrusion attacks. The detection accuracy in their experiments was 84.7%, which is very high for this experiment. Decision trees were also used in [100], where the authors replaced the misuse detection engine of SNORT with decision trees. SNORT is a known tool that follows the signature-based approach. The authors utilized clustering of rules and then derived a decision tree using a version of an ID3 algorithm. This rule clustering reduced the number of comparisons to determine which rules were triggered by given input data, and the decision tree located the most discriminating features of the data, thereby achieving parallel feature evaluation. This method achieved superior performance to that of SNORT when applied to the 1999 DARPA intrusion detection dataset. The results varied drastically depending on traffic type. The fastest were up 105%, with an average of 40.4% and minimum of 5% above the normal detection speed of SNORT. The number of rules was also increased from 150 to 1581, which resulted in a pronounced speed-up versus SNORT. Ensemble learning techniques such as random forest (RF) have also been explored in cybersecurity research. Random forest uses multiple decision trees to draw a conclusion and can be more accurate than a single decision tree. In [101], the authors employed a random forest algorithm on the KDD dataset for misuse, anomaly and hybrid-network-based intrusion detection. Patterns were created by the random forest and matched with the network for misuse detection. To detect anomalies, the random forest detected novel intrusions via outliers. Using the patterns built by the model, new outliers were also discovered. The study implemented a full system solution including anomaly detection. Data were grouped into majority attacks and minority attacks. This hybrid system achieved superior performance for misuse detection, where the error rate on the original dataset was 1.92%, and it was 0.05% for the balanced dataset. Regression algorithms are useful for forecasting a continuous target variable or numeric values, such as total phishing attacks over a period of time or network packet properties. In addition to detecting the core causes of cybercrime, regression analysis can be utilized for various risk analysis forms [102]. Linear regression [67], support vector regression [86] and random forest regressor are some of the popular regression techniques. The main distinction between classification and regression is that, in regression, the output variable is numerical or continuous, but in classification, the projected output is categorical or discrete. Ensemble learning is an extension of supervised learning that mixes different shallow models, e.g., XGBoost and random forest learning [72], to complete a specific security task. A summary of supervised approaches is shown in Table 3.
## 1 Introduction
The smart meter, data concentrator, and central system are all constantly communicating with one another as part of AMI [30]. The meter data management system receives data from electricity meters via the AMI host system. MDMS is in charge of organizing and analyzing the data that utility systems send to it. Utilities and service providers can save costs and improve service quality due to the AMI system [31]. A Process Control System called SCADA enables the real-time monitoring, measuring, and analyzing data from the power grid. However, SCADA can also guarantee connections over short and long distances, making it ideal for installations [30]. The three main parts of this system are the Human Machine Interface, Master Terminal Unit, and Remote Terminal Unit [32]. There are three parts to the remote Terminal Unit. The first component has data processing capabilities, the second component has logic program execution capabilities downloaded from the MTU Master Terminal Unit, and the third component is primarily in charge of network configuration [33]. Another element of SCADA that assists in controlling and keeping track of the RTU is the MTU. The system's final element, the HMI, gives the SCADA operator a graphical user interface. Demand Side Management is a crucial part of the smart grid. This system regulates residential energy use. Demand Side Management can improve power market stability by balancing supply and demand [34]. Demand-side management has several benefits, including improved short-term reliability, lower peak-to-average demand and power supply ratios, cheaper user bills, and lower production costs. The stretcher of paper is shown in Figure 4.  
Figure 3: Essential components of the Smart grid [29].
