esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization
## 3 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**FUNCTION**} & \multicolumn{2}{c}{**Proposed**} & \multirow{2}{*}{**Proposed (F:0.5)**} & \multirow{2}{*}{**Proposed (F:0.9)**} \\  & **(Population = 200)** & & \\ \hline ACKLAY & 5818 & 4647 & 2829 \\ \hline BF1 & 5585 & 4303 & 2847 \\ \hline BF2 & 5008 & 3502 & 2334 \\ \hline BF3 & 4282 & 3034 & 2045 \\ \hline BRANIN & 3026 & 2300 & 1506 \\ \hline CAMEL & 3327 & 2526 & 1795 \\ \hline DIFFPOWER2 & 8864 & 6326 & 4094 \\ \hline DIFFPOWER5 & 26,065 & 19,239 & 14,008 \\ \hline DIFFPOWER10 & 29,549 & 22,112 & 15,325 \\ \hline EASOM & 2755 & 1976 & 1306 \\ \hline ELP10 & 4020 & 3080 & 2111 \\ \hline ELP20 & 6537 & 5080 & 3234 \\ \hline ELP30 & 8270 & 6300 & 4307 \\ \hline EXP4 & 3570 & 2718 & 1906 \\ \hline EXP16 & 3854 & 2938 & 1990 \\ \hline EXP32 & 3852 & 2700 & 1904 \\ \hline F1 & 6444 & 5232 & 3404 \\ \hline F5 & 2697 & 2100 & 1822 \\ \hline F9 & 4406 & 2999 & 2111 \\ \hline GKLS250 & 2315 & 1767 & 1191 \\ \hline GKLS350 & 2265 (96) & 1699 (90) & 1050 (60) \\ \hline GOLDSTEIN & 3930 & 2826 & 1949 \\ \hline GRIEWANK2 & 3850 (50) & 2803 (56) & 1883 (30) \\ \hline GRIEWANK10 & 7551 & 5821 & 3933 \\ \hline HANSEN & 4565 & 2965 & 2024 \\ \hline HARTMAN3 & 3145 & 2440 & 1613 \\ \hline HARTMAN6 & 4105 & 3036 & 2004 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Function calls with different population sizes. An additional experiment was executed using the High Elliptic function, which is defined as follows:  
\[f(x)=\sum_{i=1}^{n}\left(10^{\delta}\right)^{\frac{i-1}{n-1}}x_{i}^{2}\]  
where the parameter \(n\) defines the dimension of the function. In this particular experiment, the proposed optimization method was systematically applied to a specific mathematical function as the dimension \(n\) underwent a transition from 10 to 100. Figure 4 presents the number of objective function calls required by each algorithm across different dimensions. As the problem's dimension increases, the number of function calls needed to find a solution grows significantly for all algorithms, indicating the increasing complexity of the ELP function. For example, the PROPOSED (KMEANS) algorithm requires 3593 calls for 10 dimensions and 20,900 calls for 100 dimensions, showing a gradual increase. Similarly, the PROPOSED algorithm starts with 4020 calls for 10 dimensions and reaches 24,322 calls for 100 dimensions. IPSO stands out as the most efficient algorithm, with only 1720 calls at 10 dimensions and 3165 at 100, consistently maintaining low evaluations. In contrast, WOA exhibits the worst performance, with 10,317 calls at 10 dimensions and 62,878 at 100 dimensions. The rate of increase in calls varies among algorithms. For example, the PROPOSED algorithm increases from 4020 to 6537 calls between 10 and 20 dimensions (a 1.6 times increase), while the rise from 90 to 100 dimensions is more moderate, from 20,798 to 24,322 calls. The MEWOA, although better than the WOA, requires more calls than the
## 5 Simulation and Experimental Results of the Proposed System
### Simulation Results  
#### 5.1.1 Performance of the Converter  
The solar panel is consistently operated at the minimum and maximum power level utilizing the proposed GWO optimized algorithm. The tracking of the maximum power is purposely increased at the start in order to achieve the smooth start of the BLDC drive. The perturbation size is appropriately selected in order to avoid oscillation during the peak powerpoint. The DC link voltage across the capacitor is supplied to the VSI for the smooth start of the BLDC drive. The solar panel is operated under the conditions of dynamic variation due to changes in the irradiation level. Using the GWO algorithm, the proportional and integral gain is varied as it is mainly designed to obtain an excellent dynamic response under conditions of uncertainty and external disturbances. The GWO-based closed-loop control algorithm tracks the maximum power from the system. The output voltage (see in Figure 4a) and current (see in Figure 4b) waveforms of the solar PV panel are shown in Figure 4.  
