esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Pursue Mobility Model (PMM)  
#### 6.4.1 Packet Delivery Ratio  
Figure 17 presents the PDR as a percentage in a mobile environment utilizing the PMM mobility model. It depicts a variety of scenarios characterized by different node counts and the presence of mobile and attacker nodes. The \(x\)-axis ranges from 10 to 60 nodes, reflecting changes in network density, while each bar on the graph represents a specific network configuration. Mobile nodes represent those unaffected by the Hello Flood attack, while attacker nodes signify the presence of malicious activity. The graph distinguishes between scenarios with varying percentages of mobile and attacker nodes, both with and without the attack in the mobile environment. As the total number of nodes increases from 10 to 60, there is a noticeable drop in the Packet Delivery Ratio (PDR), highlighting how higher node density adversely affects packet delivery reliability. The presence of attacker nodes, particularly at higher percentages, including 30% and 40%, significantly diminishes the PDR, underscoring the negative impact of malicious activity on network reliability. The graph illustrates that the Hello Flood attack in the mobile environment leads to a decreased PDR compared to scenarios without the attack. Specifically, instances of the Hello Flood attack exhibit lower PDR values, indicating a considerable decrease in packet delivery reliability.  
Figure 16: APC without and with attack using NCM.  
Through various mechanisms, the PMM mobility model and the Hello Flood attack collaborated to decrease the PDR. Firstly, the structured movement pattern of nodes in the PMM model, where nodes pursue a target, can lead to congestion in specific areas as the nodes converge on the target. This congestion can result in increased packet collisions and retransmissions, thereby reducing the overall PDR. Secondly, the Hello Flood attack floods the network with excessive traffic, saturating network channels and causing interference, which further disrupts packet delivery and lowers the PDR. Additionally, the movement patterns in the PMM may introduce routing instabilities, leading to suboptimal routing decisions and increased packet loss. When combined with the disruption caused by the Hello Flood attack on routing protocols, the overall impact on the PDR is compounded. Furthermore, the increased network activity resulting from both the PMM and the Hello Flood attack can lead to increased contention for network resources, such as bandwidth and processing power. This contention further reduces the PDR by increasing packet transmission delays and causing additional packet loss. In summary, the collaboration between the PMM mobility model and the Hello Flood attack reduces the PDR by introducing congestion, interference, routing instabilities, and resource contention in the network.
# Source Rock Prediction Using Well Log and Seismic Data:
## 2 Regional Geological Overview  
### The Basin's Tectonic Characteristics  
The Cote d'Ivoire Basin is located in the central part of West Africa and the northern part of the Gulf of Guinea (Figure 1). It extends from east to west [11] across Cote d'Ivoire and Ghana, extending to the offshore area of Ghana in the east and deep into the deep-sea area of Liberia in the southwest in a narrow strip with a total area of 185.5 \(\times\) 10\({}^{3}\) km\({}^{2}\).  
Figure 1: Location and drilling distribution of the Côte d’Ivoire Basin (**a**) and column chart of formation filling (**b**) (Modified according to [12]).  
The Cote d'Ivoire Basin developed in the middle of the Paleoproterozoiczoic Craton, on the eastern edge of the Archean core of Guinea [12]. The northern part of the basin is controlled by the Saint-Paulo fracture zone and its overdland extension, the Lagunes fault zone, which runs almost parallel to the coast of Cote d'Ivoire and Ghana. The south is controlled by the Romanche fracture zone and is connected to the Ghana shelf by the Cote d'Ivoire-Ghana conversion marginal ridge. These two giant transition fracture zones are the northern and southern boundaries of the basin [13; 14; 15]. In the basin, there are several fault zones with similar spacing and a nearly vertical strike to the coast, as well as two positive tectonic zones with a parallel strike to the coast-the Abidjan marginal tectonic zone and the Grand Lahou High. A number of NWW-SEE trending anticlinal structures and basement faults developed between the Grand Lahou High and the Jacqueville Trough [16; 17], and most of the oil and gas fields discovered so far lie within this region (Figure 1a).
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 2 Related Work
### Foundation Vision Models for Remote Sensing  
SAM represents a major advancement in VFMs, as it is trained on millions of annotated images to achieve zero-shot segmentation capability. However, SAM and similar models face domain adaptation challenges when applied to specific fields like medical imaging, manufacturing, and remote sensing images. These models, predominantly trained on natural image datasets, often struggle with small or irregular objects and tend to focus on foreground features, which limits their performance in remote sensing applications. To address the high computational cost of SAM, FastSAM has been introduced, providing real-time segmentation with comparable generalization performance but at a significantly faster inference speed [37].  
