esa-sceva/satcom-chunk-collection huggingface dataset:
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 3 Results and Discussion
### Pore Statistic Analysis  
The surface porosity, mean pore size, and molecular weight cut-off are summarized in Table 1. Notably, it is interesting to observe from the table that incorporating low concentrations of fumed silica enhanced membrane porosity by up to 7.24% at FS-0.3. Simultaneously, the average pore size decreased with increasing fumed silica concentration, leading to a 13 kDa reduction in MWCO. The increased porosity suggests better pore distribution and improved interconnectivity, resulting in higher flux. This result aligns with the work of [PERSON] and colleagues [39]. They developed mixed-matrix forward-osmosis (FO) membranes by combining silica gel with polyacrylonitrile (PAN) using a layer-by-layer assembly approach. This study revealed that the incorporated silica gel particles introduced additional pores within the membrane structure, which resulted in an increase in water flux during forward osmosis. Additionally, protein rejection improves as fumed silica concentration rises, indicating a smaller pore size, as supported by data in Table 1. However, rejection decreases subsequently, likely due to protein accumulation on the membrane pores.  
MWCO is a crucial parameter in filtration processes. It defines the size of solutes that can pass through a membrane, allowing them to selectively retain or remove specific molecules for various separation applications. In this work, when fumed silica was added, the decreased MWCO value enabled the retention of smaller molecules, which can then be utilized in diverse applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Membrane Code** & **Porosity, \(\varepsilon\) (\%)** & **Mean Pore Size, \(r_{m}\) (Å)** & **MWCO, \(\alpha\) (kDa)** \\ \hline FS-0.0 & 6.59 & 40.87 & 38 \\ FS-0.1 & 6.95 & 30.02 & 25 \\ FS-0.2 & 7.20 & 28.74 & 25 \\ FS-0.3 & 7.24 & 28.25 & 25 \\ FS-0.4 & 7.18 & 29.18 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pore statistics and MWCO of PES/FS blend membranes.  
Figure 5: The water contact angle of PES membranes with varied funnel silica concentrations.
# A Method for Modeling and Testing Near-Real-Time System Scenarios
## 4 Modeling the System Scenarios with Finite-State Machines
### System Dynamics Modeling with UML State Machines
In order to determine path coverage based on such a generated graph (the state diagram always has one input and at least one output), the procedure of converting arcs into vertices should be used and the depth-first search algorithm ([PERSON] et al. [32]) should be performed on such a graph. In this case, we will obtain allowable testing paths for the state machine. Algorithms for determining more sophisticated coverage in directed graphs and applications to program testing are described, for example, in the works by [PERSON] and [PERSON] [20] and by [PERSON] and [PERSON] [22]. It should be noted that the way the state is modeled in UML state diagrams does not allow for the direct generation of integration tests but is sufficient to show the paths that need to be tested during integration and certification tests, both on the simulator and in the UAV flight.
# Comprehensive Evaluation of Lateral Performance of Innovative Post in Sand
###### Abstract  
Under environmental loads such as wind and earthquakes, piles are subjected to large lateral loads. A foundation system denoted Innovative Post (IP) that is composed of an H-pile shaft and one or two steel plates (paddles) welded to its flanges, has been developed to resist large lateral loads on sound wall systems. The present study evaluates the performance of IP installed in layered cohesionless soils through a comprehensive full-scale lateral load testing program and finite element (FE) analysis considering various pile and plate configurations. The developed FE model was validated employing the field test data and was then employed to conduct a parametric study to evaluate the performance of IP considering different paddles geometry (i.e., number of paddles, single or double, width, and length). The results demonstrated that adding the plates significantly increased the lateral capacity of H-piles. A positive relationship was identified between paddle's width and length and the load efficiency. Optimal parameter values for paddles are established based on the experimental and numerical results proposed.  
