esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 3 Experimental Results
### Classifier's Ability to Detect CWE Patterns in the Intermediate Representation  
The Intermediate Representation of the C/C+ source code snippets included in the NIST suite for 118 CWEs has been sent as input to the following classifiers: random forest, decision tree, K-nearest neighbors, neural networks, logistic regression and support vector  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Vulnerable Samples** & **Number of CWEs** & **Vulnerable Samples** & **Percent of Vulnerable** \\
**in the Dataset (C/C++)** & **in this Category** & **(Average)** & **Samples (Average)** \\ \hline \(>\)5000 & 6 & 7482 & 7.04\% \\ \(>\)2000 & 14 & 5344 & 5.03\% \\ \(>\)1000 & 23 & 3883 & 3.66\% \\ \(>\)500 & 37 & 2696 & 2.54\% \\ \(>\)100 & 95 & 1887 & 1.78\% \\ \(>\)0 & 118 & 900 & 0.85\% \\ \hline
**Number of Vulnerable Samples** & **Number of CWEs** & **Vulnerable Samples** & **Percent of Vulnerable** \\
**in the Dataset (Java)** & **in this Category** & **(Average)** & **Samples (Average)** \\ \hline \(>\)5000 & 2 & 6314 & 13.67\% \\ \(>\)2000 & 8 & 3857 & 8.35\% \\ \(>\)1000 & 11 & 3183 & 6.89\% \\ \(>\)500 & 23 & 1871 & 4.05\% \\ \(>\)100 & 29 & 1534 & 3.32\% \\ \(>\)0 & 112 & 412 & 0.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of CWE vulnerable samples in the dataset (C/C++ and Java).  
machines. Despite the good results in average of the k-NN algorithm, it behaves in an unstable way for specific CWEs (e.g., CWE 124, CWE 127, and CWE 126), producing very low values for recall (e.g., for_CWE_124, recall = 0.41).  
For the C/C++ code, random forest classifier produces the best results for the recall parameter, important for true positives detection of CWE vulnerabilities; however, its quality is impacted drastically when the number of vulnerable samples is lower, especially for the precision parameter (more false positives sent to security analysts).  
For Java source code, with CWE identification at function level, all tested algorithms (random forest, decision tree, K-nearest neighbors) produce good results, the maximum values of f1 = 0.955 and recall = 0.990 being achieved with K-nearest neighbors.  
We observe that the precision degradation with the lack of samples, for Java tests, is also less accelerate. This is the result of the new techniques identified to isolate the vulnerable and non-vulnerable code snippets (\"separation per function\"), which helps the scanner identify the even the tiniest differences between an insecure and a securely written function.  
We have extracted the best classifiers results in Table 3.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs  
The fluctuation range of \(SNR\), \(RSSI\), and LQI in different environments was analyzed. Meanwhile, the models between communication distance, \(SNR\), \(RSSI\), LQI, and \(PRR\) summarized in Section 4 were also explored in different environments. Root mean squared error (RMSE) of the estimated \(PRR\) and real \(PRR\) was chosen as the evaluation index of accuracy, as shown in Equation (15).  
\[\begin{split} RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(PRR(i )-PRR_{m}(i)\right)^{2}}{n}}\end{split} \tag{15}\]  
where \(n\) is the number of samples, \(PRR(i)\) is the practical value of the \(i\)-th sample, and \(PRR_{m}(i)\) is the corresponding estimated value using mapping models.
## 3 Fault Feature Extraction
### Selection of Basic Parameters  
#### 3.1.1 The Selection of Wavelet Basis Function  
There are several kinds of wavelet basis functions, and different wavelet basis functions have varying analysis effects. The commonly used wavelet basis functions include Gaus, Dmey, Haar, and dbN. In order to realize the transient feature extraction of phase current and zero-sequence current, dbN is selected as the wavelet basis function in this paper. The advantage of this is that the localization of the frequency domain increases with an increase in N. In practical application, DB analysis has good performance. Extensive test results show that DB6 provides best performance, so it is chosen in this paper.
