esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Background and Motivation  
The importance of the ocean has increased significantly with the advancement of human civilization. Meeting the growing demands of exploiting marine resources and establishing marine military defenses has highlighted the need for advancements in underwater cyberspace infrastructure. Currently, long-distance transmission using acoustic waves is the most well-developed form of underwater communication. However, the underwater acoustic channel is limited by its bandwidth, significant environmental noise, and susceptibility to the Doppler phenomenon and multipath effects. Consequently, it is necessary to explore reliable communication technologies that can operate effectively within the complex underwater acoustic channel environment [1]. DSSS technology is extensively utilized in underwater acoustic communication owing to its excellent resistance to interference and fading [2; 3; 4]. Therefore, evaluating the interference suppression capabilities of underwater acoustic DSSS systems is of considerable importance.  
Underwater acoustic communication systems face critical challenges, including limited bandwidth, environmental noise, Doppler shifts, and multipath effects. While Direct-Sequence Spread Spectrum (DSSS) is widely adopted for its anti-interference capabilities, existing theoretical frameworks predominantly assume long-period pseudo-noise (PN) sequences. However, practical underwater systems often rely on short-period PN sequences due to:  
1. Synchronization constraints: Short codes simplify pseudo-code synchronization in dynamic underwater channels.
2. Bandwidth limitations: Short sequences align with the narrowband nature of underwater acoustic links.
3. Real-time processing requirements: Short codes reduce computational overhead for resource-constrained underwater nodes.  
Prior studies lack quantitative models to analyze how short-code DSSS systems perform under variable interference conditions (e.g., frequency offsets, phase deviations). This gap hinders the design of robust underwater communication systems. Our work addresses this by bridging theory and practicality for short-code DSSS systems.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 3 Sensor Setting
### Sensor Design and Selection Strategy  
The first concern in designing the AAL sensor kit was to determine a minimal yet expressive set of sensors that can capture relevant user behaviors. Indeed, an extremely rich set of sensors could provide plenty of information, possibly redundant. However, such a setup may be perceived as excessively intrusive by the subjects, therefore limiting the acceptance of the study. A trade-off between these two competing requirements is therefore necessary. To define the most important behaviors that should be captured inside the home, a discussion with representatives of the formal caregivers involved in the project was organized. The following information was considered important to monitor: (1) bed and chair presence patterns, to capture the resting phases; (2) toilet use patterns, both in terms of accesses and timing; (3) door openings as a proxy of social interaction; (4) overall presence and activity in a significant location, possibly close to the entrance area of the main corridor. The AAL sensor kit was designed based on those requirements and, therefore, features 5 sensors in total.  
Another key requirement that emerged while planning the study execution is the necessity to standardize the sensor network setup, while keeping the installation time to be within one hour. A quick and transparent deployment procedure is, in fact, important to foster technology acceptability in the user homes. In terms of network design, many wireless communication protocols were taken into consideration, from ZigBee to ZWave or 6 LowPan. Such protocols, however, require the presence of a specific edge router that acts as a bridge between the local sensor network and the Internet, thus increasing the cost of each sensor kit. From such considerations it is apparent that a wireless sensor network that allows the obtaining of a scalable installation while optimizing costs consists of exploiting the pre-existing home IT infrastructure, well suits the needs and thus provides a possible solution. The IEEE 802.11 b/g/n WiFi protocol was chosen for networking. This way, each sensor can directly transmit its information over the Internet.
