esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Corrosion Behavior Under Simulated Marine CWS Conditions
This breakdown behavior highlights the deterioration of the passive film in chloride-rich environments, particularly when additional oxidizing species are present, such as dissolved oxygen and an acidic agent. In Figure 7c, at an inhibitor dosage of 15,000 ppm, the polarization behavior remained stable up to 200 ppm Cl\({}^{-}\). However, at 500 ppm, a marked increase in the current density was observed in the anodic polarization region, accompanied by hysteresis loops, indicative of the repeated breakdown and re-passivation of the passive film. Also, the corrosion potential shifted in the active direction, suggesting degradation of the passive film and increased susceptibility to localized corrosion due to chloride ions. This behavior suggests that, under conditions of rapid seawater penetration, the inhibition performance of a nitrite-based CWS becomes unstable when the chloride concentration exceeds 500 ppm at an inhibitor dosage of 15,000 ppm. Figure 7d shows the polarization curves of FCD-500 in the presence of 17,000 ppm, with varying chloride ion concentrations. The corrosion potential exhibited minimal variation, with a slight shift toward the cathodic direction, indicating the stability of the passive film, even at chloride ion concentrations of up to 500 ppm. Although minor electrochemical variations were observed at certain chloride concentrations, no significant changes in the corrosion behavior were identified. Figure 7: Potentiodynamic polarization curves of FCD-500 with varying inhibitor and chloride ion concentrations at 80 \({}^{\circ}\)C: (**a**) uninhibited, (**b**) inhibitor 9000 ppm, (**c**) inhibitor 15,000 ppm, and (**d**) inhibitor 17,000 ppm. Figure 8 presents the \(i_{corr. }\) as a function of the chloride ion concentration (0-500 ppm) and inhibitor dosage (0, 9000, 15,000, and 17,000 ppm). In the uninhibited condition (gray bars), the \(i_{corr. }\) increased sharply as the chloride ion concentration increased, reaching 174.1 \(\upmu\)A/cm\({}^{2}\) at 500 ppm. This clearly demonstrates that chloride ions accelerate corrosion by promoting anodic dissolution. At 9000 ppm (pink bars), a substantial reduction in the \(i_{corr. }\) was observed (2.9-5.1 \(\upmu\)A/cm\({}^{2}\)), with only minor variations across increasing Cl\({}^{-}\) levels. Further increases in the inhibitor dosage to 15,000 ppm (orange) and 17,000 ppm (green) maintained the \(i_{corr. }\) below 6.42 \(\upmu\)A/cm\({}^{2}\) across all the Cl\({}^{-}\) concentrations, demonstrating excellent corrosion inhibition performance.
# Dynamics Analysis and Chaos Identification of Compound Pendulum Jaw Crusher with Joint Clearance
## 2 Clearance Model  
### Revolute Joint Model with Clearance  
The shaft and bearing of the revolute joint are viewed as independent parts, and Figure 1 depicts their model [14]. Where \(i,\ \ j\) stand for the bearing and shaft, \(O_{i},\ \ O_{j}\) for the center of mass, \(Q_{i},\ \ Q_{j}\) for the collision point at impact, \(\mathbf{r}_{i}^{O},\ \ \mathbf{r}_{j}^{O}\) for the center of mass' position vector, \(\mathbf{r}_{i}^{O},\ \ \mathbf{r}_{j}^{O}\) for the position vector of collision point, and \(F_{n},\ \ F_{t}\) for the normal contact force and tangential friction force at impact.  
The eccentric vector between the bearing and shaft can be defined as follows:  
\[\mathbf{e}=\mathbf{r}_{j}^{O}-\mathbf{r}_{i}^{O} \tag{1}\]  
Its unit normal vector can be expressed as follows:  
\[\mathbf{n}=\mathbf{e}/\mathbf{e} \tag{2}\]  
where \(\mathbf{e}\) is the amplitude of the eccentric vector, \(\mathbf{e}=\sqrt{\mathbf{e}^{T}\mathbf{e}}\).  