## 5 Results and Discussion
### Performance Units
#### 5.2.5 Probability of Successful Delivery (PSD)  
High reliability is essential during safety message dissemination. PSD depends on the entire network capability as well as reliable data delivery. When compared with other protocols, MAMC-MAC displays increased possibility of effectiveness in delivering of messages. Figure 10 shows that the probability of successful delivery in standard protocols is minimized with maximization in vehicular density. However, the proposed MAMC-MAC protocol has a possibility in the range from 75% to 90% for low density, until 20 nodes. As the density increases, the possibility decreases. This reveals that an increase in number of nodes decreases the number of message deliveries.
## 4 Components and System  
### Coding Filters  
#### 4.1.1 Filter Selection  
The coding filters are used to imprint the signature of the respective receiving path into the radar signal. By evaluating the transfer function, the received signal can then be correlated to a specific path. In this process, most of the received energy must be preserved to not affect the range information of the target reflection. Therefore, narrow-band notch filters are to be used as filters. For the bandwidth of the notch, a compromise has to be found between the energy loss of the received signal and the detectability of the coding. Another aspect is the number of coding channels to be used and the available radar bandwidth.  
Since a bi-static radar system will be used, the matching of the filters is not critical. However, reflectionless filter topologies are advantageous to suppress interfering reflections in the receive path and coupling between neighboring antennas. As shown in [26] there exists a type of filter that provides both perfect match and nearly infinite stop-band attenuation by canceling the waves of two signal paths by superposition. Later, an even simpler design with coupled resonators was introduced in [27]. By scaling the line lengths, the filter type can be applied to X-band frequencies.
## 2 Study Area and Dataset
Variations in gamma ray intensity help determine the fractional volume of radioactive elements such as clay. Therefore, the formations containing mudstone typically exhibit higher natural gamma ray intensity. In addition, gamma-gamma logging is used to measure the mass density of subsurface rock formations by detecting gamma rays counts per second (CPS). It is also known as \"gamma-gamma density logging\" or simply \"density logging\". The detected gamma ray counts per second are inversely proportional to the density of the rock. Higher CPS indicates lower density, while lower CPS indicates higher density. The depth interval for the logging curves is 0.05 m. Previous studies typically treat information from different logging curves at the same depth as a single input sample, which is a straightforward and natural approach. However, this method only considers numerical values without accounting for their trends. For instance, coal beds, which have a lower density, often correspond to minimum values in gamma-gamma logging. Therefore, we obtained data from every 40 adjacent sampling points (2 m in length) as a whole to form a single input sample. After the local interception of all logging curves, a total of 70,756 samples were generated, of which 52,756 samples were used for training and 18,000 samples were used for testing. Additionally, we normalized the data to ensure that the values fall within a reasonable range, which is crucial for the convergence and stability of neural network training. To preserve the information of the data trends, normalization is carried out in two steps. First, we normalize the raw logging data for each well (Figure 3b), and then we normalize each sample (i.e., 40 sampling points) (Figure 3c). It is crucial to perform regularization in two steps. First, if only the first step is performed, it can cause data with rapid local variations to become slow changes. For example, when there are very large local values in a logging curve, after normalization, the other data may become very small, failing to reflect the data variations. If only the second step is performed, it may cause significant changes in data with slow local variations. As long as the maximum and minimum values of the data are not equal, their range will always be normalized to the 0-1 interval. By combining the data from both normalizations in each well and each sample, we generated a total of six input curves (three types of curves and two normalizations), each with 40 sampling points, to serve as the input for each sample (Figure 3).
## 6 Perspectives and Applicability  
In general, potentially expansive soils are located in areas of arid and semi-arid climates, such as the Mediterranean fringe, South Africa, southern North America, northern South America, etc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Values**} & \multirow{2}{*}{**Enhancement Percentage**} \\ \cline{2-2} \cline{4-4}  & **Before Treatment** & & **After Treatment** \\ \hline N20 (DPSH) 0.00 a 0.60 m & 4–5 & 10–11 & \\ N20 (DPSH) 0.60 a 1.20 m & 5–6 & 10–13 & \\ \hline N30 (SPT) \textless{} a 1 m & 9–10 & 19–21 & \\ N30 (SPT) \textgreater{} a 1 m & 10 & 21 & 50\% \\ \hline Strength resistance (KPa) & 24–35 & 50–70 & 48.5\% \\ Confined strength resistance (KPa) & 90–130 & 150–230 & 60\% \\ Free swelling (\%) & 2.0 & 0.0–0.2 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test values registered after the experimental campaign.  