## 3 Proposed Congestion Management Approach
### Sensitivity Analysis  
#### 3.3.1 VSI Analysis  
Steady-state voltage stability analysis includes the determination of an index called the VSI, which is an approximate measure indicating both the most critical bus and the closeness of the system to voltage collapse. To determine the most critical and congested line between buses \(i\) and \(j\), contingency ranking is implemented by determining the voltage collapse point from the post-contingency operating conditions. The maximum loading point will move in accordance with variations in both the system topology and the control variables if transmission line outages occur. Since some sensitivity exists between the loading margin and the transmission parameters, a new sensitivity-based branch outage contingency ranking method [26] is used in our study to carry out the contingency selection. This is computed using both the power flow variables and the loading parameter with respect to changes in the transmission line flows. The VSI can be found from the following equation:  
\[VSI=\frac{\left|\Delta P_{ij}\right|\bullet\left|P_{ij}\right|}{\left|\Delta \lambda_{c}\right|} \tag{31}\]  
In order to evaluate the weight of each line, a scaling method involving lines with heavy loads is applied. From Equation (31), the contingency line must be the most vulnerable line according to the smallest voltage stability margin. This line is ranked at the top of the list. This technique ranks the contingencies correctly with respect to their impacts on the VSM. The VSI can be used as a real-time operational tool because of its low computational effort requirements. In addition, the VSI is similarly associated with a security cost, which is defined as the amount expended on security improvements to satisfy the \(N-1\) criterion. The security cost contains the additional costs incurred by the system to ensure \(N-1\) security.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 4 Experiments and Results  
### Materials and Evaluation Measures  
The experimental evaluation of the proposed SFCME-LSE method is carried out on two IVUS datasets. Dataset I contains 326 frames from eight pullbacks acquired from four patients at the Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, China. These images do not disclose patient information, and informed consent to use these images has been obtained from the patients and the hospital. One set of manual labeling is provided as the ground truth. The imaging system used for the acquisition is an iLab IVUS (Boston Scientific, Fremont, United States) equipped with a 40 MHz OptiCross catheter. Dataset II, which is publicly available, consists of 77 frames from 22 patients [39]. The imaging system for Dataset II is also an iLab IVUS, but with a different catheter, the 40 MHz Atlantis SR 40 Pro catheter. Dataset II has three sets of annotations from two clinical experts for intra- and inter-observer evaluations. Dataset I and Dataset II present a good diversity of artifacts, and some frames include multiple artifacts. Specifically, the number of frames containing plaque (or calcification), bifurcation, stents, side vessels, shadow artifacts, and guidewire artifacts are 69, 16, 22, 8, 42, and 36, respectively, in Dataset II.  
The algorithm is implemented in MATLAB R2018a on a Dell Precision T7610 with Intel(R) Xeon(R) E5-2637 v2 3.5 GHz CPU.  
Three performance measures are usually chosen for the quantitative analysis in IVUS segmentation [39]. The Jaccard measure (JM) weighs the similarity between the segmented area \(R_{seg}\) and the manually defined area \(R_{truth}\) and is expressed as:  
\[JM=\frac{\left|R_{seg}\bigcap R_{truth}\right|}{\left|R_{seg}\bigcup R_{truth}\right|} \tag{20}\]  
The percentage of area difference (PAD) which computes the area difference between \(R_{seg}\) and \(R_{truth}\) is defined as:  
\[PAD=\frac{\left|Area\big{(}R_{seg}\big{)}-Area(R_{truth})\right|}{\left|Area (R_{truth})\right|}, \tag{21}\]  
where \(Area(\cdot)\) represents the area calculation. The Hausdorff distance (HD) quantifies the distance between the resulting contour \(S_{seg}\) and the manual annotation \(S_{truth}\):  
\[HD=\max_{i_{a}\in S_{seg}}\left\{\min_{i_{b}\in S_{truth}}\left\{d(i_{a},i_{b} )\right\}\right\}, \tag{22}\]  
where \(i_{a}\) and \(i_{b}\) are two points in \(S_{seg}\) and \(S_{truth}\), respectively. The higher the JM is, the smaller the PAD and HD, the better the performance of the segmentation method.
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.2 Time Efficiency  
The time cost for each gesture is displayed in Table 1. The average time consumption is 0.035 s, which is fast enough to run in real-time. Gesture \"Zero\" spends the least time of 0.016 s, while gesture \"Five\" costs the longest time of 0.048 s. The difference is due to the number of fingers in each gesture, as we need to process and analyze properties of each finger region including checking connectivity, dilating finger regions, looking for finger corners, etc.