## 3 Result and Discussion
The appearance of neutrophils is more evident in C (2.5% DSS), and its epithelial structure has maintained its position. However, the extent of cryptic abscess and epithelial tissue damage is more prominent in D (3.5% DSS). This higher extent of epithelial damage may cause death due to chronic inflammatory conditions in mice and may cause irreversible chronic inflammation [32]. Figure 4: Effect of different concentrations of DSS on colon length. The colons of the entire DSS-treated group had significantly shorter lengths than the healthy group. Furthermore, the 2.5% DSS group had a significantly shorter colon length than the 1.5% and 3.5% DSS-treated groups. N = five mice per group (\({}^{***}\)\(p<\) 0.0001). Figure 5: Histological section of the colons representing (100\(\times\)) the healthy colon (**A**), the 1.5% DSS treated colon (**B**), the 2.5% DSS-treated colon (**C**), and the 3.5% DSS-treated colon (**D**). The extent of inflammation was more noticeable in the 2.5% DSS-treated mice as compared to the 1.5% group. The crypt was fully damaged in the 3.5% DSS group. The epithelial layer was distorted more in the 3.5% DSS-treated mice. The microscopic histological damage score was assigned based on the observation of epithelial damage and infiltration of inflammatory cells, as shown in Figure 6. The mice given 2.5% DSS had significantly more epithelial damage (\(p\)-value \(<\) 0.0001) than the mice given 1.5% DSS. However, the 3.5% DSS-treated mice had higher epithelial damage than the 2.5% DSS group. To avoid a higher extent of inflammation, which may cause the death of mice, 2.5% DSS is the best suited to develop the colitis model. Finally, we investigated the effect of different levels of DSS treatment on the expression of markers of colonic inflammation namely, tumor necrosis factor alpha (TNF \(\alpha\)) and myeloperoxidase (MPO) as these markers could provide a better understanding how changing/increasing levels of DSS increase the intensity of inflammation in the colon (Figure 7). We observed that there was a statistically significant increase in levels of both TNF \(\alpha\) and MPO in the DSS-treated mice as compared to the healthy control mice which is the expected outcome. We also see a gradual increase in levels of both TNF \(\alpha\) and MPO as the dose of DSS is increased. This finding provides further evidence that the increase in the level of DSS is directly proportional to the increase in the intensity of inflammation.
## 2 Fundamental Theories  
### Wavelet Packet Transform (WPT)  
The WPT is defined by the expressions [46; 47]:\[\left\{\begin{array}{l}u_{2n}^{(j)}(t)=\sqrt{2}\sum_{k}h(k)u_{n}^{(j)}(2t-k)\\ u_{2n+1}^{(j)}(t)=\sqrt{2}\sum_{k}g(k)u_{n}^{(j)}(2t-k)\end{array};n,k=0,1,2,\cdots\right. \tag{1}\]  
where \(u_{0}^{(0)}(t)=\varphi(t)\) is the scaling function, \(u_{1}^{(0)}(t)=\psi(t)\) is the wavelet function, \(j\) is the decomposition level, \(k\) is the translation parameter, and \(n\) is the modulation parameter. The terms \(h(k)\) and \(g(k)\) are quadrature mirror filters, and the corresponding function sets \(H=\left\{h(k)\right\}_{k=z}\) and \(G=\left\{g(k)\right\}_{k=z}\) denote the low pass filter and the high pass filter, respectively. The recursive relations between the \(j\)th and the \((j+1)\)th level components of WPT are given in the equations:  
\[f_{j}^{i}(t)=f_{j+1}^{2i-1}(t)+f_{j+1}^{2i}(t) \tag{2}\]  
\[f_{j+1}^{2i-1}(t)=Hf_{j}^{i}(t) \tag{3}\]  
\[f_{j+1}^{2i}(t)=Gf_{j}^{i}(t) \tag{4}\]  
After being decomposed for \(j\) times, the sum of the component signals can represent the original signal \(f(t)\) as:  
\[f(t)=\sum_{i=1}^{2^{j}}f_{j}^{i}(t) \tag{5}\]  
The process of WPT for the original signal can be perceived as a binary tree (Figure 1), in which \(f(t)\) is at the top layer. At the first decomposition layer, \(f(t)\) is decomposed by the filters \(H\) and \(G\), giving its approximate component signal \(A\) and detail component signal \(D\). \(A\) and \(D\) are then decomposed by \(H\) and \(G\) again, respectively, leading to two new pairs of approximate and detail component signals, \(\left\{AA,AD\right\}\) and \(\left\{DA,DD\right\}\). For the next layer, the filters \(H\) and \(G\) are used in the same way to process each component signal \(AA\), \(AD\), \(DA\), and \(DD\), creating new pairs of approximate and detail component signals, and so on. In the end, at the \(j\)th layer, a total of \(2^{j}\) component signals can be produced. All the component signals generated by the WPT up to the fourth layer are displayed in Figure 1, in which each component signal is termed a wavelet packet node.  
In Figure 1, the upper-left part is the WT procedure; the component signals with yellow backgrounds D, AD, AAD, AAAD and AAAA are just the results of the WT. Hence, the WT is a subset of the WPT. Unlike the WT that merely covers the lower frequency part, the WPT provides even more and exhaustive decomposition along the whole frequency scope, because each left detail signal is decomposed again in a similar way to decompose the approximation signal. The down sampling operation of WPT causes the switch of the order of the low and high-pass components in a subsequent decomposition, hence producing a frequency content that follows a Gray code sequence [47].  
