esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 3 Experimental Results
### Dataset Preparation Techniques
#### 3.1.2 Separation Per Code Snippet  
We select the samples for vulnerable code per each CWE by removing only the non-vulnerable functions, conventionally named \"good (0\" by NIST, from the code snippets. With this approach, we isolate in the vulnerable code snippets used for machine learning training all the remaining lines of code which are external to the specially designed non-vulnerable functions. As result, the vulnerable samples of the training and testing dataset contain the entire code snippets designated as vulnerable to a specific CWE, from which we have removed the not vulnerable functions.  
For training the machine learning algorithms, the samples with non-vulnerable code for a CWE include all NIST provided code in the Juliet dataset excepting the code samples mentioned by NIST as vulnerable to that specific CWE. This approach remains valid under the NIST statement that each piece of code is drafted as such to be vulnerable to only one CWE, not more.  
This approach has the advantage that is closer to the real world, where programs include more than the vulnerable lines of code. In addition, the data flow preserves the entire lifecycle of the identifiers (maintained as generic identifiers in the Intermediate Representation).  
Figure 3: Vulnerable code snippets to CWE325 (Missing Required Cryptographic Step—Key Generator, isolated from the Java dataset published by NIST.  
The second advantage and \"reality check\" is that the non-vulnerable samples are not highly similar to the vulnerable lines of code. For example, if there is no password-related syntax in a program (captured by a vulnerable or non-vulnerable function in NIST dataset), there is no possibility to a have a \"hardcoded password\" type of technical vulnerability.  
One disadvantage is that little differences between a vulnerable and not vulnerable source code which may be identified only by a deep analysis the functions provided by NIST, are not detectable, which may lead to false negatives to the teams in charge with fixing the vulnerable code.  
A second disadvantage is that the length of the Intermediate Representation vector is higher and might lead to more processing power required for the classifiers, as it includes additional code which is not relevant to the CWE.  
We use this approach to separate vulnerable code from non-vulnerable code in our experiments with the entire NIST database for C/C++ code, and during our previous experiments [11; 12] on a limited set of CWEs in Java and C/C++ code.
## 5 Experimental Results and Discussions
### Results
#### 5.2.3 Comparison with Standard PSO Algorithm  
To better demonstrate the superiority of the proposed algorithm, we present the results obtained using the basic Particle Swarm Optimization (PSO) algorithm. The results of the standard PSO are shown in Figures 4-6.  
Compared to the results obtained with the standard PSO, it can be observed that the APSO-LS algorithm exhibits superior adaptability and efficiency. The average makespan of APSO-LS and the standard PSO are 31.30 and 39.6757, respectively. This indicates that the standard PSO struggles to handle dynamic demands, leading to higher makespans. The standard PSO may face difficulties with local optima and dynamic updates, whereas APSO-LS's adaptive perturbation and local search overcome these limitations. The stable convergence and feasible routes further validate these advantages.  
Figure 4: Convergence curve and UAV routes obtained with standard PSO (run 1), where the blue line represents the path of UAV 1, the orange-red line represents the path of UAV 2, and the yellow line represents the path of UAV 3 in the right side sub-figures.  
Furthermore, our measurements indicate that the average execution time per run is approximately 0.6192 s, which, when compared to the 0.5871 s recorded for the standard PSO, does not represent a significant reduction in runtime.
## 2 Existing Technologies and the Position of the Present Research
### Inter-Diver Communication Technology  
Yamagata Casio Co., Ltd. (Higashine, Yamagata, Japan) cells a compact inter-diver communication tool--an underwater transceiver called \"Logosease\" [8]. It uses ultrasonic waves and bone-conduction speakers and microphones to allow verbal communication in water.  
While this terminal allows verbal conversation, it cannot recognize how far apart the nodes involved are. Therefore, even if a user goes astray, other users cannot recognize it. There are other methods for underwater verbal communication but none of them can recognize the distances between nodes, and thus cannot detect stray nodes.
