esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison of Our Design Approach with the Existing Literature: Potential Benefits
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Description** & **Smart Contract Data Structure** & **Design Approach: Discussion** \\ \hline \multirow{3}{*}{[PERSON] et al. [14]} & The proposed architecture is based on three layers & A smart contract is used for both client and resource registrations. & The number of layers is optimized. The solution lacks a cloud data storage to recover data from disaster situations. \\ \hline \multirow{3}{*}{[PERSON] et al. [15]} & The suggested architecture is based on a smart contract & The proposed BC data structure is used to store manufacturer and supplier data. & The proposed architecture is composed of three layers such as sensor layer, smart contract layer and distributed ledger layer. \\ \hline \multirow{3}{*}{[PERSON] et al. [37]} & Authors propose an access & Authors propose three types & \\  & control contract to control the & such as object attribute & \\  & request sent by subjects to & management contract, subject & ML is not suggested in the \\  & IoT objects. & attribute management contract & proposed architecture. \\ \hline \multirow{3}{*}{Our architecture} & The architecture is based on five layers including physical & A smart contract data structure and algorithm are proposed for sensor access control system, & The BC components are dispatched into two layers such as BC tools and transport layer and advanced service layer. On the one hand, BC tools and transport layer provides data integrity. On the other hand, advanced service layer offers data traceability. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Security** & **GridMonitoring [12]** & **Zhao [14]** & **BSeIn [27]** & **ABAC [37]** & **Our Proposal** \\ \hline
**Integrity** & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline
**Availability** & ✓ & \(\times\) & \(\times\) & ✓ & ✓ \\ \hline
**Immutability** & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline
**Confidentiality** & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline
**Privacy** & \(\times\) & \(\times\) & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between our system and similar one in terms of security metrics.
## 3 Description of the New Control Approach
### Torque Control Approach  
The objective of the classic control approach in Region II is power maximisation, which is normally implemented by using maximum power point tracking (MPPT) algorithms (see, e.g., [23; 24]). The most common is the optimal torque control (OTC), whose control law is given by  
\[T_{g}(t)=K_{opt}\omega_{g}^{2}(t), \tag{1}\]  
where \(T_{g}\) is the generator torque, \(\omega_{g}\) is the generator rotational speed, and \(K_{opt}\) is given by  
\[K_{opt}=\frac{1}{2}\pi\eta\rho_{a}R^{5}C_{\textit{max}}/(n_{x}\lambda^{*})^{3}, \tag{2}\]and parameters \(\eta\), \(\rho_{a}\), \(R\), \(C_{pmax}\), \(n_{x}\), and \(\lambda^{\star}\) are the efficiency, air density, rotor radius, maximum power coefficient, gearbox ratio, and optimal tip-speed ratio, respectively. Variable \(t\) symbolises the time. The control scheme is given in Figure 2.  
However, the classic OTC approach is not convenient for power control. A more suitable approach for down-regulation control is the torque control with power feedback shown in Figure 3 (see [23]). The lookup table implements the inverse generator characteristic curve.  
Nevertheless, this control scheme also lacks a reference input and therefore has to be modified in order to overcome the problem. The simplest way is shown in Figure 4. The purpose of the switch is to alternate between the original and new configurations.  
The control law is then formulated as  
\[T_{g}(t)=T_{g,rated}-[K_{p}(f_{g}^{-1}(P_{ref})-\omega_{g}(t))+K_{i}\int_{0}^{ t}(f_{g}^{-1}(P_{ref})-\omega_{g}(t))]dt] \tag{3}\]  
for a PI controller (proportional-integral) with gains \(K_{p}\) and \(K_{i}\). The function \(f_{g}^{-1}\) represents the inverse generator characteristic function, which is numerically implemented.
###### Abstract  
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung-Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women's footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.  
model identification; state space models; ARIMA models; forecasting; retailing Article
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 4 The Proposed Data Collection Scheme
### REISCH's Scheme
#### 4.3.1 Entities Preparation  
To start collection and storage processes, the HWSN network should be prepared with the following points:  
* Each sensor (\(SN_{i}\)) and \(LS\) server provides \(SN_{i}\) pseudonym (\(SN_{Pseud}\)), \(SN_{i}\) pseudonym signature (\(SigLS_{i}\)(\(SN_{Pseud}\))) and \(SN_{i}\) location (\(SN_{SL}\)).
* All entities (\(SN_{i},\ CH_{i},\ LS\ and\ CS\)) generate \(K_{pu_{i}}\) and \(K_{pr_{i}}\) to apply asymmetric cryptographic.