Figure 1: Schematic of wavelet packet transform (WPT) of a time-domain signal. A: approximate component signal; D: detail component signal.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### Fully Automated End-to-End Chains  
#### 2.4.1 Flood and Water Body Mapping Pipeline  
The water extraction chain processes optical or SAR data. The workflow is almost the same for both those EO input types. As input data, GSW is used to derive samples for the classification process.  
The optical workflow illustrated in Figure 4 presents the different steps:  
1. Data Acquisition 1. Optical data download through EEO (Sentinel-2/3, Landsat-5/7/8/9) or other sources;  
Figure 3: ExtractEO SaaS main page [17].  
* 2. [leftmargin=*]
* GSW occurrence product download over the area covered by the optical data;  
* 1. [leftmargin=*]
* Spectral index computation derived from the optical data: MNDWI, AWEInsh, AWEIsh, WI [18];
* 2. [leftmargin=*]
* Stack relevant spectral indices and bands for training;
* 3. [leftmargin=*]
* Water sample generation from the GSW, using an occurrence value (which is relatively low for flood mapping and high for water body mapping); Water samples are filtered using the cloud mask generated by EEO, and the indices are computed to remove outliers and filter the training samples to the hydrological reality of the image (water extent, resolution);
* 4. [leftmargin=*]
* Training, using the Multi-Layer Perceptron classifier by default. After internal trials, this algorithm appears to be the most reliable classifier for water detection on big stacks. However, the training method can be chosen among other classifiers that are implemented, such as Random Forest or Support Vector Machine, for project needs;  
* 1. [leftmargin=*]
* Prediction of a trained model over a stacked image;  
* 2. [leftmargin=*]
* Slope and hillshade thresholds derived from HR DEMs are applied to refine the water extraction (post-processing);
* 3. [leftmargin=*]
* Mosaicking of the results coming from several tiles with the same date and projection;
* 4. [leftmargin=*]
* Minimum mapping unit (MMU) sieving to remove small features and fill holes;
* 5. [leftmargin=*]
* Vectorization step in all classic formats, including GeoJSON, ESRI Shapefile, or Keyhole Markup Language (XML);  
* 1. [leftmargin=*]
* A second stack may be included in the output folder with a separate stack of bands/indices, with a different resolution than the training/predicted stack, and used only for visualization purposes. This step is relevant when large areas are covered with the need to decrease the resolution in order to display and navigate quickly with the optical data as a background.  
Most of the modules can be run with different parameters, but a generic configuration is set by default. As illustrated in Figure 5, the SAR workflow is very similar to the optical workflow. The default training stack includes either the raw amplitude, or the output of a despeckle process. The water sample filtering step is not included in the SAR workflow. The preferred machine learning method is Random Forest, as it has been proven to be more robust on thin stacks, and SAR offers less usable bands.  
Figure 4: Optical water extraction workflow.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
###### Abstract  
Communication systems have been driven towards the fifth generation (5G) due to the demands of compact, high speed, and large bandwidth systems. These types of radio communication systems require new and more efficient antenna designs. This article presents a new design solution of a broadband microstrip antenna intended for use in 5G systems. The proposed antenna has a central operating frequency of 28 GHz and can be used in the LMDS (local multipoint distribution service) frequency band. The dimensions of the antenna and its parameters have been calculated, simulated, and optimized using the FEEKO software. The antenna has a compact structure with dimensions (6.2 \(\times\) 8.4 \(\times\) 1.57) mm. Rogers RT Duroid 5880 material was used as a substrate for the antenna construction, which has a dielectric coefficient of 2.2 and a thickness of 1.57 mm. The antenna described in the article is characterized by a low reflection coefficient of \(-\)22.51 dB, a high energy gain value of 3.6 dBi, a wide operating band of 5.57 GHz (19.89%), and high energy efficiency.  
antenna; microstrip antenna; antenna 5G; 28 GHz band; 5G wireless system; broadband antenna +
Footnote †: journal: Electronics Letters  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00000-000  
to minimize dimensions and weight, thereby enabling the use of the designed antenna in mobile terminals, as well as facilitating integration with electronic devices.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 3 Methodology
### Network Architecture  
As is widely known, U-Net [26] has demonstrated an outstanding performance in medical image segmentation. It is commonly utilized as a benchmark test model for semantic segmentation tasks.  
We proposed a network architecture named nmODE-Unet for segmenting DR lesions, as shown in Figure 2. Within this proposed network architecture, we have made minor modifications to the U-Net. Specifically, we inserted an nmODE block into each skip connection of the U-Net backbone. The shallow-level features serve as inputs to the nmODE blocks, and the outputs of the nmODE blocks replace the shallow-level features, engaging in feature fusion with corresponding deep-level features.