esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiments
### Sensitivity Analysis  
We gave a set of parameters to build the models and compared them to the results of the benchmark (\(\lambda_{a}=0.06,\ n=3,\ m=2,\ N=165,\mu_{1}=450,\mu_{2}=270,D=580,U=820\)), and formed a total of four groups of given parameters adjustment schemes for a sensitivity analysis of the FD curves. We obtained the measurement results based on each group of given parameters' values, as shown in Table 6.  
Then we plotted the comparison figure, as shown in Figure 7.  
From Figure 7a and Table 6, it can be seen that with \(\mu_{1}\) in the process of increasing from 330 to 510, the corresponding measurement result \(C\) increases from 74.64 to 100.6; \(k_{C}\) increases from 0.804 to 0.924; and \(\varphi_{d}\) increases from 133.7 to 323.5.  
From Figure 7b and Table 6, it can be seen that with \(\mu_{2}\) in the process of increasing from 150 to 360, the corresponding measurement result \(C\) fluctuates between \([91.47,\ 91.53]\); \(k_{C}\) fluctuates between \([0.862,\ 0.888]\); and \(\varphi_{d}\) decreases from 441.2 to 216.5.  
From Figure 7c and Table 6, it can be seen that with \(D\) in the process of increasing from 460 to 640, the corresponding measurement result \(C\) increases from 91.43 to 91.54; \(k_{C}\) increases from 0.859 to 0.888; and \(\varphi_{d}\) decreases from 541.7 to 138.4.  
From Figure 7d and Table 6, it can be seen that with \(U\) in the process of increasing from 700 to 880, the corresponding measurement result \(C\) fluctuates between \([90.98,\ 91.53]\); \(k_{C}\) fluctuates between \([0.840,\ 0.888]\); and \(\varphi_{d}\) decreases from 327.0 to 245.0.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters and**} & \multicolumn{1}{c}{**Benchmark**} & \multicolumn{1}{c}{\(\mu_{1}\)} & \multicolumn{1}{c}{\(\mu_{2}\)} & \multicolumn{1}{c}{\(D\)} & \multicolumn{1}{c}{\(\mu\)} & \multicolumn{1}{c}{\(U\)} & \\ \hline  & \(\mu_{1}\) & 450 & 330 & 390 & 510 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 \\ Given & \(\mu_{2}\) & 270 & 270 & 270 & 270 & 150 & 210 & 360 & 270 & 270 & 270 & 270 &
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
###### Abstract  
Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications' requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture.  
Keywords:IoT, QBAIoT; self-configuring; IEEE 802.15.4; Slotted CSMA/CA; e-health +
Footnote †: journal: Computer Science
## 2 Study Area, Data, and Methodology  
### Study Area  
Our study comprised 20 cities (Delhi, Mumbai, Dhaka, Karachi, Kolkata, Lahore, Bengaluru, Chennai, Hyderabad, Ahmedabad, Surat, Pune, Yangon, Lucknow, Faisalabad, Jaipur, Kanpur, Dhanbad, Coimbatore, and Colombo) in and surrounding India, with a population greater than 0.5 million. The urban extent of these cities and the surrounding 10 km non-urban regions were considered for this study. The urban extent of the cities was derived from the 250 m MODIS global urban product.