* Each entity broadcasts \(K_{pu_{i}}\) to network members.
* Each \(SN_{i}\) uses ECDSA signatures (\(SigSN\) and \(SigCH\)) to achieve collected data integrity.
* Each server (\(LS\) and \(CS\)) uses ECDSA signatures (\(SigLS\) and \(SigCS\)) to achieve storage data integrity.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Online Detection
#### 5.3.1 Comparison with Other Methods  
First, we replayed the DDoS dataset described in Section 4.1, utilizing Tcpdump to capture the network traffic within the 90 s time window, as well as extracting the flow feature information through CICFlowmeter at the ingress router. Then, we utilized the similarity detection model based on prior knowledge in Section 3.2. It can distinguish between benign traffic and abnormal traffic. The abnormal traffic feeds into the trained detection model and the model outputs detection prediction labels and real labels. Eventually, we saved the model response time and the malicious traffic detection capability, and finally realized the online detection of DDoS attacks and a reduction in malicious traffic in the network.  
In this section, the MFPK detection model is used to distinguish benign traffic from abnormal traffic; then the CNLS, LSAT, and CNAT models are compared, and the optimal detection model is selected. Figure 10 shows the malicious traffic detection capability and response time.  
In the CNLS model, the convolutional neural network learns the features' spatial information, but the features in the self-generated dataset have no temporal relationship,  
Figure 10: The different model performance.  
so the LSTM learns less temporal information from the features, which means that the model's detection capability is poor. As can be seen from Figure 10, CNLS has the worst malicious traffic detection capability. The MTDC is only 82, and the response time is 552 s. The LSTM is suitable for processing time series features, but the packet payload and packet header features are spatial features, so LSTM cannot learn the comprehensive features information. However, the attention mechanism can analyze the relationship between features and labels, and assign appropriate weights to features. The model can focus on learning features according to the assigned weights, which can bring useful information to the model and eliminate the weakness that LSTM cannot fully learn features. Therefore, the LSAT model performance is second-best. The MTDC is 84, and the response time is 564 s.  
In the CNAT model, CNN is suitable for processing sequence features. For malicious traffic detection, packet features are sequence features. The CNN learns the spatial information of packet payload and header features, and then uses attention to strengthen the model learning capability, which can improve the malicious detection capability. The model malicious detection capability is 86 and the response time is 66 s.  
After the above analysis, the CNAT model proposed can use the convolutional layer to mine the packet payload and header features; furthermore, the attention mechanism can fully exploit these features' information. This effectively distinguishes between different DDoS attack types. Therefore, the CNAT model proposed has the best malicious traffic detection capability and response time.
## 3 Results and Discussions
### Acoustic Emission Results: Cumulative Counts and Cumulative Energy
Both these specimens experienced the initial rupture at a very early stage, 0.1 s for JLS 2 and 0.35 s for JLS 3. The cumulative counts and energy have also increased steeply at these time periods. Beyond that point, the cumulative counts and the cumulative energy increase almost linearly with one another. This represents the delamination in the adhesive bonded region. The adhesive peeling is occurring at this stage, which is the reason for the simultaneous increase in the counts and energy. While reaching the final stage of failure, JLS 2 and JLS 3 have probably experienced fiber breakage which is indicated by the sudden increase in counts and energy near the final rupture region. While looking at Figure 6, all the three specimens have almost similar patterns in terms of load vs. time, cumulative counts and energy. However, the simultaneous increase in the counts and energy of the SLS specimens must not be compared or to be confused with the JLS specimens. Although in both the cases the cumulative counts and cumulative energy increased linearly, the slope of the increment is entirely different. The slope of the increment in SLS specimens is higher than the JLS specimens. Therefore, they do not represent the same damage mode although it looks similar. In the SLS specimens, the cumulative counts and energy are very low during the initial stages of loading until the specimens have suffered their initial rupture. In the SLS specimens, most of the load is distributed in shearing and these low-energy and low-count AE signals indicate the shearing occurring in the adhesive region. AE signals connecting to fiber breakages and transverse cracks growth release acoustic data with higher energy