esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 4 Problem Statement and Motivation
### Forwarding Functions Analysis
Value of \(E_{r}\) is between 0 and 70 J and this results \(ECF\) values between [\(0,70\)] which is very different from the range in \(SQI\) based forwarding function. \(ECF\) is also modified to include transmission range and depth threshold. If node is in deepest region of networks with depth greater than \(D_{2}\), \(DDF\) based forwarding function is employed using following equation. \[DDF=\frac{SQI\times l_{i}}{D_{i}} \tag{15d}\]  
Range of \(DDF\) is also given in Table 2. For a certain value of depth threshold \(d_{th}\) maximum and minimum values of \(DDF\) are roughly one tenth of the corresponding \(SQI\) function. Holding time is the time for a certain node during which it avoids any transmission and waits for the more appropriate forwarder to transmit the packet. Its value for each node is related to forwarding function of the underlying scheme. Difference of values of holding times for two successive nodes should be sufficiently large enough to avoid any collision. On the other hand very large value of holding time causes delay during transmission process. This delay may be negligible on a single node but in multi hop communication accumulated sum of all delays eventually causes larger considerable delay. So an optimum value of holding time is required to ensure redundant packet suppression and minimum delay. Holding time calculated for AMCTD and iAMCTD is given in following equation. \[H_{T}=(1-F_{F})\times H_{tmax} \tag{16}\]  
Holding time calculation depends on two factors \(H_{tmax}\) and forwarding function \(F_{F}\). There is no appreciable explanation of \(H_{tmax}\) which is termed as system parameter in previous literature. Whereas values for different forwarding functions can have range very much different from each other and these values are not even comparable with each other. Holding time calculated with these forwarding functions has very abrupt fluctuations for different depth nodes which causes increased delay in some regions and redundant transmission and collision in other depth regions. So there is need to have a relation providing comparable values of holding times. At least one must be able to predict the range of values to be assigned as holding time.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Summary of Calibration Methods  
The error estimation and correction of any single channel of the TIADC system can be realized in Sections 2.1-2.4. Through the strict distribution and management of the phase and clock of each channel of the TIADC system, after the error correction of each channel is completed, the output of each channel is merged to realize the output correction of the TIADC system.
###### Abstract
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Extraction Techniques** & **Advantages** & **Disadvantages** \\ \hline \hline \multicolumn{4}{c}{Traditional techniques} \\ \hline \multirow{4}{*}{Maceration (solvent extraction)} & - & Simple, low-cost equipment. & - & Time-consuming (hours to days). \\  & - & Effective for a wide range of compounds & - & Large solvent consumption. \\  & - & (polar and nonpolar). & - & Poor selectivity, may extract unwanted \\  & - & Adaptable to many plant materials. & - & Loss of volatile or thermolabile compounds. \\ \hline \multirow{4}{*}{Soxhlet extraction} & - & Efficient for continuous extraction with small & - & Long extraction times (up to 24 h). \\  & - & solvent volumes. & - & Not suitable for thermally sensitive \\  & - & Ideal for nonpolar substances like lipids. & - & compounds. \\  & - & High extraction yields. & - & Energy-intensive due to continuous heating. \\ \hline \multirow{4}{*}{Ultrasound-assisted extraction (UAE)} & - & Reduces extraction time and solvent use. & - & Ultrasound can cause degradation of \\  & - & Enhances solvent penetration and extraction & & thermolabile compounds. \\  & - & efficiency. & - & Optimization of frequency and duration is \\  & - & Low energy consumption. & & essential to avoid undesired effects. \\ \hline \multirow{4}{*}{Hydro-distillation} & - & Widely used for essential oils extraction. & - & High temperatures may degrade volatile \\  & - & Simple process using water as solvent. & - & Long processing times. \\  & & & - & Yields can be low for nonvolatile compounds. \\ \hline \multicolumn{4}{c}{Innovative techniques} \\ \hline \multirow{4}{*}{Microwave-assisted extraction (MAE)} & - & Significant reduction in extraction time & - & High temperatures can degrade thermolabile \\  & (minutes). & compounds. & compounds. \\  & - & Less solvent required. & - & Requires specialized equipment. \\  & - & Uniform heating enhances efficiency. & - & Best suited for compounds stable at \\  & - & Green and eco-friendly. & & moderate to high temperatures. \\ \hline \multirow{4}{*}{Pressurized liquid extraction (PLE)} & - & Faster extraction with lower solvent use. & - & High temperatures can degrade \\  & - & High pressure accelerates solvent & - & heat-sensitive compounds. \\  & - & penetration, reducing time. & - & Equipment costs are higher. \\  & - & Can use GRAS solvents like ethanol. & - & Not suitable for small-scale labs due to \\  & - & Green solvent, no toxic residues. & - & High equipment costs. \\  & - & High selectivity, adjustable for various & - & Inefficient for polar compounds unless a \\  & & compounds. & - & cosolvent (e.g., ethanol) is used. \\  & - & Ideal for thermolabile compounds due to & - & Requires optimization of pressure and \\  & & mild conditions. & - & temperature for each matrix. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main advantages and disadvantages of traditional and innovative methods for the extraction of valuable compounds from agri-food byproducts. For each method, exemplificative applications are described in the manuscript.
