esa-sceva/satcom-chunk-collection huggingface dataset:
# Eulerian-Eularian Model for Agglomeration Behavior of Combusted Iron Particles
## 5 Conclusions  
The agglomeration hydrodynamics of the iron powder particles in a fluidized bed were analyzed using a CFD numerical model. The model was developed with a simpli  
Figure 8: Solid velocities/ms\({}^{-1}\).  
Figure 6: Pressure drop variation for increased velocity V = 0.12 m/s.  
Figure 7: Solid fraction variation for V = 0.12 m/s.  
fied approach of a two-fluid method involving the implementation of the user-defined subroutines written by the authors. The conclusions of the study are as below:  
1. An Eulerian-Eulerian multiphase model, where the solid phase is explicitly simulated, was implemented through user-defined subroutines and is successfully extended to cohesive dense gas-solid flows by including temperature-dependent solid bridge (sintering) force.
2. The iron fine powder agglomeration due to the sintering force developed in the model qualitatively agrees with the experiment and CFD-DEM studies. The solid temperature of the iron particles was found to be a significant parameter in estimating the sintering force effect. Higher gas superficial velocities can delay the defluidization in the fluidized bed combustion. However, the bed still leads toward the defluidization regardless of the gas's superficial velocity.
3. It was observed that there is a significant impact on particle cohesion of the smooth surface/nine-micro contact-point model (C = 1/1.251) model (2.4/3 scaling factor) results in higher temperatures, which is closely aligned with the realistic phenomena. This behavior is similar to that of the CFD-DEM study of the same experiment. Therefore, the smooth surface/nine-micro contact-point model sintering force model is more suitable for representing the true phenomena observation in the study of combusted iron powder.
4. The normalized pressure drop curve follows a similar pattern for each temperature and model type similar to the experiment. With the temperature and sintering force factor increase, the higher pressure drop variation could be observed attributing to higher standard deviations. Nevertheless, the standard deviation of the pressure drop significantly drops in clear agglomeration cases at higher temperatures.
5. The study proposed a cost-effective, simplified CFD model for predicting particle agglomeration in fine iron particles. The results of the model reasonably agree with the other referenced CFD-DEM work along with the experiment observations. Future work can lead to the implementation of the model on a larger scale to minimize cost.  
The authors declare no conflict of interest.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 3 Proposed Routing Protocol
### Routing Protocol
#### 3.1.4 GA and ALO  
The objective function in (9) and the system constraints in (10) form a Mixed-Integer Linear Programming (MILP) problem, where the decision variable \(x_{i,nx}\) has only binary values 0, 1 and all system constraints are linear. To solve this problem, two loops are introduced in this paper. The first loop (outer) works by proposing different possibilities of paths, then evaluates each proposed solution (generation) in the second loop (inner) based on Equations (9)-(11). The GA works as an outer loop. In each iteration, several solutions are extracted using GA's operators (crossover and mutations) utilizing the fitness value of the proposed objective function, then the ALO algorithm (i.e., inner loop) produces the best path with the minimum fitness value of the current iteration. The GA keeps generating new solutions, and ALO picks out the best one till the process is finished. Figure 5 shows the flowchart of the proposed routing algorithm. Algorithm 1 shows how the proposed routing algorithm works to determine the optimal path and the best fitness value of the system, which in turns extends the lifetime of the network of IoTs. During the implementation process, we have tried another MILP solver to solve the problem, but the problem was mainly with the high final fitness value that resulted from other solvers that have been used. As mentioned previously, the aim of this study (optimization problem) is to minimize the total amount of energy consumption for each path, which in turn leads to minimizing the total energy consumption for the system as a whole. Among the other MILP solvers that have been incorporated with GA to solve the problem is the Branch and Bound (B&B) algorithm. The Particle Swarm Optimization (PSO) was also used with the GA as well. However, based on the implementation results, the lifetime of the network was less than the (GA-ALO) solvers by 10-15%.  
```
0:\(N\) and BS (characteristics), \(S_{r}\) sends \(P_{k}\) to BS
0:\(Optimal\ path,E_{n}(L,D)\)
1:Initialization
2:K = max # (GA iterations)
3:\(S\) = current GA's population set
4:\(ii=1\)
5:while\(ii<K\)do
6: GA generates initial population \((S)\)
7: ALO calc En (L, D) for \(S\)
8: ALO returns best fitness value with its path to GA
9: GA saves optimal (\(E_{n}(L,D)_{i,mx}\) & path) comes from ALO
10: GA updates \(S\) using crossover and mutation
11:\(ii++\)
12:endwhile
13: GA returns optimal value with its path
```  
**Algorithm 1** The proposed routing protocol (EFRP)  
Figure 5: Flowchart of our proposed protocol.