Globally, 28% of existing geological formations contain clays with expansive capacity. Among them, 70% are found in climates where there are representative humidity changes that are also form the subsoil of large cities such as London, Madrid, Cape Town, etc.  
An excellent example is the city of Madrid (Spain), as in recent years the housing demand in the city of Madrid has led to the expansion of the urban layout and the search for new areas for urban development. By the end of 1997, 14 urban development plans (namely PAU, as referred to in Spanish) were approved with a construction schedule ending in 2015. However, the economic crisis, which began in 2008, has directly affected the approved development plans by significantly delaying the construction dates, although, fortunately, they have not been canceled so there are reactivation possibilities in the medium term.  
Such is the case that until the year 2020, the greatest urban development in Spain is expected to be found in the city of Madrid, focused in the east and southeast of the city. The plan is known as \"The East and Southeast Strategy\", which covers a total surface area of nearly 53 million square meters that provides for the construction of 149,000 housing placements, with 15-20% to be built under cooperative systems. Thousands of residents have been waiting for their homes for more than 10 years.  
The peculiar geological characteristics in the areas where these urban development actions are located, coinciding with those described in this article, will condition the planning of the urban issue, among other issues. Ultimately, this contribution aims to provide insights into the methodology and techniques for using the land.
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 3 Impact Load Sparse Identification Model
### Assessment and Analysis of MC Penalty Function Impact Load Sparse Identification Model  
Both the MC penalty function based sparse algorithm and the L1 norm based sparse algorithm need to adjust the model sparsity parameter to optimize the model. The accuracy results under different parameters k are shown in Figure 4.  
It can be seen from Figure 4 that the accuracy of the sparse decomposition algorithm based on MC penalty function has a low sensitivity to the value of \(k\). As the value of \(k\) increases, the accuracy of the MC model basically stays at a level state. However, the L1 norm model increases with the value of \(k\), showing a slight floating phenomenon. During this period, the accuracy of sparse operation increases with the increase of \(k\) value when the \(k\) value is \(0<k<1\), and maintains at 96% when the \(k\) value is near 1. When the \(k\) value is more than 1, the accuracy decreases rapidly. Then, with the increase of \(k\) value, the accuracy returns to a high point. It is found that this phenomenon is mainly caused by excessive sparse model.  
The variation between the MC penalty function model and L1 norm model was verified by increasing the input sample size. The accuracy variation results under different input sample sizes are shown in Figure 5.  
According to the results of Figure 5, the accuracy of the MC penalty function model and L1 norm model are gradually improved with the increase of input sample size. However, it is not difficult to see that the improvement speed of the MC penalty function model is significantly better than that of the L1 norm model, and the improvement process is relatively smooth. On the contrary, the L1 norm model appears jitter  
Figure 4: Different sparsity parameters \(k\) robustness. (**a**) robustness of sparsity parameter \(k<1\), and (**b**) robustness of sparsity parameter \(k>1\).  
Figure 3: Process of sparse recognition method based on MC penalty function.
## 3 Simulations of Atmospheric-Based Disruption Including Turbulence and Clouds
### Joint Characterization of a Space-Ground FSO System Based on Scintillations and Clouds  
Once the simulated normalized histogram of the atmospheric-turbulence-induced attenuation (Figure 5) together with the considered probability density function of cloud attenuation (Equation (18)) modeled as a Log-Normal distribution is provided, Multivariate  
Figure 8: Clouds characterization: (**a**) modified Gamma PSDs for the primary six types of clouds; (**b**) modelling of Mie scattering efficiency \(Q_{sca}\) versus size parameter x.  
Statistical Analysis is considered. Having both attenuation variables (i.e., atmospheric turbulence and clouds losses) defined in the same probability space, the simulated joint probability density function on all possible optical attenuation pairs of output is provided in Figure 9.  
