esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Detection Approaches
#### 3.8.2 Anomaly based Ransomware Detection Approaches  
In anomaly detection the normal behavior is modelled to build a normal profile. This normal profile is always different than the profile built from the anomalies [24; 81]. This detection is performed by building a profile with use of benign applications. Any deviation to normal profile is considered as anomaly.  
The authors in [82] developed the new ransomware variants to describe a dynamic ransomware detection system. A local virtual client server environment was developed and named as File tracker. This was a behavioral based system performed detection in two modes, i.e., user and kernel modes. Normal and Abnormal access profiles were built by using aggregated data from all the 50 clients. Suspicious features were classified into three categories with respect to subject, process, and impact of the attack. Whenever a suspicious feature was observed the user and server were notified. This system possessed certain limitations as it can detect the existing and new ransomware variants with a loss of up to 20 files. Delays were also observed due to client server architecture.  
An anomaly based detection and prevention model for ransomware was developed in [83] which performed detection in four phases. Early detection model was developed by carrying out study on the unstructured data of petya and WannaCry logs, stored in an institution EcuCERT. A corpus was created consisted of the most important features in the log files. Corpus contained the behavior pattern of vulnerabilities found in Microsoft windows systems. The study explained the features that best represent the vulnerabilities in the system by using the user log files. Study utilized big data analysis tool with machine learning to identify behavior patterns of ransomware.  
A behavioral and anomaly based ransomware detection model was presented in [84]. This model was developed by the integration of two modules, one dealt with behavioral data and other dealt with anomaly data. The anomaly detection module was responsible to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ransomware Detection Approaches**} \\ \hline \multirow{2}{*}{**Sr\#**} & \multirow{2}{*}{**Study**} & \multicolumn{4}{c}{**Misuse Based**} & \multirow{2}{*}{**Anomaly**} \\ \cline{3-3} \cline{5-6}  & & **Signature** & & **Behavioral** & & **Hybrid** \\ \hline  
& & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ransomware detection approaches.  
find zero-day ransomware attacks. The anomaly module was built by using the legitimate programs. Any deviation from the normal profile resulted in triggering the alarm.  
A deep learning-based ransomware detector DeepRan [85] utilized the anomaly concept to detect the ransomware attacks. This detection model monitored the abnormal activity of infected host in a network environment of bare metal server. The normal profile for the model was developed by collecting the normal host logs. Model was developed by using the deep learning classifier BiLSTM-CRF to classify the normal and infected host activities.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 3 Test Cases
### Ebro River--Spain (2015, 2018, 2021)  
In recent years, several events have affected the Ebro River in Spain (2015, 2018, and 2021). The river stretches 930 km (approximately 580 miles) from the Cantabria Mountains to the Mediterranean Sea, playing a crucial role in the surrounding areas and the whole of Spain.  
In February 2015, the Ebro River experienced major flooding in the Zaragoza region. The river flow rate reached an impressive 2400 cubic meters (84,700 cubic feet) per second during this event. The swollen river cut through Zaragoza, affecting approximately 700,000 people and submerging large areas of nearby farmland [46].  
In April 2018, the Ebro River threatened the city of Tudela. Due to snowmelt in river catchment areas and a period of heavy rain, the river's rising waters prompted authorities to issue an orange alert at the second-highest level on the Spanish scale [39]. The water level reached over five meters before gradually receding [47]. It is worth noting that the water level was lower than that recorded in 2015 only because of the measures taken to improve the safety of the populations closest to the channel after the 2015 event [48].  
Another flood affected Zaragoza in December 2021. This flood was triggered by heavy rainfall and rapid snowmelt over a 72 h period, leading to extensive damage and disruption in the upper and middle Ebro River basin that runs through Zaragoza. Spain's Meteorological Agency (AEMET) reported that on 10 December, the Arga River in Pamplona reached 4.80 m, whereas the Ebro River in Castejon reached 7.94 m by 11 December. Flooding along the Ebro later affected areas of the Aragon region, including the capital Zaragoza, where the river reached 5.52 m on 14 December 2021 [49]. Areas along the river in the city were flooded, and the Spanish Red Cross reported that patients from a hospital in the city were evacuated [49]. Unfortunately, these floods highlight the increasing frequency of such events in Spain due to climate change. Authorities continue to monitor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Areas** & **Event Date** & **Image** & **Mission Identifier** & **Date of Acquisition** & **Acquisition Mode** \\ \hline Spain (Zaragoza) & 10 December 2021 & After Flood & Sentinel-1A & 16 December 2021 & Descending \\ Spain (Tudela) & 12 April 2018 & After Flood & Sentinel-1A & 13 April 2018 & Descending \\ Spain (Zaragoza) & 2 February 2015 & During Flood & Sentinel-1A & 2 February 2015 & Descending \\ UK (Selby) & 27 December 2015 & After Flood & Sentinel-1A & 29 December 2015 & Descending \\ Italy (Sesia) & 3 October 2020 & During flood & Sentinel-1A & 3 October 2020 & Descending \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the Sentinel-1 SAR images used in the study areas.  