## 2 PV System Configuration
### Modeling of the PV Module  
A single-diode model of a PV cell is depicted in Figure 2[13]. The accuracy of the single-diode model is high and its complexity is low [48; 49; 50; 51; 52] compared to two-diode modeling [52; 53; 54; 55]. A single-diode model is widely used by researchers [13; 56] and is a reliable model for MPPT algorithm analysis [48; 57; 58; 59; 60]. The physics of the p-n junction of a PV cell are governed by five main parameters which are photon current I\({}_{\text{ph}}\), output current I, terminal voltage V, shunt resistor R\({}_{\text{sh}}\), and series resistance R\({}_{\text{s}}\). The shunt resistor, R\({}_{\text{sh}}\), shows the leakage current of the p-n junction, while series resistance, R\({}_{\text{s}}\), represents the combination of the contact resistance, metal grid, and p and n layers [59].  
Figure 1: Standalone PV system with boost converter and MPPT controller.  
Figure 2: Equivalent circuit of a single-diode model of a PV cell.  
The characteristics of the single-diode model of the PV cell are illustrated in Equation (1) [61; 62]. It is noted that the characteristics of the PV cell involve nonlinearity [59].  
\[\mathrm{I}=\mathrm{I_{ph}}-\mathrm{I_{o}}\left(\mathrm{e}^{(\frac{q(V+\mathrm{I_{ ph}})}{N_{\mathrm{SnRT}}})}-1\right)-\mathrm{I_{sh}} \tag{1}\]  
I represents the output current of the PV cell, \(\mathrm{I_{ph}}\) indicates the photon current, \(\mathrm{I_{o}}\) represents the reverse saturation current of the cell, q indicates the electronics charge, V is the output voltage of the cell, n is the ideality factor, K is Boltzmann's constant, and T is the absolute operating temperature. \(\mathrm{N_{s}}\) is the total number of cells in series. \(\mathrm{I_{sh}}\) indicates the current flow through the shunt resistor.  
The photon current is denoted by Equation (2) [63].  
\[I_{ph}=\left(\frac{S}{S_{ref}}\right)\left(I_{ph,ref}+\mu_{Isc}\left(T_{c}-T_{ c,ref}\right)\right) \tag{2}\]  
\(S\) represents the actual irradiance (W/m\({}^{2}\)), \(S_{ref}\) is the irradiance under the reference condition (W/m\({}^{2}\)), \(I_{ph,ref}\) is the light current under the reference condition (A), \(\mu_{Isc}\) is the manufacturer-supplied temperature coefficient of the short-circuit current (A/K), \(\mathrm{T_{C}}\) is the actual cell temperature (K), and \(T_{C,ref}\) is the actual reference temperature (K).  
The reserve saturation current is given by Equation (3) [64].  
\[I_{o}=I_{o,ref}\left(\frac{T_{c}}{T_{c,ref}}\right)^{3}exp\left(\frac{qE_{go} }{nK}\left(\frac{1}{T_{c,ref}}-\frac{1}{T_{c}}\right)\right) \tag{3}\]  
\(E_{go}\) is the band gap energy, and n is the ideality factor.
# Locating Image Sources from Multiple Spatial Room Impulse Responses
## 2 Methods
### Geometry Calibration  
The previous sections have described how direct sound and early reflection estimates are extracted from each source-receiver measurement. Up to this point, the data from each receiver is presented in its own coordinate frame. Yet, in order to estimate the image source positions from multiple measurements, the data needs to be transformed into a global coordinate frame. To do so, one needs to have knowledge of the receiver orientations and positions in the global frame.  
There are several options for identifying the receiver layout, requiring different amounts of information. On one hand, there is the option to measure all positions and orientations manually. For a large number of receivers, this approach is laborious and susceptible to measurement errors. In our experience, measuring the array rotation with high accuracy is especially difficult in practice. On the other hand, there are fully automatic microphone array geometry calibration approaches using, for example, DoA, ToA or TDoA information [11]. If the measurement system delay was not determined or contains errors, the ToA is easily biased. However, an unbiased DoA estimate of the direct sound from each source to each receiver is readily available, so one could use DoA-based microphone array calibration methods. Algorithms for calibration in 2D were presented in [12; 13], and could be extended. Such DoA-based approaches typically rely on non-linear least squares optimization and have strong similarities to photogrammetry. There, multiple pictures of the same scene allow for creating a 3-dimensional reconstruction, which requires estimation of the camera positions and orientations. The advantage of DoA-based automatic calibration is that it is sufficient to measure 3 source or receiver positions manually. Moreover, it does not depend on ToA information, which might suffer from the mentioned systematic biases.  
