esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 3 General Operation States of Underwater Gliders
### Descent and Ascent States  
In Figure 3, the schematic diagram of inertial forces (red) and the hydrodynamic reactions (blue) in the descent and ascent state of the glider are shown. In the descent state, the negative net buoyancy \(B\) causes the vehicle to glide downwards in the water, eventually reaching a stable state at a constant speed \(U\) through the balance of the hydrodynamic forces and inertial forces. In the ascent state, the positive net buoyancy \(B\) causes the vehicle to glide upwards, also reaching the steady state. The result in both states is the forward movement of the vehicle with an estimated glide angle \(\gamma\) (Section 4). In the steady state, the resultant hydrodynamic force \(R\) is in the opposite direction of the net buoyancy force \(B\).  
Figure 3: Underwater glider movement at steady state for: (**a**) descent state (**b**) ascent state.  
Figure 2: Sawtooth pattern and main operation states in the underwater glider operation cycle.  
According to [PERSON]'s first law, for the vehicle to be in a steady state at constant speed, the vehicle must be in equilibrium (the sum of forces and moments is equal to zero), therefore the dive velocity \(U\) will be the result of the balance of the inertial forces and the hydrodynamic reactions.  
Considering that the pitch angle \(\theta\) is controlled by the unbalance of the internal masses of the glider (the pitch mechanism), the analysis to estimate the flight angles, as a function of the glide angle \(\gamma\), is presented in Section 4.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spatial Domain-Based Methods
#### 2.1.3 Other Methods Based on Spatial Domain  
[PERSON] et al. [46] obtained the quality score by calculating the maximum local variation (MLV) of image pixels. The standard deviation of the weighted MLV distribution was used as a metric to measure sharpness. The study shows that this method is characterized by high real-time performance. [PERSON] et al. [47] developed a sharpness model by analyzing the autoregressive (AR) model parameters point-by-point to calculate the energy and contrast differences in the locally estimated AR coefficients and then quantified the image sharpness using a percentile pool to predict the overall score. This evaluation method that calculates local contrast and energy based on a mathematical model is also a spatial domain method. [PERSON] et al. [48] proposed a new independent feature similarity index to evaluate the sharpness by calculating the structure and texture differences between two images. [PERSON] et al. [49] presented a no-reference image blur metric based on the study of human blur perception for varying contrast values. The method gathered information by estimating the probability of detecting blurring at each edge in the image and then calculated the cumulative probability of blur detection to obtain the evaluation result. [PERSON] et al. [50] proposed an adaptive definition evaluation algorithm, which achieved a better evaluation effect than the standard adaptive definition algorithm. [PERSON] et al. [51] presented a no-reference image quality evaluation metric based on Cartoon Texture Decomposition (CTD). Using the characteristics of CTD, the image was separated into cartoon parts with prominent edges and texture parts with noise. Then the ambiguity and noise levels were estimated respectively to predict the results.  
The spatial domain-based methods require less computation, but the above methods rely much on the details of the image and are easily affected by noise.
## 2 Materials and Methods
### Physicochemical Characterization Tests
#### 2.1.8 X-ray Fluorescence (XRF)  
The method is used when X rays crosses the wavelength and is quantitatively recorded by the X-ray count. In this way, the elemental composition (total content) of samples (pure and activated allophane) is obtained physically. This method is also known as a non-destructive test because when exposed by high-energy X-ray radiation, the sample does not present any type of modification.  
The test begins with the mechanical preparation where the sample is dried, to decrease the particle size to approximately 75 microns, and then a quartet is performed to obtain a representative sample. The samples pass to the physical-chemical preparation area, where fusion is performed in order to obtain a homogeneous glazed disc for analytical reading. In instrumental reading, an X-ray fluorescence spectrometer is used per dispersive wavelength in which the optical part is housed for the identification and quantification of certain elements.  
The elements determined for this study are the identification and quantification of silica and alumina present in the allophane due to the ratio of silicon/aluminum equal to one, as mentioned above.  
The adsorption capacity is influenced by the Si/Al ratio due to its chemical composition, since the lower the number of aluminum atoms, the fewer exchange cations, so the silicon content is inherently hydrophobic, generating an affinity with hydrocarbons.  
The allophane is characterized by containing in its structure elements considered predominant such as aluminum and silicon. This synthesis originates from soils from volcanic ash that generally indicate a greater presence of silicon.  
\"The allophane Al/Si ratio is in the range 1.3-1.4. The said allophane is called allophane or allophane flow of silica and represents an intermediate between the allophanes of soils rich in Al (Al/Si = 2 known as allophane proto-imologie or allophane of imologie type) and allophanes rich in Si (Al/Si = 1 is phazom or allophane-type allophane)\" [9].  
