esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The open loop gain of the proposed operational amplifier circuit is obtained by AC simulation in LTSpice. From Figure 3, the open loop gain of the proposed circuit is 70 dB and the unity gain frequency is approximately 34 MHz.  
Figure 1: Signal readout block diagram.  
Figure 2: Proposed gallium nitride (GaN) operational amplifier. Exact values of the components cannot be shown here due to confidentiality pertaining to patent filing.  
To compute the common mode rejection ratio (CMRR) of the OPA, a simulation setup shown in Figure 4a is designed and a sine wave of 2 V peak-to-peak is applied to the input terminal. Figure 4b shows the simulation result, the output peak-to-peak voltage obtained is 619 mV. CMRR is calculated by using the following equation:  
\[\text{CMRR}=\bigg{(}1+\frac{R_{F}}{R_{1}}\bigg{)}\cdot\bigg{(}\frac{Vin(\text{ peak}-\text{to}-\text{peak})}{Vout(\text{peak}-\text{to}-\text{peak})}\bigg{)} \tag{8}\]  
On putting the values in Equation (8), we obtain:  
\[\text{CMRR}=\bigg{(}1+\frac{560\text{k}\Omega}{100\Omega}\bigg{)} \cdot\bigg{(}\frac{2\text{V}}{619\text{mV}}\bigg{)}=16158.3\] \[\text{CMRR}\text{ in dB}=20\log(16158.3)=84.2\text{dB} \tag{9}\]  
Slew rate is another important factor of the OPA which can be measured by applying a step signal at the input of OPA and the rate of change of the signal from 10% to 90% at the output of OPA is measured [41]. For computing the slew rate, the proposed OPA is configured in unity gain mode and an ideal pulse of 1 V amplitude is applied to input terminal as shown in Figure 5a. The corresponding simulation output is shown Figure 5b. Slew rate is computed by the following equation:  
\[\text{Slew rate}=\frac{\Delta V_{out}}{\Delta time}=\frac{(V_{out90\%}-V_{out10 \%})}{(t_{90\%}-t_{10\%})} \tag{10}\]  
Figure 4: (**a**) Common mode rejection ratio (CMRR) simulation setup. (**b**) Simulation output.  
Figure 3: Open loop gain of proposed operational amplifier having a unity gain frequency of 33.76 MHz.  
On putting the simulated value from Figure 5b in Equation (10), the slew rate obtained is given as  
\[\text{Slew rate}=\frac{(898.96\text{mV}-97.92\text{mV})}{(1.045\text{\mu s}-1.005 \text{\mu s})}=20.03\ \frac{\text{V}}{\text{\mu s}} \tag{11}\]
# Studies to Overcome Brain-Computer Interface Challenges
## 2 Studies to Overcome BCI Limitations
### Correction Using Image Processing  
We could estimate movement with a non-invasive method through the above study. However, the accuracy for controlling a robot arm to grasp a target is generally low. For example, success rates of the invasive BCI method were 20.8-62.2% for reaching and grasping movements, although the experiment task was easy [19]. One softball was presented on the flexible stick as a target in the experiment. Although the robotic arm approximately reached the target, grasping often failed because the robotic arm did not precisely reach the object. A small inaccuracy in the movement prediction caused task failure.  
We proposed a novel prediction method, a feedback-prediction algorithm (FPA), to increase the accuracy using image information as shown in Figure 1[24]. Positions of objects can be easily calculated using image processing technology. The FFA compensates for the prediction based on object positions. The method predicts the target among several objects based on the predicted direction. It corrects the prediction based on the target position. We applied the Kalman filter to compensate for the prediction. The compensated vector multiplied an automatically calculated weight value to reach the target easily. Even if the user changes the movement direction, the predicted trajectory can be corrected to the changed direction because the FPA predicts and corrects at every step. The accuracy of the movement prediction was significantly improved using the FPA for all nine subjects and 32.1% of the error was reduced. Although there were some studies modifying the prediction using target position, the previous studies predicted and compensated for movements on the 2D screen [25, 26]. Therefore, the previous methods are unsuitable for controlling a neural prosthesis in real life.