## 6 Discussion  
The increasing capabilities of generative AI are significantly influencing the field of cybersecurity. Threat actors are leveraging generative AI to scale the scope and sophistication of their attacks. In this paper, we introduced GenSQLi, a generative AI-based framework designed to help cybersecurity defenders strengthen their systems against SQL \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Definition/Calculation** \\ \hline True Positives (TP) & Number of attacks correctly blocked by the WAF: \(TP=469\) \\ False Negatives (FN) & Number of attacks not blocked by the WAF: \(FN=6\) \\ True Negatives (TN) & Number of normal samples correctly not blocked by the WAF: \(TN=1596\) \\ False Positives (FP) & Number of normal samples incorrectly blocked by the WAF: \(FP=0\) \\ \hline Accuracy & \(\frac{TN+TP}{TN+TP+FN+FP}=0.9956\) \\ Precision & \(\frac{TP}{TP+FP}=1.0\) \\ Recall & \(\frac{TP}{TP+FN}=0.9874\) \\ F1-Score & \(2\cdot\frac{\text{Precision-Recall}}{\text{Precision}+\text{Recall}}=0.9936\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance metrics for generated ModSecurity security rules. injection (SQLi) attacks. GenSQLi employs generative AI to synthesize novel SQLi attack patterns capable of bypassing existing Web Application Firewall (WAF) security rules. The framework then combines machine learning with generative AI to generate defense rules that address entire classes of these attacks. The approach employed by GenSQLi is in contrast to the traditional approach of the manual crafting of WAF security rules. Table 9 compares the generative AI approach with the manual approach across multiple features. This approach exemplifies the potential of using generative AI to proactively enhance cybersecurity defenses by synthesizing potential attack scenarios before they are encountered in real-world systems. The process can be iterated with multiple generation strategies until a satisfactory level of robustness is achieved. Existing SQLi generation techniques, such as context-free grammars and evolutionary algorithms (see Section 2), can serve as in-context learning examples to create families of similar attacks. Additionally, newly discovered attacks can immediately be incorporated as in-context examples to generate similar attack vectors and corresponding defense rules. Although GenSQLi primarily targets rule-based firewalls, the generated SQLi attacks can also enhance machine-learning-based attack detection models by supplying additional training data. Overall, the proposed generative AI framework provides a foundation for automated defense against a wide range of cybersecurity threats. An advantage of using generative AI in this capacity is the relative ease of generating both attacks and defenses, particularly when compared to traditional methods like context-free grammars.This lowers the technical barrier, enabling a broader range of cybersecurity practitioners to enhance system defenses against cyberattacks. Unlike traditional manual drafting of firewall rules, a generative AI-driven approach offers faster adaptation to
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 4 Data Analysis and Results
### Effect of the Previous Knowledge of the Students  
The analysis for questions \(Q3\), \(Q4\), and \(Q5\) was also performed for the four EM sections and the EF section alone (see Table 1). The results are presented in Table 8 and Table 9, respectively.  
From Table 8, it can be observed that the students from the EM sections showed the same trend discussed above for the entire student sample. They obtained significant learning gains for questions \(Q4\) and \(Q5\), especially in this latter, where almost all students answered it correctly in the post-test. On the other hand, the observed learning loss for \(Q3\) was also not statistically significant.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{N=24}\)} & \multicolumn{2}{c}{**Correct Answers Proportion**} & \multicolumn{2}{c}{**Gains**} & \multicolumn{2}{c}{\(\mathbf{t}\)**-Test**} \\ \cline{2-7}  & _Pre_ & _Post_ & \(G\) & _Grel_ & _p_-Value (Two-Tails) \\ \hline \(Q3\) & 0.805 & 0.770 & \(-0.035\) & \(-0.042\) & 0.5516 \\ \(Q4\) & 0.310 & 0.552 & 0.242 & 0.351 & 0.0010 \\ \(Q5\) & 0.540 & 0.977 & 0.437 & 0.950 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average learning gains per question for the four Electricity and Magnetism course (EM) sections.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{N=87}\)} & \multicolumn{2}{c}{**Correct Answers Proportion**} & \multicolumn{2}{c}{**Gains**} & \multicolumn{2}{c}{\(\mathbf{t}\)**-Test**} \\ \cline{2-7}  & _Pre_ & _Post_ & \(G\) & _Grel_ & _p_-Value (Two-Tails) \\ \hline \(Q3\) & 1.000 & 0.917 & \(-0.083\) & \(-0.083\)(*) \\ \(Q4\) & 0.917 & 0.917 & 0.000 & 0.000 \\ \(Q5\) & 0.833 & 1.000 & 0.167 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average learning gains per question for the Electromagnetic Fields (EF) section.  