Keywords:innovative post; paddled pile; lateral loading; sand; model tests; three-dimensional (3D) numerical analysis; finite element; design optimization +
Footnote †: journal: Applied sciences
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 3 Type of Attacks on IoT and IIoT Devices
### Software Attacks  
Another type of possible attack is a software attack. This may be launched whenever vulnerabilities are associated with the software system used for IoT systems. Viruses, worms, adhere, spyware, and trojan horses are malicious programs that may steal information, are capable of tampering with the data, and may launch DoS or DDoS. Some malicious programs may even infect data present in IoT devices, clouds, and data centers. According to IoT domain experts, due to the wide range of applications in commercial as well as industrial areas, we may have 75 billion devices by 2025. As it is comparatively new in w.r.t. personal computers and the internet, it has more security concerns and more possible attacks. In 2016, an IoT botnet was used for the deadliest DDoS attack on DNS service providers by Mirai Botnet and Jeep Hack.  
**Countermeasures against Software Attacks**: In the direction of countering software-based attacks, previous research [21] presented a framework combining multiple aspects to defend hardware-implanted Trojans. The three security aspects used in the proposed model are an encryption mechanism to prevent unauthorized access, vendor diversity to gain trust among untrusted participants, and mutual auditing to check the encryption status and message content. A similar kind of work was performed by [22], in which a secure hardware-based design was proposed to prevent Trojans. This mechanism directly prevented Trojan's injection into the network.
## 5 Conclusions  
To improve the long-term RUL prediction performance of the lithium-ion battery with capacity regeneration phenomenon, a hybrid prediction method based on the RVM and the GM algorithm is explored. The proposed method decouples regeneration phenomenon and normal degradation and predicts them separately. After obtaining the amplitude of regeneration and the regeneration cycle number, an exponential model is proposed to describe the degraded region. Moreover, an iterative updating method is proposed to predict the normal degradation. The RVM and the GM are alternately used to carry out a multi-step prediction. The RVM-GM model prediction results update the training dataset, and the updated training dataset retrains the RVM-GM model. Finally, we add the prediction results of each regeneration region into the normal degradation prediction sequence to get the overall SOH prediction results.  
As compared with a pure RVM and a pure GM, our hybrid method has higher accuracy and stability. Moreover, the RVM and the GM in the proposed hybrid method only rely on historical data, and it can construct a prognostic model without any prior knowledge about the complex battery degradation. Thereby, this method can predict the RUL of different types of batteries with historical data accumulation.  
The limitations of our hybrid method in real applications are that the training data should have typical data reflecting the regenerated phenomena, and the future battery use plan should be known to determine the rest time in the prediction phase. In addition, other factors such as temperature and hysteresis can also affect the accuracy of the prediction. Therefore, our future work will investigate how to build the degradation model in the case of insufficient data and will consider the influence of unknown rest time, temperature, data lag, and other factors on the prediction uncertainty.  
[PERSON] and [PERSON] conceived the method and structured the whole paper; [PERSON] and [PERSON] performed the experiments, analyzed the data, wrote and revised the paper.  
This research was funded by the National Natural Science Foundation of China (Nos. 61633008, 61773132 and 61803115).  
Thanks to the NASA Ames Laboratory for providing open data.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Battery Number**} & \multirow{2}{*}{
\begin{tabular}{c} **Prediction Start** \\ **Cycle** \\ \end{tabular} } & \multicolumn{2}{c}{**Absolute Error of RUL Prediction Result**} \\ \cline{3-4}  & & **Hybrid Method** & **RVM-KF Method** \\ \hline \multirow{3}{*}{\#5} & 60 & 28 & 4 \\  & 80 & 8 & 1 \\  & 100 & 4 & 4 \\ \hline \multirow{3}{*}{\#6} & 60 & 32 & 15 \\  & 80 & 2 & 15 \\  & 90 & 3 & 2 \\ \hline \multirow{3}{*}{\#7} & 60 & 39 & 1 \\  & 80 & 19 & 7 \\  & 100 & 1 & 1 \\ \hline \multirow{3}{*}{\#18} & 60 & 4 & 8 \\  & 80 & 1 & 5 \\ \cline{1-1}  & 100 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of prediction results of the hybrid RVM-GM method with RVM-KF.