## 2 Pulse-Type near Fault Ground Motion
Therefore, it is very sensitive to pulse-type near-fault earthquakes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **Code** & **Earthquake** & \begin{tabular}{c} **Station and** \\ **Direction** \\ \end{tabular} & \begin{tabular}{c} **Magnitude** \\ **(M\({}_{w}\))** \\ \end{tabular} & \begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **PGV** \\ **(cm/s)** \\ \end{tabular} & \begin{tabular}{c} **PGV/** \\ **PGA** \\ \end{tabular} & \begin{tabular}{c} **Fault** \\ **Distance (km)** \\ \end{tabular} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Fling-step effect \\ near-fault \\ earthquakes \\ \end{tabular} } & A1 & Chi-Chi & TCU052-NS & 7.6 & 0.411 & 95.7 & 0.233 & 0.06 \\  & A2 & Chi-Chi & TCU052-EW & 7.6 & 0.341 & 159.0 & 0.466 & 0.06 \\  & A3 & Chi-Chi & TCU068-NS & 7.6 & 0.452 & 263.0 & 0.581 & 0.50 \\  & A4 & Chi-Chi & TCU068-EW & 7.6 & 0.555 & 176.5 & 0.318 & 0.50 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Rupture forward \\ directivity \\ near-fault \\ earthquakes \\ \end{tabular} } & B1 & Northridge & WTJ-316 & 7.1 & 0.319 & 67.4 & 0.211 & 7.10 \\  & B2 & Northridge & WTJ-046 & 7.1 & 0.446 & 92.7 & 0.208 & 7.10 \\  & B3 & Chi-Chi & TCU102-EW & 7.6 & 0.308 & 87.2 & 0.290 & 1.19 \\  & B4 & Chi-Chi & TCU120-EW & 7.6 & 0.233 & 62.6 & 0.280 & 9.87 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Non-pulse-type \\ near-fault \\ earthquakes \\ \end{tabular} } & C1 & Chi-Chi & TCU071-EW & 7.6 & 0.530 & 69.83 & 0.080 & 4.88 \\  & C2 & Chi-Chi & TCU072-EW & 7.6 & 0.480 & 85.51 & 0.180 & 7.87 \\  & C3 & Chi-Chi & TCU078-EW & 7.6 & 0.442 & 42.14 & 0.100 & 8.27 \\  & C4 & Chi-Chi & TCU079-EW & 7.6 & 0.590 & 64.49 & 0.110 & 10.95 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Far-field \\ earthquakes \\ \end{tabular} } & D1 & Taft & No.095-S09E & 7.7 & 0.172 & 17.2 & 0.099 & 43.0 \\  & D2 & Taft & No.095-N21E & 7.7 & 0.153 & 17.8 & 0.116 & 43.0 \\ \multirow{2}{*}{
\begin{tabular}{c} Far-field \\ earthquakes \\ \end{tabular} } & D3 & EI Centro & No.117-S90W & 7.1 & 0.214 & 36.9 & 0.172 & 40.0 \\  & D4 & EI Centro & No.117-S00E & 7.1 & 0.349 & 33.5 & 0.096 & 40.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Earthquake records and their characteristic parameters. Figure 1: Acceleration and velocity time history curves for different types of earthquakes: (**a**) fling-step effect; (**b**) rupture forward directivity; (**c**) non-pulse-type; and (**d**) far-field.
###### Abstract  
This study aims to investigate the effect of high temperature on the mechanical properties of low-slump polypropylene fiber (PPF) concrete, and tests the tensile and compressive properties of 204 groups of low-slump PPF concrete with eight different dosages and four different lengths at normal temperature and after high temperature. The results of the compressive test showed that PPF can significantly improve the mechanical properties of concrete after high temperature when the fiber content is small, and the compressive strength of low collapse polypropylene fiber concrete after high temperature showed a tendency to rise and then fall at the same temperature with an increase of the fiber admixture. When the fiber content was 0.5 kg/m\({}^{3}\), the compressive strengths of 3 mm, 9 mm, 15 mm and 19 mm reached their maxima, which were 9.65%, 11.33%, 7.90% and 2.87% higher than that of ordinary concrete, respectively. With an increase in fiber length, the effect of PPF on the compressive strength of concrete is not obvious. PPF at high admixture further increases the pore and air content in concrete, which decreases the compactness of the concrete, thus leading to a decrease in the compressive strength of the concrete. When the temperature was 800 \({}^{\circ}\)C and the fiber admixture was 5.0 kg/m\({}^{3}\), the compressive strength of PPF concrete with different lengths reduced by 17.83%, 17.27%, 22.59% and 23.92%, respectively, compared to normal concrete. In addition, according to the results, the optimal combinations of strength at room temperature and after high temperature were 3 mm fiber length and 1.0 kg/m\({}^{3}\) dosing and 9 mm fiber length and 0.5 kg/m\({}^{3}\) dosing, respectively, which increased the compressive and tensile strengths by 17.15% and 25.72% at room temperature and by at least 6% and 20% after high temperature, compared to the concrete without fiber dosing. Moreover, the stress-strain constitutive equations of PPF concrete at normal temperature and after high temperature were established, which can be used for finite element simulation and related mechanical analysis of PPF after high temperature.  
