esa-sceva/satcom-chunk-collection huggingface dataset:
# Susceptibility of _Staphylococcus epidermidis_ to Argon Cold Plasma Jet by Oxygen Admixture
## 3 Results and Discussion
### Anti-Bacterial Activity of Non-Thermal APPJ
In an in vitro  
Figure 7: Effect of argon non-thermal APPJ generated at different oxygen admixtures (0.2 to 1.2%) on the colony-forming unit (CFU) \(\times\) 10\({}^{6}\)/mL of _S. epidermidis_ at different exposure times. test [39], the researchers showed that cold atmospheric argon plasma applied for 25 min at the 40 W was lethal for the growth of _Aspergillus flavus_ on the medium, while the application of plasma for 20 min at the same voltage was important in the protection of brown rice cereal bars from the contamination of _A. flavus_ up to 20 days under storage conditions of 25 \({}^{\circ}\)C and 100% relative humidity. The longer treatment time, as compared with our data (16 min), is probably due to the high voltage they employed. [PERSON] et al. [40] have reported the ability of atmospheric plasma for the decrease of microbial populations on surfaces of fresh produce. [PERSON] et al. [41] stated that cold atmospheric gas plasma applied for 15 min accomplished 2.72, 1.76, and 0.94 log-reductions of _Salmonella typhimurium_ on lettuce, strawberry, and potato, respectively. It is suggested that the electrostatic force caused by charge accumulation on the outer surface of the cell membrane weakens the tensile strength of the membrane, thus initiating its rupture [34; 42]. In a related study, [PERSON] et al. [43] investigated the efficacy of low-temperature plasma treatments against bacteria using an atmospheric-pressure plasma jet and show that plasma treatments can inhibit both Gram-positive (_S. aureus_) and Gram-negative (_E. coli_) bacteria on solid and porous surfaces. Moreover, both direct plasma treatment and plasma-activated media were effective against the bacteria suspended in liquid culture. They also indicated that reactive oxygen species are the key mediators of the bactericidal effects of cold plasma, and the presence of hydrogen peroxide is necessary but not sufficient for antibacterial effects. In addition, they suggest that bacteria exposed to plasma do not develop resistance to further treatment.
## 4 GNSS-Based Location Context Profiling
### Applications of Location Context Profiling
#### 4.3.2 Context Semantic Analysis  
Our studies in Section 3.1 also suggest that GNSS measurements can represent the context of environments, which can be used to trigger sensor fusion for better localization. We aim to create a context database containing labelled training representations and allow a new representation to query for a context label. Since our representation is learned from records, a semantic label is assigned to a trip segment (of length \(\mathcal{T}\)) rather than a spot sample. Moreover, manually labeling representations can be meaningless, given the great variety in human perception, so we adopt a bottom-up clustering mechanism to build up the labeled database. Each training representation has two levels of labels.  
* **Lower-level clustering**: For a set of representations, we use DBSCAN to cluster them into small clusters, assign each cluster with a predefined label as the _detailed context_ and calculate the center of the cluster. Detected noise is labeled as \"noise\". Lower-level clustering captures scenario information, such as \"on street\", \"blocked one one side by building\", \"building enclosing\", and \"on void deck\". Figure 4a,b demonstrate two traces after lower-level clustering.
* **Higher-level clustering**: Cluster centers and \"noises\" from lower-level clustering are further combined into upper-level clusters, and assigned a unified label, such as _general context_. Higher-level clustering captures general information, such as satellite patterns. The noise vector detected from the upper level of clustering are treated as a separate cluster, since it may capture an infrequent satellite pattern at the moment, but can be further clustered into other groups when the database is enlarged. To illustrate the correspondence with lower-level clustering, Figure 4c shows the representations with the color label of upper-level clusters.  
At the query stage, each representation will be firstly matched to the nearest upper-level clusters to determine its general context, and then compared with all representations inside the cluster to find its k nearest neighbors and assign the final label by majority voting. We set a threshold for finding the kNN so that queries with the nearest distance and larger than the threshold will not be assigned any label, as we expect it to be a new context representation that is not included in the training database.  