## 3 Results and Discussion
### Peptides Hydrolysed by Alcalase and Pepsin  
Enzymatic hydrolysis is a technique for specifically fragmenting a raw material in order to extract peptides of interest in the presence of proteases. During enzymatic hydrolysis, proteases will break the peptide bonds between two adjacent amino acids in the primary sequence of a protein, thus generating at least two peptides. The enzymatic hydrolysis of a protein extract makes it possible to obtain peptide fractions potentially endowed with biological activities. Alcalase and pepsin were used separately to release peptides from _Rhodomonas_ sp. protein extract. The use of specific or even nonspecific proteases is the most common way to produce bioactive peptides, as it takes less time to reach the degree of hydrolysis, and it is possible to control the hydrolysis process in order to achieve peptides with specific molecular weights and a specific amino acid composition [55]. Alcalase is considered the most effective enzyme to produce a high percentage of protein recovery [55]. In addition, it has been reported that pepsin hydrolysate exerts stronger biological activities, which may be due to the random cleavages of hydrophobic amino acids by pepsin [56].  
The peptides obtained after enzymatic hydrolysis revealed a DH of 34.58% and 43.24% using alcalase and pepsin, respectively. These results indicated that _Rhodomonas_ sp. protein extract was suitable as substrate for both used proteases.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 3 Proposed Method
### Detection Based on Logits Dispersion by Other Three Features
#### 3.4.2 Detection Based on the Variance of Filtered Values  
The optimization objective of adversarial examples is to introduce an adversarial perturbation to yield the target expression. However, it does not suppress its original expression. The original expression may shift and spread in the adversarial perturbation. That will result in adversarial examples with slight logit variance. Focusing on this issue, this paper designs a statistical detection method based on the variance of filtered values so that under the same calculation method, the logit variance of adversarial examples can be smaller, and the logit variance of normal speech can be larger. The filter mechanism can distinguish adversarial examples from normal speech more clearly.  
In the sparse logit ASR model, values greater than the hyperparameter \(p\) in the speech logits to be detected must be screened, and the variance of the non-negative values in matrix \(A_{(n-t)\times(m-1)}\) is derived as the logit evaluation index:  
\[\sigma^{+2}=\frac{1}{(n-t)\times(m-1)-r}\sum_{i=1}^{(n-t)}\sum_{j=1}^{(m-1)} \left(a_{ij}-\overline{x}^{+}\right)^{2} \tag{7}\]  
where \(a_{ij}=\left\{\begin{array}{ll}x_{ij},&x\geq p\\ \overline{x}^{+},&x<p\end{array}\right.\), \(\overline{x}^{+}\) denotes the average of all values greater than \(p\) in this non-sparse matrix \(A_{(n-t)\times(m-1)}\)  
\[\overline{x}^{+}=\frac{1}{(n-t)\times(m-1)-r}\sum_{i=1}^{(n-t)}\sum_{j=1}^{(m- 1)}x_{ij}^{+} \tag{8}\]  
where \(x_{ij}^{+}=\left\{\begin{array}{ll}x_{ij},&x\geq p\\ 0,&x<p\end{array}\right.\), \(r\) denotes the number of all values greater than \(p\) in matrix \(A_{(n-t)\times(m-1)}\).  