## 5 Results and Discussion
Table 2 indicated that the hybrid NSGA2 + Bi-directional + GR with the five classifiers is steady for three subset features selected by the hybrid NSGA2 + Bi-directional + GR to build the SVM classifier, recording the highest accuracy of 93% vehicle collision detection in the IoV. Three subsets of IoV collision detection features are consistent with the SVM, GNB, DT and RF classifiers, except for KNN with four subset features. Hence, it can be deduced that SVM can produce better accuracy in detecting vehicle collision in the IoV, with only three features selected by the hybrid NSGA2 + Bi-directional + GR. It is observed that the feature selection results by each of the classifiers outperformed the classifiers developed with the maximum IoV vehicle collision detection features. Figure 8 depicts the different classifiers with selected features based on hybrid NSGA2 + Bi-directional + GR. The results of the hybrid NSGA3 + Bi-directional + GR feature selection approach in Table 2 show that the SVM outperforms the compared classifiers based on the subset features selected by the hybrid NSGA3 + Bi-directional + GR. Thus, by comparing the performances of both hybrid NSGA2 + Bi-directional + GR and hybrid NSGA3 + Bi-directional + GR, it is found that the hybrid NSGA3 + Bi-directional + GR with SVM performs better in terms of vehicle collision detection accuracy. As observed in the case of hybrid NSGA2 + Bi-directional + GR, hybrid NSGA3 + Bi-directional + GR shows better performance with subset selected IoV vehicle collision detection features compared to the result with features without selection. Figure 9 shows the performance of each classifier build based on subset features selected by hybrid NSGA3 + Bi-directional + GR. The hybrid NSGA3 + Bi-directional + GR was stable on three subsets of features. Hybrid NSGA3 + Bi-directional + GR with KNN, DT, GNB, SVM and RF are all stable on three subset features. Comparing the performance of all the five classifiers in terms of accuracy clearly shows that the SVM outperforms all other algorithms. Figure 10 depicts the NSGA2 + GR and NSGA3 + GR performance across filter-, wrapper- and hybrid-based feature selection approaches. The SVM in each of the categories of feature selection methods was selected for comparison, because in each case it performed the best, except in the case of NSGA2 + GR where GNB outperformed the SVM. It is found that the best performing feature selection algorithm is the hybrid NSGA3 + Bi-directional + GR, as it successfully minimizes the subset features and maximizes the accuracy in detecting vehicle collision in the IoV environment better than the compared algorithms: hybrid NSGA2 + Bi-directional + GR, filter-based NSGA3 + GR, wrapper-based NSGA2 + Bi-directional and wrapper-based NSGA3 + Bi-directional. However, regarding the issue of minimum subset features, most of the algorithms in  
different scenarios selected three subset features followed by four, five and two as shown in Table 2.
## 3 The Impact of the Electromagnetic Interference on Drones
### Influence of Electromagnetic Interference of Converter Station on UAV Communication  
The measurement and control communication system that the UAV will be affected by electromagnetic interference can be divided into two parts [17]: UAV data link communication system, which is responsible for transmitting the uplink control signal and the downlink image transmission signal. UAV navigation and positioning system, which is responsible for its own positioning and navigation.
###### Abstract  
Focal and non-focal Electroencephalogram (EEG) signals have proved to be effective techniques for identifying areas in the brain that are affected by epileptic seizures, known as the epileptogenic zones. The detection of the location of focal EEG signals and the time of seizure occurrence are vital information that help doctors treat focal epileptic seizures using a surgical method. This paper proposed a computer-aided detection (CAD) system for detecting and classifying focal and non-focal EEG signals as the manual process is time-consuming, prone to error, and tedious. The proposed technique employs time-frequency features, statistical, and nonlinear approaches to form a robust features extraction technique. Four detection and classification techniques for focal and non-focal EEG signals were proposed. (1). Combined hybrid features with Support Vector Machine (Hybrid-SVM) (2). Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4). A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN). Prior to feeding the features to classifiers, statistical analyses, including t-tests, were deployed to obtain relevant and significant features at each approach. The proposed feature extraction technique and classification proved effective and suitable for smart Internet of Medical Things (IoMT) devices as performance parameters of accuracy, sensitivity, and specificity are higher than recently related works with a value of 99.7%, 99.5%, and 99.7% respectively.  
