esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Classification of Botnet Using Lightweight CNN
## 1 Introduction  
The Internet of Things (IoT) is a dynamic ecosystem growing quickly in scale, connectivity, and variety of applications. This ubiquitous ecosystem is similar to other emerging technologies in that it permeates every aspect of our existence. Unfortunately, despite all the benefits that the Internet of Things brings, the increased attack surface that it creates has become even more significant. Vulnerabilities are made worse by the intrinsic limitations of devices, which frequently lack strong security mechanisms [1]. There has been a noticeable trend recently in which the IoT ecosystem is becoming more compromised by botnet threats [2]. An enormous network of infected IoT devices can have devastating consequences since it can grow quickly and launch powerful attacks. As a result, finding practical ways to strengthen IoT systems has emerged as a crucial and complex field of study. Among these, machine learning-based methods are particularly attractive since they allow for the early identification of possible assaults and the identification of unusual behavior [1].  
In the realm of cybersecurity, an IoT botnet integrates itself into a network by executing a series of malicious operations on computing devices. These operations comprise three key steps: first, identifying susceptible devices through scanning; next, installing a compatible bot tailored to the vulnerable device's architecture for propagation; and finally, launching anattack through a command and control operation, as elucidated by [PERSON] [3]. A concrete example is the Mirai botnet, which encompasses attack vectors, a scanning process actively seeking other devices for compromise, and a command and control system governing the compromised devices (bots). This orchestrates further propagation and instigates attacks, exemplifying the sophisticated nature of contemporary botnet strategies [4].
###### Abstract  
Oily skin is a problem for a large number of people, especially in tropical countries. This condition results in comedones, inflammatory acne, and other aesthetic problems in the skin. Emblica (_Phyllanthus emblica_ L.) has a number of benefits for the skin; for instance, there were some studies that suggested that emblica has skin whitening effects, and anti-collagenase and anti-elastase activities; however, its anti-sebum efficacy has not been reported.The objective of this research was to study the anti-sebum efficacy of emblica toner on facial skin. The toner base was formulated, accelerated stability was tested, and preferences were evaluated in 10 volunteers. The toner base with the highest preference score was mixed with emblica extract. Then, the toner base and emblica toners were assessed for skin irritation by a single patch test in 30 volunteers. The anti-sebum efficacy was conducted using the randomized, single-blind, placebo-controlled, split-face method with unwashed and only-washed skin positions added to the middle of the forehead in the same group of volunteers assessed by a skin sebum measurement, SebumScale(r), at 1 h before the test, once after washing, and 1, 2, 3 and 4 h after applying the toners on forehead and cheek skin. The stable toner base with the highest preference (85.6 \(\pm\) 1.8%) was mixed with 1%, 2%, and 3% emblica extract. The toners were stable and did not cause any skin irritation. The 3% emblica toner was chosen for efficacy evaluation. The casual sebum levels of the forehead skin and cheek skin were 66.66 \(\pm\) 7.01 and 56.12 \(\pm\) 7.75 \(\upmu\)g/cm\({}^{2}\), respectively. The sebum level of the unwashed skin position changed (5.0 \(\pm\) 1.66%) insignificantly up to 4 h (\(p>\) 0.05). In comparison, the sebum level of the only-washed skin position was recovered to the casual sebum level (99.4 \(\pm\) 1.23%) within 3 h. Furthermore, the anti-sebum efficacy of the emblica toner (23.5 \(\pm\) 1.24%) was higher than that of the toner base (12.0 \(\pm\) 1.52%) (\(p<\) 0.05). The anti-sebum efficacy of emblica toner on cheek skin (26.9 \(\pm\) 1.78%) was higher than that on forehead skin (20.1 \(\pm\) 1.34%) (\(p<\) 0.05). In summary, the model of evaluation of anti-sebum efficacy used in this study has been found to be practical, and the emblica toner is safe and has apparent anti-sebum efficacy on facial skin.  