In the dense logit ASR model, the variance of non-negative values in matrix \(A_{(n-t)\times m}\) is calculated as the logit evaluation index, and the calculation method is the same as the above formula. Finally, \(\sigma^{+2}\) is compared with the statistical threshold \(k\), and those less than the threshold are judged as adversarial examples.
## 3 Tests and Methodology
### Permeability Quality Evaluation Test  
Under the dynamic scouring of surface runoff, the clogging materials fill the internal pores of the pavement, which will cause a decrease of the porosity and permeability coefficient of the pavement surface. Thus, the change of mass, the change of porosity, and the change of permeability coefficient were defined as three indexes to evaluate the clogging behavior of OGFC containing OSW filler during the spring and summer seasons. After the physical clogging procedure, the clogged Marshall specimens were collected for surface cleaning and drying, and then the mass, porosity and permeability coefficient of the Marshall specimens were measured. The porosity of the Marshall specimens was measured according to Chinese standards T 0706-2011. The constant head permeability test was conducted to measure the permeability coefficient of the permeable asphalt mixture in accordance with Chinese standards JTG D50-2006, and the permeability coefficient (K) was obtained by using Equation (1).  
\[K_{20^{\rm{FC}}}=\frac{QL}{At\Delta t} \tag{10}\]  
where \(Q\) refers to the quality of the water permeating through the specimen during t time (cm\({}^{3}\)); \(L\): infiltration length (cm); \(A\): cross-sectional area of the specimen (cm\({}^{2}\)); \(t\): infiltration time (s); \(\Delta t\): water head difference (cm).
## 3 Material and Methods
### DL-Based Text Classification  
The detection of propaganda in COVID-19-related public discussions presents unique challenges, particularly in ML-MC classification tasks. Traditional text classification methods, such as bag-of-words (BoW) approaches, lack the ability to capture contextual relationships between words, limiting their effectiveness. Our study addresses these limitations by employing an advanced transformer-based model tailored for propaganda detection in COVID-19-related discourse.  
This study leverages the strengths of DL models to automatically extract context-sensitive features from raw text. These features allow for the identification of nuanced relationships and patterns within the data, which are crucial for classification. Specifically, we use a transformer model enhanced with TF-IDF and \(n\)-gram embeddings to improve feature representation. This approach enables our model to distinguish subtle propaganda techniques while maintaining scalability for large datasets. Key innovations of our DL-based classification approach include the following:  
Figure 4: The propaganda data sample six classes and themes.  
Active max poolingThis mechanism highlights the most relevant textual features, ensuring that critical information influencing classification is prioritized. This improves the model's ability to detect fine-grained propaganda elements in text.  
Two-fold cross-entropy loss functionDesigned for multi-label classification, this loss function enhances the model's performance by addressing overlapping labels and improving predictive accuracy across multiple classes.  
Hidden bottleneck layersBy introducing these layers, our approach refines text representations, reduces model size, and optimizes computational efficiency without sacrificing performance.  
In contrast to prior work, which often focuses on tasks like fake news detection or sentiment analysis [53], our approach specifically targets propaganda detection in the context of COVID-19. This focus fills a critical gap by providing tools for continuous monitoring and prevention of misinformation in public health discourse. Additionally, our model's architecture is designed to handle the vast label and feature spaces inherent to multi-label classification, ensuring robustness and scalability.  
This research underscores the importance of proactive propaganda detection, particularly for healthcare organizations and policymakers. By integrating advanced DL techniques into public health strategies, our methodology provides a practical and scalable solution for addressing misinformation during global health crises. The emphasis on innovative modeling techniques and their application to real-world challenges highlights the main contribution of this study, advancing the state of the art in ML-MC text classification.