EEG; DNN; deep CNN; WT-SST; DWT; SVM +
Footnote †: journal: Applied Sciences
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model
### Layer 2: Proposed Routing Protocol  
In a real OppNet involving human activity where mobile nodes are carried by humans, the mobility is not truly random. Human activity imposes patterns on the mobility that has a characteristic time interval, depending on what the human is doing. The proposed routing approach partially depends on the probability that the current node will encounter the destination node based on the last meeting time. Moreover, the new model depends on an acknowledgment table to remove the acknowledged messages from the network. To overcome high overhead, the protocol efficiently detects which messages should be deleted from the buffer upon congestion.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Different Languages
#### 4.7.2 Singing Voice  
We evaluate the singing alignment on the five most prominent languages in DALI, which are English, French, German, Italian, and Spanish. We used a five-fold evaluation strategy on 50 songs (the same as for our baseline Playlist 50). However, we measure the alignment error on each song, not their concatenation as for Playlist 50, and report the average metrics on Table 4. The DALI track IDs for these additional languages are also available on our companion website.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Language** & **MAAE [ms]** & **Q50\% [ms]** & **Q95\% [ms]** & **Q99\% [ms]** & **PCO [\%]** \\ \hline Arabic * & 42 & 29 & 116 & 236 & 100 \\ Chinese * & 30 & 27 & 75 & 112 & 100 \\ Czech & 27 & 7 & 78 & 109 & 100 \\ Dutch & 79 & 82 & 124 & 158 & 100 \\ French & 49 & 45 & 97 & 115 & 100 \\ German & 25 & 24 & 54 & 95 & 100 \\ Greek * & 37 & 36 & 85 & 92 & 100 \\ Italian & 80 & 79 & 129 & 165 & 100 \\ Spanish & 77 & 80 & 137 & 206 & 100 \\ \hline \end{tabular}
\end{table}
Table 3: Comparing the alignment performances of our CTC-based aligner (trained in English) on speech in different languages. * The text in these languages has been transliterated to Latin alphabet before alignment.  
For English, we obtain similar results to that obtained for Playlist 50. As each song here is evaluated individually and as it is simpler to perform a short than long alignment, we even obtain slightly better performances. However, performances clearly degrade in other languages. In fact, there exist extremely severe outliers (Q95% and Q99% metrics) that naturally degrade the MAAE. When analyzing some of them, we observe cases in which the first word of the next paragraph is predicted at the beginning of a long instrumental bridge, or a word is emitted because of background vocals that are not part of the original transcript.  
An interesting conclusion, though, is that the median error remains under 100 ms for all languages. Since it will always be hard to account for the huge diversity existing for the singing voice in song recordings, the median value is certainly a better metric than MAAE to assess alignment performances in the singing voice, especially with background music.
# Optimization Design of Filament Wound Composite Pressure Vessel Based on OpenSees
## 1 Introduction
These are advanced analysis methods with high analysis accuracy and computational efficiency. (b) Standard analysis methods Some standard industrial programs such as NASTRAN, ANSYS, ABAQUS, and GENESIS also can analyze the mechanical properties of filament wound pressure structures. These standard programs are based on the displacement of the finite element method with powerful functions, good numerical properties, simplicity, universality, and versatility [9]. Optimal design is a new design idea proposed in the 1960s. It applies the optimization principle and computer technology to the design and proposes an important method for scientific design in engineering. Firstly, under the given design constraints, the design problem in the project is transformed into an optimal problem, and then the computer is used as a tool to find the optimal design scheme to achieve the predetermined design goal. This has greatly improved the efficiency and quality of design, which is widely used in various product designs and various industrial design fields. In the field of intelligent optimization computing, there are two swarm intelligence algorithms: ant colony optimization algorithm and particle swarm optimization algorithm. The former is derived from the simulation of the food-searching behavior of the ant community and has been successfully applied to many combinatorial optimization problems. These small particles, called \"swarms\", can adjust their trajectory and fly to the best position they have experienced before as well as the best position other particles have experienced. All particles should adjust their speed and remember the best position they have experienced to achieve this goal. By coordinating and communicating with each other, particles always follow the optimal particles in the solution space to search, so they have faster computational efficiency [10; 11; 12; 13]. Based on the above background, this paper uses the combination of OpenSees and ANSYS to carry out finite element analysis of composite pressure vessels and uses the optimization method of Nelder-Mead to optimize the fiber angle and fiber thickness in the filament winding part. This method can simplify the calculation process and find the best winding scheme through function optimization, which has certain guiding significance for the fiber winding layer of a composite winding structure.