anti-sebum; _Phyllanthus emblica_ L.; toner; skin +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods--Porosity Classification and Fracture Identification
### Classifying Bright and Dark Pixels  
All the images are in grayscale with an average pixel intensity of 101/image. Each pixel in a grayscale image represents only the amount of light or the intensity information. Grayscale images are a black-and-white or gray monochrome that is composed exclusively of shades of gray. We measured the intensity (\(\mathcal{L}_{i,j}\)), the measure of brightness /darkness) of any pixel \(\mathcal{P}_{i,j}\in\mathcal{G}_{n}\in\mathcal{M}\). We classify a pixel as dark or bright if \(\mathcal{L}_{i,j}\in[0,I]\) and \(\mathcal{L}_{i,j}\in(I,255]\) respectively, where \(I\) will be the threshold with range \(0<I<255\) (see Algorithm 1). Here, \(\mathcal{L}_{i,j}=0\) means fully dark, whereas \(\mathcal{L}_{i,j}=255\) classifies as fully bright. \(I\) mostly depends on the resolution of the available image dataset, and we humbly encourage the readers to choose suitable \(I\) values based on their expert judgments. In easy words, we classify pixels (as bright or dark) based on their computed \(\mathcal{L}_{i,j}\) values. The proposed image processing technique will be applied only in the effective region of every individual image, which we will term as the red boundary. The proposed image processing technique understands the red boundary by analyzing the brightness of the bordering pixels based on their \(\mathcal{L}_{i,j}\) values.  
```
0:Grayscale image \(\mathcal{G}_{n}\) of rock contain \(l\times s\) pixels. Variables & Parameters:\(\mathcal{L}_{i,j}\), \(\mathcal{P}_{i,j}\)\(\forall\)\(i\leq l,j\leq s\); Outputs: Classification of every pixel as dark and bright.
1:procedurePIXEL.CLASSIFICATION(\(\mathcal{G}_{n}\))
2:for(\(i=0\); \(i<l\); \(i++\)) do {
3:for(\(j=0\); \(j<s\); \(j++\)) do {\(\triangleright\) starting pixel at \(i\),\(j\)=0, 0 (top left)
4:if(\(\mathcal{L}_{i,j}\)\(\in[0,I]\)) then\(\mathcal{P}_{i,j}\in\mathcal{G}_{n}\) is dark
5:elseif(\(\mathcal{L}_{i,j}\)\(\in\)(\(I,255]\)) then\(\mathcal{P}_{i,j}\in\mathcal{G}_{n}\) is bright } } }
6:End.
```  
**Algorithm 1** Pixel Classification Procedure
## 3 Materials and Methods
### Temperature Information Collection  
Temperature data from wireless sensors are wirelessly transmitted to the edge router using LoRa radio. We installed nine wireless sensors in the selected building. We were able to use only a single edge router due to an excellent radio link budget offered by LoRa radio working in the 433 MHz band. The edge router transmits the data to a Message Queuing Telemetry Transport (MQTT, ISO/IEC 20922) broker. The communication may be optionally encrypted by transport layer security (TLS). The data are then retrieved by a web application written in C# that stores them in a relational database, provides an application programming interface (API) to retrieve historical data, and provides a graphical user interface to read current values. The optimization algorithm described in the next part of this paper also receives the most up-to-date data from the MQTT broker to process it. The information flow is shown in Figure 5.  
Any resident of a flat in which a sensor was installed could freely access the readouts by scanning a quick-response code (QR code) placed on the cover of the device. A list containing information about all installed sensors was also available via a web browser. Both screens are shown in Figure 6.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 4 Case Study  
Reports should discuss not only their results but also how they can be interpreted from the perspective of previous studies and their own working hypotheses. These findings and their implications should be discussed in the broadest possible context possible, and future research directions may also be highlighted.  
To verify the feasibility and effectiveness of the optimization scheme here, as well as the superiority of the improved SMC and EKF based on the immune PSO algorithm, a simulation model of the flywheel HSPMSM system based on the EKF is built using MATLAB and Simulink.  
