esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Programmable Logic Controllers and Operational Cycles  
Programmable Logic Controllers (PLCs) are specialized digital systems designed for use in industrial environments. PLCs must conform to the IEC 61131 standard [13]. Central to a PLC is the CPU, which executes the user-defined logic program, manages the system's operational cycle, and interfaces with peripheral devices.  
The operational cycle of a PLC is a repetitive sequence controlled by the CPU. This cycle begins with scanning and interpreting input data, processing the data according to the user-defined program, and then updating outputs to control the industrial process.  
The user program is stored in programmable memory, while temporary data are held during execution. This three-phase cycle, known as the scan cycle, comprises the following steps: (1) reading inputs, (2) processing the inputs, and (3) writing outputs.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 3 Results and Discussion
### Density and Porosity  
In order to evaluate the porosity and the density, and to study their influence on the mechanical properties of gypsum, measurements were carried out on both hydrated SG and AG samples. Porosity is one of the most important physical characteristics, governing the mechanical strength and density; it is influenced by the grain size, particle shape, and grain distribution. The values of rock density \(\rho_{\rm{R}}\), powder density \(\rho_{\rm{P}}\), porosity \(\rm{P}\), and water content \(\rm{W_{c}}\) of the studied gypsum are summarized in Table 4. It can be observed that the porosity of the SG sample was slightly higher than that of the AG sample, which also had higher water content; it is confirmed by SEM images (Figure 8), where it has a higher percentage of dark area (pores).  
As expected, due to the higher porosity, the SG samples had a lower density.
## 6 Challenges and Future Research Directions
### Self-Learning and Adaptation  
In recent times, the pattern of attack has changed, and more attack vectors are now being employed to launch DDoS attacks. As a result, new attack scenarios with different features are evolving daily. It is possible that the features identified to detect one category of attack vector may not be sufficient to successfully detect another category of attack. In addition, the frequency and pattern of DDoS attacks are expected to increase with the development of 5G networks, which have made it possible for more devices to be connected. Hence, there is a need to further develop a DDoS attack detection framework that can swiftly learn and adapt to changing attack patterns, while transiting from the current-day network to 5G and beyond, with reduced computational time and increased accuracy. Further investigation in this area is essential.
## 3 Experiment and Results
### Evaluation Indicators  
To ensure a thorough evaluation, we utilized the leave-one-out cross-validation approach for each individual patient. The evaluation criteria employed in this research encompassed metrics such as _AUC_, sensitivity, and specificity. _AUC_ represents the area under the curve and serves as an indicator of the model's classification capability. Sensitivity and specificity, on the other hand, assess the accuracy of preictal prediction during the interictal period. The value for this particular metric was determined by examining all seizure prediction scores within the leave-one-out cross-validation for each patient.  
\[Sensitivity=\frac{TP}{TP+FN}, \tag{6}\]  
\[Specificity=\frac{TN}{TN+FP}, \tag{7}\]  
\[AUC=\frac{\sum\left(p_{i},n_{j}\right)_{p_{i}>n_{j}}}{P\times N}, \tag{8}\]  
In Equations (6) and (7), true positive (_TP_) refers to correctly classified pre-ictal periods, false negative (_FN_) refers to misclassified interictal periods, true negative (_TN_) refers to correctly classified interictal periods, and false positive (_FP_) refers to misclassified pre-ictal periods. In (8), \(P\) represents the number of positive samples, \(N\) represents the number of negative samples, _p\({}_{i}\)_ represents the prediction score for positive samples, and _n\({}_{j}\)_ represents the prediction score for negative samples.
## 5 Conclusions  
To improve the linearity and stability of the fabric-based strain sensor, this paper proposes a three-step dip-coating method by using MWCNTs and rGOs. By means of coating the stretched fabric, the micro-folded structures consisting of two rGO layers are formed. Therefore, the sensing mechanism is dependent on the variation of the contact resistance between two rGO layers, which has a linear relation with the strain of the fabric. The SEM characterization and experiments validated that the fabric-based MWCNTs/rGO/rGO sensor possesses good linearity and stability. Additionally, the potential applications of the fabric-based strain sensor were demonstrated by the stable and reliable measurements of different physiological signals. In future, the Wheatstone bridge circuit instead of digital source meter will be implemented to measure the strain signals. This could not only further improve the sensor's accuracy but also enhance its portability. Moreover, in order to widen the measurement frequency of the sensor, we will investigate the sensor's performance by adopting a metasurface fabric as the substrate.  