**Remark 1**.: _All the training procedures presented in Section 4 are performed offline, making the online computations (basic algebraic operations) in smartphones very light-weight._
###### Abstract  
Over the past years, a number of studies have demonstrated the relevance of strain assessed by two-dimensional speckle tracking echocardiography (STE) in evaluating ventricular function. The aim of this study was to analyze changes in left (LV) and right ventricular (RV) longitudinal strain associated with variations of heart rate (HR) in participants with and without chronic heart failure (CHF). We enrolled 45 patients, 38 of these diagnosed with CHF and carrying an implantable cardioverter defibrillator, and seven patients with pacemakers and without CHF. The frequency of atrial stimulation was increased to 90 beats/min and an echocardiogram was performed at each increase of 10 beats/min. Global LV and RV longitudinal strain (LVGLS and RVGLS, respectively) and RV free well longitudinal strain (RVfwLS) were calculated at each HR. When analyzed as continuous variables, significant reductions in LVGLS were detected at higher HRs, whereas improvements in both RVGLS and RVfwLS were observed. Patients with a worsening of LVGLS (76% overall) were more likely to present lower baseline LV function. Only a few patients (18% for RVGLS and 16% for RVfwLS) exhibited HR-related deteriorations of RV strain measures, which was associated with lower levels of baseline RV function and higher pulmonary systolic pressures. Finally, 21 (47%) and 25 (56%) participants responded with improvements in RVGLS and RVfwLS, respectively. Our findings revealed heterogeneous RV and LV responses to increases in HR. These findings might ultimately be used to optimize cardiac functionality in patients diagnosed with CHF.  
definition Article  
Influence of Heart Rate on Left and Right Ventricular Longitudinal Strain in Patients with Chronic Heart Failure [PERSON], 1,2  
[PERSON]  
1,2  
[PERSON]  
1,2  
[PERSON]  
1,2  
[PERSON]  
1,2  
[PERSON]  
a reduced left ventricular ejection fraction (LVEF) is present [10]. On the other hand, an increased heart rate during exercise could favor a rise in cardiac output. Moreover, the HR response to LV and RV could be different. Finally, the method for programming optimal paced heart rate responsiveness in patients carrying implantable devices is not well defined.  
Thus, this study was aimed to evaluate the changes in LV and RV strain in response to increases in HR in participants who have been either diagnosed or not with CHF.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 6 Evaluation
### Using UDP  
In this simulation, we generated 40 constant bit-rate (CBR) flows, where the sources were randomly picked from 100 nodes in a 300 \(\times\) 1500 m sized space. Among the CBR flows, 50% of them are 40-byte CBR packet flows, while the other 50% are 500-byte CBR packet flows. We use 40-byte and 500-byte packets to represent the actual Internet traffic presented in [31]. Figure 11 shows the throughput, average end-to-end packet delay, and normalized control overhead of the simulation. The throughput increase over min-hop when only MTM is used is about 16%, while the throughput increase in MARY over min-hop is 22%. This is due mainly to the medium time saved by the small-sized packet flows sent by the min-hop metric. The average end-to-end delay of MARY also decreases about 260 msec more compared to MTM. The normalized control overhead is the value obtained by dividing the total number of control packets by the total number of received packets. We can see that MARY generates less network control overheads compared to MTM.