The angle between the eccentric vector and the positive direction of the X-axis can be defined as follows:  
\[\varphi=\cos^{-1}(\mathbf{n}\cdot\mathbf{I}) \tag{3}\]  
where \(\mathbf{I}\) is the unit vector in the positive direction of the X-axis.  
The position vector of the collision point can be defined as follows:  
\[\mathbf{r}_{k}^{Q}=\mathbf{r}_{k}^{O}+R_{k}\mathbf{n}\quad(k=i,j) \tag{4}\]  
where \(\mathbf{r}_{i},\ \ \mathbf{r}_{j}\) are the radius of the bearing and shaft, respectively.  
The velocity vector at the collision point is produced by taking the derivative of Equation (4) with respect to time, which can be stated as follows:  
\[\dot{\mathbf{r}}_{k}^{Q}=\dot{\mathbf{r}}_{k}^{O}+R_{k}\dot{\mathbf{n}}\quad(k=i,j) \tag{5}\]  
The velocity vector at the collision point is projected onto the collision plane in the normal and tangential directions, then the relative normal and tangential velocities can be defined as the following:  
\[\left\{\begin{array}{l}v_{n}=\left(\dot{\mathbf{r}}_{i}^{Q}-\dot{\mathbf{r}}_{j}^{Q} \right)^{T}\mathbf{n}\\ v_{l}=\left(\dot{\mathbf{r}}_{l}^{Q}-\dot{\mathbf{r}}_{j}^{Q}\right)^{T}\mathbf{t}\end{array}\right. \tag{6}\]  
where \(\mathbf{t}\) is the tangential vector, obtained by \(\mathbf{n}\) rotating 90\({}^{\circ}\) counterclockwise.  
The embedding depth between the bearing and shaft can be expressed as follows:  
\[\delta=\mathbf{e}-\mathbf{c} \tag{7}\]  
where \(\mathbf{c}\) is clearance size, \(\mathbf{c}=R_{i}-R_{j}\).
## 3 Methodology and Implementation
### Architecture and Principle of Operation of Victim Identification Module
#### 3.1.1 Face Extraction  
The Multi Task CNN (MTCNN) method described by [PERSON], [PERSON], [PERSON] and [PERSON] [43] was used to detect and extract faces from photographs. The solution described here uses a pre-trained MTCNN architecture implemented using the PyTorch deep learning framework [44]. The chosen MTCNN model consists of three convolutional networks operating in a cascaded manner: P-Net \(\rightarrow\) R-Net \(\rightarrow\) O-Net. Subsequent architectures are, respectively, responsible for:  
* binary classification (face/no face) and proposing \"candidate\" of bounding boxes of different dimensions on which faces are visible;
* verification and rejection of false \"candidates\", as well as merging all bounding boxes into one final selection for each analyzed image;
* detection of facial landmarks (left and right eye, tip of the nose, left and right corner of the mouth), for an even more detailed description of the face in an image.
## 4 Theoretical Considerations
### BDS Method
The quantities \(z_{l}\), \(z_{p}\), and \(z_{D}\) are measured from the input plane of the setup. Furthermore, \(k_{TO}\) and \(k\) are the wave numbers of the TOs and PB, respectively. The detailed expression describing \(\Theta_{mR}\) and \(\Theta_{ml}\) can be found in the literature [41]. The change in the PB's optical path in the gas in which it propagates (introduced by both the PB's deflection by IoR gradients and a change in the gas IoR) causes variations in the PB phase, expressed as Equation (17) [41]:  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]4  
where \(\Delta\Phi_{d}\) is the change in the PB phase caused by PB deflection by thermal gradients, whereas \(\Delta\Phi_{f}\) is the change in the PB phase introduced by the change in the refractive index of the air in which the PB travels. At the detector plane (\(z_{D}\)), \(\Delta\Phi_{d}\) and \(\Delta\Phi_{f}\) are written as  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]5  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]6  
where \(\xi_{D0}=x_{D1}(1+iz_{D1}z_{RC})^{-1}\) is the ray coordinate in the input plane of an experimental system for a given observation point for an undisturbed PB [41]. Finally, the PB intensity variations after one pass through TOs at the detector can be written as  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]7  
where \(U(x_{D1},y_{D1},z_{D1})\) is the PB's electric field distribution, and \(I_{0g}(x_{D1},y_{D1},z_{D1})\) is the undisturbed PB intensity that has the form  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]8  
where \(x_{D},y_{D},\) and \(z_{D}\) are the coordinates of the ray at the detector plane. The change in the PB intensity introduced by TOs results in a photodetection (BDS) signal that, for a detection performed by a quadrant photodiode (QP), is in the form  
\[q_{sl}(x,t)=2^{-1}\gamma_{i}I_{0}\exp[\sum_{m=1}^{i-1}\gamma_{m}(L_{m-1}-L_{m} )+\gamma_{i}(L_{i-1}-x)]\exp(2i\pi ft) \tag{6}\]9  
where \(K_{d}\) is the detector constant, \(k\) is the PB wave number, \(z_{0}\) is the PB height over the sample surface, \(A_{BDS}\) is the change in the PB amplitude caused by deflection, \(\phi_{BDS}\) is the variation in the PB phase resulting from the fluid refractive index change because of TOs and the deflection of the PB by thermal gradients.
