esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Evaluation
#### 3.1.4 Comparison with Existing Models  
As shown in Table 6, we compared the GRU-GAN model with existing malware detection models, focusing on key metrics such as accuracy, training time, testing time, and memory usage. Our proposed GRU-GAN model demonstrated exceptional performance, achieving an accuracy of 99.99%, surpassing models by [PERSON] et al. (2020), [PERSON]  
Figure 13: Computation resources used by the BiLSTM, BiGRU, and GRU–GAN models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Dataset 1**} \\ \hline  & **Training Time** & **Testing Time** & **Memory Usage (MB)** \\  & **(s)** & **(s)** & **Memory Usage (MB)** \\ \hline BiLSTM & 80 & 12.7 & 120.3 \\ \hline BiGRU & 69 & 8.4 & 90 \\ \hline
**GRU–GAN** & **57** & **7.2** & **75** \\ \hline  & **Dataset 2** & & \\ \hline BiLSTM & 127 & 14.8 & 147 \\ \hline BiGRU & 112 & 11.5 & 104 \\ \hline
**GRU–GAN** & **98** & **9.2** & **90** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation resources used by the BiLSTM, BiGRU, and GRU–GAN models.  
et al. [29], [PERSON] et al. [4], [PERSON] et al. [14], and [PERSON] et al. [31]. In terms of computational efficiency, our model was highly efficient, with a training time of 57 s, significantly faster than the model proposed by [PERSON] et al. [14] at 2569.2 s and [PERSON] et al. [29] at 35.721 s. Only the model proposed by [PERSON] et al. [2] was faster at 0.40 s but with lower accuracy (84.0%). The GRU-GAN model achieved a testing time of 7.2 s, which is competitive, although slower than [PERSON] et al. [4] at 0.011 s and [PERSON] et al. [29] at 0.177 s. It achieved this while maintaining higher accuracy, indicating a balanced solution for real-world applications where both speed and accuracy are critical. The GRU-GAN model used 75 MB of memory efficiently. Many compared studies did not report memory usage, highlighting a gap in evaluation that our study addresses thoroughly.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 3 Experiments
### Discussion
Some have corners detected, like the handle of the mug and the bill of the rubber duck, while some are circular with absolutely no detected features at all, like the ball and duct tape (Figure 19, Object 3). The algorithm is able to derive a stable grasp about the object centroid. Avoiding the corners contributes to the stability of the grasps as well. The rubber duck had two detected corners at the beak and the tail. These would likely make for very small contact points with the gripper and result in a less stable grasp. The object which the algorithm had the most difficulty with is the toy slinky (Figure 19, Object 36). Due to the highly irregular shape of the object, the algorithm had difficulty in identifying clear features of the object. Moreover, the malleable nature of the object made it challenging for the robot to grasp, as it tends to change its shape and move out of the intended grasp position. However, in spite of these challenges, the algorithm is still able to execute a stable grasp by targeting the approximate centroid of the object. This allows the object to be more balanced and less likely to slide out of the gripper. Hence, the algorithm still sports a high success rate in grasping the toy slinky, further showing its flexibility in dealing with challenging situations. The algorithm sports a significantly higher grasp success rate compared to other existing methods on a significantly smaller training dataset. This can be attributed to the greatest advantage of this algorithm, which is flexibility. In other existing methods, when encountering an object, the algorithm finds the closest match based on what it is trained on. However, as illustrated in Figure 17, even within a single object, there can be many orientations, positions, and configurations. This makes it impossible to train an algorithm to be able to deal with every possible combination. In contrast, the features of an object do not change as easily. Even when changing configurations, the key features of the object remain unchanged. The algorithm is equipped to reactively deduce a grasp position based on the features presented, providing it with a high level of flexibility. Unlike other methods where the grasp position of an object in every orientation and configuration has to be labelled, this study has demonstrated that with feature detection, this tedious labelling process is not required and the algorithm has the flexibility to handle a large variety of cases.