For the data presented here, we applied an approach that relies on measuring only the source positions manually. In practical measurements, this requires significantly less effort than measuring all positions. The receiver positions are computed from the direct sound DoAs.  
Apart from the receiver positions, the geometry calibration stage also determines the ToA bias explicitly. This is not only important for the geometry calibration itself, but also for the image source estimation stage, as the biases described next apply to all sound events.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## Appendix A Impulse Response Function  
A SAR scene \(h\) that contains a point target located in its center can be expressed analytically as an impulse response function in polar coordinates ([4], Equations (9)-(11))  
\[h(e,\varphi)=\frac{\mathrm{e}^{\mathrm{i}\varphi}}{e}h_{\rm s}(\varrho,\varphi )+\phi_{0}\frac{\mathrm{e}^{\mathrm{i}\varphi}}{\varrho}\sum_{n=-\infty}^{ \infty}\frac{\mathrm{i}^{n}h_{\rm jn-1}(\varrho)}{\mathrm{e}^{\mathrm{i}(n-1) \varphi}}\operatorname{sinc}\left(n\frac{\phi_{0}}{2}\right), \tag{12}\]  
where  
\[h_{\rm s}(\varrho,\varphi)=B_{\rm r}\operatorname{sinc}\left[ \frac{B_{\rm r}}{2}\varrho\cos\left(\frac{\phi_{0}}{2}-\varphi\right)\right] \mathrm{e}^{\mathrm{i}\varrho\cos\left(\frac{\phi_{0}}{2}+\varphi\right)- \mathrm{i}\frac{\phi_{0}}{2}}\\ -B_{\rm r}\operatorname{sinc}\left[\frac{B_{\rm r}}{2}\varrho \cos\left(\frac{\phi_{0}}{2}+\varphi\right)\right]\mathrm{e}^{\mathrm{i} \varrho\cos\left(\frac{\phi_{0}}{2}+\varphi\right)-\mathrm{i}\frac{\phi_{0}}{ 2}} \tag{13}\]  
and  
\[h_{\rm jn-1}(\varrho)=\left(1-\frac{B_{\rm r}}{2}\right)\!J_{n-1}\!\left[ \left(1-\frac{B_{\rm r}}{2}\right)\!\varrho\right]-\left(1+\frac{B_{\rm r}}{ 2}\right)\!J_{n-1}\!\left[\left(1+\frac{B_{\rm r}}{2}\right)\!\varrho\right]\!. \tag{14}\]  
Here, \(\varrho\) and \(\varphi\) are related to azimuth and range via \(\xi=\varrho\sin\varphi\) and \(\rho=\varrho\cos\varphi\), respectively, \(\mathrm{J}_{n-1}(\cdot)\) is the Bessel function of the first kind [31], \(B_{\rm r}=B/f_{\rm c}\) the relative bandwidth, and \(\phi_{0}\) the integration angle. Note that analytical realizations of SAR-scene cuts \(h(0,\rho)\) and \(h(\xi,0)\) can be obtained from the impulse response function (12) for \(\varphi=0\) and \(\varphi=\pi/2\), respectively.
## 3 Data and Methods
### Digital Terrain Model Data  
A digital terrain model of the whole region of Sardinia is available on the Regione Autonoma della Sardegna website (www.sardegnageoportale.it, accessed on 29 November 2019). The DTMs have been realized by the point clouds acquired using a laser scanner flight. The procedure of processing the point clouds is available on the Regione Autonoma della Sardegna website ([[http://webigs2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:BFPNM](http://webigs2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:BFPNM)]([http://webigs2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:BFPNM](http://webigs2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:BFPNM)) accessed on 29 November 2019). The DTMs resulting from the processing of the laser scanner have a resolution of 1 m on the horizontal plan and 30 cm on the vertical accuracy.