Conceptualization, F.W. and R.X.; methodology, R.X.; software, L.S.; validation, X.Z.; formal analysis, M.C.; resources, Y.M.; data curation, L.S.; writing--original draft preparation, R.X.; writing--review and editing, X.Z.; visualization, J.C.; supervision, F.W.; project administration, F.W.; funding acquisition, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific research project of Jiangsu advanced textile engineering technology center grant number XJFZ/2018/08.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Study Evaluation
### Model Analysis  
To confirm the effectiveness of the _neural network_ models for the lung disease detection task, below, we present the receiver operating characteristic (i.e., roc) analysis plot.  
The roc analysis plot, shown in Figure 6, is generated by drawing the rate related to the rate of the true positive feature vector and the rate of the false positive feature vector by considering different thresholds.  
As shown in Figure 6, the roc curve related to the _neural network_ model exhibits the best prediction trend; in fact, the closer the curve comes to the 45-degree diagonal of the roc space in Figure 6, the less accurate the test is (as shown by the _kNN_ roc curve).  
This confirms the effectiveness of the _neural network_ model for lung disease detection from respiratory audio sessions. As a matter of fact, there are several advantages in the adoption of the neural network architecture. For instance, different from the _kNN_, the _SVM_, and the _logistic regression_ algorithms, they offer the possibility to perform incremental updates with stochastic gradient descent (differently, for instance, from decision trees, which consider inherently batch-learning algorithms). Moreover, they are able to model more arbitrary functions (for instance, nonlinear interactions) and, for this reason, they can often be more accurate. Relating to the disadvantages, neural networks certainly require a longer learning time (if compared, for instance, to the decision tree algorithm), but considering that learning is carried out only once, this does not represent a problem in the adoption of the proposed method in a real-world context.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **F-Measure** & _Specificity_ & _Sensitivity_ \\ \hline Asthma & 1 & 1 & 1 \\ Be & 10.92 & 1 & 0.86 \\ Bl & 0.92 & 0.86 & 1 \\ COPD & 0.96 & 0.97 & 0.95 \\ LRTI & 0.80 & 0.67 & 1 \\ Pneumonia & 0.92 & 0.86 & 1 \\ URTI & 0.90 & 0.93 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lung disease characterisation classification result from the single disease. We use the \(Be\) and \(Bl\) notations to indicate bronchiectasis and bronchiolitis pulmonary disease, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Actual Class** & & & & & & \\ \hline  & & **Asthma** & **Be** & **Bl** & **COPD** & **LRTI** & **Pneumonia** & **URTI** \\ \hline Predicted class & Asthma & 1 & 0 & 0 & 0 & 0 & 0 \\ B & 0 & 6 & 1 & 0 & 0 & 0 \\ BI & 0 & 0 & 6 & 0 & 0 & 0 & 0 \\ COPD & 0 & 1 & 0 & 60 & 1 & 1 & 1 \\ LRTI & 0 & 0 & 0 & 0 & 2 & 0 & 0 \\ Pneumonia & 0 & 0 & 0 & 0 & 0 & 6 & 0 \\ URTI & 0 & 0 & 0 & 2 & 0 & 0 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lung disease characterisation confusion matrix. We use the \(Be\) and \(Bl\) notations to indicate bronchiectasis and bronchiolitis pulmonary disease, respectively.
# The Emerging Role of Long Noncoding RNAs in Sorafenib Resistance Within Hepatocellular Carcinoma
### Sorafenib Resistance in HCC  
The efficacy of Sorafenib is often hampered by the development of both primary and secondary resistance, which limits its therapeutic success. Sorafenib resistance can be divided into intrinsic (also known as primary) resistance and extrinsic (also known as secondary) resistance. Primary resistance occurs when cancer cells are inherently non-responsive to sorafenibib at the outset of treatment. This can be attributed to the genetic heterogeneity of cancer/tumor cells. The reason behind the lack of effectiveness and sensitivity of sorafenibib in the early stages of treatment in HCC and RCC is that liver and kidney cancer cells have resistance factors even before sorafenib treatment. The most common type of renal cancer is RCC, and the genes described as altered are V.H.L., PBRM1, SETD2, KDM5C, PTEN, BAP1, mTOR, TP53, TCEB1 (E.L.O.C.), SMARCA4, and ARID1A, frequently reported in RCCtherapy [18; 22].  