low slump concrete; polypropylene fiber (PPF); after high temperature; stress-strain curve; mechanical properties +
Footnote †: journal: Applied Sciences
# Fast Format-Aware Fuzzing for Structured Input Applications
## 3 Methodology
### Indirect Dependency Recognizer  
We briefly describe the overall design of _Indirect Dependency Recognizer_ based on the _Selective Flip_ strategy and its advantages over existing works first, then introduce the key parts _Non-deps exclusion_ and _Deps detection_ of the _Selective Flip_ strategy in detail in the following.  
For the byte that has indirect dependencies with the _cmp_ operand, once we flip it, the value of the corresponding operand will change. On the contrary, if we flip the _non-deps_ fragment, no _cmp_ operand value is altered. Based on this intuition, we propose the _Selective Flip_ strategy and apply it in _Indirect Dependency Recognizer_, to identify and pass the _non-deps_ fragments in the _trailer_ fields, only to flip those metadata-intensive fields.  
_Indirect Dependency Recognizer_ contains two main modules: _Field Schedule_ and _Suspensive Field Analysis_. Figure 8 shows how _Indirect Dependency Recognizer_ processes dependency recognition intelligently.  
_Indirect Dependency Recognizer_ takes the I2S field records as the input and recognizes and records all the indirect dependencies. The major parts are as follows:  
* **Field Schedule module**: Load I2S field records, and collect all the fields that have not yet recognized dependencies initially. Pick out the largest one, and output it to the _Suspensive Field Analysis_ module. Load the field that needs to be analyzed further, split it in half, and push them into the set of suspensive fields.
* **Suspensive Field Analysis module**: Load the suspensive field from the _Field Schedule_ module. Perform _Non-deps exclusion_ if its length is greater than _minSpan_; otherwise, perform _Deps detection_. Once the target field is _non-deps_, _Suspensive Field Analysis_ will just abandon it to reduce the cost. Otherwise, output it to _Field Schedule_. After _Depsdetection_, all the recognized dependencies will be recorded. Finally, if no suspensive field exists, _Suspensive Field Analysis_ outputs the dependencies records to assist other fuzzing parts. Additionally, \(minSpan\) is configured as Equation (A1).  
Additionally, we regard a comparison instruction as \"uncontrollable\" when the values of its operands change frequently and are not controlled by the input. To collect those uncontrollable ones, the target binary will be run twice with the same input. All the changed \(cmp\) operands are regarded as uncontrollable and collected in the set \(skip\_cmp\).  
Like WEIZZ and SLF, we identify indirect dependencies through byte-by-byte analysis. Module _Suspensive Field Analysis_ will catch and pass _non-deps_ fragment in the _Non-deps exclusion_ step, and the costly analysis will be performed centrally in those metadata-intensive fields.  
As a result, all the fields associated with the target \(cmp\) operands have been recorded, guiding the fuzzer to mutate the initial input and test the deeper code with a higher probability.