## 2 HEV Modelling and Control
### HEV Control Level: DP  
When it comes to the HEV control level, a DP-based algorithm was chosen as the strategy to compute the optimal torque split and gear engaged of each HEV candidate through the different drive cycles. This optimizer runs the drive mission backwards and, at every time step,  
* It sweeps all the discretized control values;
* Then, it computes the state values for all the combinations of controls using the vehicle modeling equations given in the section above;
* Finally, it calculates a user-defined cost function that depends on the abovementioned values.  
Then, the objective of this algorithm is to find the optimal time trajectories for gear number and torque split that minimize the cost function while complying with the imposed constraints [15; 16]. In (10), the state space \(X\), the control space \(U\), and the cost function \(J_{DP}\) used for this study are found.  
\[X=\left\{\begin{array}{c}ICE_{state}\\ SOC\\ n_{gear}\end{array}\right\}\,\ \ \ \ \ U=\left\{\begin{array}{c}n_{gear}\\ \gamma\end{array}\right\}\ J_{DP}=\int_{0}^{t_{end}}\Bigl{(}FC+\alpha_{1} \cdot\mu_{gearshift}+\alpha_{2}\cdot\mu_{ICE,on/off}\Bigr{)}dt \tag{10}\]\(X\) is composed of three variables, i.e., the binary ICE activation state (either on or off), the discretized battery SOC, and the gear engaged. \(U\) embeds again the gear engaged and the torque split term \(\gamma\) introduced in Equation (7). Lastly, \(J_{DP}\) is the cost function to be minimized throughout the overall driving mission up to its final time instant \(t_{end}\). \(J_{DP}\) not only holds the instantaneous fuel consumption \(FC\) as obtained by interpolating in the ICE BSFC map, but it also considers two penalty terms aimed at improving drivability and comfort. These two terms are composed of the flags \(\mu_{gearshift}\) and \(\mu_{ICE,an/off}\) that are triggered when gear-shifting and ICE activation are encountered, respectively. \(\alpha_{1}\) and \(\alpha_{2}\) are constant weight factors for the respective penalty terms. Thus, the control strategy defined by the DP will not only be optimized for the fuel consumption, but it also will reduce both the number of gear shifts and ICE activations, in order to improve passengers' comfort. Finally, as already mentioned, the DP-based optimizer accounts for operational constraints and accepts only the control trajectories for which
* The ICE, the EM, and the battery operate within the corresponding operating limits;
* The actual vehicle velocity matches the trajectory imposed by the drive cycle;
* Final and initial battery SOC values are similar in simulating charge-sustaining HEV operation.
## 2 Concept of Micro-Grid Systems
### Power Electronics Converter Control
#### 2.2.3 Tertiary Controller  
The principal goal of a tertiary controller is to regulate power flow and load sharing between converters and between several hybrid microgrid systems if they are connected in a group and finally synchronize individual incremental costs. Additionally, a tertiary controller is utilized to update the set point for the secondary controller depending on the production cost preferences by updating the loading ratio of each converter [38]. Compared to the secondary controller, the tertiary controller is able to suppress voltage fluctuations and change voltage operating points to attain the optimal dispatch of power production.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 5 Heuristic Solution Algorithm