The model consists of the HSPMSM, SVPWM converter, and controller. The improved SMC replaces the PI controller and is obtained by the EKF based on the immune PSO. The \(i_{d}=0\) vector control strategy is adopted, and the immune particle swarm algorithm optimizes the six-dimensional array synthesized using the EKF covariance matrices \(Q\) and \(R\), namely, the optimal values of 6 parameters. Then, the estimated rotational speed and rotor angle position are calculated using the EKF. The selected flywheel motor parameters are shown in Table 4. Figure 6 shows the improved sensorless vector control block diagram of the HSPMSM based on the EKF. The traditional control strategy uses a PI controller for both the outer loop of speed and the inner loop of current, whereas in this thesis, a new  
Figure 5: Characteristic curve under the u\({}_{4}\)(t) control law. **(a)** Angle tracking. **(b)** Angular velocity tracking. **(c)** Control input. **(d)** Phase trajectory.  
SMC controller with an improved convergence law is used to replace the outer loop PI controller to control the speed, after which all the comparison graphs of the simulation results refer to the comparison of the control results under the two control strategies of the outer loop PI controller and the outer loop of the improved SMC controller.  
Two experiments are conducted here. The system speed is set at 1000 rpm and 8000 rpm, PI and SMC control are improved, and then comparative analysis is performed after improving the optimization algorithm.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Overview of Traditional Methods of Wake Detection
#### 3.2.3 Other Methods
Then, [PERSON] et al. [19] proposed a method for cascaded detection of ship hulls and wakes using high-resolution satellite optical imagery from Gaofen-1. Candidate hulls are identified using the phase spectrum of the Fourier transform, followed by hull refinement to acquire accurate shapes and eliminate false alarms based on shape and texture features. The inclusion of wake information is used to increase the probability of correctly identifying true ships. Detected ships are classified using a fuzzy classifier that combines hull and wake information. [PERSON] et al. [24] investigated the detection of Kelvin ship wakes in numerically simulated SAR images using advanced signal processing techniques. They modeled the Kelvin wake geometry based on classical ship wave theory and calculated the scattering echo using a two-scale method. To detect the wakes within these images, they reconstructed them in the Radon domain using Cauchy proximal splitting (CPS), incorporating a non-convex regularization. They found that Kelvin wakes are more readily detectable in HH polarization, at larger incidence angles, and in the X-band frequency range.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 4 Challenges of Short Text Clustering  
Short texts contain several issues, including a lack of information due to documents that include few words [1]. Short texts are used in various applications, including microblogs, Facebook, Twitter, Instagram, mobile messages and news comments. These texts are usually about 200 characters long, which is very short [168]. For instance, Twitter determines the length of each tweet to be no more than 280 characters [169; 170; 16], and Instagram sets a 2200 characters maximum caption length [17]. A short mobile message is limited to 70 characters. To be precise, short texts exhibit the following problems:  
1. Lack of information: A short text has only a few words, leading to a lack of information and poor document representation. Each short text does not include sufficient information on word co-occurrence, and most texts are likely created for only one topic [171].
2. Sparsity: The length of a short text is limited. This short text can represent a wide range of topics, and each user uses unique word choice and writing style [172]. A given topic has a wide range of content, so determining its features is difficult.
3. High dimensionality: Representing the short text using standard text representation methods, such as TF-IDF vectors or BOW [27], leads to high-dimensional features that are less distinct for measuring distance. In addition, the computational time required is extensive [18; 28; 29].
4. Informal writing and misspelling: Short text is used in many applications, such as comments on microblogs, which contain noise and many misspellings, and the presence of a particular language style [47]. In other words, users of social media platforms such as Twitter tend to use informal, straightforward and simple words to share their opinions and ideas. As an illustration, many people on Twitter may write '4 you' rather than 'for you' when posting tweets. In addition, users may create new abbreviations and acronyms to simplify the language: 'Good9t' and 'how r u' are widespread on social networks. Furthermore, the online questions and search queries do not use the grammar seen in official documents.  
According to [173], the lack of information and sparsity considerably impact short text clustering performance. The typical clustering algorithms cannot be applied directly to short texts because of the many variations in the word counts of short texts, and the limited number of words in each post. For example, the accuracy of using the traditional K-means [24] algorithm to group short text is lower than when using K-means to group longer text [25]. This issue complicates feature space extraction from the short text for text clustering.