## 3 Comparative Analysis of Existing Evaluation Methods
### Existing Evaluation Methods
#### 3.1.3 [PERSON]'s Liquefaction Probability Assessment Method  
After compiling the sand and silt databases of [PERSON] et al. (1999) [30], [PERSON] et al. (2004) [31], [PERSON] et al. (2005) [32], and [PERSON] et al. (2013) [15], [PERSON] et al. [35] obtained a total of 380 liquefaction cases and 234 non-liquefaction cases, amounting to 614 historical datasets. When establishing the probabilistic correlation curve, they incorporated the seismic safety factor of nuclear power plant sites, avoiding misclassifying liquefaction sites as non-liquefaction sites. They repeatedly adjusted the parameters to fit the boundaries of liquefaction and non-liquefaction areas, deriving a relatively conservative new \(CRR\) evaluation formula:  
\[CRR=\exp\left[\frac{V_{S1}}{86.4}+\frac{V_{S1}}{134.0}^{2}-\frac{V_{S1}}{125.2 }^{3}+\frac{V_{S1}}{158.5}^{4}-4.8\right] \tag{10}\]  
[PERSON] et al. [35] considered the sample imbalance in the database, where the number of liquefaction datasets exceeded the non-liquefaction datasets, and introduced the weight factors (wNL = 1.2 and wL = 0.8) proposed by [PERSON] et al. [39] to balance the data imbalance. Based on the simplified liquefaction probability model proposed by [PERSON] et al. [40], they established a shear wave velocity-based liquefaction probability assessment formula for sand and silt, as follows:  
\[P_{L}=\frac{1}{1+\left(\frac{\widetilde{F_{S}}}{0.509}\right)^{2.511}} \tag{11}\]  
\(\widetilde{F_{S}}\) is the nominal safety factor.
## 2 The Proposed Methods
### Kde-Adasyn  
The KDE-ADASYN algorithm is utilized to address more complex data imbalance issues. This algorithm generates new samples that maintain the original probability distribution of the minority class while simultaneously increasing the quantity and improving the quality of samples near decision boundaries. The specific steps are as follows:  
**Step 1**: Define the number of minority class samples as \(N_{\text{min}}\), the number of majority class samples as \(N_{\text{max}}\), and the total number of samples as \(N\). For each minority class  
Figure 1: The KAMS fault diagnosis framework.  
vibration signal sample, use the Euclidean distance to find its \(K\) nearest neighbors. Then, calculate the ratio of majority class samples within these \(K\) neighbors to obtain the weight \(r_{i}\), which reflects the degree of class imbalance around each sample.  
\[r_{i}=\frac{Number\ of\ majority\ class\ samples}{Total\ number\ of\ K-nearest\ neighbors} \tag{1}\]  
Next, normalize \(r_{i}\) as follows:  
\[\hat{r}_{i}=\frac{r_{i}}{\sum_{i=1}^{N_{\min}}r_{i}} \tag{2}\]  
**Step 2**: For each minority class vibration signal sample, use kernel density estimation to obtain the probability density function of the minority class samples.  
\[p(x)=\frac{1}{N_{\min}+h}\sum_{i=1}^{N_{\min}}K\left(\frac{x-x_{i}}{h}\right) \tag{3}\]  
where \(h\) is the bandwidth parameter and \(K\) is the kernel function. In this paper, the Gaussian kernel is used in the following equation:  
\[G(x)=\frac{1}{\left(\sqrt{2\pi h}\right)^{d}}\exp\left(-\frac{1}{2}\frac{(x-x _{i})^{T}(x-x_{i})}{h}\right) \tag{4}\]  
Therefore, Equation (3) can be rewritten as  
\[p(x)=\frac{1}{N_{\min}+h}\sum_{i=1}^{N_{\min}}G(x) \tag{5}\]  
By introducing the normalized weight coefficient \(\hat{r}_{i}\), the weighted probability density distribution of the minority class samples is obtained:  
\[\hat{p}(x)=\frac{1}{N_{\min}+h}\sum_{i=1}^{N_{\min}}\hat{r}_{i}G(x) \tag{6}\]  
**Step 3**: Calculate the total number of new samples that need to be generated:  
\[G=(N_{\max}-N_{\min})\times\beta \tag{7}\]  
where \(\beta\) is a custom parameter within the range \([0,1]\).  
**Step 4**: Based on the probability density distribution obtained from Equation (6), new samples are generated through sampling until the number of newly generated minority class samples reaches \(G\). These new samples conform to the weighted probability distribution of the original minority class samples.