## 3 Results
### Mechanisms of BFRT
#### 3.2.2 Muscle Fiber Recruitment  
Muscle fiber recruitment has been shown to play an essential role in the observed effects of BFFT. The recruitment of type II muscle fibers seems to be important for inducing muscle hypertrophy, as these have a higher occurrence of signaling proteins compared to type I muscle fibers [29; 30]. Muscle fibers are recruited according to the'size principle', in which the smaller motor units associated with type I muscle fibers are activated initially at lower intensities, whereas the larger motor units related to type II muscle fibers are recruited at higher exercise intensities. Since the level of MPS is determined by the amount of muscle fibers activated, it is generally accepted that higher intensities (>70% of 1 Repetition Maximum (1 RM)) are required to induce positive muscle adaptations in terms of hypertrophy and strength, as HLRT recruits both type I and type II muscle fibers (whereas LLRT mostly recruits type I fibers) [13]. Interestingly, previous research using integrated electromyography (iEMG) has demonstrated that during BFFT, recruitment of  
Figure 1: Overview of mechanisms using Blood Flow Restriction.  
type II muscle fibers happens even at low intensities (\(\sim\)20% 1 RM), due to the low oxygen levels and metabolite accumulation induced by vascular occlusion, which causes the type I fibers to fatigue more rapidly [28; 31; 32; 33]. Furthermore, group III and IV afferents located within a muscle are also stimulated by these metabolic processes. Stimulation of these afferents causes inhibition of the alpha motor neuron, thereby further enhancing muscle fiber recruitment to maintain adequate muscle force and protect against conduction failure [28].  
MPS occurs independently of exercise intensity if fatigue is reached. As BFRT effectively induces muscle fatigue due to significantly decreased venous return and arterial inflow, it enables optimal fiber recruitment for muscle hypertrophy purposes [28].
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 1 Introduction  
### Motivation  
Anomaly detection is a critical problem in many applications, ranging from security and fraud detection to quality control and health monitoring. With the increasing amount of data generated in today's world, it is becoming increasingly important to develop efficient and accurate methods for detecting anomalies in large and complex datasets.  
Finite mixture models are a popular statistical technique for modeling complex data distributions [1]. Gaussian mixture models (GMMs), a specific type of finite mixture model, assume that the underlying distribution of the data is composed of a mixture of multiple Gaussian distributions. GMMs have been widely used in various scientific fields, including computer vision, pattern recognition, and supervised and unsupervised learning. Gaussian mixtures have also proven to be a valuable and flexible tool in analyzing biological data, ranging from identifying different populations within a dataset to modeling complex and multimodal distributions. For instance, [PERSON] et al. [2] and [PERSON] et al. [3] applied Gaussian mixtures for clustering gene expression data, while [PERSON] et al. [4] employed Gaussian mixtures to identify differentially expressed genes between two or more groups of samples. GMMs were also used for the identification of gene pathways and interactions by [PERSON] et al. [5] and for predictive modeling in protein dynamics by [PERSON] and [PERSON] [6].  
In the context of anomaly detection, GMMs can be used to model the distribution of the bulk of the data and to identify those observations that deviate significantly from the expected pattern. This can be performed by computing the density of each data point according to the estimated GMM and flagging any observations with low densities as anomalies. However, for this approach to be effective either reliable and robust estimatesof the mixture parameters are required, or the model should explicitly incorporate an additional component for the presence of noise and outliers. In fact, a potential disadvantage of the GMM framework is its sensitivity to the presence of outliers and noise in the data. This can negatively impact the estimation of model parameters and the performance of the model. Furthermore, outliers and noise can be especially problematic in anomaly detection tasks, where the goal is to identify those cases that deviate from the distribution of most data points.