Additionally, the tumor microenvironment plays a significant role in primary resistance. Cancer-associated fibroblasts (CAFs) and other stromal components can secrete growth factors and cytokines that activate alternative signaling pathways, such as the PI3K/ AKT pathway, which can bypass the inhibitory effects of sorafenib [23]. Furthermore,epigenetic modifications, such as DNA methylation and histone modifications, can lead to the silencing of tumor suppressor genes and the activation of oncogenes, contributing to intrinsic resistance. Secondary resistance is defined as when cancer/tumor cells become less sensitive to sorafenibity after a period of treatment, which ultimately can result in treatment failure [24; 25]. Sorafenib resistance limits its therapeutic effect, so gaining a better understanding is paramount.  
One common mechanism of secondary resistance is the acquisition of new genetic mutations. For example, secondary mutations in the BRAF gene can arise during sorafenib treatment, resulting in continued activation of the RAF/MEK/ERK pathway despite the presence of the drug [26]. Moreover, cancer cells can undergo phenotypic changes, such as epithelial-to-mesenchymal transition (EMT), associated with increased invasiveness and drug resistance. EMT is regulated by various transcription factors, which can be induced by prolonged exposure to sorafenib. Another mechanism involves upregulating drug efflux pumps, such as ATP-binding cassette (ABC) transporters, which actively pump sorafenibity out of cancer cells, reducing its intracellular concentration and effectiveness [27]. Additionally, cancer cells can activate compensatory signaling pathways to circumvent the inhibitory effects of sorafenib. For instance, increased JAK/STAT3 pathway activity has been observed in sorafenib-resistant HCC cells, promoting cell survival and proliferation. Primary and secondary resistances significantly hinder the therapeutic effectiveness of sorafenibity. Thus, it is essential to understand these resistances. The significant determinants involved in sorafenib resistance are presented in Table 1.
###### Abstract  
As the proportion of distributed generation (DG), including Photovoltaic (PV) generation, in the power grid system increases, there are dropouts of large-scale distributed power generation sources due to some transient conditions that negatively affect the power grid stability and power quality. Accordingly, the inverter for DG generation is in a transition period, requiring more complex control performance. Anti-islanding function requirements in particular are becoming more strict because sophisticated grid-connection requirements are demanded, such as voltage ride-through, frequency ride-through, rate of change of frequency, and other functions. Thus, highly advanced anti-islanding methods are required to detect the islanding condition quickly and accurately to stop the inverter. This paper presents the improved anti-islanding method based on reactive power variation (RPV) under grid-support environments for single-phase DG inverters. In order to verify the validity of the proposed method, PSIM simulation was conducted. The proposed method meets the requirements of IEEE Std. 1547-2018 and KS C 8564:2021 by preventing islanding within 0.007 s under the newly adopted voltage/frequency trip setting, while the conventional RPV method fails.  
anti-islanding; grid-connected inverter; distributed generation; grid support function; renewable energy 2022
# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 2 Methods
### Features (Variables)  
The outcome variable in this study was whether the participant had Long COVID-19. In the survey, this was measured as whether respondents who had previously reported having COVID-19 symptoms reported a return to their baseline level of health or were experiencing ongoing symptoms. As such, participants who had not previously reported COVID-19 symptoms were excluded, yielding the final dataset, a subset of the total cohort with a sample size of 601. As the Long COVID-19 variable was first included in the November 2020 survey wave, the previous wave being in September 2020, ongoing symptoms had to have been present for a minimum of two months. As aforementioned, participants without positive serology for COVID-19 were excluded from the final dataset, thereby eliminating the possibility of those who reported having symptoms of COVID-19 without having evidence of infection being captured as having Long COVID-19 and affecting the results. Therefore, the outcome variable in this study reflects participants in the UK aged 16 and over with past symptomatic COVID-19 infection experiencing ongoing symptoms for a minimum of two months.  