## 3 Results and Discussion
Therefore, we consider that the inhibitory effects of magnetic nanoparticles on PCR amplification is induced by interaction between Taq polymerase and magnetic nanoparticle clusters, rather than individual magnetic nanoparticles. The complex interaction between nanoparticles and PCR reagents is caused by the particle aggregation due to a decrease in particles dispersibility, and the mechanism behind such interactions will be helpful to understand similar interactions between polymerase and other nanoparticles. The present results also suggest that the effect of superparamagnetic particle clusters on PCR can be improved using an external magnetic field, and will be applied to novel PCR techniques in an external magnetic field. Additionally, using the external magnetic field, we have already demonstrated that the size, structure, and dynamics of particles clusters suppress PCR efficiency directly [21]. Therefore, our related report has justified the mechanism that we clarified in this paper. Those results also suggest that the effect of superparamagnetic particle clusters on PCR can be improved using various external magnetic fields, and will be applied to novel PCR models in various external magnetic fields. If such a field is applied, a magnetic dipole moment is induced in each particle, the magnetic particles form chain clusters, and consequently the size and the structure of the particle cluster can be controlled. Moreover, [PERSON] _et al._[22] simulated the chaotic mixing induced by a magnetic chain in a rotating magnetic field. Consequently, it can be considered that magnetic nanoparticles will work as a nano-size magnetic stirrer in the PCR process under an external varied magnetic field. This PCR technique could be applied to high frequency induction heating using AC magnetic fields.
## 3 Results and Discussion
### Assessments of Practical Application  
Conventional heterotrophic denitrification techniques are severely restricted by the low C/N ratio of wastewater, thereby causing the high NO3\({}^{-}\)-N content in STWW [33; 34]. To further assess the practical application of AEE-3, the dynamic column test of NO3\({}^{-}\)-N uptake onto AEE-3 was performed using a real STWW effluent which was collected from a local sewage treatment plant, containing the following quality parameters: pH, 6.54; NO3\({}^{-}\)-N, 20 mg/L; COD, 39 mg/L; TDS, 436 mg/L The breakthrough curves of AEE-3 are shown in Figure 11. An inconspicuous deviation between Run 1 and Run 3 breakthrough curves was found, further confirming its potential application in the removal of NO3\({}^{-}\)-N from real STWW effluent.  
Figure 10: NO3\({}^{-}\)-N breakthrough curves of AEE-3 (**a**) and A520E (**b**) for presence of HA, TA and SDBS.
## 3 Results and Discussion
### Ablation Experiments  
This article presents the results of ablation experiments conducted to analyze the comparative effects of several factors on the image restoration process. The factors in question are the effects of increasing the attention mechanism, adjusting the loss function and changing the training scheme. In the experiment, the video sequence was initially processed by CycleGAN following the basic VVC codec and then compared with the results obtained after various processing methods. As can be seen from Table 4, the PSNR value of the video sequence before and after the initial processing is 27.6238, and the LPIPS value is 0.2946. The first processing method was to add the attention mechanism on the basis of the original network structure. The PSNR value increased by 2%, while the LPIPS value decreased by 3.02%, representing improvements in both cases. Method 2 involved the use of an enhanced loss function on the foundation of Method 1, resulting in a negligible change in the PSNR value and a further decrease in the LPIPS value. The PSNR decreased by 0.46%, while the LPIPS value decreased by 3.18%, indicating that the human eye perception was still enhanced. Processing Method 3 altered the training method based on processing Method 2, resulting in an increase of 7.38% in the PSNR and a decrease of 4.59% in the LPIPS value. Both metrics demonstrated a notable improvement.
# Optimizing Nigella Oil Extraction Temperature for Sustainable Production
## 3 Results and Discussion
### Oil and Solid Components
(2018) investigated different combinations of feeding rates (36, 45, and 54 g/min), screw press temperatures (40, 55, and 70 \({}^{\circ}\)C), and seed moisture contents (6%, 12%, and 18%) to study their effect on Nigella oil yield. They observed both increasing and decreasing patterns of oil yield with increasing temperature in their study. For example, when expelling seed samples with 6% moisture content at different feeding rates and temperatures, they noted an increase in oil yield with temperature, whereas they observed a decrease in oil yield when expelling samples containing 12% moisture ([PERSON] et al., 2018). This variability underscores the complexity of optimizing screw press extraction conditions to achieve maximum oil yield from Nigella seeds, highlighting the need for careful consideration of multiple factors influencing the extraction process. The solid components (sludge, cake, and waste left inside the screw press machine during extraction) were also quantified (Table 1). It is noteworthy from the table that the sludge content in the oil obtained from different extraction temperatures ranged from 3.4 to 3.7%, with no significant differences. The factors such as moisture content and pretreatment methods, pressure, extraction duration, and screw speed might exert a more pronounced effect on sludge content compared to temperature ([PERSON] et al., 2019). Furthermore, the % of waste and cake in the present study ranged between 16.9 and 23.5% and 51.8 and 55.5%, respectively. The overall solid component (sludge, cake, and waste left in the screw and barrel) obtained from the extraction ranged between 72.1 and 82.6%. The highest value of solid content was observed in the 40 \({}^{\circ}\)C extraction, while the lowest was observed in the 80 \({}^{\circ}\)C extraction. Overall, the present study reveals a direct relationship between screw press extraction temperature and oil yield but an inverse relationship with byproducts (solid components). Therefore, optimizing the temperature for extracting Nigella oil can have significant environmental and economic benefits from a sustainability perspective. For example, many global manufacturing companies produce large volumes of cold-pressed Nigella oil every year, which necessitates a substantial amount of raw materials, resulting in lower oil yields and higher amounts of byproducts. Optimizing the extraction temperature with sustainability in mind could lead to more efficient use of raw materials and a reduction in waste byproducts. This would not only increase the economic efficiency of oil production but also reduce the environmental impact. By minimizing waste and maximizing yield, the overall footprint of Nigella oil production could be significantly lowered. This approach aligns with sustainable development goals, emphasizing resource efficiency, waste reduction, and the use of environmentally friendly production methods.