## 4 Evaluation Results
### Performance Evaluation of Emergency Message Board  
ADRS utilizes Tiboo embedded boards to maintain a temporary network, so that victims trapped inside a building can still post emergency messages to a Tiboo embedded board when connections to the outside are disrupted. This experiment studies the effect of the number of connections on the success rate of packet transmission. The size of a message is 20 bytes, which represents a short emergency message in a real scenario. For each experiment, we repeat the transmission 100 times. The average results are shown in Figure 16. As is shown, the Tiboo embedded board can maintain a highly success rate of packet transmission. When the number of connection increases to four, the success rate slightly decreases to 94.64%. These figures indicate that ADRS can be applied to real scenarios for practical use.  
In addition, we conducted an experiment to evaluate the effect of node failure on transmission time. Figure 14 shows the experimental environment, in which \"A\", \"B\", \"C\" and \"D\" represent the  
Figure 16: Stability of emergency message board.  
Figure 15: Comparison of evacuation time.  
embedded boards, respectively. Each smart phone can connect to each embedded board in one hop. When a node fails, the original connection to the failure node should be rebuilt on other nodes, thus increasing the traffic on the affected nodes. Therefore, we investigated the effect of the number of connections on transmission time to simulate the scenario of node failure. The results are shown in Figure 17, in which the \(x\)-axis represents the number of connections on Node A. The more failure nodes there are, the higher the number of connections on Node A there is. Due to the sufficient network bandwidth, we found that the transmission time increases slightly when the number of connections increases. We also measure the transmission time of three different sizes of emergency message. These are 10-byte, 20-byte and 30-byte emergency messages. Our results show that the transmission time remains almost the same as long as the size of an emergency message is more than 20 bytes and the number of connections is small. For example, when the number of connections becomes two, the transmission time of a 10-byte emergency message is 3.24 s and that of a 20-byte emergency message 3.45 s. Our results also show that emergency messages can be posted to a node in 10 s in the experiments we have tested.
## 2 Topics and Results
### Blood Flow  
Hemorheology is a science studying the biomechanical properties of blood and blood cells in conditions of flow in a stream. It may be added that it is a science of knowing and studying the viscosity of the blood. Due to the increased oxygen needs of the muscle cells during exercise, the blood flow from the heart to the stressed muscle groups increases. In addition to increasing the frequency of heart activity, increased blood flow is also achieved through the expansion of blood vessels, which supply the muscles with the necessary oxygen (O\({}_{2}\)).  
According to well-known Hagen-Poiseuille's equation, blood flow \(Q\) depends on perfusion pressure \(\Delta P\), vascular geometry (length \(L\) and radius \(r\) of the blood vessel) and blood viscosity \(\eta\) (1).  
\[Q=\frac{\Delta P\pi r^{4}}{8\;L\;\eta} \tag{1}\]  
The greatest influence on blood flow is the radius of the blood vessel \(r\) (calculated by the fourth power in the equation). This means that a small expansion of the internal diameter (d = 2r) of the corresponding blood vessel leads to a significant increase in the blood flow \(Q\) passing through its lumen (if the other factors of Equation (1) do not change). Perfusion pressure \(\Delta P\) increases blood flow \(Q\) directly, while vessel length \(L\) and blood viscosity \(\eta\) have an inverse effect.