## 5 Results  
This section presents the results obtained from the experimental evaluation of the proposed Machine-Learning (ML) and Deep-Learning (DL) models for the detection of Distributed Denial-of-Service (DDoS) attacks in IoT and Cloud environments. The objective is to assess the effectiveness of the implemented models Random Forest, XGBoost, and LSTM by analyzing their performance under different configurations and data processing conditions. The experiments were conducted using two widely accepted datasets in the literature, CIC-DDoS2019 and N-BaIoT, with specific attention to data preprocessing, feature selection, and hyperparameter optimization techniques. The evaluation metrics used include accuracy, precision, recall, and F1-score, in addition to ROC curves and confusion matrices, providing a comprehensive understanding of the strengths and limitations of each approach. The subsections below detail the results associated with feature selection strategies, data processing workflows, model training, and final comparative analysis. The datasets used in this study, CIC-DDoS2019 and N-BaIoT, have been widely adopted in the literature due to their structural richness and diversity of attack patterns. CIC-DDoS2019 provides network flows generated in a controlled environment with multiple current DDoS attack vectors, while N-BaIoT collects traffic from real IoT devices infected by botnets such as Mirai and Bashlite. While both datasets represent synthetic scenarios, their composition based on realistic traffic and quality labeling make them valid benchmarks for evaluating detection models. Nevertheless, it is recognized that, given the rapid evolution of attack techniques, it is necessary to complement these evaluations with validations in real operational environments, as proposed in future work.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Proposed Approach
#### 3.1.4 Differential Global Information Fusion Module  
The DGIF is designed to effectively combine global and differential features, capturing both broad contextual information and localized changes. As shown in Figure 6, the module utilizes three parallel dilated convolutions with dilation rates of 1, 2, and 3, which extract multi-scale features. This design allows the module to efficiently expand the receptive field, capturing both fine-grained details and large-scale patterns without significantly increasing computational costs.  
The module begins by concatenating the global and differential input features, which are then passed through the three dilated convolutions. These parallel convolutions with increasing dilation rates enable the module to focus on changes at different spatial resolutions, ensuring that both small, subtle changes and broader patterns are effectively captured.  
To further enhance the fused features, the module incorporates residual connections by adding the original global and differential features, along with their scaled versions. This residual connection not only preserves the original information but also helps the network maintain gradient flow during training, making the module more stable and effective. The final refined output is generated through additional convolutions, which ensure that the integrated features are fully optimized for downstream tasks.  
By combining multi-scale feature extraction, residual connections, and efficient fusion, the DGIF module ensures that both global and differential information are fully utilized, resulting in a more robust and accurate representation for change detection.
# Analysis of Cement Sheath-Rock Damage Mechanism--A Case Study on Water Injection Wells
###### Abstract  
In the field of water injection wells within oilfields, comprehending the intricate mechanics of water channeling and the resulting rock damage on the external cemented surface holds paramount significance for the efficient management of reservoirs. This paper presents a comprehensive study aimed at illuminating the complex nature of rock damage on the external cemented surface of casings and deciphering the underlying mechanisms that underpin water channeling occurrences. To this end, a robust constitutive model is established and refined to capture the multifaceted interactions inherent in rock damage on the cemented surface. This model introduces a modified bonding force approach to enhance shear stress precision and thoughtfully accounts for the profound effects of elastic-plastic behavior, cracking damage, and elastic-cracking coupling damage on damage progression. Subsequently, the refined model is employed to investigate rock damage on the external cemented surface of water injection wells, encompassing variations in confining pressure, rock width on the cemented surface, and the ratio of Young's modulus between the cement sheath and the rock. The research findings emphasize the interplay between cracking and elastic damage as the catalyst for rock damage on the cemented surface. Impressively, the accuracy of the refined constitutive model for the cemented surface has advanced by over 5% compared to prior studies. The manipulation of confining pressure and the Young's modulus ratio enhances peak fracture water pressure, signifying substantive strides in comprehending damage propagation mechanics. Furthermore, the study discerns the negligible influence of rock width on the cemented surface regarding damage patterns. These findings have important implications for the effective management of water injection wells, providing insights for the restoration of water channeling wells and proactive measures against water channeling phenomena. They also contribute to the refinement of well cementing practices and the proficient management of water channeling and water flooding in oilfields. The research findings have profound implications for the domain of water injection wells, offering novel insights into the restoration of water channeling wells and the implementation of preemptive measures against water channeling phenomena. These findings hold the potential to guide the refinement of well cementing practices and the adept management of water channeling and water flooding wells within the studied oilfield.  