and take precautionary measures to mitigate the impact of future floods in Zaragoza and its surrounding areas.
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 4 DNN-Based Guidance (DNNG) Law
### DNNNG Architecture  
The DNNNG architecture consists of a fully connected layer. The output for each layer can be expressed mathematically, and the output of the \(i\)-th layer can be represented as shown in Equation (7).  
\[o_{i}=a_{i}(W_{i}o_{i-1}+b_{i}) \tag{7}\]  
where \(a_{i}\) is the activation function, \(W_{i}\) are the weighting vectors, \(o_{i-1}\) is the output of the previous layer, \(b_{i}\) is bias and \(i\) is the number of hidden layers.  
The selection of an activation function is very important in designing a DNN model. In this study, we experimented with various activation functions, and among them, the DNN model consisting of only a hyperbolic tangent function had the best performance. Thus, in this study, the activation function selects a hyperbolic tangent function as defined in Equation (8), which applies for both the output and hidden layers.  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\quad(x=W_{i}o_{i-1}+b_{i}) \tag{8}\]
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 4 Data Processing
### PM2.5 and PM10 Mass Concentration Estimation
\[PM2.5=11.749-0.283\times AOD_{blue}+0.412\times AOD_{green} \tag{9}\]  
\[PM10=10.782-0.037\times AOD_{blue}+0.065\times AOD_{green} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **N** & **Max** & **Min** & **Mean** & **StdEv** \\ \hline PM2.5 (\(\text{\text{\text{\text{\text{\text{\text{\text  
Based on the regression models, the PM2.5 and PM10 mass concentrations in the whole Dong Thap Province were estimated, as illustrated in Figure 8.
## 2 Attack Position Assessment  
### Bayesian Game Model  
The process of offense and defense in traffic signal systems involves incomplete information and uncertainty. Bayesian games can transform this uncertainty into probability distributions. This allows each decision-maker to choose the optimal decision strategy based on probabilistic judgments of the behaviors of other decision-makers. Bayesian games can help analyze the interactions and influences among various decision-makers under conditions of incomplete information and uncertainty, leading to the identification of optimal strategies. Additionally, Bayesian games can provide a quantitative evaluation of the likelihood of survey results, rather than simply accepting them completely or rejecting them entirely, as is common in traditional decision-making methods.  
In urban traffic control, traffic signal controllers are positioned at intersections and are primarily responsible for receiving data from upper management systems and various traffic detectors. They control the operation of traffic lights based on these instructions. Generally, the upper management system is installed at traffic management agencies, coordinating with traffic signal controllers through dedicated network resources.  
Assumption 1: Both the attackers and defenders participating in the game are purely rational. Therefore, the traffic network security value S must exceed any costs; otherwise, there would be no incentive for either party to adopt strategies aimed at enhancing security. Thus,  
\[S>max(Cau,Cas,Cdc,Cdp,Cdcp) \tag{1}\]  
where \(Cau\) denotes the cost of attacking the UC; \(Cas\) denotes the cost of attacking the TSM; \(Cdx\) represents the cost corresponding to the defense strategy of \(dx\in\{dcp,dc,dp\}\), \(dcp\) means using information and control strategy defense at the same time, \(dc\) means using only information strategy defense, and \(dp\) means using information and control strategy defense.  
Next, we will discuss the benefits derived from the attack positions under various strategies of both parties in the game.  