Predictor variables were considered for inclusion in the final model based on a review of the literature surrounding risk factors for developing Long COVID-19. Variables were excluded if their incidence was low in the sample dataset. In particular, some comorbidities were excluded from the total pool of variables for the model if their incidence was less than 50 in the full dataset. Seventy-five predictors were selected for inclusion in the total pool of variables, relating to respondent demographics, long-term health comorbidities, lifestyle choices and markers of socioeconomic status. Variable coding for categorical variables can be found in Supplementary Materials Tables S1 and S2. Missing values were mitigated by assigning missing responses and responses such as \"inapplicable\" a value of 0, and these were excluded from the final model. Two-way or higher interaction terms were not added to the model, as there was no a priori reason for including specific interactions, and including all two-way interactions would result in too many features given the study's sample size.
## 3 Numerical Results and Discussion
### Entropy Combinations
#### 3.2.1 Entropy Difference NNetEn as a Feature for Signal Separation  
We can consider the relationship between the F-ratio and the difference in entropies. In Figure 9, the F-ratio distributions for three variants of difference are shown: NNetEn(D1)-NNetEn(D1) (Figure 9a), NNetEn(D2)-NNetEn(D2) (Figure 9b), NNetEn(D2)-NNetEn(D1) (Figure 9c). For the same time series of signals, the entropy difference was computed with different settings.  
In Figure 9a, the maximum F-ratio (~127) is only 3 points higher when using only Dataset 1, whereas when using one feature. In Figure 9b, F-ratio reached a maximum of 50 using only Dataset 2, which is 20 points higher than when using one feature. As a result of combining both datasets (Figure 9c), the F-ratio reached 140, which is 13 points higher than all previous options combined. A feature difference is therefore even more powerful than an individual feature. It can be explained by a change in the relation between entropies. For example, for Pair A and Dataset 2 (Figure 7b) with \(N\)_set_ = 9 NNetEn\({}_{\text{nav}}\) (\(r\) = 1.1918) \(<\) NNetEn\({}_{\text{nav}}\) (\(r\) = 1.2243), and for  
\(\text{mboxemphNset}=12\)\(\text{NNetEn}_{\text{nav}}\)\((r\) = 1.1918) \(>\) NNetEn\({}_{\text{nav}}\) (\(r\) = 1.2243), therefore, the entropy difference at these settings changes more strongly from class to class. As a result, we see the maximum F-ratio for the point (9,12) in Figure 9b.
## 4 Discussion and Comparisons
The scattered far-field patterns \(|\mathrm{E}_{\mathrm{z}}^{\mathrm{sc}}(\varphi_{1})|\) and \(|\mathrm{H}_{\mathrm{z}}^{\mathrm{sc}}(\varphi_{1})|\) are depicted in Figures 9 and 10, respectively, for the dielectric cylinders' quadruplet shown in the insets. E-polarization and several values of \(\theta^{\prime}\), for \(\theta^{\prime}=45^{\circ}\), are considered in Figure 9, while H-polarization and several values of \(\varphi^{\prime}\), for \(\theta^{\prime}=90^{\circ}\), are assumed in Figure 10. On the one hand, as regards the patterns of the electric field intensity, Figure 9 suggests that the main lobe remains at \(\varphi=225^{\circ}\) regardless of the angle \(\theta^{\prime}\). On the other hand, as far as the patterns of \(|\mathrm{H}_{\mathrm{z}}^{\mathrm{sc}}(\varphi_{1})|\) are concerned (Figure 10), no trend is observed regarding the lobes; the formation of the latter depends strongly on the angle \(\varphi^{\prime}\).
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Power Management  
Voltage and current data showed that the aircraft used slightly less peak power than expected for the design. However, the maximum power demand caused the system voltage to drop by almost 25% compared to the no-load voltage at the end of the flight. This significant drop in voltage caused the current to increase further to maintain the same power demand, nearly exceeding the designed maximum allowable of 400 A. This large voltage sag is due to minimized battery size to limit vehicle weight, uniquely constrained for electric VTOL aircraft configurations. A 30% voltage drop from the no-load voltage is a conservative preliminary estimate for battery and wire sizing in future similar hybrid gas-electric powered aircraft with a VTOL mission profile.