## 2 Methodology  
Natural gas pipeline failures caused by natural forces such as landslides, earthquakes, and floods can have severe consequences on pipeline operators, nearby communities, and the environment. These incidents can lead to major disruptions in the supply of natural gas, property damage, injuries, and even fatalities. Moreover, predicting these incidents is challenging due to the complexity of the underlying factors involved. Therefore, there is a need to develop a natural force damage modeling framework to classify the causes of natural force damage to pipelines in the U.S.  
The proposed methodology involves collecting a dataset of pipeline incident data points and a corresponding dataset of climate change data points. These datasets contain various features related to pipeline failure, including location, time, pipeline age, pipeline material, temperature, humidity, and wind speed. The pipeline incident data undergoes pre-processing, feature selection, and descriptive statistical analysis. Supervised machine learning algorithms are utilized using the scikit-learn software machine learning library for the Python ecosystem [24]. The algorithms used in this study include the k-nearest neighbors (KNN), multilayer perceptron neural network (MLPNN), random forest, multiclass support vector machine (multiclass SVM), and extra gradient boosting classifier (XGBoost). These algorithms are widely used in supervised learning applications. The model is trained on a subset of the collected dataset, and its accuracy is evaluated by comparing the predicted outcomes with actual outcomes from a testing set. If necessary, adjustments are made to the model to ensure accurate predictions. Finally, the model is integrated into a production pipeline that inputs pipeline incident data and climate change data and outputs the probability of natural gas pipeline failures caused by natural forces.  
The proposed AI algorithm shown in Figure 1, is expected to contribute to preventing future incidents and minimizing the catastrophic losses associated with natural gas pipeline failures caused by natural forces. By predicting the likelihood of pipeline failures, the algorithm will help operators take proactive measures to address potential issues before they become critical. Additionally, the algorithm will enable regulators to make more informed decisions on safety regulations, thereby improving safety for both operators and the public. In short, the proposed natural force damage modeling framework, which uses machine learning algorithms, can accurately predict the likelihood of natural gas pipeline failures caused by natural forces. This framework will aid in proactive measures to prevent pipeline failures, minimize catastrophic losses, and improve safety for both operators and the public.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 2 Preparatory Knowledge
### Identity-Based Cryptosystems  
IBC (identity-based cryptography) [19] addresses the challenges associated with the supervision of digital certificates in public-key infrastructure (PKI). Within IBC, an entity's identification ID serves as its public key, while the private key is created using the KGC's (key generation center) master keys alongside the entity's ID. We employ the Chinese national standard algorithm SM9 as an example of IBC, and the SM9 algorithm is introduced as follows [20]:  
Define \(P_{1}\) as the generator of an additive cyclic group on an elliptical curve \(G_{1}\), \(P_{2}\) as the generator of a similar group on an elliptical curve \(G_{2}\), \(H(\cdot)\) as a hash function, and \(e(\cdot)\) as a bilinear pair. Considering A as the signer and [PERSON] as the verifier, the digital signature process for SM9 is as follows:  
Key Generation: The KGC selects a random number \(k\)s \(\in[1,N-1]\) as the master private key for signing and computes \(P_{pub-s}=[ke]P_{2}\) as the master public key. Therefore, the master key pair is established as \(\left(ke,P_{pub-s}\right)\). The identification of user A is \(ID_{A}\). To create [PERSON]'s private signing key \(ds_{A}\), the KGC computes \(t_{1}=H(ID_{A},N)+ks\) and \(t_{2}=ks\cdot t_{1}^{-1}\) within the field \(F_{N}\), subsequently obtaining \(ds_{A}=[t_{2}]P_{1}\).  
Signing: To sign a message \(M\), [PERSON]'s signing process is as follows:  
1. \(g=e\left(P_{1},P_{pub-s}\right)\);
2. Select a random number \(r\in[1,N-1]\);
3. \(w=g^{r}\), \(h=H_{2}(M\|w,N)\), \(l=(r-h)modN\);
4. \(S=[l]ds_{A}\). Then, [PERSON]'s signature is \((\mathsf{h},\mathsf{S})\).  