All of them are iteratively solved with the combined use of a greedy algorithm and the numerical solver 'lp_solve' [63], giving solutions of resulted LP. As a consequence, the decision variables of the considered problem, i.e., \(\mathbf{y}\), \(\mathbf{x}\) and \(\mathbf{\Lambda}t^{\text{C}}\), \(\mathbf{r}^{\text{T}}\), \(\mathbf{\Lambda}t^{\text{ARR}}\), \(\mathbf{\Lambda}t^{\text{DEP}}\) are the results of Algorithms 1, 2, 3, 4, together with TGA, respectively. The matrices \(\mathbf{r}^{\text{T}}\), \(\mathbf{\Lambda}t^{\text{ARR}}\), \(\mathbf{\Lambda}t^{\text{DEP}}\), along with \(\mathbf{x}\), form the new timetable \(\mathbf{T}\mathbf{T}\mathbf{=}\left[<x_{p}\cdot[r^{\text{T}}_{p,i,j,k}]_{i,j\in\mathbf{V}\in \mathbf{K}},[t^{\text{ARR}}_{i,p}+\Delta t^{\text{ARR}}_{i,p}]_{i\in\mathbf{V}},[t^{ \text{DEP}}_{i,p}+\Delta t^{\text{DEP}}_{i,p}]_{i\in\mathbf{V}}>\right]_{p\in\mathbf{ T}\mathbf{R}}\). Similarly, the time shifts for closures \(\mathbf{\Delta}t^{\text{C}}\), together with \(\mathbf{y}\), define the resulted track closure plan \(\mathbf{S}\mathbf{=}\left[S_{n}\right]_{n\in\mathbf{C}}\), where \(S_{n}\mathbf{=}<t^{\text{C}}_{n},t^{\text{CSE}}_{n},t^{\text{CSL}}_{n},T^{\text{C} }_{n}>\), \(r^{\text{C}}_{n}=R^{\text{C}}_{n,i,j,k}\cdot y_{n}\), \(t^{\text{CSE}}_{n}=\max\{t^{\text{CSE}}_{n},t^{\text{CSE}}_{n}+\Delta t^{ \text{C}}_{n}\}\), \(t^{\text{CSL}}_{n}=\min\{t^{\text{CLS}}_{n},t^{\text{CLS}}_{n}+\Delta t^{ \text{C}}_{n}\}\). Algorithms 1, 2, 3, 4 and TGA are introduced in the next subsections. The detailed description of Algorithm TGA can be found in Reference [5]. It is assumed that all algorithms have access to all data concerning infrastructure, train runs, and track closures. The necessary auxiliary notation is given in Table 5.
## 3 Results  
### Water Quality  
Water samples from Well B, Red Dam and Sokoru Dam were evaluated, and their analysis revealed several critical findings regarding water quality (Figure 4a,b accordingly). The Well B samples (Figure 1), indicated the presence of cyanuric acid at 10 mg/L, though not regulated, suggested some chemical contamination. Turbidity, nitrate, and pH values were within acceptable ranges (Table S1).  
The Sokoru Dam (Figure 1) showed extremely high turbidity levels (335 NTU) and lead concentration (11 mg/L), both significantly exceeding WHO/EPA limits, indicating severe water quality issues. Nitrate and pH levels were within acceptable limits, but the overall water quality necessitated substantial intervention (Table S2). The presence of lead at these concentrations poses a significant threat and substantial health risks. Regulatory bodies, such as the U.S. Environmental Protection Agency (EPA) and WHO, have set stringent standards for lead in drinking water. The EPA's action level for lead in drinking water is 0.015 mg/L (15 ppb) [16], and WHO allows 0.01 mg/L (10 ppb) [17], significantly lower than the 11 mg/L levels found in the reservoir.  
The Red Dam also presented severe water quality problems, with an extremely high turbidity of 12,000 NTU, indicating extreme contamination. Hydrogen sulfide at 2.5 mg/L and Cyanuric acid at high concentrations (200 mg/L) also harm aquatic lifeforms (Table S3). Overall, the compounds found necessitated comprehensive treatment. Lead was not detected, which is positive, but overall water quality issues required significant remediation (Figure 4).  
In summary, the water quality parameters indicated significant issues, particularly with turbidity, heavy metals (lead), and chemical contaminants in certain water sources. Immediate interventions were necessary to address these concerns and ensure the water met safety standards for consumption and use.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 5 Verification and Analysis  
The experimental results of the MEMS accelerometer temperature experiment are shown in Figure 10. It can be seen that when the temperature changes from \(-10\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C, the acceleration output signal is approximately 5367 g. This means that the output of HGMA is greatly affected by temperature. To compensate for the accelerometer signal temperature, three temperature compensation models (RBF NN, RBF NN + GA, and RBF NN + GA + KF) are established here, as shown in Figure 11.  