## 3 Proposed Target Channel Visiting Order Design Algorithm
### Introduction of PSO  
PSO is a bio-inspired optimization method which is inspired by observing the bird flock [20]. In PSO, each solution is a bird in the swarm and is referred to as a \"particle\". In order to solve optimization problems in a discrete number space, [PERSON] and [PERSON] [21] developed a discrete binary version of PSO, which is the focus of this paper.  
To begin with the iteration, a swarm with \(S\) particles is initialized. Let \(\mathbf{x}_{i}^{k}=[x_{i1}^{k},x_{i2}^{k},...,x_{iD}^{k}]\) denote the position of particle \(i\) (\(1\leq i\leq S\)) at iteration \(k\), where \(D\) is the number of dimensions to represent a particle. \(x_{id}^{k}\) takes binary values from {0,1}. The velocity of particle \(i\) at iteration \(k\) is denoted as \(\mathbf{y}_{i}^{k}=[y_{i1}^{k},y_{i2}^{k},...,y_{iD}^{k}]\), \(\mathbf{y}_{id}^{k}\in R\). Each particle in the swarm is assigned a fitness value indicating how good it is for an optimization objective. We use \(\mathbf{p}_{i}^{k}=[p_{i1}^{k},p_{i2}^{k},...p_{iD}^{k}]\) and \(\mathbf{p}_{g}^{k}=[p_{g1}^{k},p_{g2}^{k},...,p_{gD}^{k}]\) to denote the best solution that particle \(i\) and the whole swarm have obtained until iteration \(k\), respectively. At each iteration, each particle adjusts its velocity according to its last velocity, its distance to the best solution it has obtained and its distance to the best solution of the swarm. The velocity of the particle is updated as follows:  
\[y_{id}^{k}=y_{id}^{k-1}+\xi_{1}r_{1}(p_{id}^{k-1}-x_{id}^{k-1})+\xi_{2}r_{2}(p _{gd}^{k-1}-x_{id}^{k-1}) \tag{6}\]  
where \(\xi_{1}\) and \(\xi_{2}\) are two positive constants, \(r_{1}\) and \(r_{2}\) are random numbers uniformly chosen from the range [0,1]. Furthermore, the velocity is transformed to a value in the range [0,1] by using the following sigmoid function:  
\[\text{sig}(y_{id}^{k})=\frac{1}{1+\exp(-y_{id}^{k})} \tag{7}\]  
where \(\text{sig}(y_{id}^{k})\) denotes the probability of \(x_{id}^{k}\) taking 1. According to \(\text{sig}(y_{id}^{k})\), \(x_{id}^{k}\) can be updated as:  
\[x_{id}^{i}=\begin{cases}1,&\text{if }r<\text{sig}(y_{id}^{k})\\ 0,&\text{else}\end{cases} \tag{8}\]  
where \(r\) is a random number uniformly distributed in [0,1]. In the discrete PSO, a maximum velocity \(V_{\text{max}}\) is used to avoid \(\text{sig}(y_{id}^{k})\) approaching 0 or 1, _i.e._, \(y_{id}^{k}\in[-V_{\text{max}},+V_{\text{max}}]\).