When both parties in the game choose strategy \((a,nd)\), the attacker benefits \(S-Cau\), while the defender also benefits \(-S\). When the strategy is \((a,dx)\), the defender benefits \(\alpha S-(1-\alpha)S-Cdx=(2\alpha-1)S-Cdx\), and the attacker's benefit is the defender's benefit minus the cost of the attack: \((1-\alpha)S-\alpha S-Cau=(1-2\alpha)S-Cau\). In the case of \((na,dx)\), the detection system has an error, resulting in the defender receiving benefit: \(-Cdx\). The strategy benefits of attacking the UC or TSM can be obtained, as shown in Table 2.  
In the payoff matrix, \(Cx=Cau\) in case UC, and \(Cx=Cas\) in case TSM. \(N_{a}\) denotes the attacker, the strategy includes \(\{a,na\}\), \(a\) means attacking, and \(na\) means not attacking; \(N_{d}\) denotes the defender of the system, including UC and TSM, the defense strategy includes \(\{dcp,dc,dp,nd\}\), and \(nd\) means not defending; \(S\) denotes the value of the security of the transportation network; \(\alpha\) represents the detection rate; \(\beta\) represents the false alarm rate.
# Dwell Time Estimation of Import Containers as an Ordinal Regression Problem
## 3 Materials and Methods
### Dwell Time Estimation as a Supervised Classification Problem  
To determine the supervised classification algorithms, we have used the Auto-WEKA tool [46] included in WEKA version 3.8.3. Auto-WEKA compares the performance of 30 supervised learning algorithms regarding a metric selected by the user using a cross-validation test with ten folds. Since our datasets present a class imbalance in a ratio of \(\frac{1}{350}\) and \(\frac{1}{150}\), respectively, we configured Auto-WEKA to select the classification algorithm with the highest F1 measure.  
Auto-WEKA outputs a different supervised classifier for each dataset. The algorithm with the highest F1 measure -0.91- estimating the dwell time for the instances in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Dataset1\_T\&V** & **Dataset2\_T\&V** \\ \hline OCC + IBk k = 1 & 3.331590404 & 7.08567135 \\ OCC + Kernel Logistic Regression & 2.431074447 & 6.396608677 \\ OCC + LibSVM + LinearKernel & 3.152352826 & 7.64170435 \\ OCC + LibSVM + PolyKernel & 5.221540441 & 13.03492621 \\ OCC + LibSVM + RBFKernel & 4.982034132 & 12.13935106 \\ OCC + LibSVM + SigmoidKernel & 6.131968673 & 12.35616552 \\ OCC + C4.5 & 3.703891075 & 9.843052078 \\ OLM & 3.085444781 & 8.04021403 \\ OCC + MLP \(-\) RELU & 2.643779925 & 8.273726134 \\ OCC + SimpleLogistic & 4.857876774 & 14.13145068 \\ CVR + LinearRegression & 3.251455833 & 8.364204478 \\ CVR + M5P & 3.745751895 & 8.440288676 \\ Reg + DecisionTable + RMSE + IBk + Round & 5.558925374 & 12.06379664 \\ Reg + LibSVM + LinearKernel + Round & 5.788572492 & 12.13272507 \\ Reg + LinearRegression + Round & 5.798134705 & 12.13779264 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(MAE\mu\) achieved by 15 ordinal regression algorithms with both datasets for training and validation. The Ordinal Class Classifier with a Kernel Logistic Regression achieved the lowest error (\(MAE\mu\)) on both datasets.  
Dataset1 T&V was Lazy-IBK with the parameters [\(-\)E, \(-\)K, 29, \(-\)X, \(-\)I]. However, Lazy-KStar with the parameters [\(-\)B, 4, \(-\)M, a] reached the highest F1 measure -0.90- estimating the dwell time for the instances in the Dataset2_T&V.  
Previously, we found that the algorithm OCC with Kernel Logistic Regression reached the lowest \(MAE\mu\) for both datasets by estimating the dwell time as an ordinal regression problem. To achieve this result, we performed several experiments manually configured. Contrarily, Auto-WEKA found a different supervised classifier for each dataset. However, since we aim to show that estimating the dwell time for import containers in a yard must be solved as an ordinal regression problem, we shall compare the three algorithms, OCC+Kernel Logistic Regression, against Lazy-IBK on Dataset1_Test and OCC+Kernel Logistic Regression against Lazy-KStar on Dataset2_Test.