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 5 Evaluation
### Number of Hops under Different Scales of Incoming Traffic  
In the investigation of the number of hops under varying scales of incoming traffic, Figure 9 serves as a visual representation of the measurements. The results for the topology with a sparsity of 15% highlight a notable contrast between the perfect matching and the balanced group method. Specifically, perfect matching demonstrates a smaller number of hops in comparison to the balanced group method. Expanding our analysis to different network scenarios, Figures 10 and 11 present the results for networks with sparsity levels of 35% and 55%, respectively. In the case of the sparse network with 35% sparsity, there is a substantial difference in the number of hops between the perfect matching and balanced methods. This discrepancy underscores the impact of network topology on the efficiency of these routing strategies. As the number of flows increases, the IDSs integrated into the service chain experience a heightened load. In response to this increased load, there arises a necessity for load balancing, resulting in an augmented number of hops. This phenomenon is particularly evident in the perfect matching method, where the load balancing is not as inherently managed as in the balanced group method. The escalation in the number of hops contributes to an increased delay in both the detection process and overall data transmission.  
This analysis sheds light on the intricate relationship between the traffic scale, IDS deployment strategies, and network efficiency in terms of the number of hops. The observed  
Figure 8: Detection time for topology 3 (Sparsity = 55%). (**a**) Delay with one IDS. (**b**) Delay with fixed two IDS. (**c**) Delay with long IDS chain.  
Figure 7: Detection time for topology 2 (Sparsity = 35%). (**a**) Delay with one IDS. (**b**) Delay with fixed two IDS. (**c**) Delay with fixed two IDS.  
tends emphasize the importance of considering both the network topology and load-balancing mechanisms for optimizing the performance of service chains under diverse traffic scenarios.
## 4 Deployment of Cloud-Native Core Network in ComEdge  
In addressing the deployment of a cloud-native edge core network on the ComEdge platform, we formulated the following reasonable research hypotheses. Due to the ability to fully leverage the dynamic temporal relationships between user service requests, core network deployment and operational costs, DQN-CNFDA can utilize fewer resources while effectively reducing system overhead. Other approaches that consider only a single factor exhibit relatively poorer performance in terms of platform deployment, operational costs, and resource utilization.  
To validate our research hypotheses, in this section, we first introduce the key processes and deployment examples of the core network. Afterward, we formulate the deployment optimization problem for the cloud-native edge core network, aiming to minimize the deployment and control traffic overhead of the core network.
# Recent Advances in the Development of Mammalian Geranylgeranyl Diphosphate Synthase Inhibitors
## 5 In Vivo Studies Using GGDPS Inhibitors  
While limited in scope, studies performed in mice suggest that GGDPS inhibitors are active in vivo, and thus, may prove beneficial in the treatment of cancer and other diseases. In a mouse model of prostate cancer, a DGBP analog was found to disrupt Rap1a geranylgeranylation and decrease adrenal gland tumor burden by \(\sim\)50% [64]. A follow-up study assessed the ability of the inhibitor to slow the development of prostate cancer metastasis in a preventative murine model [65]. Treatment of mice with this inhibitor resulted in a reduction of whole body tumor burden and prolonged survival compared to vehicle-treated animals. A pyridinium bisphosphonate (**12**) proposed to function as a dual FDPS-GGDPS inhibitor delayed tumor growth in SK-ES-1 sarcoma cells in a mouse xenograft model [51]. Collectively, these studies suggest that GGDPS inhibitors may have anti-tumor activity in vivo.  
In addition to their application as anti-cancer drugs, GGDPS inhibitors may be of use for the treatment of fibrotic lung disease [66]. The small GTP-binding protein, Rac1, mediates hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) production in alveolar macrophages and H\({}_{2}\)O\({}_{2}\) is directly linked to the development of fibrosis. It was hypothesized that because geranylgeranylation regulates Rac1 activity, GGDPS inhibitors may render Rac1 inactive resulting in a decrease in H\({}_{2}\)O\({}_{2}\) production. In fact, treatment with DGBP inhibited Rac1 import into the mitochondria, leading to impaired oxidative stress and an attenuated fibrotic response [66].