## 4 Evaluation and Analysis
### Evaluation Measures  
We chose four evaluation measures: accuracy, recall, precision, and f-score to ensure the reliability of the model. Below, we illustrate terms that can be useful for evaluation measure analysis. The accuracy measure is calculated by TP (True Positive rate: correctly recognized samples) divided by N (all the samples of all activities). The recall measure is calculated by TP (True Positive rate: correctly recognized samples) divided by TP+FN (False Negative rate: samples wrongly recognized as other activities samples). The precision measure is calculated by TP divided by TP+FP (False Positive rate: samples of other activities wrongly recognized as one activity samples). F-score is computed as the harmonic mean of recall and precision.  
\[Accuracy=\frac{TP}{N} \tag{16}\]  
\[Recall=\frac{TP}{TP+FN} \tag{17}\]  
\[Precision=\frac{TP}{TP+FP} \tag{18}\]  
\[F-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{19}\]
## 3 Results and Discussion
### Regression Analysis of Polishing Efficiency and Torque  
In this research study, a relationship between two response variables and the four input variables are modeled and analyzed by using regression analysis. Two response variables are \(PE\) and \(T\). Predictor or variables are control factors (\(T_{g}\), \(\omega\), \(F_{z}\), and \(V_{f}\)). Two linear regression equations are modeled for polishing efficiency and torque. Some important parameters to judge the regression models are \(R\)-\(sq\) (it describes, how much variation is present in the response), \(R\)-\(sq\) (\(adj\)) (it is a modified \(R\)-\(sq\) that has been adjusted for the number of expressions), and \(R\)-\(sq\) (\(pred\)) (how good the model predicts the response for new observations). For the good regression model, these values must be high. Equation (10) shows the linear regression for the polishing efficiency \((PE_{L})\), which has \(R\)-\(sq\) = 91.47%, \(R\)-\(sq\) (\(adj\)) = 88.84%, and \(R\)-\(sq\) (\(pred\)) = 83.88%. Equation (11) shows the linear regression for Torque \((T_{L})\), which has \(R\)-\(sq\) = 94.34%, \(R\)-\(sq\) (\(adj\)) = 92.60%, and \(R\)-\(sq\) (\(pred\)) = 87.63%.  
\[PE_{L}=\ 0.808-54.9\ T_{g}+0.000147\ \omega\ +\ 0.03523\,F_{z}\ -\ 0.00092\ V_{f} \tag{10}\]  
\[T_{L}=\ -\ 0.497\ +\ 17.8\ T_{g}+0.000043\ \omega\ +\ 0.03188\,F_{z}\ -\ 0.00283\ V_{f} \tag{11}\]  
Figure 8a,b compares the experimental value (actual value) for the polishing efficiency and torque to the predicted ones achieved by the linear regression model which is closely matched with the actual values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sources** & **DF** & **Adj SS** & **Adj MS** & **F Ratio** & \(p\) **Value** & **\% Contribution** \\ \hline \(T_{g}\) & 1 & 0.0014 & 0.0014 & 1.4100 & 0.2630 & 0.66 \\ \(\omega\) & 2 & 0.0065 & 0.0033 & 3.2300 & 0.0830 & 3.01 \\ \(F_{z}\) & 2 & 0.1963 & 0.0982 & 97.0700 & **0.0000 \({}^{*}\)** & 90.54 \\ \(V_{f}\) & 2 & 0.0024 & 0.0012 & 1.2000 & 0.3400 & 1.12 \\ Error & 10 & 0.0101 & 0.0010 & & & 4.66 \\ Total & 17 & 0.2168 & & & & \\ \hline \hline \end{tabular} Notes: \({}^{*}\) Significant factors at 95% confidence level.  