Verification: For verifying a signature \(\left(\mathsf{h}^{\prime},\mathsf{S}^{\prime}\right)\) on the message \(M^{\prime}\), [PERSON] follows the following steps:  
1. \(g=e\left(P_{1},P_{pub-s}\right)\);
2. \(t=g^{h^{\prime}}\), \(h_{1}=H(ID_{A},N)\);
3. \(P=[h_{1}]P_{2}+P_{pub-s}\), \(u=e(g^{r},P)\), \(w^{\prime}=u\cdot t\);
4. \(\mathsf{h}_{2}=\mathsf{H}_{2}\left(\mathsf{M}^{\prime}\|\mathsf{w}^{\prime}, \mathsf{N}\right)\), if \(\mathsf{h}_{2}=\mathsf{h}^{\prime}\), the signature verification is successful; if not, it fails.
###### Abstract
For the first time, [PERSON] et al. (2006) introduced PSO to the interpretation of geophysical inversion results. They employed this algorithm to interpret the inversion of electrical sounding data [41]. Later, many researchers began to study the application of PSO to geophysical inverse problems. Researchers have introduced PSO to many geophysical fields, such as magnetic techniques, seismology, and gravity, as well as demonstrated its versatility. Researchers have also systematically examined the relationships between the parameters (e.g., the inertia coefficient, as well as the individual and social acceleration coefficients) of the PSO algorithm and improved its convergence rate for geophysical inverse problems, thereby laying a foundation for higher-dimensional inversion [42, 43, 44, 45]. By constraining the objective function with a \(k\)-means clustering model, [PERSON] et al. (2018) identified particle swarms from multiple magnetic sources in the presence of complex magnetic anomalies [46]. [PERSON] et al. (2019) successfully inverted two-dimensional magnetotelluric data using a hierarchical PSO algorithm with time-varying acceleration coefficients (HPSOCVAC) [40]. However, PSO has yet to be applied in 3D electromagnetic (EM) inversion, which is mainly because the high complexity of the 3D models of the EM method restricts the convergence rate of PSO. In particular, the decrease in the modified step size and convergence rate of PSO near an optimum solution renders it susceptible to premature convergence. In addition, the increase in the number of parameters amplifies the problem of over stochasticity associated with PSO and consequently reduces the continuity of the model information in the inversion result, which necessitates the smoothing of the model [26, 46]. Through a comprehensive consideration of the advantages and disadvantages of model- and data-driven inversion algorithms, a new 3D inversion algorithm, referred to as the PSO-GD algorithm, was developed for SATEM in this study. In this algorithm, PSO is first used to fit the inversion to ensure global convergence, and then the GD algorithm is employed to accelerate the process of fitting the inversion. A cosine probability function is used as a weighting function for the model-updating equation in the new algorithm to ensure a smooth transition from global to accelerated convergence. The \(\alpha\)-trimmed filter function is selected as a regularization constraint for the new algorithm. The model is smoothed to suppress the problem of over stochasticity associated with the 3D PSO-based inversion process. The rest of this paper is organized as follows: First, the basic principle and implementation of the PSO-GD algorithm are presented. Then, the stability and reliability of this algorithm are evaluated based on numerically simulated data. Finally, the PSO-GD algorithm is applied to the 3D inversion of real-world measurements, and conclusions and suggestions on further improving the algorithm are given.
## 2 Materials and Method
### Drought Recovery Duration  
The sensitivity of GPP to drought is well documented and its spatiotemporal patterns can be estimated in several ways [73; 74]. GPP, a metric of photosynthetic activity, is used in this study to detect the recovery duration of terrestrial ecosystem after drought episodes. First, the normal GPP threshold, which is the mean GPP over the study period, is calculated for each grid at 8-day time step [75]. Then, the ecosystem recovery from a drought episode is defined when the post-drought GPP reverts and stays above the normal condition (GPP normal threshold) within one-month (4 consecutive 8-day periods) [48; 75]. Figure 1 provides a schematic overview of the methodology and analysis of this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Description** & **Percentiles (\%)** \\ \hline N & Normal/wet condition & 31 to 100 \\ D0 & Abnormally dry & 21 to 30 \\ D1 & Moderate drought & 11 to 20 \\ D2 & Severe drought & 6 to 10 \\ D3 & Extreme drought & 3 to 5 \\ D4 & Exceptional drought & 0 to 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: United States Drought Monitor (USDM) drought categories employed in this study to categorize drought intensity.  