While the simulated joint probability density function offers a good assessment of atmospheric effects influencing FSO systems, only atmospheric turbulence is emulated based on the delivered hardware. Cloud attenuation is static compared to fast turbulence fading and is out of interest for the prototyped near-space FSO link.
## 2 Proposed Framework and Experimental Methodology
### Computational Analysis and Models  
GPU Performance Estimation Model: Amdahl's Law was applied to estimate theoretical GPU speedup based on the parallelizable fraction of encryption tasks.  
\[Speedup=\frac{T_{CPU}}{T_{GPU}}+\frac{1}{(1-p)+\frac{p}{k}} \tag{2}\]Time Complexity Modeling: Linear regression analyzed encryption time as a function of data size:  
\[T=aN+b \tag{3}\]  
Resource Utilization Analysis: GPU core usage, memory bandwidth, and clock speed were evaluated using:  
\[U=\frac{Used\ Resources}{Total\ Resources}\times 100\% \tag{4}\]  
Cost-Performance Analysis: GPU cost versus encryption throughput was calculated:  
\[C=\frac{Cost}{Throughtput} \tag{5}\]  
Energy Consumption Estimation: Power consumption during encryption was estimated based on GPU TDP and encryption time:  
\[E=P\times T \tag{6}\]
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Latency Reduction
#### 4.3.5 MR-2  
The latency reduction results for the MR-2 benchmark are available in Figure 10. The difference between CBPC2 and CBPC is negligible for the Wi-Fi network environment and significant for LTE and 3G environments, with particular significance in LTE environments with a gain in latency reduction of 11 percentage points. Compared to the ZIP compression method, CBPC2 and CBPC methods generate the best addition in the reduction for LTE and 3G networks. ZIP produces 45% reduction in latency regarding raw image transfer for the Wi-Fi network environment, which, paired with a relatively weak result of CBPC2 and CBPC compressions, renders them unplausible to use.
## 1 Introduction
To solve the problem, the alternating multiplier iterative method (ADMM) [32] and the majorization-minimization (MM) algorithm [33] were used to split the complex problem into several subproblems. Furthermore, an accelerated ADMM with a restart [34] is used to solve the new model (OGS-Lp-FAST for short). In this way, a large amount of spatial-domain calculations are transferred to the frequency domain, which significantly reduces the complexity of the algorithm and speeds up its convergence. The anisotropic total variation (ATV), isotropic total variation (ITV), total generalized variation (TGV), overlapping group sparsity with L1-norm (OGS-L1), overlapping group sparsity with pseudo-norm (OGS-Lp), and our method are compared experimentally using criteria such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and runtime. The model and algorithm proposed here could further improve the image denoising performance. The contributions of this study are as follows: (1) By introducing OGS into TV, a new regularization was proposed, which incorporated the advantages of TV and OGS models. In the OGS-TV model, the neighborhood difference of each point is fully utilized to augment the difference between the smooth and edge regions. It could balance the staircase artifacts and \"spots\" effects well. (2) We adopt the Lp-pseudo-norm instead of the L1-norm to describe the fidelity term of impulse noise, extending the L1-norm-based OGS-TV to the OGS-Lp model. (3) The ADMM framework was employed to solve the proposed model. In the ADMM framework, the complex multi-constraints optimization problem is changed to several decoupled subproblems, which are easier to solve. Fourier transform technology is introduced to transform the matrix operation from the spatial domain to the frequency domain, which avoids large-scale matrix calculations. (4) In order to achieve faster convergence speed, the rapid ADMM with a restart process is adopted to improve the speed of the proposed algorithm. This improved model is named OGS-Lp-FAST. The rate of convergence of the model increases from \(O(\frac{1}{R})\) to \(O(\frac{1}{R^{2}})\). This paper is organized as follows: Section 2 gives a review of the traditional TV model; Section 3 describes the incorporation of overlapping group sparsity and Lp-pseudo-norm shrinkage into the TV model, and uses accelerated ADMM with a restart to solve the new model; Section 4 seeks to validate the proposed algorithm with standard images, and compares it with four other models; and Section 5 summarizes this paper and proposes future work.