# Model for Wall Shear Stress from Obliquely Imping Planar Underexpanded Jets
###### Abstract  
Though inclined under-expanded planar jets are used in many practical applications, the wall stress resulting from their impingement has not been adequately characterized. Reduced-order models for wall shear as a function of jet parameters have not been reported. This work uses computational fluid dynamics to determine wall shear stress as a function of the nozzle parameters and jet angle. The simulations of the impinging jet are validated against the experimental data and direct numerical simulation; then, the jet parameters are varied to formulate an empirical relationship for maximum wall shear stress as a function of a nozzle pressure ratio, standoff distance, jet Reynolds number, and impingement angle. The global expression for shear stress agrees with  
The flow field of jets impinging normally and obliquely has been studied experimentally and by numerical methods to develop relationships with wall shear stress. [PERSON] (2018) found a similarity solution for subsonic 2-D incompressible non-orthogonal stagnation point flow. [PERSON] (2018) used Preston tube measurements to calculate the wall shear stress of the incompressible oblique planar jets. The behavior of compressible jets and characterization of wall shear stress was not considered. [PERSON] et al. (2018) studied the mass transfer of obliquely impinging planar jets but did not examine wall shear stress. [PERSON] et al. (2018) conducted a computational study of obliquely impinging slot jets, but the \(k-e\) turbulence model used is unreliable for wall shear stress measurement. [PERSON] et al. (2018) studied erosion from obliquely planar jets; however, their results do not directly apply to particle resuspension from a rigid surface.  
Investigating inclined impinging jets can close a knowledge gap in microparticle resuspension applications where shear stress is closely correlated with particle resuspension (2018; 2018; 2018; 2018). This work presents a parametric study of obliquely impinging, under-expanded planar jets. Impingement angles from 0\({}^{\circ}\) to 60\({}^{\circ}\) are examined for jets standoff height to nozzle width ratios of 15-30 and nozzle pressure ratios from 1.0-3.4. The computational data is used to develop a simple relationship for maximum wall shear stress as a function of only the jet parameters: impingement angle, jet width, jet standoff height, and jet nozzle pressure. This reduced-order relationship allows evaluating wall shear stress without performing extensive CFD simulations or performing labor-intensive experiments.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description  
The Broker initiates the operation of the system by creating a grid of Ad-Dealers who are given access to specialized networking equipment and two public/private key pairs, one used for encryption/decryption and one for authentication. The Broker then develops the mobile client software and makes it accessible for users to download. Finally, the Broker accumulates ads from various Advertisers and forwards them to the Ad-Dealers. The ads are organized into groups based on their targeted audiences. When appearing at close proximity of an Ad-Dealer, the mobile client on a user's device can establish an anonymous connection and download ads for specific consumer interests on request. Additionally, users forward requests and adverts among themselves. When two mobile clients (users) A and B come into proximity, A sends ad requests to B. User B is responsible for collecting the relevant adverts from the Ad-Dealer by forwarding the requests and then delivering the ads back to A. The system comprises a sequence of sub-protocols that are triggered when certain events happen, e.g., when two users meet. We describe these sub-protocols and the corresponding triggers in Figure 1.
## 4 Discussion
In line with previous research [47], foot shots were the most common finishing method following an attacking situation (Table 3). Additionally, in the present study, 17.5% of the goals were scored with headers. Although playing styles vary between teams [7], these results were consistent with the 17.7% observed in the English Premier League during the 2008/2009 season [18]. Header shots exhibited excitatory patterns for goals near the posts and inhibitory patterns for saves in the inner columns, and they were most effective within the six-yard box. This should be considered both when choosing the direction of the shot and when delivering crosses into the box. The analysis of shot laterality revealed notable trends based on the foot used (Table 4). Shots taken from the left flank with the left foot and from the right flank with the right foot showed lower effectiveness when aimed at the near post, but higher effectiveness when directed toward the far post. Conversely, shots taken from the left flank with the right foot and from the right flank with the left foot also showed low effectiveness when aimed at the near post. These insights can help to optimize finishing strategies and inform goalkeepers' defensive actions. This is the first study to jointly analyze the field zones, the striking leg, and the goal zone. Previous studies only examined the latter two variables [19]. However, further research is needed to fully understand the nuances of shot placement and goalkeeper positioning. Although this pattern may be due to the skewed distribution of right- and left-footed players (i.e., in this study, 4920 left-footed and 7775 right-footed shots were analyzed, indicating an imbalance in the number of shots taken with each foot). The present study provides insights into the effectiveness of goalkeeper and forward actions, yet there are several limitations. The primary one is that it only considers shots on target recorded by FotMob, excluding missed attempts, which restricts a comprehensive evaluation of shot effectiveness. Future research should incorporate all shooting attempts to provide a more thorough understanding. Another limitation is the cross-sectional design; a longitudinal approach would enhance robustness, reduce seasonal variations, and yield more consistent results over time. However, the inclusion of data from five European leagues strengthens the generalizability of the findings, mitigating the reliance on the characteristics of a single league. Additionally, the study does not differentiate the lateral origin of corner kicks, an element that could provide crucial insights into tactical preferences and their effectiveness. Lastly, incorporating data on the goalkeeper's position at the moment of the shot--specifically whether they are aligned with the shot's bisector--could improve the analysis. Examining how goalkeepers adjust their positioning could improve the understanding of defensive effectiveness and their reactions to different shot situations. Future research should consider these variables to further enrich the tactical analysis of finishing situations.