and lower counts, owing to their lower order symmetric nature. Since the energy and counts recorded are low during these stages, these signals are higher order asymmetric in nature that could be connected only to shearing phenomena [20]. Beyond the region of the initial rupture, in all three SLS specimens, the cumulative counts and energy start to increase simultaneously until they reach the final failure. This indicates that the cumulative counts and energy were very low until the crack opens in the adhesive. Generally, the crack openFs at the adhesive and adherend interface, which is governed by the geometry of the overlapping area. Once the crack had opened, due to the brittle nature of the adhesive, it progressed faster through the thickness of the adhesive. This interlaminar crack growth always generates higher counts and higher energy. This pattern continued until the specimens failed at the final rupture.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Therapeutic Innovation in Oncology  
As precision medicine evolves, new strategies are emerging to improve cancer therapy outcomes. Two promising and complementary approaches are the repurposing of existing drugs for new oncology uses and the development of entirely novel therapeutics with the help of advanced AI techniques. These innovations aim to address current gaps in treatment, reduce the time and cost of drug development, and ultimately improve patient survival and quality of life.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drug** & **Original Indication** & **Repurposed Oncology Use** & **Mechanism of Action** & **Reference** \\ \hline Thalidomide & Sedative, leprosy & Multiple myeloma & Immunomodulatory, anti-angiogenic & [94] \\ Metformin & Type 2 diabetes & Breast, ovarian cancer & AMPK activation, mTOR & [95] \\ Lovastatin & Hypercholesterolemia & Breast cancer stem cells & Inhibits SOX2, reduces mammosphere formation & [96] \\ Thioridazine & Antipsychotic & ER+ breast cancer & ER\(\alpha\) degradation, antiproliferative & [97] \\ Fingolimod & Multiple sclerosis & Under investigation in solid tumors & S1P receptor modulator & [98] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Examples of non-oncological drugs that have been successfully repurposed or are under investigation for cancer treatment. The table summarizes their original indications, new oncological applications, proposed mechanisms of action, and key supporting references.
## 3 Results and Discussion
### Volatile Compounds Analysis Using GC-qMS
#### 3.4.2 Alcohols  
Amino acids could be metabolized under the enzymatic activity of yeast strains during the wine fermentation process to yield higher alcohols (fused alcohols) [3]. Higher alcohols have been confirmed to affect the overall aroma of wine. It has been reported that thepresence of higher alcohols in wine with a low concentration (lower than 300 mg/L) could favor the overall aroma of wine through enhancing the aromatic complexity, whereas higher alcohols with a high concentration (higher than 400 mg/L) could result in an off-flavor of the wine aroma [6]. In this study, three wine samples showed a total higher alcohol concentration above 400 mg/L, which might result from the massive consumption of amino acids by these yeast strains during fermentation [21]. Isobutanol and isopentanol were found to be the dominant higher alcohols in these wine samples, and their concentration represented approximately 90% of the total fused alcohol content (Table 3). Isobutanol is normally produced through deamination and decarboxylation from leucine, whereas the transamination of valine could result in the formation of isoleucine during the wine fermentation process. Isobutanol and isobutanol both possess solvent and alcohol scents [21; 34; 40]. These wine samples showed a significant difference in the isobutanol level. The highest and lowest concentrations of isobutanol were found in the VI and CY wine, respectively. In addition, 2-heptanol was found to have a similar concentration in these wine samples. Isobutanol exhibited the lowest content in the BV-fermented passion fruit wine sample (Table 3). This fused alcohol was also detected in grape wines, with little flavor contribution due to its low concentration [21; 38]. However, its moldy, mushroom and fruity aroma could significantly contribute to the passion fruit wine aroma since its concentration was higher than its threshold in these wine samples.henyl ethyl alcohol had the highest level in the CY wine, followed by the VI and BV wine samples. The ES-fermented wine sample exhibited the lowest phenyl ethyl alcohol concentration. Phenyl ethyl alcohol possesses sweet and rose scents and its floral notes could be fully incorporated into the overall aroma of the passion fruit wines due to its high OAV. Benzyl alcohol is described as a roasted and sweet aroma [21; 35]. The CY and BV passion fruit wine exhibited the highest and lowest level of benzyl alcohol. Its aromatic contribution in the passion fruit wine was limited since its concentration was below the threshold in these wine samples.