From Figure 4, it is observed that the output voltage from the PV panel is approximately 55 Volts, and the output current generated by the PV panel is 17 Amps. Here, the PV panel is integrated with the MPPT technique. For prove the effectiveness of the proposed controller in MPPT tracking, a comparative study was done using various MPPT algorithms.  
In Figure 5, the output waveform of the modified LUO converter using the GWO algorithm is shown. Initially, a peak is observed, and later at the time > 0 s the voltage value settles to a value of approximately 300 Volts. The output voltage produced by the proposed modified LUO converter is compared with the P&O and Fuzzy logic algorithms.  
Figure 4: Solar Photovoltaic (PV) panel output. (**a**) Output voltage in Volts (V); (**b**) Output current in Amps (A).  
The input and output currents drawn by the proposed system are shown in Figure 6a,b, respectively. Here, the current ripples are maintained within the permissible limit. The three-phase output voltage waveform of the voltage source inverter is shown in Figure 7.  
Figure 5: The output voltage waveform of the modified LUO converter using the GWO algorithm.  
Figure 6: Current waveforms of the modified LUO converter using the GWO algorithm. (**a**) Converter input current; (**b**) Converter output current.  
Figure 7: The output waveform of the voltage source inverter.
## 2 Materials and Methods
### Lipid Oxidation Analysis
#### 2.8.4 DSC Analysis  
The measurements of oxidative stability were carried out with a Perkin-Elmer DSC 7 device (PerkinElmer, Corp., Norwalk, CT, USA) equipped with an Intracooler II and Pyris software 10.0. Nitrogen (99.98% purity) was the purge gas and oxygen (99.999% purity) was the oxidation agent. The DSC calorimeter was calibrated for temperature and enthalpy using indium (m.p. 156.6 \({}^{\circ}\)C, \(\Delta\)Hf = 28.45 Jg\({}^{-1}\)) and n-dodecane (m.p. \(-\)9.65 \({}^{\circ}\)C, \(\Delta\)Hf = 216.73 Jg\({}^{-1}\)). Isothermal determination of oxidation induction period (IP) was performed accordingly to the method described for oxidative stability measurements [23]. The samples of extracted fat of 8-10 mg were weighed into open aluminum pans (Perkin Elmer, No. 02190041, Waltham, MA, USA). At least three samples were analyzed of each fat. The thermal program includes heating from 25 to 140 \({}^{\circ}\)C at a scanning rate of 5 \({}^{\circ}\)C min-1 under nitrogen flow and isothermal heating at 140 \({}^{\circ}\)C under oxygen flow of 20 mL/min. The reference was the same open and empty aluminum pan. After normalization of oxidation DSC curve, the IP parameter was determined as the intersection of the extrapolated baseline and the tangent line (leading edge) of the recorded exotherm.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 3 PQ Mitigation Techniques
### Future Mitigation Trends
#### 3.2.2 Transmission Technology  
Offshore installation is one of the main trends in WTGs technology and might have a more favorable energy balance compared with onshore turbines. Offshore WTGs harvest 50% more energy than a turbine placed on a nearby site. The reason behind this is less friction on the sea surface. Additionally, offshore turbines have a longer life expectancy than onshore turbines, which is around 25-30 years. The longer life is because of the lower fatigue loads on the WTGs due to the flow turbulence at sea. On the other hand, the construction and installation of offshore turbines are more difficult than onshore turbines. However, the transmission of the power generated by offshore WPPs to the distribution system is another aspect to be carefully considered. HVAC is a simple and economical solution for power transmission from grid-connected offshore wind farms. However, the length and power capacity of HVAC cables are limited. The reason is that the distributed capacitance of undersea cables is much higher than that of overhead transmission lines. However, HVDC is a secure and efficient way to connect offshore WPPs to the grid. HVDC transmission system offers many advantages in comparison with HVAC. It has a higher power transmission capability and the power flow is controllable. Further, cable power losses are low, and offshore WTs are isolated from any disturbances. However, the classical HVDC transmission system is based on the CSCs with naturally commutated thyristors, which are the so-called LCC. LCC-based HVDC transmission technology suffers independent control of the active and reactive power. Furthermore, it produces a big amount of harmonics. VSC based HVDC transmission system provides more advantages for the connection of large offshore WPPs. Such a system does not require a strong offshore or onshore ac network and can even start up against a dead network. Additionally, the reactive and active power can be controlled in a VSC-based HVDC transmission system. Transmission technologies can help in stabilizing the grid at PCC [33].