Figure 1: The framework for analyzing terrestrial drought recovery considering Gross Primary Productivity (GPP) and assessing Water Use Efficiency (WUE) response to drought and decomposing the influential factors.
## 3 Seismic Attributes and Application
### First Edge Enhancement  
Even though the direction amplitude contrast cube showed good fault continuity, it was not sufficient to extract faults automatically. To enhance the continuity of faults, edge evidence of seismic attributes was applied to the directional amplitude contrast. Edge evidence uses integrals to detect faults in the image, which allows the delineation of the more enhanced fault edges from the results of amplitude contrast. This attribute retrieves lines with significantly different values in all directions, and it provides an estimate of the best line that passes the main point.  
Figure 3: Amplitude contrast for specific azimuth direction. A change in amplitude contrast is shown when the azimuth direction increases from \(0^{\circ}\) to \(170^{\circ}\) at \(10^{\circ}\) intervals.  
In this study, the multiple edge evidence attribute was applied. As a result, noise was attenuated, owing to which the location of the discontinuity could be distinguished more clearly. It can be applied in both horizontal and vertical directions or along the direction of line orientation. A combination of these characteristics in sequence is expected to have a higher effect. Figure 5 shows the results of the sequential application of edge evidence considering the horizontal and vertical combinations. The results of horizontal application show that, although edge enhancements were generated, the overall noise remained strong. However, after the subsequent application of edge evidence in the vertical direction, most of the noise disappeared, and the discontinuity became more prominent.
# An Overview of Hybrid RANS-LES Models Developed for Industrial CFD
## 4 Embedded LES (LES)  
ELES is an approach whereby zones for RANS and LES are pre-defined--typically with the LES zone embedded into a larger RANS zone. In such a set-up, one typically employs a synthetic turbulence generator method, which converts RANS turbulence from upstream of the RANS-LES interface to LES content downstream of the interface.  
A challenging test case for ELES is the flow over a hump with a relatively large separation zone on the leeward side. Figure 16 shows the setup ([PERSON] et al. [43]), with an LES zone embedded into a wider RANS zone. The RANS region was computed with the SST model ([PERSON] et al. [44]) and the LES zone with a WALE ([PERSON] et al. [45]) and separately with a WMLES model ([PERSON] et al. [45]). At the RANS-LES interface, synthetic turbulence was generated using the method proposed by [PERSON] et al. ([PERSON] et al. [45]). The LES grid consisted of 2 million hex cells. The momentum thickness Reynolds number of the boundary layer just upstream of the LES zone was high, with \(Re_{\Theta}=U_{\delta}\Theta/\
u=7000\) (\(U_{\delta}\) = boundary layer edge velocity, \(\Theta\) = momentum thickness, \(\
u\) = kinematic viscosity).  
Figure 17 shows the skin friction and wall pressure coefficient distributions from the simulations. The use of the algebraic WMLES model ([PERSON] et al. [45]) in the LES zone results in very close agreement with the data, even though the skin friction is known to be very sensitive to simulation details. Interestingly, in the simulations using the WALE model, the wall shear stress drops off immediately after the RANS-LES interface to unrealistically small values due to the lack of resolution. This is expected, as the Reynolds number is too high for a WRLES on the given grid. The use of a WMLES with the RANS layer close to the wall can remedy this shortcoming and provide good agreement with the experimental data.  
Figure 15: Sound pressure level (SPL) comparisons between the computational (GEKO1-SBES and GEKO2-SBES) and experimental data (courtesy of FCA Italy) at the measuring probe locations on the front side-window of the Alfa Romeo Giulietta: (**a**) measuring locations; (**b**) SPL comparisons at measuring probe locations (labeled 8, 9, and 10 from left to right, respectively). (Courtesy of FCA Italy.).  
One of the weak points of the current ELES models is that there is no ability to convert back LES results to RANS at the outflow of the LES zone. This is acceptable in some cases but not in all, and can create inconsistencies downstream of the LES zone. An alternative proposal for handling such situations is given by [PERSON] et al. (2020).