\end{table}
Table 6: ANOVA table for Torque (T).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{4}{c}{**Polishing Efficiency (\(PE\))**} & \multicolumn{4}{c}{**Torque (\(T\))**} \\ \cline{2-9}  & \(T_{g}\) & \(\omega\) & \(F_{z}\) & \(V_{f}\) & \(T_{g}\) & \(\omega\) & \(F_{z}\) & \(V_{f}\) \\ \hline
1 & **0.767 \({}^{*}\)** & 0.655 & 0.582 & **0.751 \({}^{*}\)** & **0.368 \({}^{*}\)** & **0.350 \({}^{*}\)** & **0.243 \({}^{*}\)** & 0.390 \\
2 & 0.712 & 0.761 & 0.772 & 0.725 & 0.386 & 0.387 & 0.388 & 0.379 \\
3 & – & **0.802 \({}^{*}\)** & **0.864 \({}^{*}\)** & 0.741 & – & 0.393 & 0.498 & **0.366 \({}^{*}\)** \\ \hline \hline \end{tabular} Notes: \({}^{*}\) Optimum levels of the factors.  
\end{table}
Table 7: Cell mean of the control factor Response table for cell means.
## 3 Results and Discussion
### Characterization of PDMSB-Grafted PS Nanoparticles
#### 3.5.2 DLS Measurements of PDMSB-Grafted PS Particles  
To prove the successful grafting-from approach for DMSB on the surface of PS particles additionally to TEM studies, DLS measurements were done to obtain changes of the hydrodynamic radii and diffusion coefficients of the grafted particles. In all cases, the correlation functions, g\({}_{2}\), were found to be very close to exponential, and no indication of polydispersity was found. The correlation time, \(\tau\), is related to the translational diffusion coefficient, \(D\), by \(D=1/(\tau q^{2})\), and the expected linear variation of \(<\tau>^{-1}\) with \(q^{2}\) was very nicely fulfilled by our data. Figure 8 shows, therefore, the scattering vector (\(q^{2}\)) dependence of the reciprocal average relaxation time \(<\tau>^{-1}\) for bare and grafted PS nanoparticles in THF. Applying the Stokes-Einstein relation allowed to calculate hydrodynamic radii, R\({}_{\rm h}=k_{B}\)T/6\(\pi\)mD, with \(k_{B}\), T and \(\eta\) as the Boltzmann constant, the absolute temperature and the solvent viscosity, respectively. From results in Figure 8, it can be concluded that the slope decreases after PDMSB-grafing of the PS nanoparticles and, hence, indicating an increase in hydrodynamic radii. Obtained R\({}_{\rm h}\) increases from 148 nm for bare PS nanoparticles to 209 nm for sample PDMSB(@PS-1 and to 175 nm for sample PDMSB(@PS-2, which corresponds to a shell thickness of 61 nm for the first and 27 nm for the second sample, respectively, evidencing a successful particle grafting with PDMSB.  
Figure 7: _Cont._
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 4 Solution Using Matching Game-Based U-S. A and U-S. P Algorithms  
In this section, we propose two matching algorithms to solve the U-S. A and U-S. P formulated sub-problems. Since O.P.T1 \(-\) U \(-\) S. A and O.P.T1 \(-\) U \(-\) S. P sub-problems are formulated as a one-to-many and one-to-one matching game, respectively. The matching function of U \(-\) S. A is defined by \(\mu_{\text{U$-$\text{S. A}}}\) with a tuple \((\mathcal{B},U,\Theta,\succ_{\mathcal{B}},\succ_{U})\) and the U \(-\) S. P is represented as \(\mu_{\text{U$-$\text{S. P}}}\) with a tuple \(\left(\mathcal{E},E,\widetilde{\Theta},\succ_{\mathcal{I}},\succ_{E}\right)\). In the U \(-\) S. A matching function, \(\succ_{\mathcal{B}}=\{\succ_{b}\}_{b\in\mathcal{B}}\) and \(\succ_{U}=\{\succ_{u}\}_{u\in U}\) indicates the preference relations of the BSs and UEs players, respectively. As for the U \(-\) S. P matching function, \(\succ_{\mathcal{I}}=\ \{\succ_{l}\}_{l\in\mathcal{I}}\) and \(\succ_{E}=\{\succ_{\epsilon}\}_{c\in E}\) denote the preference relations of the associated uRLLC UEs and eMBB UEs with the same BS, respectively. Assume that \(\Theta\) and \(\widetilde{\Theta}\) are the quota for O.P.T1 \(-\) U \(-\) S. A and O.P.T1 \(-\) U \(-\) S. P, respectively.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Hypothesis-Driven Studies in Identification of mTOR Signaling Components