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 4 Data Collection Technologies
\begin{table}
\begin{tabular}{c c c c} \hline
**Technologies** & & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Inductive loops} & - & provide basic traffic & & \\  & & parameters (e.g., volume, & \\  & & occupancy, speed, & - & installation requires pavement \\  & & presence, headway) & & cut and lane closure \\  & - & well-defined detection zone & - & spatial coverage is limited \\  & - & well-known technology & - & implementation and \\  & - & accurate and reliable & & maintenance costs are high \\  & & traffic data & - & lifetime depends on \\  & - & negligible influence of & & pavement quality \\  & & weather conditions & & \\ \hline \multirow{8}{*}{Point sensors} & - & can provide the largest set & & \\  & & of data & & \\  & & - & feasible integration of & - & affected by \\  & & traffic collection and & & weather conditions \\  & & traffic supervision & - & calibration issue \\  & & - & can replace several loops & - & cover occurrence \\  & & - & non-intrusive sensor—no & & \\  & & pavement cut needed & & \\ \hline \multirow{8}{*}{Radar sensors} & - & provide speed, vehicle counts, & & \\  & & vehicle classification & - & susceptibility to \\ \cline{1-1}  & - & is not affected by & & electromagnetic interferences \\ \cline{1-1}  & & weather conditions & - & cover occurrence \\ \cline{1-1}  & - & multiple detection zone & & \\ \hline \multirow{8}{*}{Acoustic sensors} & - & multiple lane & - & spatial coverage is limited \\ \cline{1-1}  & & operation available & - & high costs for setting up \\ \cline{1-1}  & - & passive detection & & and maintaining \\ \cline{1-1}  & - & record vehicle’s passage, & - & unsuitable for urban areas \\ \cline{1-1}  & & presence, and speed & & with dense traffic \\ \hline \end{tabular}
\end{table} 
Table 3: Advantages and Disadvantages of Data Collection Technologies.
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## 5 Conclusions  
The proposed neural network model based on multi-head self-attention has been found to be effective for SQL injection detection. The model has the advantages of low cost for model deployment, fast inference computation, and high SQL injection detection performance. While having comparable detection accuracy, precision, and recall rate values to the existing lightweight BERT models, the proposed model requires significantly lower weight size. In fact, the weight size of the proposed model is only 69,269, which is 0.1% of that of the DistilBERT model. Therefore, the proposed model can be deployed in edge devices with limited memory capacity such as UP Board. The average inference time of the proposed model for the SQL injection detection for each query is only 51.55 ms on a PC. Furthermore, the average inference time for the Up Board is 175.92 ms. For the test set where the length of each query is less than 1000 characters, the accuracy, precision rate, recall rate, and F1 score of the proposed model are 98.98%, 98.01%, 99.84%, and 98.92%, respectively. The detection performance can be effectively maintained for the test set with queries above 1000 characters in length. It can be concluded from the experimental results that proposed model is an effective alternative for applications requiring fast SQL injection detection with high accuracy and low deployment overhead.  
Conceptualization, R.-T.L. and W.-J.H.; methodology, R.-T.L. and T.-M.T.; software, R.-T.L.; validation, R.-T.L., W.-J.H., and T.-M.T.; investigation, R.-T.L.; resources, W.-J.H. and T.-M.T.; writing--original draft preparation, W.-J.H.; writing--review and editing, T.-M.T.; supervision, W.-J.H.; project administration, W.-J.H. and T.-M.T.; funding acquisition, W.-J.H. All authors have read and agreed to the published version of the manuscript.  
The original research work presented in this paper was made possible, in part, by the National Science and Technology Council, Taiwan, under grants MOST 111-2221-E-003-009-MY2 and NSTC 113-2221-E-003-027-MY2.  
Not applicable.  
The training and test sets supporting the reported results can be found in the link [[https://www.kaggle.com/datasets/rayten/sql-injection-dataset](https://www.kaggle.com/datasets/rayten/sql-injection-dataset)]([https://www.kaggle.com/datasets/rayten/sql-injection-dataset](https://www.kaggle.com/datasets/rayten/sql-injection-dataset)) (accessed on 8 November 2024).  