c 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Physical and Mathematical Model
### Building Analysis Model  
According to the typical spatial layout and the evolution of rural buildings in the Lhasa area [33], three types of indoor space-arrangement modes were derived and shown in Figure 8 as the building analysis models. In Figure 8, mode \(a\) is \"north-south through\" type, mode \(b\) is \"transfixion and separation combination\" type and mode \(c\) is \"north-south separation\" type. Among them, mode \(a\) and mode \(b\) represent the transitional modes in the development of indoor space-arrangement of local rural buildings, while mode \(c\) represents the current space-arrangement of the modern rural building. The height of the room in all modes is 3.3 m and the parameters of the space-arrangement modes are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Room**} & \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Arrangement Mode**} \\ \cline{3-5}  & & **Mode**\(a\) & **Mode**\(b\) & **Mode**\(c\) \\ \hline \multirow{4}{*}{Bedroom 1} & Width/m & 5.4 & 5.4 & 5.4 \\  & Depth/m & 8.1 & 8.1 & 5.4 \\  & Space-volume ratio/\% & 33.3 & 33.3 & 22.2 \\  & Orientation & South-North & South-North & South \\  & position & West & West & West \\ \hline \multirow{4}{*}{Bedroom 2} & Width/m & 5.4 & 5.4 & 5.4 \\  & Width/m & 5.4 & 5.4 & 5.4 \\  & Depth/m & 8.1 & 8.1 & 5.4 \\  & Space-volume ratio/\% & 33.3 & 33.3 & 22.2 \\  & Orientation & South-North & South-North & South \\  & Position & East & East & East \\ \hline \multirow{4}{*}{Living room 3} & Width/m & 5.4 & 5.4 & 5.4 \\  & Depth/m & 8.1 & 5.4 & 5.4 \\  & Space-volume ratio/\% & 33.3 & 22.2 & 22.2 \\  & Orientation & South-North & South & South \\  & Position & Middle & Middle & Middle \\ \hline \multirow{4}{*}{Kitchen 4} & Width/m & / & 5.4 & 5.4 \\  & Depth/m & / & 2.7 & 2.7 \\ \cline{1-1}  & Space-volume ratio/\% & / & 11.1 & 11.1 \\ \cline{1-1}  & Orientation & / & North & North \\ \cline{1-1}  & Position & / & Middle & Middle \\ \hline \multirow{4}{*}{Storage room 5} & Width/m & / & / & 5.4 \\  & Depth/m & / & / & 2.7 \\ \cline{1-1}  & Space-volume ratio/\% & / & / & North \\ \cline{1-1}  & Orientation & / & / & West \\ \hline \multirow{4}{*}{Storage room 6} & Width/m & / & / & 5.4 \\ \cline{1-1}  & Depth/m & / & / & 2.7 \\ \cline{1-1}  & Operation & / & / & North \\ \cline{1-1}  & Position & / & / & West \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of space-arrangement modes.
## 4 Simulation Results
### GPS Total Outage  
During GPS total outages, the final navigation solution depends solely on the INS. In this case the quality of the MEMS IMU sensors plays an important role in the accuracy of the solution. Moreover, in order to handle long GPS outages, the errors of the sensors should be well modeled and the EKF well-tuned. An example of a position solution during several GPS total outages with different durations is shown in Figure 21. It can be seen that the longer the outage, the higher the drift in the position, which even increases during turns.  
In order to check how long the navigation solution is valid during GPS outages, the position error is computed and compared to a certain threshold. This threshold will obviously depend on the accuracy requirements. For example, if the maximum allowed position error is set to 20 m, it can be seen from Figure 22 that during the last four outages, the position error takes an average of 22 s to reach 20 m.  
Figure 21: Position solution when GPS total outages occur.  
Figure 20: Feed-forward frequency uncertainty for the visible SV 19 (**left**) and the non-visible SV 11 (**right**).  
This means that with typical MEMS inertial sensors such as the one assumed, a maximum GPS total outage of 22 s can be handled. If the quality of the inertial sensors is higher, the navigation time during GPS total outage will be longer, and if the accuracy requirements are higher, the navigation time will be shorter.  
The uncertainty of the feed-forward frequency aiding is shown in Figure 23. As expected, the quality decreases during total outages. For the majority of the satellites this will not allow maintaining tracking during outages based on the feed-forward component only, and consequently re-acquisition will be needed to track the satellites once they reappear. For example, for SV 1, the feed-forward uncertainty drifts by roughly 1.33 Hz/s, this means that after 19 s the uncertainty will be 25 Hz. If the satellite does not appear within this time, a PLL with a bandwidth of 4 Hz and integration time of 5 ms will not be able to tolerate this error (see Figure 4), and re-acquisition is needed.  
In summary, during GPS total outages, the navigation solution totally depends on the quality of the IMU sensors. In this case, the MEMS inertial sensors are not well suited to handle long outages and can provide a reliable navigation solution for only a very short period of time. Consequently, feed-forward aiding is only temporarily possible, and if the satellite does not reappear before the uncertainty of the aiding drift becomes intolerable, re-acquisition is necessary.  
Figure 23: Feed-forward frequency uncertainty during GPS total outages.  
Figure 22: Position uncertainty during GPS total outages.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs  
This section explores various security threats targeting MANETs. Due to their open, accessible, and mobile nature, MANETs are particularly susceptible to a wide range of attacks. These attacks can be categorized in several ways, including the following [27; 28].