#### 4.4.4 Hypothesis-Driven Identification of Sestrin 2 as a Leucin Sensor for mTORC1 Activation  
In mammalian cells such as HEK293T, removal of either leucine or arginine inhibited mTORC1-mediated S6K phosphorylation, consistent with these two types of amino acids being the major mTORC1 activity regulators. Given that the pentameric GATOR2 complex is required for amino acid-induced mTORC1 activation [51] and Sestrins (Sestrin 1, 2 and 3) were previously identified as a GATOR2 binding partner that suppresses mTORC1 activation with unknown mechanism(s) [53], [PERSON] et al. examined if Sestrins binding to GATOR2 was regulated by amino acids and found that only leucine, but not arginine, depletion in HEK293T cells led to increased Sestrin 2 binding to GATOR2 [88]. Further evidence supported that leucine specifically disrupted Sestrin 2 binding to GATOR2 in cells and in vitro, thus establishing a critical role of Sestrin 2 as a leucine sensor to control mTORC1 activation--upon leucine availability, leucine releases Sestrin 2 suppression on GATOR2, leading to mTORC1 activation [88]. Further structure work with a complex of Sestrin 2 binding to leucine revealed the critical binding features [89].
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 2 Review of Related Literature
### KSP Algorithms  
The KSP problem has a rich history spanning seven decades, leading to the diverse solution methods listed below. [PERSON]. [15] proposed a brute-force approach enumerating and ranking all paths between a given origin-destination (OD) pair. This method suffers from exponential worst-case complexity and is limited to smaller networks. [PERSON]. [16] introduced the first path deviation-based algorithm using a shortest path tree representation superimposed on the network. Storing several candidate paths is a potential setback of this method, especially on large networks. This method was later adapted by [17], using a dynamic programming approach for KSP to a single destination from all origins.  
[PERSON] proposed labeling algorithms [18; 19; 20] to find KSP from a single origin to all destinations that fall under two categories--label-correcting and label-setting--that primarily differ in the order of processing the network links. Comparing the computational performance of the labeling algorithms for KSP, [PERSON]. [20] found label-setting to be efficient on low-density sparse networks such as transportation and communication networks. The worst-case time complexity of [PERSON]'s labeling approaches, as given by [21], is \(O\left(k^{2}n^{2}\log n\right)\), where \(n\) is the number of nodes in the network. As an extension of [PERSON] and [PERSON]'s [22] single origin to all destinations algorithm, [PERSON]. [23] proposed several label-correcting algorithms differing in their node selection strategies. They also note that label-setting has a better worst-case complexity among the labeling algorithms for the KSP problem.  
Another approach was developed by [21], which is a path deletion procedure that modifies the network at each iteration to find the next shortest path by removing the current path from the network and re-running any efficient shortest path algorithm. A significant limitation of this approach is that the network size grows with the number of paths to be found. [PERSON] et al. [24; 25; 26] improved this approach to limit the network size and require only a single application of a shortest path algorithm. [PERSON]. [27] came up with a better way of network modification to improve the practical performance of the path deletion algorithm [21], yielding a computational complexity of \(O(m+kn\log n)\).  
[PERSON]. [4] proposed an algorithm with the best worst-case complexity on directed graphs with non-negative link costs, maintaining shortest paths in a heap with a time complexity of \(O(m+n\log n+k\log k)\) for a single OD pair and \(O(m+n\log n+kn\log k)\) for a single origin to all destinations. However, the algorithm does not explicitly output the paths themselves. The path lengths can be obtained in a constant time per path, but listing the links along the path requires an additional \(O(m)\) time per path.