A decrease in Si/Al implies an increase in the aluminum content, avoiding an interaction between them, since the AlO\({}_{4}\)-tetrahedra are far from each other.  
The equipment used for the method is BRUKER S8 TIGER.
## 4 Discussion
### Limitations and Future Developments  
Some parameters, such as implant diameter, have a reduced range of variation. Thus, the description of responses could be improved by expanding these ranges. The responses chosen as mechanical criteria in the present study (average stress in the bone and stress jump) are global responses that do not take into account local effects. This is one of the reasons why the angle of inclination has only a small influence on the system. Local results, such as maximum strain or stress, could be envisaged in further investigations. Consideration of this type of response could enable physiological limitations to be taken into account with the bone's mechanostat theory [72]. Modelling of the bone's anatomy and quality also has limitations as only cortical thickness and Young's modulus of cancellous bone were considered. Today, considerable progress is being achieved in automation of the digital chain, particularly in segmentation of scanner images [73]. A digital model can, therefore, be produced quickly and almost automatically, taking into account the patient's real anatomy and bone density levels. Mechanical criteria could thus be implemented during the planning stage of implant placement by the dental surgeon. Another hypothesis was the sticky contacts in the parametric model, in particular, the interface between bone and implant. It does not allow modelling of different levels of osseointegration. Different methods have been used in the literature, such as varying the coefficient of friction [16] or the number of force transfer nodes at this interface [74].
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 4 View Factors for Complex Shapes and Obstructions
### Complex Shapes with Curvature  
While RADIANCE natively supports curved surfaces such as spheres and cones, it does not have the functionality to support other analytical curved shapes. Furthermore, the functionality of exporting complex curved shapes in the form of native RADIANCE spheres or cones is not supported by most 3D modeling software. Consequently, curved shapes must be approximated through planar surfaces.  
As shown through the examples in Figure 15, it is possible to generate and thereby calculate view factors shapes of greater complexity by using planar surfaces. The calculation process for these shapes is the same as that discussed for the previous use-cases.
## 2 Methodology
### Experimental Procedures
#### 2.2.1 Compressive Strength Testing  
Compressive strength experiments were conducted on casting mortar blocks under three different conditions: a dry environment, a pure water environment, and a slurry environment. In each environment, 100 mm \(\times\) 100 mm \(\times\) 100 mm cubic specimens were cast using a funnel to simulate the conduit method. To match the on-site shaft wall casting model, core specimens were extracted to obtain \(\Phi\)50 mm \(\times\) 100 mm cylindrical specimens. The compressive strength of these cylindrical specimens was measured at 1, 3, 7, and 28 days to investigate the strength development of substitute mortar for concrete. The long-term performance evaluation of this material will be further studied in future research. The specific procedures are as follows:  
Dry environment: Mortar is first poured into the mold box using a funnel. The mixture is then vibrated on a vibration table to ensure proper compaction. After curing at room temperature for 12 h, the specimens are demolded and cored. Finally, they are cured in a standard curing box and tested for compressive strength according to the curing age.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bentonite (g)** & **CMC** & \multirow{2}{*}{**Water (g)**} & **Anhydrous** & **Sodium** & \multirow{2}{*}{**Density**} & **Apparent** & **Colloidal** \\
**Solution (g)** & & & **Sodium** & & **Tripolyphosphate** & **(g/cm\({}^{3}\))** & **Viscosity (cp)** & **Rate (\%)** \\ \hline
140 & 30 & 530 & 1.0 & 1.0 & 1.183 & 22.3 & 98.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selected slurry formulations and their characteristic parameters for the experiment.  
Figure 4: Relationship curves of slurry colloid rate under different formulations.  
Pure water environment: Water is added to the casting box until it is higher than the height of the mold box. The mortar is then poured into the mold box using a funnel, forming the specimens in the pure water environment. After curing at room temperature for 12 h, the specimens are demolded and cored. They are then cured in a constant-temperature water bath and tested for compressive strength according to the curing age.  
Slurry environment: Prepared slurry is placed in the casting box. To observe the casting process and ensure completion, mortar is poured into the mold box using a funnel when the slurry level reaches half the height of the mold box. The slurry level is then continuously increased while pouring the mortar. After forming the specimens in the slurry environment, they are cured at room temperature for 12 h, demolded, cored, and finally cured in a constant-temperature water bath. The specimens are tested for compressive strength according to the curing time.  
A schematic diagram of the preparation process of the specimens is shown in Figure 5.