## 4 Results and Discussion
### Performance Analysis
#### 4.5.1 Confusion Matrix  
A confusion matrix is deliberated as a tabular representation used to assess the efficacy of the classification model, especially in terms of differentiating between normal and attack samples. Predictions are characterized into four sets, such as true negatives (TNs), true positives (TPs), false negatives (FNs), and false positives (FPs), by the confusion matrix in the framework of IDS. TPs are samples that are correctly labeled as attacks, whereas TNs are samples that are suitably classed as normal. On the other hand, FNs occur if attacks are erroneously classed as normal, and FPs occur if normal instances are incorrectly labeled as attacks. Certain attack types, including zero-day exploits or advanced persistent threats (APTs), might cause errors in the confusion matrix because they show subtle patterns or have less visibility in network data, making them more difficult to detect. Also, misclassifications may result from attacks that closely resemble normal activity, like specific kinds of denial-of-service (DoS) attacks.  
Figure 6 presents the confusion matrix of a proposed EDRL model for the classification phase. The proposed classification framework classified the features into normal and attack instances based on the enhancement of DAE and IFSA. In the confusion matrix, it can be seen that very few samples are misclassified, whereas the overall classification accuracy of the approach is high. Among the samples, 36,562 samples are accurately classified as normal, which is shown as blue color in the figure, and there is a misclassification for 164 samples. In the case of attack samples, 50,806 samples are accurately classified as attacks, which is shown in navy blue color in the figure and the remaining 138 samples are misclassified as normal instances. Overall, it can be justified that there is only a minimum misclassification rate, and the overall classification rate is much better and more effective.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMC and PDSMC  
In this section, simulation results of SMC and PDSMC are presented. Parameter values can be seen in Table 2. In the simulation, we considered external disturbance a constant value (500) and the parameters of the system were selected on the basis of this disturbance. As discussed earlier, the gain K should be greater than the upper bound of perturbation in Equation (9). That's why the value of switching gain K = 550 (SMC) was greater than disturbance 500. In the PDSMC case, the gain K\({}^{\prime}\) = 275 follows Equation (44).  
Step input was given to the system and the following results were observed.
# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 2 Research Model
Additionally, the initial SOC value (\(SOC_{0}\)) estimation relies heavily on accurate OCV measurements and the conditions under which the OCV experiment was conducted, including factors such as temperature and charge/discharge rates. These factors significantly influence the precision of SOC estimation and should be carefully controlled during the modeling process. However, in some intervals, there may be data irregularities, which may be due to errors in the measurement process or the characteristics of specific battery cells. All graphs follow a similar pattern, but there may be subtle differences depending on the characteristics of the battery cells. Here, \(C_{n}\) is the rated capacity and \(i\) is the current at each time step. \(C_{n}\) is a variable value depending on the actual degree of battery aging, but since this paper uses short-cycle data, it is calculated assuming a constant capacity. In this study, we proceeded with the process shown in Figure 4 to train an LSTM model for data preprocessing, model learning, and performance evaluation, predict battery capacity using test data, and evaluated the model's performance using performance indicators. We evaluated prediction performance to determine which model produced the most accurate and reliable SOC estimates.