# Quantification of the Effective Detectable Period for Concrete Voids of CLP by Lock-In Thermography
## 3 FEM Modeling and LIT Simulation
### Condition of LIT Simulation  
In order to implement various LIT inspection conditions for the concrete voids on the back of the CLP, as shown in Table 2, four cases (Cases 1 to 4) were simulated at two levels for heat flux and frequency, respectively. The real-time temperature change and distribution of the CLP surface for each case can be predicted through the thermal distribution analysis of FEM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Unit** & **CLP** & **Concrete** & **Voids** \\ \hline \multirow{3}{*}{Volume} & \multirow{3}{*}{mm\({}^{3}\)} & 400 \(\times\) 400 & 400 \(\times\) 400 & \#1: 75 \(\times\) 75 \(\times\) 15 \\  & & \(\times\)6 & \(\times\)75 & \#2: 105 \(\times\) 105 \(\times\) 15 \\  & & & \(\times\)75 & \#3: 75 \(\times\) 75 \(\times\) 30 \\  & & & & \#4: 105 \(\times\) 105 \(\times\) 30 \\ \hline Mass & kg & 7 & 26 & - \\ \hline Thermal & \multirow{2}{*}{W/m K} & \multirow{2}{*}{60.4} & \multirow{2}{*}{0.72} & \multirow{2}{*}{0.026} \\ conductivity & & & & \\ \hline Specific heat & J/kg\(\cdot\)K & 496 & 780 & 1007 \\ \hline Density & kg/m\({}^{3}\) & 7820 & 2300 & 1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties and size of the CLP mockup.  
Figure 3: FEM model and plate mesh image: (a) FEM model with implied voids and (b) mesh image of the CLP front surface.  
In the LIT simulation through the FEM model, as shown in Figure 4, the LIT device consisting of an IR camera, a function generator, an amplifier, a PC for control and analysis, and halogen lamps was considered. Additionally, in order to induce high emissivity, the black paint application condition applied to the CLP surface was also considered. These assumptions are similar to the actual LIT inspection experiments on CLP mockup specimens. The LIT simulation of each case was performed by the FEM model, and the environmental conditions equally applied to all simulations are as follows. The heat transfer coefficient by natural convection of gas-state fluid is suggested as 5-25 W/m\({}^{2}\) K [25]. This study was conducted in a stable laboratory environment, and the minimum value of 5 W/m\({}^{2}\) K was set:  
1. Initial temperature: 22 \({}^{\circ}\)C;
2. Emissivity (\(\varepsilon\)) 0.95 of the plate surface;
3. The convection coefficient is 5.0 W/m\({}^{2}\) K;
4. Heat load of 3 cycles of a positive (+) half-sine wave as shown in Figure 5;
5. Frame rate is considered 50 times per cycle.  
It was set so that 150 frames of thermography could be acquired for each case.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL  
In this section, we summarize each of the schemes reviewed. Although it is feasible to categorize them by any of the classification criteria described in the previous section, we divide the schemes in two categories based on whether they forward packets via a single parent or multiple parents.
## 4 Discussion
However, line features have instability and complexity in matching, such as incomplete line segments and repeated parallel lines, leading to less robust performance in certain scenarios. Additionally, the results indicate that the proposed method, which incorporates principal direction information, outperforms the method integrating line features. We attribute our improvement to the following critical reasons: (1) the principal direction can characterize the global geometric features of feature points and focuses on parts with significant data changes, effectively adding additional structural information constraints. Moreover, diverse constraints can help avoid local optima; (2) the Pareto optimal solution ensures optimal overall performance, enhancing the quality of the results. Next, in the computational efficiency tests, compared to ORB SLAM3, the proposed method achieves an average improvement of 24.23%, which is dependent on the input image size. As detailed in Reference [18], the ORB feature-extraction process is closely related to image size. Our method reduces the number of feature extraction levels, decreasing the computational load associated with feature extraction. Since the first level (level 0) has the largest image size and accounts for the majority of the computation time, reducing the number of levels by half does not correspond to a proportional reduction in the computational time. The integration of line features results in significantly higher computational costs. It necessitates the simultaneous extraction and matching of both point and line features, increasing the complexity of the entire process. The preprocessing of line features, such as line segment merging and removal, contributes non-negligibly to the computational burden. The increase in the computational cost raises the probability of unreliable estimations, which limits the overall performance improvement of the system. Finally, extensive multi-scenario testing on various datasets demonstrates that our method improves the computational efficiency without compromising the localization accuracy. Although reducing the number of pyramid levels might introduce feature-extraction and matching errors, which could lead to pose estimation deviations, including principal direction information compensates for these potential inaccuracies. The experimental results align with our expectations.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 3 Research Methodology  
The primary objective of this study is to develop a profiling model that analyzes power consumption data to extract users' behavioral insights in the smart grid. The key challenges include the limited scope of the dataset used in this work, as it contains data from only a single household. Additionally, the use of DTW for pattern analysis introduces computational constraints. Another challenge is balancing data utility with privacy preservation in user profiling.