For the EF section, the hypothesis test was not performed to compare the proportions because the well-known large sample criterion \(N*P>5\) and \(N*(1-P)>5\) is not met [31], where \(P\) is any proportion of correct answers in \(Pre\) or \(Post\). From Table 9, it is interesting to note that for the more advanced students of the EF section, the learning gain increased only for \(Q5\) (plane charge). Note also that all EF students obtained the right answer for question \(Q5\) after using the simulators (\(<G_{rel}>=1\)).
## 4 Model Building and Training
### Model Training  
#### 4.2.1 Model Training CatBoost  
Table 6 shows the parameters of the final CatBoost model: the number of iterations corresponding to the number of decision trees is 500; tree depth and learning rate are defined in Section 4.1.1; RMSE (3) is used as the loss function; the greedy search algorithm provides for sequential deepening of the tree; training is stopped when the error value does not decrease within 30 iterations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Parameter** & **Value** \\ \hline
1 & Number of neighbors & \(2,5,7,10,15,20\) \\ \hline
2 & Sheet size & \(1,3,5,10,20\) \\ \hline
3 & weight function & “uniform” \\  & & “distance” \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for the k-nearest neighbor model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Parameter** & **Value** \\ \hline  & & “linear” \\
1 & Kernel type & “poly” \\  & & “rbf” \\  & & “sigmoid” \\ \hline
2 & Regularization parameter C & \(1,2,3,4,5\) \\ \hline
3 & Epsilon & \(0.1,0.2,0.5,1,1.5,2,3\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters for SVR model.  
Figure 6 shows the training schedule, according to which 65 iterations are sufficient for the model, determined by setting the overfitting detector.  
The interpretation of the gradient boosting algorithm is facilitated by the ability to represent the decision rules in the form of a visual tree structure. Figure 7 shows part of one of the decision trees. As you can see from the figure, the same functions are used to create left and right splits at each level of the tree. Owing to the peculiarities of the structure of decision trees, gradient boosting is able to cope with nonlinearities.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Num** & **Parameter** & **Value** & **Optional Description** \\ \hline
1 & Number of iterations & 500 & Number of decision trees \\ \hline
2 & Tree depth & 8 & Tree structure depth \\ \hline
3 & Learning rate & 0.1 &
\begin{tabular}{c} A parameter that determines the step size at each iteration when moving toward the minimum of the loss function \\ \end{tabular} \\ \hline
4 & Metric used for learning & RMSE & Formula (4) \\ \hline
5 & Greedy search algorithm & Symmetric tree &
\begin{tabular}{c} The tree is built level by level until it reaches the required depth \\ \end{tabular} \\ \hline
6 & \
\begin{tabular}{c} Type of overfitting \\ detector \\ \end{tabular} & Early stopping &
\begin{tabular}{c} Stops training when the error value does not decrease within 30 iterations \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 6: Model parameters based on CatBoost.  
Figure 7: Visualization of the tree structure.
## 3 Preliminaries
### Design Goals
#### 3.2.3 Bandwidth Efficiency  
Systems for video analysis using drones need to be designed with bandwidth efficiency as a key consideration in order to be scalable. Each drone streams high-definition data,which when not managed properly, can quickly saturate network bandwidth. The goal is to design a system that can seamlessly accommodate as many drones as possible with minimal performance degradation.  
Not all data captured by drones are useful. For video analytics, drones need to be equipped with onboard models or heuristics that can determine if some footage is important enough for transmission. The bandwidth usage can be further optimized using data compression techniques, which may be dependent on the nature of the specific use case.
## 4 Conclusions  
The daily mean mass concentration of PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\) were 63.20, 119.63 and 211.95 ug\(\cdot\)m\({}^{-3}\), respectively. The average mass concentrations of OC and EC in PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) were 11.37 and 5.87, 19.24 and 7.36, and 27.04 and 10.27 ug\(\cdot\)m\({}^{-3}\), respectively. The average proportions of TM for the three particle sizes of PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) were 43%, 36% and 26%, respectively. These results revealed that the carbonaceous components in fine particles is high, and decrease along with the increase in particle size.  