###### Abstract  
This study evaluates the dynamic performance of a reference footbridge under human-structure interaction (HSI) effects using real-time hybrid simulation (RTHS). The footbridge, designed with precise multi-axial dynamic sensitivity, is tested under pedestrian gait velocities of 1.20, 1.50, and 1.80 m\(\cdot\)s\({}^{-1}\). The RTHS framework involves an analytical continuous model of the footbridge as a numerical substructure and real human gait loads as the experimental substructure. The results reveal significant dynamic coupling between pedestrian-induced loads and the responses of the structure. Lateral vibrations exhibit a fundamental frequency of approximately 1.0 Hz, whereas vertical vibrations peaked near 2.0 Hz. Dynamic synchronization, particularly at higher gait velocities, amplified the structural vibrations, with lateral loading increasing by up to 300% in the middle span. Vertical loads show substantial amplification and attenuation depending on gait velocity and footbridge location. Lateral accelerations display a dispersion of approximately 15.0%, whereas vertical accelerations showed higher variability, with dispersions reaching up to 20%. The RTHS technique demonstrates high fidelity and accuracy, with global errors below 2.95% and delays of less than 2.10 ms across all evaluated directions. These results emphasize the critical importance of accounting for HSI effects in the design of pedestrian footbridges because human-induced vibrations can significantly impact structural serviceability and user comfort. This study offers important insights into optimizing footbridge design to mitigate the risks of excessive vibrations and ensure both safety and functionality under typical pedestrian loads.  
Keywords:human-structure interaction; footbridge; human-induced loads; human gait; vibration; real-time hybrid simulation +
Footnote †: journal: Applied Sciences
## 3 Results  
### Baseline  
Distributions of individual differences in the RP for the baseline are shown in Figure 3A (i.e., the RP with the COI subtracted from the RP without the COI). No significant individual differences were found for any frequency interval when using a Wilcoxon signed-rank test. The median and interquartile range (IQR) of each distribution was as follows: endothelial NO-independent (\(-0.04\) [\(-0.04\) [\(-0.13\)-\(0.14\)], \(p\) = 0.81), endothelial NO-dependent (\(-0.04\) [\(-0.19\)-\(0.10\)], \(p\) = 0.42), neurogenic (0.02 [\(-0.06\)-\(0.10\)], \(p\) = 0.59), myogenic (0.002 [\(-0.06\)-\(0.07\)]), \(p\) = 0.71), respiratory (0.003 [\(-0.02\)-\(0.04\)], \(p\) = 0.65), and cardiac (0.005 [\(-0.03\)-\(0.03\)], \(p\) = 0.50).  
Group-level comparisons for each frequency interval without and with the COI are shown in Figure 4A. The median and IQR for each frequency interval without and with the COI-correction were as follows: endothelial NO-independent (without: 0.85 [0.35-1.35] vs. with: 0.76 [0.22-1.38]), endothelial NO-dependent (without: 1.04 [0.79-1.57] vs. with: 1.15 [0.96-1.40]), neurogenic (without: 1.44 [1.14-1.76] vs. with: 1.50 [1.17-1.71]), myogenic (without: 1.13 [0.84-1.47] vs. with: 1.08 [0.83-1.59]), respiratory (without: 0.68 [0.43-0.76] vs. with: 0.66 [0.44-0.80]), and cardiac (without: 0.73 [0.28-1.06] vs. with: 0.68 [0.35-1.11]).
## 2 Materials and Methods
### Application Layer
#### 2.4.4 Clash Detection  
During the operation stages, repairs and rehabilitation activities are common. Traditionally, the utility information is either unavailable or is present in 2D CAD drawings. Using 2D CAD drawings for underground repair works is time-consuming, costly, and not reliable. In addition, it is very difficult to retrieve the depth, type, and material of the underground utility from 2D drawings that cause a potential collision between utility lines. With the proposed framework, the 3D models can be utilized to check the automatic design and constructability. Many BIM tools such as Civil 3D and Naviswork and GIS tools such as ArcGIS provide capabilities to detect collisions between 3D utility models. It is supposed that new utilities will be developed in the future or old systems will be extended. In this case, the developed framework can be used to obtain the 3D model and the semantic information about the type, material, etc. that will help in further actions.