## 2 Data and Methods
### Datasets
#### 2.2.3 Evapotranspiration Dataset  
The China PML-V2 terrestrial evapotranspiration, i.e., PML-V2 (China) [61], was fed to SM2 RAIN to estimate precipitation. This product has a spatial resolution of 500 m and a temporal resolution of 1 day. Previous study has demonstrated the ability of PML-V2 (China) to accurately represent local evapotranspiration [61]. It is available from the Central Tibetan Plateau Data Center (TPDC, [[https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)]([https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)), accessed on 11 October 2022) and provides four ET components, including vegetation transpiration, soil evaporation, vaporization of intercepted rainfall, and evaporation from water bodies, ice, and snow. The sum of these four components was used as input to the SM2 RAIN algorithm.  
Note that as SMAP data are available since 2015 and CMA precipitation is available till 2018, the time window of 2015-2018 is used for the subsequent analysis.
# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Mean DSI (Algorithm, Expert)**} & \multicolumn{2}{c}{**Mean Hausdorff Distances in mm (Algorithm,**} \\ \cline{2-5}
**Comparative Studies** & **Computed after Registration in the:** & **Expert) Computed after Registration in the:** \\ \cline{2-5}  & **3D-iCEUS\({}_{start}\)** & **3D-iCEUS\({}_{end}\)** & **3D-iCEUS\({}_{start}\)** & **3D-iCEUS\({}_{end}\)** \\ \hline NGF vs. Expert & **0.870** & **0.868** & **13.272** & **13.170** \\ NMI vs. Expert & 0.838 & 0.832 & 15.116 & 15.570 \\ NCC vs. Expert & 0.815 & 0.852 & 15.198 & 15.950 \\ \hline \end{tabular}
\end{table} 
Table 2: Mean DSI values and Hausdorff distances computed for the three similarity measures and by comparison with the expert registration. Figure 5: Illustration of vascular structures identification for five cases: (**a**) preoperative cT1 MR; (**b**) vascular structure selection in cT1 MR; (**c**) overlapping of vascular structure segmented in cT1 MR on CEUS\({}_{start}\) before registration; (**d**) overlapping of vascular structure segmented in cT1 MR on CEUS\({}_{start}\) after registration; (**e**) overlapping of vascular structure segmented in CEUS\({}_{start}\) on CEUS\({}_{end}\) before registration; (**f**) overlapping of vascular structure segmented in CEUS\({}_{start}\) on CEUS\({}_{end}\) after registration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Similarity Measure** & **cT1 MR \(-\) 3D-iCEUS\({}_{start}\)** & **3D-iCEUS\({}_{start}\) \(-\) 3D-iCEUS\({}_{end}\)** \\ \hline NGF & 6.1 & 12.8 \\ NMI & 3.5 & **3.1** \\ NCC & **3.2** & 8.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean processing time in \(s\) during the registration computed for the successful cases (patient 10 excluded). An illustration of using vascular structures from cT1 MR and 3D-iCEUS\({}_{start}\) for correcting the brain shift is depicted in Figure 6. First, the blood vessels are extracted from the both modalities and by overlapping them it is clearly observed that they are not aligned. Second, a registration process is performed to align the segmented structures. By registering the targets, a matrix denoted \(T\) which describes the spatial transformation necessary for aligning structures is obtained. Then, this transformation matrix is applied on the cT1 MR images so as to achieve its matching with the 3D-iCEUS\({}_{start}\) data. Finally, the superimposing of these image modalities is presented for the input before registration and for the output after registration.
## 4 Experimental Results  
### Network Implementation  
As an optimization training method, we employed the ADAM optimizer [44] to update network parameters iteratively, using the conventional learning rate 0.001, with hyper-parameters \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The implementation of the MSCDnet was done using TensorFlow framework on a PC of Intel(r) Core i7-7700 @ 3.60 Hz CPU and a Nvidia GTX 1080 Ti GPU.