The authors would like to thank [PERSON] for the collections of test set for the evaluation of neural network models.  
The author [PERSON] is employed by the company \"NVIDIA\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS)  
Five grams of onion sample was placed into a 20 mL screw-capped vial. Then the sample was incubated at 40 \({}^{\circ}\)C for 5 min and extracted for 30 min. An automatic headspace sampling system (MultiPurpose Sample MPS 2 with an SPME adapter, from Gerstel Inc., Mulheim, Ruhr, Germany) with a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Inc., Belle-fonte, PA, USA) was applied to extract volatile components. As a result, the SPME fiber was inserted into the headspace and absorbed at 40 \({}^{\circ}\)C for 40 min. After extraction, the loaded SPME fiber was immediately eliminated from the sample vial and inserted into the injection port of GC-MS at 250 \({}^{\circ}\)C for 5 min in order to conduct further analysis. The analysis of each extract was repeated three times.  
GC-MS analysis was conducted on an Agilent 7890B gas chromatographic column which was equipped with an Agilent 5975 mass selective detector. In order to isolate and characterize the aroma compounds, an INNOWAX capillary column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) was applied. The carrier gas was helium (99.999%), with a constant flow rate of 1.0 mL/min and an inlet temperature of 250 \({}^{\circ}\)C. The sample was injected in splitless mode. The temperature program was as follows: the initial oven temperature, staying at 50 \({}^{\circ}\)C for 5 min, was ramped to 160 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and then held at that temperature for 3 min, later, was raised to 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and maintained there for 2 min. The temperature of the transfer line was 250 \({}^{\circ}\)C. With ionization energy being 70 eV, the electron ionization mode (EI) was used. The ion source temperature was 250 \({}^{\circ}\)C, and the mass range was from m/z 45 to 500.
# Potential Study of Solar Thermal Cooling in Sub-Mediterranean Climate
## 4 Economic Feasibility
### Absorption Chiller Monitoring Results  
The absorption chiller was monitored on several sunny summer days between May and July 2015. The cooling capacity and the thermal COP as a function of generation temperature (\(T_{g}\)) are shown in Figure 7[29]. As shown in Figure 7, the cooling capacity varies and the maximum value reached about 30 kW with a mean daily generator temperature reaching values not higher than 80 \({}^{\circ}\)C. The thermal coefficient of performance is defined as the ratio of the cooling achieved in the evaporator of the absorption chiller to the driving heat applied to the generator. The COP of the absorption chiller varied significantly depending on the operating conditions. Low COP values were observed, varying between 0.09 to 0.54 compared to 0.79 as claimed by the manufacturer regarding of the operating conditions. The absorber and condenser mean daily temperature is between 24 \({}^{\circ}\)C and 38 \({}^{\circ}\)C with maximum ambient air temperatures reaching 41 \({}^{\circ}\)C. The mean daily evaporator temperature is between 8 \({}^{\circ}\)C and 15 \({}^{\circ}\)C.  
The main result to investigate is the observed cooling capacity of just 18.7%, at maximum of the nominal cooling capacity value (160 kW). in addition, an average thermal COP of less than 0.3 can be observed. An explanation was listed by [29], who attributed this to the operation of the absorption chiller on non-demanding days (the University is closed on Fridays), the frequent operation of the solar dry cooler, and some control and instrument issues.  
Figure 7: Cooling capacity and thermal Coefficient of Performance (COP) of the absorption chiller in GJU as a function of driving temperature, modified from [29].