## 3 Experimental Results and Discussion
### Mineral Compositions and Micro-Morphologies
#### 3.4.2 Micro-Morphologies of Specim  
SEM was adopted to observe the surface and cross section of shotcrete specimens to obtain the surface morphology, particle distribution, and particle connection of shotcrete specimens. The micro-morphologies of shotcrete without the replacement of cement and with the partial replacement of cement with gangue powder were compared. Test results are shown in Figures 12 and 13.  
The analysis of Figures 12 and 13 shows that the two types of shotcrete both have rough surfaces, pores, and fractures. In relative terms, the pores and fractures are larger in shotcrete with the partial replacement of cement with gangue powder. This is attributed to the lower hydration reactivity of coal gangue powder compared to cement, despite ball-milling activation. The reduced hydration products lead to higher porosity and unfilled cracks in shotcrete. These pores and fractures induce localized stress concentrations and diminish the effective load-bearing area, causing premature failure upon reaching theoretical strength thresholds. Therefore, the mechanical performance of shotcrete without the replacement of cement is superior to that of shotcrete with the partial replacement of cement with gangue powder.
## 4 IoT Protocols
### Infrastructure Protocols  
#### 4.3.1 Network Technologies  
The network technology layer consists of the physical/device layer and link layer protocols.  
* **Zigbee:** It uses the IEEE 802.15.4 standard and operates in the 2.4 GHz frequency range with 250 kbps [59]. The maximum number of nodes in the network is 1024, and has a capacity of up to 200 m. Zigbee can use 128-bit AES encryption.
* **Bluetooth Low Energy (BLE):** It provides the same range as classic Bluetooth with considerably less energy consumption [60]. This is used in beacons used to send contextual information based on location (Google Beacon Platform, Google Physical Web, Apple ibeacon)
* **Near Field Communication (NFC):** It is a short-range, high frequency (13.56 MHz) wireless technology based on RFID [61]. Two devices have to come closer to initiate the transaction, such as a phone and payment terminal.
* **IEEE 802.15.4:** It is a standard that specifies the physical layer and media access control for low-rate wireless personal area networks [62]. It has the Zigbee, Wireless HART, and MiWi specifications. It is used with 6 LoWPAN and standard Internet protocols to build a wireless embedded Internet.
# Structural Damage Identification Using a Modified Directional Bat Algorithm
## 4 Numerical Simulation
### Beam-Type Bridge Model
The maximum relative error of other bat variants algorithms were 5.7% (DBA), 8.6% (TMBA) and 23.4% (LBA), respectively. DBA had a small error alarm in element 4. Due to the small population and few iterations, the other three variations of BA did not perform well in the accuracy of structural damage identification. Therefore, the proposed MDBA has better superiority under the condition of a small population and few iterations. Next, the robustness of MDBA was verified, and different degrees of noise were applied to the modal information. The identification result under the same small population and few iterations is shown in Figure 8 and Table 3. It is clear that with the increase of noise level, the relative error of the identification results also gradually increased. However, the maximum relative error was only 5.64% in the noise levels of 5%. Under the condition of a small population and few iterations, MDBA also showed good identification results and robustness in the case of different noises. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Damage Severity (Relative Error)**} & \multicolumn{2}{c}{**Obvious Error Alarm**} \\ \cline{2-4}  & **Damage Element 2** & **Damage Element 7** & **Damage Severity @ Element** \\ \hline MDBA & 19.99\% (0.05\%) & 25.00\% (0\%) & none \\ DBA & 18.86\% (5.70\%) & 26.01\% (4.04\%) & 2.65\%@4 \\ LBA & 15.32\% (23.40\%) & 23.51\% (5.96\%) & none \\ TMBA & 21.28\% (6.40\%) & 22.85\% (8.60\%) & none \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The identification results of the damaged case by other variations of BA. Figure 7: **(a)** The convergence curves achieved by other variations of BA for the objection function. **(b)** The identification results of the damaged case by other variations of BA.