Based on the established model, the results of the above models are calculated, as can be seen from Figure 11. All three models can approximate the original data very well, but RBF NN + GA + KF does a good job at suppressing noise. This work then employs the RBF NN + GA + KF denoising method and temperature drift method to improve HGMA precision. As can be seen from Figure 12, when the temperature changes from \(-10\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C, the acceleration output signal is approximately 385 g. This means that the fusion algorithm can effectively reduce the influence of temperature drift on HGMA precision.  
Figure 11: The three temperature energy models influence drift of the accelerometer.  
Figure 10: The output of MEMS in the temperature experiment.  
Allan variance is employed to evaluate the denoising and temperature compensation result of the methods; the results are shown in Figure 13. The acceleration random walking and bias stability (referring to angular random walking and bias stability of gyroscope) results of the original signal, RBF NN denoising, RBF NN + GA and RBF NN + GA + KF are 17130 g/h/Hz\({}^{0.5}\), 4720 g/h; 11470 g/h/Hz\({}^{0.5}\), 3595 g/h; 10760 g/h/Hz\({}^{0.5}\), 3416 g/h; 691
## 4 Results
### Field Trial  
Treatment significantly affected the number of live SHBs recovered from hives at the end of the two-week trial (\(\chi^{2}\) = 44.05 (2), \(p\) \(<\) 0.001) (Table 3). The greatest number of live adult SHBs was recovered in hives with acetone traps (86.8 live SHB/hive) (Table 3). The number of live SHBs recovered in hives with acetamiprid traps (11.6 live SHB/hive) was 7.48\(\times\) lower than that recovered from hives with acetone traps (\(t\) = \(-\)5.50, \(p\) \(<\) 0.001) (Table 3; Figure 3A). Comparatively, the number of live SHBs recovered in hives with fipronil traps (10.4 live SHB/hive) was 8.35\(\times\) lower than that recovered from hives with acetone traps (\(t\) = 5.76, \(p\) \(<\) 0.001) (Table 3; Figure 3A), but not different from that recovered in hives with acetamiprid traps (\(t\) = 0.28, \(p\) = 0.958) (Figure 3A).  
The average number of dead SHBs in traps varied significantly by treatment (\(\chi^{2}\) = 108.23 (2), \(p\) \(<\) 0.001) (Table 3). The number of dead SHBs found in acetamiprid traps (71.4 dead SHB/hive) was 44.63\(\times\) higher than that found in fipronil traps (1.6 dead SHB/hive; \(t\) = 10.40, \(p\) \(<\) 0.001) (Table 3; Figure 3B). No dead SHBs were found inside the acetone traps (Table 3; Figure 3B). Based on the lack of overlap of confidence intervals, the number of dead SHBs in acetone traps was significantly lower than that found in acetamiprid traps, but not different from that in fipronil traps (Figure 3B).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Compound** & **n** & **LC\({}_{50}\) (\(\mu\)g/g)** & **95\% CI** & \(\chi^{2}\) (df)** \\ \hline Coumaphos & 488 & 1250 & 1159–1328 & 1.672 (4) \\ Acetamiprid & 300 & 20.5 & 10–31 & 4.939 (3) \\ Fipronil & 398 & 1.78 & 1.58–2 & 11.312 (5) \\ \hline \end{tabular}
\end{table}