These four episodes were categorized as a short evolution pattern (PE1 and PE3) and a long evolution pattern (PE2 and PE4). Unstable metrological conditions, such as air mass frequently fluctuating or heavy wind blowing, was the main reason for the short evolution pattern. In comparison, stagnant condition facilities to atmospheric chemical reaction, which would last for more days in long evolution pattern. Carbonaceous components contributed to PE1 and PE2 under drier conditions through transportation and local combustion emission, while they were not main species in PE3 and PE4 for haze explosive growth under suitable RH, whether for the short or long evolution pattern. These results highlight that local primary emissions such as biomass combustion were important sources of haze formation in Central China, especially in dry conditions.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/10/3498/s1](http://www.mdpi.com/2076-3417/10/10/3498/s1)]([http://www.mdpi.com/2076-3417/10/10/3498/s1](http://www.mdpi.com/2076-3417/10/10/3498/s1)), Figure S1: Mass concentration of PM\({}_{2.5}\) and meteorological conditions (wind speed and relative humidity) during sampling period, Figure S2: The backward air mass trajectories of 48 h starting form Xinxiang, (a) for PE1, (b) for PE2, (c) for PE3 and (d) for PE4, Table S1: Meteorological parameters during the sampling period.  
Conceptualization, G.Y. and Z.L.; methodology, J.Z.; software, P.Z.; validation, G.Y., Z.L. and Y.W.; formal analysis, G.Y.; investigation, J.Z.; resources, G.Z. and Z.C.; data curation, P.Z.; writing--original draft preparation, J.Z.; writing--review and editing, G.Y. and Z.L.; visualization, G.Y.; supervision, Y.W.; project administration, G.Y.; funding acquisition, G.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ministry of Science and Technology of China (grant no. 2017 YFC0210000), the National Natural Science Foundation of China (41807327, 41805123), and the Program for Innovative Research Team in Science and Technology in University of Henan Province (No. 20 IRTSTHN011).  
The authors declare no conflict of interest.
# A Fast Sparse Coding Method for Image Classification
## 1 Introduction
However, to further improve its performance, some problems need to be solved. First, it cannot effectively handle large training sets because the dictionary learning algorithm has high computational complexity [18; 19]. Second, local features are dealt with separately, thus the mutual dependence among local features is ignored, which results in highly variable sparse codes even for similar features [16]. To solve these problems, we propose a self-representation sparse coding strategy that separates dictionary learning algorithm from sparse coding. Under this strategy, sparse dictionary is built with low-level feature extracted from some important subregions. This is different from existing sparse coding method, which learns a dictionary by employing linear combination of low level feature extracted from all subregions in training dataset. We employ cluster centers of \(k\)-medoids cluster to build our dictionary, because this cluster builds cluster centers by stably selecting local feature extracted from important subregions and can reduce the learning complexity of Lagrange dual. Therefore, the proposed method can solve the sparse coding's problem of high computational complexity and variable sparse codes for similar features. Based on this strategy, we propose an image classification framework, as shown in Figure 1. Firstly, SIFT descriptors are extracted from each of subregions. Secondly, \(k\)-medoids cluster is applied to learn a dictionary from SIFT descriptors. Then, ScSPM method is employed to generate image feature. Finally, classification is performed to classify images according to image feature.
## 4 Parameterization and Analysis of the Proposed TDL Model
### Average PDP and Tap Characteristics
Variables including UAV speed, UAV altitude and Tx-Rx distance may cause significant differences in parameters (e.g., number of taps \(M\), tap power \(a_{m}\), and tap delay \(\tau_{m}\)) and, therefore, shall be considered in the model. As one important characteristic of propagation channel, the Doppler shift depends on the UAV speed and wave direction. Further, the Doppler spread and its spectrum shape indicate the scatterer properties. In scenarios FS4 to FS6, the same number of taps, tap power, and tap delay are obtained at different altitudes, and similar parameter variation trends are observed. Thus, we conclude that the impact of UAV altitude on the TDL model is relatively small if the UAV speed and Tx-Rx distance are fixed.