# Deep-Learning Based Positron Range Correction of PET Images
###### Abstract  
Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.  
positron emission tomography; positron range; neural network; deep learning 2020
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 2 Related Works
### Multilayer Perceptron for Remote Sensing  
As a representative neural network structure, multilayer perceptron (MLP) is widely used in remote sensing tasks such as remote sensing image classification [32; 33; 34], object detection [35] and change detection [36; 37]. The MLP can have multiple hidden layers between the input and output layers, but the simplest MLP has only one hidden layer. Despite its simple structure, numerous computer vision experiments [38] have demonstrated that the MLP still has the same feature representation capabilities as traditional convolution and transformers, even under complex network and large dataset conditions. In addition, the MLP has excellent compatibility with CNNs [39]. For example, by combining the spectral features extracted by MLP with the spatial features represented by a CNN, [PERSON] et al. [40] used a rule-based decision fusion method to integrate the MLP with the CNN and achieved excellent classification accuracy of very fine spatial resolution remote sensing images. It is also inspired by meta-learning [41] and makes it possible to use MLP for the automatic determination of hyperparameters such as weights. Therefore, we adopt the MLP as the main structure of our AWNet to automatically acquire active and passive loss weights, which, in turn, improves its generalisation.
## 3 Bandwidth of Double-Dielectric Patch Antenna  
As discussed in Section 2, in order to fulfill the bandwidth requirements, we chose air as the second dielectric in the double-dielectric substrate of the patch antenna. We aimed at obtaining antenna bandwidth of at least 8 MHz operating at the center frequency of 754 MHz.  
The bandwidth of a patch antenna above a dielectric substrate depends on the dimensions of the patch and substrate permittivity. A number of approximate formulas for antenna bandwidth are available from the literature. Here, we based our bandwidth estimation on the approach developed in [10].  
Considering a resonant half-wavelength patch antenna with moderate patch width \(W_{p}\) satisfying \(0.35<W_{p}/\lambda_{0}<2\), where \(\lambda_{0}\) is the free space wavelength, the relative antenna bandwidth can be estimated [10] as  
\[BW\approx\frac{1}{Q_{md}}\approx\frac{4h}{W_{p}\sqrt{\varepsilon_{r,q}}\left( \frac{W_{p}}{\lambda_{0}}-\frac{2}{\pi^{2}}\right)}, \tag{1}\]  
where \(Q_{md}\) is the radiation quality factor of the antenna, \(\varepsilon_{r,q}\) is the equivalent relative substrate permittivity, and \(h\) is the substrate thickness. From this formula, increasing \(h\) and decreasing \(\varepsilon_{r,q}\) resulted in a larger bandwidth for an antenna with a given patch width.  
As the air has a lower dielectric constant (\(\varepsilon_{r}=1\)) compared to the average dielectric constant of the patch substrate (FR4), combining an air layer with a dielectric layer resulted in a reduction in the effective dielectric constant of the double-dielectric substrate (FR4 + air). Such a compound substrate is characterized by an equivalent relative permittivity \(\varepsilon_{r,q}\) that can be estimated by the following formula:  
\[\varepsilon_{r,q}=\frac{1+r}{1+\frac{r}{\varepsilon_{r}}}, \tag{2}\]  
where \(\varepsilon_{r}\) is the relative permittivity of the FR4 substrate, and \(r\) is a parameter determined by the thicknesses of the substrate and air layers \(h_{s}\) and \(h_{a}\). In plate-capacitor approximation, \(r\approx\frac{h_{s}}{h_{a}}\). A more accurate result could be obtained with the conformal mapping approach [10], in which  
\[r=\frac{d_{s}}{d_{a}} \tag{3}\]  
where, \(d_{s}=\frac{K(k_{s})}{K^{\prime}(k_{s})}\), \(d_{a}=\frac{K(k_{s})}{K^{\prime}(k_{s})}-d_{s}\), with \(k_{s}=1/\cosh\frac{\pi W_{p}}{4h_{s}}\), \(k_{a}=1/\cosh\frac{\pi W_{p}}{4(h_{s}+h_{a})}\), and \(K(x)\) is the complete elliptic integral of the first kind [11]. On the one side, when \(h_{s}\ll h_{a}\), \(r\to 0\) and the equivalent permittivity approaches the permittivity of air. On the other side, when \(h_{s}\gg h_{a}\), \(r\rightarrow+\infty\) and the equivalent permittivity approaches the permittivity of the substrate. In our case, as shown in Section 4, air height \(h_{a}\) reached 3.2 mm.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 5 Discussion  