Table 2: Small hive beetle acute toxicity Lethal Concentration 50 (LC\({}_{50}\)) values with 95% confidence intervals and chi-square values generated in PoloPlus.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**Live SHBs**} & \multicolumn{3}{c}{**Dead SHBs in Traps**} \\ \cline{2-6} \multirow{2}{*}{**Treatment**} & **Total \#** & **Avg. \# SHBs/Hive** & **Avg. \# SHBs/Hive** & **Avg. \# SHBs/Hive** & **SE** \\  & **SHBs** & **(95\% CI)** & **SE** & **SHBs** & **(95\% CI)** & **SE** \\ \hline Acetone & 434 & 86.8 (50.8–148.4) a & 21.6 & 0 & 0 b & 0 \\ Acetamiprid & 58 & 11.6 (6.4–21.1) b & 3.59 & 357 & 71.4 (60.6–84) a & 5.8 \\ Fipronil & 52 & 10.4 (5.7–19.1) b & 2.93 & 8 & 1.6 (0.7–4) b & 2.93 \\ \hline \end{tabular}
\end{table}
Table 3: Total number, average, 95% confidence intervals, and standard error of live and dead small hive beetles (SHBs) collected after two weeks of treatment exposure via CD trap.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### Synthesis and Scanning Electron Microscopy (SEM) of CTAB-Stabilized AuNRs (CTAB-AuNRs)  
To produce the coated systems, CTAB-AuNRs synthesized as previously reported [15] were used as starting material. Briefly, CTAB-AuNRs were produced through seed-mediated growth procedure. In a first step, gold seeds were prepared via NaBH\({}_{4}\) (300 \(\mu\)L, 0.01 M) reduction in Au III from HAuCl\({}_{4}\) (25 \(\mu\)L, 0.05 M), under vigorous stirring and in the presence of hexadecyltrimethylammonium bromide (CTAB 0.1 M) as stabilizing agent. The seeds were then allowed to elongate, reaching a cylindrical shape, in a growth CTAB solution (0.1 M) containing 500 \(\mu\)L of 0.05 M HAuCl\({}_{4}\) and 950 \(\mu\)L of HCl 1 M in which 600 \(\mu\)L AgNO\({}_{3}\) 0.01 M, 400 \(\mu\)L of ascorbic acid 0.1 M, and 120 \(\mu\)L of gold seeds were added. After incubation at 28 \({}^{\circ}\)C, CTAB-stabilized AuNRs (CTAB-AuNRs) were thus obtained.  
Scanning electron microscopy (SEM) was performed to investigate the size and morphology of CTAB-AuNRs and AuNRs/LA,GG, using a scanning electron microscope ESEM Philips XL30. CTAB-AuNRs in aqueous dispersion were washed 2 times with Milli-Q water to remove the excess surfactant, placed on a TEM copper grid (10 \(\mu\)L, 0.001 mg\(\cdot\)mL\({}^{-1}\)) and allowed to dry for 24 h. SEM analyses were carried out after deposition of the grid on a double-sided adhesive tape, previously applied on a stainless-steel stub.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 3 Solution Method
### Discretizing Wind Distribution
#### 3.1.2 Discretizing Wind Speed Distribution  
The aim of the discretization scheme is to group the values of the continuous random variable into a three finite group.  
The continuous sequence of wind speed can be discretized into three points, as shown in the following formula:  
\[y_{i}=\mu_{x}+z_{i}\cdot\sigma_{x}. \tag{15}\]  
Notice that  
\[\left\{\begin{array}{ll}z_{i}=\frac{\lambda_{3}}{2}+(-1)^{3-i}\sqrt{\lambda _{4}-\frac{3{\lambda_{3}}^{2}}{4}},i=1,2\\ z_{3}=0\end{array}\right. \tag{16}\]  
where \(y_{i}\) is the discrete wind speed  
Define  
\[\left\{\begin{array}{ll}\mu_{x}=\int\limits_{0}^{M}xf(x;\lambda,k)dx\\ \sigma_{x}=\int\limits_{0}^{M}(x-\mu_{x})^{2}f(x;\lambda,k)dx\\ \lambda_{i}=\int\limits_{0}^{M}(\frac{x-\mu_{x}}{\sigma_{x}})^{i}f(x;\lambda, k)dx\end{array}\right. \tag{17}\]  
where \(\mu_{x}\) is the mean value of \(x\); \(\sigma_{x}\) is the standard deviation of \(x\); \(\lambda_{i}\) is the \(i\)th central moment of \(x\).  