## 2 Proposed PLL Architecture
### Band Selection Procedure  
Band selection execution is implemented by the proposed circuit block realization shown in Figure 2a. As shown in the schematic diagram, the four-bit ADC architecture is implemented to generate the 11 reference voltages by using a resistor network and transmission gates. Here, two non-equal customized resistor values are implemented in the divider network to produce the two most probable reference voltage ranges. These two unique voltage levels' resistive circuit is shown in Figure 2b, while the customized voltage values are displayed in Figure 3. Two 3 \(\times\) 8 decoders were designed to select any one of the 11 reference voltages by enabling the ASIC digital control. One PMOS input pair comparator for a low-range reference voltage and one NMOS input pair comparator for a high-range reference voltage were designed with a hysteresis of 5 mV. These two dynamic comparators compare reference voltages with the DC control voltage Vctr. The comparator output moves to the regenerative latch to strengthen the logic value. To make the circuit compact, two comparators compare the Vctr and reference voltage levels vrefp_sel and vrem_sel multiplexed with eleven voltage levels. Eleven clock cycles are applied sequentially to compare all subdivided voltage reference levels and then obtain the final output voltage at dout.  
Figure 2: (**a**) Proposed PLL with an automatic band selection circuit; (**b**) four-bit ADC sub-block with two indigenous custom voltage steps.  
Based on the comparator signal, the band moves/switches to the upper or lower frequency band or may remain in its present frequency band, if Vctr is among Vrefm_sel and Vrefp_sel. There are eleven voltage levels from 390 mV to 1.7 V as reference voltages in the ADC sub-block, out of which two indigenous voltage steps of 420 mV were designed along with the other 60 mV steps for the fast locking of the PLL, with the flexibility of a wide range selection.  
In the case that PLL power down is observed for the Vctr, there may be an output at the dout. To overcome this unwanted condition, the circuit block comprises an NMOS, and an NOR gate is implemented at the output (dout), whose inputs are connected with the multiplexing output and ensure a ground path for the Vctr. Hence, based on ASIC mapping, these outputs (dout) move to the iDAC and provide a suitable change in the bias voltage of the delay cell. In the proposed PLL, the most probable locking voltage range is 0.7-1.7 V. The complete locking mechanism is displayed in Figure 4 along with the band selection simulation in Figure 5. In Figure 5, the automatic band selection simulation graph shows the ringing of the DC control voltage and the final convergence. It sequentially compares all eleven voltage references represented by the DAC data latch for one cp_break_d high pulse, then Vctr increases, and the pattern repeats until achieving the final lock-in range.
## 4 Results and Discussion
### Hydrodynamic Performance of TLC Reactor
#### 4.2.2 Power Consumption  
The hydraulic mixing power consumed by the TLC reactors is the product of flow velocity, surface inclination, and water depth. With the increase in mass flow rate and slope angle, flow velocity increases such that the reactor consumes more power to move the liquid along the reactor (Figure 8a,c). The hydraulic power consumption increases by more than twofold for the slope angle of 3\({}^{\circ}\) in comparison with the reactor inclined at 1\({}^{\circ}\). Higher velocities require considerably more energy. High cell densities can be achieved by faster flow rates, but the energy cost to achieve this is very high [14].  
Moreover, an increase in water depth resulted in the slow movement of fluid, which indicates that the reactor with increased water depth requires more power to move the liquid along the reactor (Figure 8b). In other words, greater reactor dimensions require greater power. The hydraulic power  
Figure 8: Hydraulic power consumption (**a**) for different mass flow rates at a water depth of 5.6 mm and slope angle of 1\({}^{\circ}\), (**b**) for various water depths at a mass flow rate of 2.4 kg/s and slope angle of 1\({}^{\circ}\), and (**c**) for the complete reactor with the variation in mass flow rate and slope angle.  
required by each compartment of the reactor is presented in Figure 8a. The power required by the lower channel of the reactor is greater than that required by the upper channel because of the flow reversal module. The flow reversal module directs the fluid at the lower channel by increasing the flow velocity in the lower channel. The high velocity results in increased power consumption in the lower channel.