## 3 Results
### Antimicrobial Activity  
The antimicrobial potential of the extracts was evaluated against three representative microorganisms, namely, a Gram-negative bacterium, _Escherichia coli_, a Gram-positive bacterium, _Staphylococcus aureus_, and a fungal species, _Candida albicans_ (Table 3). Regarding the bacterial inhibition of _E. coli_, extracts E1 and E2 (with higher percentages of ethanol used for the extraction procedure--50%) should be highlighted since the lower concentrations of extracts tested (0.0075 and 0.75 mg\(\cdot\)mL\({}^{-1}\)) were significantly different (\(p<0.05\) or less) from the same concentrations for the other extracts (Table S1--complete statistical analysis). However, for the other concentrations (1.5 and 3 mg\(\cdot\)mL\({}^{-1}\)), no significant differences were found between extracts, with the exception of E2 and E4 that significantly differ from each other (\(p<0.05\)). The highest values of inhibition were found for E1 and E2, for 0.75 and 1.5 mg\(\cdot\)mL\({}^{-1}\), reaching values near to 40% of inhibition.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Extracts**} & \multirow{2}{*}{**EtOH (\%)**} & \multirow{2}{*}{**Yield (\%)**} & \multicolumn{4}{c}{**ORAC (\(\underline{\underline{\text{\text{\text{\text{\text{\text  
In the case of _S. aureus_ inhibition, E3 showed to be the most promising extract against this bacterium, reaching values of inhibition close to 60% between 0.75 and 3 mg\(\cdot\)mL\({}^{-1}\) (significantly different from the other extracts, \(p\) < 0.05 or less), and only the lowest concentration, 0.0075 mg\(\cdot\)mL\({}^{-1}\), presented values of inhibition below 10%. It is also possible to verify that hydroethanolic extracts (E1-E4) were more efficient at inhibiting the growth of _S. aureus_ (mostly at 0.75 and 3 mg\(\cdot\)mL\({}^{-1}\)) when compared with aqueous extracts (E5-E6), with E6 at 0.0075 mg\(\cdot\)mL\({}^{-1}\) being responsible for the opposite effect--bacterial growth promotion.  
The antimicrobial activity of the extracts against _C. albicans_ showed a more variable profile between extracts and concentrations when compared to bacteria. In fact, the highest values of inhibition correspond to the E4 and E6 extracts at 0.0075 mg\(\cdot\)mL\({}^{-1}\) (_p_ < 0.0001) and the lowest values (fungal growth promotion) were found for the same extracts but at 0.75 mg\(\cdot\)mL\({}^{-1}\) (_p_ < 0.05 or less). Except for those cases, E1 and E2 were the extracts that reached lower values of inhibition.  
Comparing the ability of the extracts in the study against the three microorganisms, globally, the higher inhibition (near to 60%) was found against _S. aureus_ (E3) and _C. albicans_ (E4 and E6) with different extracts, while against _E. coli_ were the extracts E1 and E2 that were responsible for the higher antibacterial inhibition (near to 40%).
## 3 MSA-LMS Adaptive Filter  
### Step Size of MSA-LMS Adaptive Filter  
The size of input signal power affects the performance of an algorithm. Therefore, a strong input signal power requires a relatively large step size. In this case, stable values cannot be found frequently in the steady state. Conversely, a weak input signal power requires a relatively small step size and, consequently, convergence is slowed down.  
Figure 1: Structure of least mean squares (LMS) adaptive filter.  
In this study, the step size is first divided into an autocorrelation matrix of input signal power as shown in the following equation, and it is changed according to signal size.  
\[\frac{1}{[u(k)u(k-1)][u(k)u(k-1)]^{T}} \tag{3}\]  
In the case of a general adaptive filter, the larger the step size is, the faster the convergence can be. The step size normalized by the input signal power changes according to the input power and does not diverge. In addition, it facilitates much faster convergence compared with a general algorithm.  