## 7 Discussion  
We have introduced the hesitant fuzzy entropy-based opportunistic clustering and data fusion scheme for WSNs. The previous entropy-based clustering schemes were designed without consideration of multiple attributes (especially asynchronous working-sleeping cycle) involved in the data flow from sensor nodes to CHs and to the BS. HFECS utilizes multiple attributes like the time frequency parameter, residual energy, remaining buffer capacity, link quality factor, the distance to the BS and the number of supported sensor nodes etc. in terms of local information of sensor nodes to measure hesitant fuzzy entropy and its corresponding weight coefficients for the CH election procedure and reliable data fusion, which improves the energy utilization and also increases the network lifetime of sensor nodes. The proposed designed of HFECS could be applicable to the following different approaches in future:  
* Designing a HFE-based multipath opportunistic routing protocol for wireless cognitive sensor networks.
* Designing a HFE-based clustering scheme with ring routing protocol.
* Unmanned Aerial Vehicle (UAV) sensor networks
* Internet-of-Things (IOT)-enabled home energy systems
* Cyber-physical systems
* Energy harvesting wireless sensor networks [48].
* Hesitant fuzzy entropy analysis in a density-based and grid-based opportunistic clustering scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{6}{c}{**Number of Rounds**} \\ \cline{2-9}  & **500** & **1000** & **1500** & **2000** & **2500** & **3000** & **3500** \\ \hline SEP-E & 3.26 & 16.18 & 28.57 & 36.11 & 50 & 66.92 & 97 \\ DEEC-E & 5.56 & 17.91 & 40 & 48.48 & 50 & 43.75 & 71.43 \\ EBCS & 0 & \(-\)2.47 & \(-\)1.56 & 4.26 & 9.09 & 9.52 & 20 \\ TEEN & 3.26 & 25.4 & 40 & 53.13 & 61.43 & 87.09 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Percentage improvement of HFECS in terms of PDR.  
Figure 9: Packet delivery ratio (PDR) (%) against number of communication rounds.
###### Abstract
Although regional climatic conditions exert an important control on the state of the cryosphere, it is also heavily influenced by various external parameters. For example, frozen ground is shaped by snow cover, vegetation, peat, wildfires, and other phenomena [23; 24; 25]. Debris (negative or positive) [26] and light-absorbing impurities (positive) [27] are significant factors that influence the melting of mountain glaciers, and changes to snow cover are related to vegetation [28], topography [29], elevation [30], and other factors. Therefore, the cryosphere may not always be shrinking at the local scale. For example, the glaciers in the Karakoram Range are currently experiencing the so-called \"Karakoram anomaly\" [31], with increased snow cover observed across the high altitudes of northern Pakistan [32] and stable permafrost present in the northern Great Khingan Mountains, China [24]. Determining the nature and extent of cryospheric changes caused by global warming will facilitate cryospheric water resource utilization and risk management. The Altai Mountains of Inner Asia remain a relatively pristine landscape. However, recent socio-ecological changes have introduced challenges regarding the management of natural resources, particularly water. The Altai Mountains form the 'Water Tower' of a wider region, with the populations in the highland to lowland regions of the surrounding four nations depending on the river, stream, and lake waters derived from melting snow and glaciers for their livelihood and economic activities [16; 33]. Climate change has increased in mountainous regions at an ever-accelerating pace over the last century [34], with the Altai Mountains being no exception to these drastic changes. The southern Altai Mountains (SAM) experienced significant temperature and precipitation increases over the period 1966-2015 [35]. Our field investigations indicate that summer droughts, which have caused serious livestock losses in the SAM, are preceded by winters with limited snowfall. The SAM has been experiencing increasing water stress in recent years, highlighting the urgent need to assess the effect of cryospheric changes on water resources. This study aims to: (a) discover the change characteristics of main cryospheric elements during the period 1961-2015, as well as any spatiotemporal trends that may exist, and (b) assess the opportunities and challenges that these cryogenic changes pose to the regional water resource management, including the hydrological processes, water resources, water management (hydropower, agriculture, tail-end lakes, fisheries, aquatic life, and water rights associated with transboundary rivers), and their correlation with SDGs. The findings of this assessment will be useful for sustainable development in the SAM.