Notice that  
\[\left\{\begin{array}{ll}P_{i}=\frac{(-1)^{3-i}}{z_{i}(z_{1}-z_{2})},i=1,2\\ P_{3}=1-P_{1}-P_{2}\end{array}\right. \tag{18}\]  
where \(P_{i}\) is the probability corresponding to \(y_{i}\).  
A ten-year daily wind speed data for the city of Madison, USA, was utilized to fit the Weibull distribution. Specifically, the rated power for wind generation was 113 MW with the total maximum load of 283 MW, and the cut-in, cut-out, and normal speed of wind turbines was 3.5 m/s, 40 m/s, and 13.5 m/s, respectively. The three-point discrete distribution was calculated and is shown in Figure 1.
## 2 Materials and Methods
### FTIR Measurement  
The Agilent Cary 630 FTIR spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA), fitted with an attenuated total reflectance (ATR) accessory, was utilized for analysis. A small quantity of insect powder was positioned on the ATR crystal and secured using a pressure clamp. Spectral scanning was conducted over the range of 650-4000 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\) and 32 scans per spectrum. This range was sufficient for the major peaks corresponding to the structures of amide A, amide B, alpha-chitin structure, amide I, amide II, and amide III. Data acquisition and management were performed by MicroLab PC 5.7 software (Agilent Technologies Inc., Santa Clara, CA, USA). Measurements were conducted in triplicate for each powder batch.
## 2 Preliminaries
### Elliptic Curve Algorithm  
The Elliptic Curve Digital Signature Algorithm (ECDSA) [22] stands as a widely employed digital signature algorithm within the realm of public key cryptography and is renowned for its robust security and efficient performance. In the elliptic curve algorithm, two fundamental operations, namely Point Addition and Point Multiplication, play a pivotal role.  
Consider an elliptic curve described by the equation \(y^{2}=x^{3}-7x+9\). Drawing a straight line intersecting the curve at three distinct points--\(P\), \(Q\), and \(R\)--becomes a pivotal illustration. According to the definition of the point addition operation, the sum \(P+Q+R\) equals the identity 0, implying \(P+Q=-R\). The negation of \(R\) is defined as a point obtained through symmetry about the x-axis, as depicted in Figure 3, encapsulating the essence of point addition.  
If we adjust the position of this straight line such that points \(P\) and \(Q\) coincide, adhering to the aforementioned point addition rules, we can derive the point \(2P\). Following the same principle, by consecutively connecting point \(P\) to \(nP\), we obtain \(3P\), \(4P\), and so forth, up to \((n+1)P\). The dot product is denoted as \(K\times P\), signifying \(K\) iterations of the point addition operation for point \(P\).  
Figure 2: Organizational structure of DIDs. A DID Document is a collection of identity information that is invisible to the outsider and is claimed by the DID Controller field. The DID-ID part is the unique identifier of the DID Document, from which the specific DID Subject can be located. The DID URL is the external service provided by the DID, including authentication. The Verifiable Data Registry is the agency responsible for issuing verifiable credential.  
2.3.1. Security, Encryption, and Decryption Performance  
ECDSA is founded upon the discrete logarithm problem on elliptic curves, leveraging the mathematical complexity of this problem to provide formidable security. In comparison to traditional algorithms such as the Rivest-Shamir-Adlema (RSA) algorithm, ECDSA achieves significant security strength, even with shorter key lengths. This characteristic renders it particularly suitable for resource-constrained environments such as IoT devices and mobile applications.  
ECDSA exhibits notable advantages in encryption and decryption performance when compared to traditional algorithms. The efficiency of elliptic curve operations, as opposed to integer factorization, allows ECDSA to deliver substantial security with relatively short key lengths, thus easing the challenges associated with key management and computational resources. This efficiency positions ECDSA favorably in scenarios with heightened computing performance demands, including wireless sensor networks, smart cards, and mobile devices.