## 4 Analysis and Suppression Method of Short Circuit Current in MMC-Based MTDC Power Grid
### DC Short Circuit Current Suppression Method in MMC-Based MTDC Power Grid
#### 4.2.2 Fault Current Limiting Devices
Under DC short circuit fault conditions, the inductance of the SFCL increases rapidly in order to restrain the rising rate of the short circuit current as shown in Figure 15. However, the permeability and the coil turns limit the size of the proposed inductive SFCL [50]. Moreover, superconductive FCL needs a high technological level and huge capital cost [47], which makes it unable to be widely applied in MMC-based MTDC power grids. * NSFCL: a kind of structure of NSFCL, called capacitor-based FCL (CBFCL), is proposed in [57] and the power circuit structure of the CBFCL is shown Figure 16. The CBFCL consists of three main sections:
* isolation transformer and rectifier;
* a bypass switch; and,
* capacitor bank section with DC bus and corresponding switches. *  
Figure 14: The working principle of the inductive SFCL: **(a)** Topology; **(b)** Short circuit current path. Figure 15: Short circuit current with the improved inductive SFCL. Switch S1 is applied to flow the normal operation current, while S2 is applied to flow the fault current. Energy is transfered between the capacitor bank section and the DC bus through S\({}_{\text{C}}\) and S\({}_{\text{d}}\). The operation modes that are under normal conditions are shown in Figure 16a,b and operation modes under fault conditions are shown in Figure 16c,d. Under normal conditions, S\({}_{\text{C}}\) is turened off and the capacitor bank section is charged while taking into account resistor \(r_{\text{C}}\) in series with capacitor bank after a time constant. Because the capacitor bank section receives electrical energy from the DC bus, which leads to a voltage rise. \(S_{\text{C}}\) is conducted after the voltage of capacitor bank section reached a determined level. The proposed CBFCL circuit has little impact on the normal operation of the MMC-HVDC system. When a DC short circuit fault occurs, S\({}_{1}\) is conducted. By turning on S\({}_{2}\) in short circuit current limitation, it makes the capacitor bank section and inductor participate insert in current path. When comparing with previous studies, there are no switching strategies, such as pulse width modulation in the CBFCL for limiting the fault current and all of the switches behave like Contactors in which they are closed or opened. This feature makes it possible to use cheaper switches and a better lifetime of switching instruments. Moreover, the CBFCL not only cuts off the short circuit current, but also saves energy in the capacitor that can be utilized after fault clearance [57].
# Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS
## 1 Introduction  
Home security systems have been growing and are expected to grow to USD 84.4 billion by 2027 [1]. While home security and smart home devices provide convenience and ease of mind for users, their associated data are also increasingly used as forensic evidence in criminal cases [2, 3]. Although home security devices help secure and monitor homes, they can become great sources of forensic evidence. With the rise of self-installed home security devices, the important question to ask is how well these devices protect user data. Home security systems are categorized into (1) traditional, professionally installed and monitored systems, (2) self-installed and professionally monitored, and (3) self-installed and self-monitored smart IoT devices. The do-it-yourself self-installed systems are predicted to have the fastest growth and surpass USD 32 million by 2030 [4]. The growth is due to an increase in awareness of home security systems, the emergence of Internet of things (IoT) technologies, and easy-to-use do-it-yourself (DIY) home security systems. Among the self-installed home security systems, SimpliSafe [5] is usually ranked in the top three favorite systems [6].  
In this paper, we investigate the SimpliSafe home monitoring system due to its high popularity, focusing on the interaction between the companion app and devices, as well as a network analysis while the base device connects to servers. SimpliSafe is a self-installed smart home security system that includes monitoring sensors and cameras.  
The main contributions of our study are as follows:We conducted a mobile forensic analysis of the SimpliSafe home security app on both Android and iOS smartphones.
* We performed a forensic analysis of the network traffic generated when a user interacts with the SimpliSafe home security system.
* We identified any privacy and security concerns that arose due to the way the SimpliSafe home security app stores data on smartphones and transmits data over a network.
* We provide a forensic road map to investigators tasked with examining this and similar home security systems.  
This paper is organized as follows. In the next section, we share background information and related works. In Section 3, we explain the methodology followed in this paper. Section 4 describes our findings. In Section 5, we discuss our results and challenges. Finally, in Section 6, we conclude our analysis and discuss future work.