More than 90% of COSMIC-2 RO profiles can detect the very lower troposphere. The percentages of COSMIC-2 RO profiles that cannot go below 200 m above the surface due to strong vertical gradients of refractivity, strong tangential horizontal gradients of refractivity, and clouds are about 7.3%, 1.4%, and 0.7%, respectively. There are still a few RO profiles whose lowest altitudes are relatively high but are not affected by the above factors. We examined the uncertainty (LSW) and confidence (CP) of the bending angle for these RO profiles in order to see if there are any obvious differences between the RO profiles whose lowest altitudes are low or high (figures omitted). Both the uncertainties and confidences of the RO profiles with low and high lowest altitudes increase with decreasing altitude. The variations and magnitudes of the uncertainties and confidences for RO  
Figure 12: Scatter plots of the RO lowest heights versus the along-the-track horizontal gradient of refractivity in the tangent direction at the perigee point (H. T. Grad. of \(N\)) for ROs with their vertical gradients of \(N-N(r)\) less than \(-65\) N-unit km\({}^{-1}\) in August 2020 over (**a**) the SEP and (**b**) SCP areas. The cloud contribution to the refractivity (i.e., \(N_{cloud}\) ) at the RO lowest heights is indicated in colors, and the number of the lowest RO data with the “H. T. Grad. of \(N^{\prime}\) being less than 0.15 N-unit km\({}^{-1}\) (black dashed line) at 0.2-km interval is shown by black curve.  
profiles with the high lowest altitudes do not differ greatly from those with the low lowest altitudes. Therefore, the reason that these RO profiles that have weak refractivity gradients and are unaffected by clouds cannot penetrate down is not known and requires further investigation. Fortunately, they only account for about 0.6% of COSMIC-2 RO profiles.
## 3 Results  
The results of the FCM-DSS testing for the four case studies are shown in Figure 6. The figure presents the system's confidence levels (on a scale of 0-1) for recommending specific medical specialties based on the input symptoms. Each line in the graph corresponds to a different specialty: pulmonologist (black), ENT (red), neurologist (blue), and speech-language pathologist (SLP; green). The height of each line indicates the confidence level for recommending that specialty, with a threshold of 0.6 required to make a recommendation. For example, in Case Study 1, the system strongly recommends a pulmonologist, as the confidence level for this specialty exceeds the threshold.  
It is notable that in all cases where a medical specialty is recommended, SLP consistently appears as the second-highest confidence level. This pattern highlights the overlap in conditions managed by SLPs and other medical specialists, reflecting the tool's ability to capture the interconnected nature of long COVID symptoms. However, the severity of symptoms in these case studies often necessitated primary referral to a medical specialist rather than an SLP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study** & \multicolumn{3}{c}{**Fuzzy Valued Input Concept**} \\ \hline \multirow{4}{*}{1} & C1. Difficulty Functioning—High & C6. Changes in Voice Quality—Medium \\  & C2. Difficulty Breathing—Medium & C9. Increased Effort to Speak—Medium \\  & C3. Shortness of Breath—High & C11. Difficulty with Activity Execution—High \\  & C4. Coug—High & C12. Post-Covid Stress—High \\ \hline \multirow{4}{*}{2} & C1. Difficulty Functioning—High & C6. Changes in Voice Quality—High \\  & C2. Difficulty Breathing—Medium & C7. Horaseness—High \\  & C3. Shortness of Breath—Medium & C8. Voice Fatigue—High \\  & C4. Coug—High & C9. Increased Effort to Speak—High \\ \hline \multirow{4}{*}{3} & C1. Difficulty Functioning—High & C16. Difficulty Maintaining Conversation Topic—Medium \\  & C10. Feeling of Confusion—High & C17. Difficulty Maintaining Attention—High \\  & C11. Difficulty with Activity Execution—High & C18. Difficulty Organizing Activities—High \\  & C12. Post-Covid Stress—Medium & C19. Time Management Difficulties—High \\  & C13. Mental Fatigue—High & C21. Memory Problems Worsening Post-Covid—High \\  & C14. Difficulty Recalling Information—High & C23. Word Finding Problems—High \\  & C15. Difficulty Retaining New Information—High & C24. Difficulty Remembering Words—High \\  & C16. Difficulty Remeaning New Information—High & C25. Difficulties Understanding Language—Low \\  & Topic—Medium & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case fuzzy input concept values for the FCM-DSS.