## 3 Methods
### Evaluation of the Tracking Performance  
In general, the Metrics-1 that is used to evaluate the tracker's ability calculates the ratio of area C to the real object area B. Area A, formed by the tracker inferring the four coordinates, is marked with the symbol \(D\). Area B, formed by the four coordinates of the object's ground truth, is marked with the symbol \(G\). Area C, marked with symbol \(G\cap D\), is where area A and area B overlap, as in Figure 7 and Equations (8) and (9).  
We believe that such an overlap ratio loses its validity in several situations. In the first case, when the tracker determines that the size of the object surrounds the real object, the overlapping area is equal to the area of the real object. At this time, using the general tracking evaluation method will determine the tracking ability of the tracker to be 100%, as in Figure 8a. Another situation is that the tracker determines that the size of the object is surrounded by the real moving object, so the overlapping area will be equal to the area of the moving object identified by the tracker, so the size of the object identified by the tracker cannot reflect the size of the object, as in Figure 8b.  
\[\text{Non}-\text{overlap area} =(G-(G\cap D))+(D-(G\cap D)) \tag{10}\] \[\text{The overlap ratio} =[3\ \times\ (G\cap D)-(G+D)]/G \tag{11}\]  
Therefore, we adopted a different tracking ability estimation method, Metrics-2. It finds the ratio of the difference between the overlapping area and the area of non-overlapping to the area of the real object, as Equations (10) and (11). By subtracting the area  
Figure 8: There are two overlap conditions: (**a**) the first overlap condition; (**b**) the other overlap condition.  
Figure 7: The overlap is used to evaluate the tracking ability.  
of non-overlapping from the overlapping area to magnify the components of the tracking inaccuracy, it can highlight the effect of the tracker in tracking.
## 7 Conclusions  
Real-time and accurate evaluation of rock mass quality is crucial for the design and construction of underground engineering projects. However, due to the geological complexity of deep rock engineering, comprehensive geological surveys are challenging, making it difficult to accurately assess the quality of surrounding rock with limited geological data. To address this issue, this study established a rock mass quality classification database comprising various underground engineering projects and developed a rock mass classification prediction model using XGBoost. To enhance the model's predictive performance, ZOA and BO algorithms were introduced to optimize XGBoost's hyperparameters. The specific conclusions are as follows:  
1. Performance evaluation results indicate that the ZOA-XGBoost model with a population size of 30 achieved the best predictive performance. The evaluation metrics on the test set were Accuracy of 0.923, Kappa of 0.887, Precision of 0.932, Recall of 0.932, and F1-score of 0.922.
2. Multiple feature importance analysis methods demonstrated that RQD and UCS are the key input variables for predicting rock mass quality. ICE analysis revealed that higher RQD and UCS values correspond to better rock mass quality, consistent with actual engineering experience.
3. To further validate the performance of the ZOA-XGBoost model, an additional rock mass quality classification dataset containing missing values was constructed. The results showed that ZOA-XGBoost achieved an accuracy of 80.00% on the incomplete dataset, significantly outperforming other machine learning models with imputed missing values. This confirms the reliability and effectiveness of the hybrid model developed in this study for practical engineering applications.  
Conceptualization, B.Y. and D.L.; Data curation, B.Y. and Y.L.; Formal analysis, Q.Z.; Funding acquisition, Q.Z. and D.L.; Investigation, Q.Z.; Methodology, Y.L.; Project administration, Q.Z. and D.L.; Resources, Y.L.; Software, B.Y. and Z.L.; Supervision, Q.Z. and D.L.; Validation, B.Y., Y.L. and Z.L.; Visualization, B.Y.; Writing--original draft, B.Y.; Writing--review & editing, Q.Z. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant nos. 52374153 and 52304113.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare that they have no conflicts of interest.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods  
Figure 3 illustrates the methodology adopted in this study. To comprehensively evaluate and select the optimal magnitude prediction model, we conduct a systematic experimental design in subregion A of the southern segment of the North-South Seismic Belt (25\({}^{\circ}\) N\(\sim\)30\({}^{\circ}\) N, 100\({}^{\circ}\) E\(\sim\)105\({}^{\circ}\) E). The earthquake catalog data, which formed the basis of this study, were used to calculate seismicity parameters. These parameters are organized into a time series and used as core features for model training and optimization. The data were divided into training and test sets to evaluate model performance and prevent overfitting effectively. Specifically, two ratios were used: 80% training and 20% test and 70% training and 30% test to ensure adequate validation of the model's ability to generalize to unknown data. During model training, grid search was used for hyperparameter tuning to find the optimal combinations in the predefined parameter space, maximizing prediction accuracy and stability. As a result, the selected model exhibits high reliability and robustness. Finally, the optimal prediction model was chosen based on evaluation metrics to predict the maximum magnitude within the following year.  