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 3 The Proposed Method
### Embedding Learning
#### 3.3.1 A Basic KGE Model  
Different KGE models have different score functions that aim to obtain a suitable function to map the triple score to a continuous true value in [0, 1], i.e., \(\phi:\mathcal{E}\times\mathcal{R}\times\mathcal{E}\rightarrow(0,1)\), which indicates the probability that the triple holds. We follow [17; 18] and choose ComplEx [26] as a basic KGE model. It is important to note that our proposed framework can be combined with an arbitrary KGE model. Theoretically, using a better base model can continue improving performance. Therefore, we also experiment with RotatE as a base model. Below, we elaborate on the methods of ComplEx and RotatE. ComplEx assumes that the entity and relation embeddings exist in a complex space, i.e., \(e\in\mathbb{C}^{d}\) and \(r\in\mathbb{C}^{d}\), where \(d\) is the dimensionality of the complex space. Using plurals to represent entities and relations can better model antisymmetric and symmetric relations (e.g., kinship and marriage) [26]. Through a multilinear dot product, ComplEx scores every triple. In contrast to ComplEx, RotatE defines the functional mapping induced by each relation \(r\) as an element-wise rotation from the head entity \(h\) to the tail entity \(t\):  
\[F_{ComplEx}(h,r,t)=\text{Re}(h^{\text{T}}\text{diag}(r)\bar{t})=\text{Re}(\sum_{ i}[h]_{i}[r]_{i}[t]_{i}), \tag{2}\]  
\[F_{RotatE}(h,r,t)=-\|h\circ r-t\|, \tag{3}\]  
We utilize the \(\text{Re}(\cdot)\) function to extract the real part of a complex value, and \(\circ\) is the Hadmard (or element-wise) product. Additionally, the \(\text{diag}(\cdot)\) function is employed to construct a diagonal matrix from the variable \(r\). Furthermore, we define \(\bar{t}\) as the conjugate of the variable \(t\), and \([\cdot]_{i}\) is the \(i\)-th entry of a vector.  
\[\phi(h,r,t)=\sigma(F(h,r,t)), \tag{4}\]  
where \(\sigma(\cdot)\) is the sigmoid function and \(F(\cdot)\) is the score function. By minimizing the logistic loss function, ComplEx learns the relation and entity embeddings:  
\[\sum_{(h,r,t)\in\mathcal{T}\cup\mathcal{T}^{\prime}}\log(1+\exp(-y_{htr}\cdot \phi(h,r,t))), \tag{5}\]  
where \(\mathcal{T}^{\prime}\) is negative triples and \(y_{htr}\) is the label of a triple.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 2 System Model  
We considered an AmBC-NOMA system, as depicted in Figure 1: the fixed base station (BS) sent a data signal \(x(t)\) to the downlink users \(D_{i}\) (\(1\leq i\leq D\)) and to the BD. Furthermore, as the data signal \(x(t)\) excited the circuitry of the uplink BD, the BS and the BD simultaneously transmitted signals to the NOMA users. We assumed that all nodes of the AmBC-NOMA systems were fitted with an antenna. As the BD was a passive component, we assumed that the information processing delay of the BD and the power consumption of the circuit had a negligible effect on the reflected signal. Due to the complex radio environment in urban districts, all the channels in Figure1 were assumed to obey Rayleigh fading. In order to simplify the analysis, all the nodes were considered a single antenna in the AmBC-NOMA systems. Our future work will consider complicated scenarios (e.g., multi-antenna and multi-cell scenarios). On the premise of not losing generality, the fading channel gains ordering between the BS and the users were \(\left|h_{1}\right|^{2}\leq\cdots\leq\left|h_{d}\right|^{2}\leq\cdots\leq \left|h_{D}\right|^{2}\), where \(g_{i}\) indicates the efficient channels gains from the BD to users, \(i\in[1,D]\). Considering that the channels between the BD and the users were heavily attenuated, SIC technology was adopted for users to extract desired signals: specifically, the user first decoded its signal from the superimposed signal, and then decoded the backscatter signal \(c(t)\) afterwards.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 5 Covert Channel Attacks in 2.5D/3D ICs
### Existing Works
#### 5.2.2 Thermal Covert Channels in 3D ICs
In TSV3D technology, the bandwidth increased by 10%, primarily due to greater temperatures, since the resistance to heat sink increased in a 4 tier stack. In this 4 tier system, a noise application executed in the tier directly below the receiving core resulted in a lowered bandwidth of 100 bps with a BER of approximately 2.5% [65]. These results indicated that the vertical thermal coupling in 3D systems was strong enough that additional tiers did not prevent covert channel communication. However, this strong coupling could also facilitate the development of effective jamming-based countermeasures.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 2 Review of [PERSON] et al.'s Scheme
### Absence of a Verification Mechanism for a Wrong Password  
As stated in [15; 16], in real life, people need to manage a large number of accounts for different applications, so it can easily happen that someone inputs a wrong password. The verification mechanism for a wrong password at a device is an ideal feature for the authentication protocol, which can not only reduce needless communication, but also save calculation costs. However, this valuable mechanism is absent in [PERSON] et al.'s scheme. The consequence of this shortcoming is that a session initiated by a wrong password will be continued until the server finds some errors, and the client will not realize there is a password error until the request is out of time. In this way, much communication and computational resources are wasted, and the authentication process is made ineffective.