Second, to achieve convergence with a large step size in the initial stage and to obtain good stability in the steady state, an exponential function as follows is introduced to converge to a small value.  
\[e^{-\gamma\cdot[e(k)\cdot e(k-1)]} \tag{4}\]  
Finally, an autocorrelation function of the instantaneous error is used so that adjustment of a misadjustment is not needed. Because the power of the error is large, the parameter \(\gamma\) can control the convergence time as well as adjust the misadjustments.  
\[\gamma\cdot e(k)\cdot e(k-1) \tag{5}\]  
Instead of fixed LMS step size, \(\gamma\) can be adjusted to vary the step size and thereby improve the algorithm in respect to both robustness and flexibility. The optimal step size is achieved by adjusting \(A\) (\(0<A<1\)), \(B\) (\(0<B<1\)), and \(C\) (\(0<C<1\)) in the expression as follows:  
\[\mu=\frac{A}{[u(k)u(k-1)][u(k)u(k-1)]^{T}[B\cdot\mu(k-1)+C\cdot e^{-\gamma \cdot[e(k)\cdot e(k-1)]}]} \tag{6}\]
# Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment
###### Abstract  
The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.  
artificial potential field; path planning; obstacle avoidance; dynamic window; danger index Article
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 4 Materials and Methods
### Experimental Setup
#### 4.3.1 Dataset Source and Background  
One business metric used to evaluate customer experience is called the Net Promoter Score (NPS(r)), a registered trademark of Sattmetrix Systems, Inc., Redwood City, CA, USA (Bain and Company and Fred Reichheld). This score gauges the likelihood of customers recommending a company. Customers who are highly likely to recommend, scoring 9 or 10, are categorized as promoters. On the other hand, customers scoring 6 or below are referred to as detractors. Customers who fall in between are classified as passive, as they do not express strong advocacy or dissatisfaction.  
For our experiments, we used the NPS (Net Promoter Score) dataset, which comprises customer feedback data collected on heavy equipment repair. We specifically chose this dataset to maintain consistency with related works, enabling us to offer a comparable assessment of our methodology. This also allowed us to adopt similar data pre-processing steps as those outlined in the related works. The dataset includes information from 38 companies and encompasses 340,000 customers across sites in the USA and Canada. Each customer survey is stored in a database, with each question (benchmark) represented as a feature in the dataset. The benchmarks consist of numerical scores ranging from 0 to 10, which indicate the quality of service provided. Examples of benchmarks include questions relating to job correctness, customer satisfaction, likelihood to refer, and more. The decision attribute in the dataset is labeled \"PromoterStatus\", which classifies each customer as a promoter, passive, or detractor. The primary objective of the decision problem is to enhance customer satisfaction and loyalty, as measured by the Net Promoter Score. By applying action rules, the aim is to identify minimal sets of actions that can \"reclassify\" customers from \"Passive\" to \"Promoter\", thus improving the NPS. For our experiments, we utilized surveys obtained from customers of four companies during the year 2015.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Time-Frequency Domain Features Extraction Method
#### 3.1.3 Time Gap  
During the time slot when there is no jamming signal and the power margin exists, transmission is almost not affected by jamming. If such a time slot exists, a time gap exists.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Feedstock Degradation Pathway
#### 2.2.2 (b) Acidogenesis  
Acidogenesis is the second step of AD, where the hydrolyzed products of macromolecules are subjected to the fermentation step following different metabolic pathways. Different facultative and obligate anaerobic bacteria (_Lactobacillus, Escherichia, Staphylococcus, Bacillus, Sarcina, Veillonella, Desulfobacter, Desulfomonas, Eubacterium limosum, etc._) are involved in this step to produce volatile fatty acids (VFA), hydrogen, carbon dioxide, and alcohols. Sugars and amino acids are the main substrates of this step. Results of glycerol fermentation are propionate production and biomass generation [19; 20]. Acidogenic microorganisms produce high concentrations of hydrogen during this phase, which may cause inhibition of the production of acetate.