Figure 3: Flow chart of the experiment.  
Figure 2: Schematic diagram of sliding time window.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 3 Background
### IoT Botnet
#### 3.3.3 Basic Components of IoT Botnets  
Understanding the way Internet botnets work is very important in order to find new and effective ways to discover these bots and deal with them to limit their damages. Understanding the workings of these bots more comprehensively will help us to resist them and keep cyberspace clean, and thus help to ensure the security of the Internet. After the source code for Mirai was published, many cybercriminals cloned and adjusted the code and issued copies of IoT malware that aimed to create Internet of Things (IoT) botnets and compete in controlling the largest possible number of IoT devices [26]. Therefore, most IoT malware has similar working steps. In the following steps, how these IoT botnets work will be explained (see Figure 4).  
Figure 4 illustrates how IoT botnets work. It is clear from the figure that botnets work in several steps. They are generalized and summarized in seven steps as follows:  
* The bots search the IP address space for devices running Telnet or SSH and try to log in using a hard-coded IoT credential dictionary.
* Once successful, a bot reports the IP address of the victim and related credentials to a report server.  
Figure 3: Phases of an IoT botnet’s life cycle.  
* The report server dispatches this information to the loader server.
* The loader server simultaneously forces the device to be infected in a way dependent on the architecture of the victim.
* The attacker sends a command to the command and control server specifying the target and the requested details to start the attack.
* The command and control server informs the bots in preparation for an attack.
* The bots trigger the attack against the specified target.  
It is noteworthy that malware for the Internet of Things is developing and being modified for different reasons. These reasons include having different purposes for the IoT botnets, and exploiting new vulnerabilities. Therefore, maintenance [27] is performed for such malware. Reconfiguration of such a botnet or adding new devices to it increases its size and makes it more powerful. These changes may make IoT botnets more sophisticated and increase the difficulties in IoT botnet detection.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### Devices  
In the future there will be an even greater deal of heterogeneity in devices connected to the IoT, both intelligent as well as inert (e.g., simply labelled as Radio Frequency Identification, RFID). Moreover, the vast majority of smart devices lack the computing resources needed to implement the IP protocol stack and this situation will continue in the future for many of the devices used in IoT applications [14]. This is the case of WSNs which consist of devices designed to be deployed in large numbers and multiple environments, including remote regions. For these reasons, maximising lifetime, power consumption and cost of deployment are some of the challenges that these networks present. Specifically, energy is the scarcest resource of WSN nodes and is a decisive factor in the useful life of the network. Thus, energy supply for devices integrated in WSN will increasingly depend on different energy harvesting technologies (solar, thermal, wind, etc.) and on new battery miniaturization and ultra capacitor technologies [15; 16; 17]. As these technologies mature, WSN will be applicable in a wide range of scenarios that require long-life batteries. Regarding cost, according to an IDTechEx forecast [18], the average price per node will drop from $9 per piece in 2011 to $5 per piece in 2021. Communications merit a separate section.
# Early Ransomware Detection with Deep Learning Models
## 3 PARSEC Dataset
### Data Collection  
We chose to use Windows 7 for malware analysis because, despite being an older operating system, it remains a target for malware attacks due to its widespread use in slower-to-upgrade environments [40]. Therefore, malware analysis on Windows 7 provides insights into threats still exploiting older systems. Additionally, many malware variants designed to target Windows platforms maintain compatibility with Windows 7 due to its architectural similarities with newer versions. Note that our method and results are also applicable to the Windows 10 OS and its server counterparts. It is important to note that, for our purposes, Windows 7 and Windows 10 are API-compatible. This means a sample identified as ransomware on Windows 7 would also be classified as ransomware on Windows 10.  
We used Process Monitor [41] (PM) on a Windows 7 Service Pack 1 (SP1) environment within VirtualBox v6.1 [42] to record API calls from both benign and malicious processes. Process Monitor (v3.70) is a sophisticated tool developed by Sysinternals (now part of Microsoft) that can capture detailed API call information [43].  
We collected the data for malicious and benign processes separately. For each API call of a process, we recorded the call's name, result, parameters, and execution time. Then, we filtered the API calls and their parameters from every process to construct our datasets; this procedure is shown in detail below. Figure 1 shows the pipeline of our data collection.  
Note that our set of ransomware and benign processes is extensive, but it does not contain all possible processes. However, choosing a few representatives from each application group is an acceptable practice in benchmarking, and we consider the selected set of benign applications to be adequately representative. While ransomware can theoretically differ significantly, in practice, it generally follows the same patterns as other malware. There are several notable examples, such as Locky, WannaCry, and Ryuk, from which all others are derived [44; 45].