## 4 Partitioning Detection and Connectivity Restoration
### Primary Monitoring and Failure Detection  
Once each critical actor selects an appropriate backup, it is notified through regular heartbeat messages. The pre-designated backup starts monitoring its primary through heartbeats. Missing a number of successive heartbeats at backup indicates the failure of the primary. After failure detection, the backup triggers the post-failure recovery process as detailed in the following section.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 2 Related Work
[22] analyzed a large number of real web shells to reveal their functions and studied how attackers use web shells by establishing \"honeypots\". However, it may take a lot of time to configure the \"honeypot\" and obtain the corresponding attack intentions. Therefore, it is hard to ensure the real-time ability and effectiveness of web shell detection. In addition, some web shells have a \"honeypot\" detection function, which will automatically disable certain malicious behaviors in the \"honeypot\" to fight the detection engine. [PERSON] et al. [5] proposed a web shell detection method based on network traffic, which uses convolutional neural networks to monitor and model the web shell runtime traffic. This method has two disadvantages. First, if the attacker stretches the front of the attack and executes the required command only once in a while, and the command self is proposed as normal behavior, this can undoubtedly bypass this detection scheme. Second, running web shells in real-time may cause key nodes/data of the system to be destroyed, and the overall risk is high. The web shell detection method proposed in this paper is based on the static detection of different characteristics of web shell, which can effectively avoid the above defects. [PERSON] et al. [7] and [PERSON] et al. [23] use regular matching and keyword feature matching to identify web shells. Although suspicious scripts can be effectively identified to a certain extent, web shells are usually written in high-level languages. The lexical and syntactic features cannot be fully reflected by regular expressions. The method proposed in this paper comprehensively considers the lexical, syntactic, and abstract features existing in such high-level languages (especially the PHP language) and proposes a heuristic ranking method of features based on the Fisher score, which can detect web shell accurately and efficiently.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### Dataset  
Dataset collection and generation processes were obtained from the [PERSON] and Glenn-Jocher databases, as shown in Table 1[34; 35]. For the experiment, we used a secondary dataset that contains a collection of indoor and outdoor fire images [36]. The dataset we used provides two folders: train and val. The full fire image dataset was split into training (75%) and test (25%) sets. The train folder was for the training images, and the val folder was for image validation. Both folders contained a set of unlabeled images as well as labeled images to train, test, and validate the model. Additionally, since we did not have wildfires, we used YouTube videos that had different shapes and types of fire to test our model and check its accuracy.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 3 Scenario and Test Bench
### Scenario Description for Underwater Measurements
When performing measurements in underwater environments, it is needed to ensure that signal does not spread out of the water. To guarantee this fact, we have proceeded to establish an ad hoc wireless connection between both antennae outside the water. After that, one of these antennae has been progressively introduced inside the water. The connection between both devices is checked every 5 cm until 30 cm. At this depth, the antenna located inside the water does not detect any signal from the antenna placed outside the water. This simple test helps us to ensure that measures are valid and the signal does not spread out of the water. From this moment, the second antenna can be put inside de water to start the tests.