In the context of remote sensing change detection, the first adaptation of SAM, known as SAM-CD [38], has been proposed to explore the model's potential in this domain. However, while SAM-CD leverages the powerful feature extraction capability of SAM, it does not explicitly consider the importance of differential information between bitemporal images, which is critical for accurately identifying changes. This limitation highlights the need for further adaptation of VFMs to effectively handle the unique challenges of change detection in remote sensing images.  
In this work, we address these limitations by utilizing a VFM-based Siamese structure to enhance the model's ability to extract semantic latent features and effectively capture both global contextual information and differential features, enabling robust and accurate change detection in remote sensing imagery.
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on Methodology
#### Microfluidics for Hepatitis Diagnosis  
Microfluidic devices allow for the continuous flow of a sample and the detection of a target analyte in the sample. Only one report was found on microfluidics-based hepatitis detection. [PERSON] et al. designed a SERS immunoassay-based microfluidic system for the detection of hepatitis B in human blood plasma samples in 2015 [19]. The system was composed of three layers, a first layer of anti-HBsAg antibodies immobilized on a GaN/Au-Ag surface, a second layer of the complementary HBV surface antigen (HBAsAg), and a third layer of fuchsin (the Raman reporter)-labeled immune gold nanoflowers (antibody-Fc-AuNFs). When reagents were allowed to enter through the inlets of the microfluidics sandwich, immunocomplexes were formed in the detection area (DA), a square-shaped chamber containing the SERS-active substrate. Raman measurements were performed here to detect the HBsAgAg by recording the SERS spectrum of basic fuchsin. A calibration curve was obtained by plotting the intensity of the SERS signal of the FC band at 1178 cm\({}^{-1}\) against the concentration of the antigen. The limit of detection for the hepatitis B virus antigen was estimated to be 0.01 IU/mL. A schematic illustration of this microfluidics system is shown in Figure 13.  
Table 2 shows a summary of these studies on Raman spectroscopy in the field of hepatitis diagnosis, indicating the type of hepatitis, the variant of Raman spectroscopy used, the sample type, and the main findings of the study. Figure 13: Sequential steps for the formation of the SERS-based immunoassay. (**A**) Preparation of the capture substrate, (**B**) synthesis of the Raman-reporter-labeled immuno-Au nanoflowers, (**C**) SERS detection of the sandwich interactions, and (**D**) a schematic illustration. Reprinted with permission from [19]. Copyright, 2014, Elsevier B.V. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No.** & **Type of Hepatitis** &
\begin{tabular}{c} **Raman Spectroscopy** \\ **Variant** \\ \end{tabular} & **Sample Type** & **Main Findings** \\ \hline
[11] HCV & \
\begin{tabular}{c} SERS + PCA-PLSR \\ \end{tabular} & Blood serum &
\begin{tabular}{c} An R\({}^{2}\) value of 0.92 and an RMSEP of 0.24 IU/L as compared to RS, which yielded an R\({}^{2}\) value of 0.77 and an RMSEP of 0.41 IU/L. \\ \end{tabular} \\ \hline
[18] HCV & \
\begin{tabular}{c} RS + PCA-LDA \\ \end{tabular} & Blood plasma &
\begin{tabular}{c} Identified biochemical markers for \\ HCV. PCA-LDA achieved 98\% sensitivity and specificity. \\ \end{tabular} \\ \hline
[19] HBV & \
\begin{tabular}{c} SERS + microfluidics \\ \end{tabular} & Antigen solution &
\begin{tabular}{c} The limit of detection for antigen \\ detection was estimated to be 0.01 IU/mL. \\ \end{tabular} \\ \hline
[20] HBV & \
\begin{tabular}{c} RS + PCA-SVM \\ \end{tabular} & Blood plasma &
\begin{tabular}{c} The PCA-SVM model showed 98\% accuracy in identifying HBV-infected samples. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies on Raman spectroscopy for hepatitis diagnosis.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques
### Linear Minimum Mean Square Error Beamformer  
The _linear minimum mean square error_ (LMMSE) beamformer exploits the temporal diversity between the LOSS and the undesired reflections by comparing the received signal with some reference signal generated locally at the receiver. GNSS signals are a clear example of the latter, where such a reference signal is the local code replica used for despreading, that is, \(\mathbf{s}(\tau,\varphi)\). The LMMSE beamformer aims at minimizing the MSE of the error between \(\mathbf{s}(\tau,\varphi)\) and the array output \(\mathbf{X}\). The solution to this problem is given by [6]  
\[\mathbf{w}_{\text{LMMSE}}(\tau_{0},\varphi_{0})=\mathbf{R}_{\mathbf{X}}^{-1} \mathbf{r}_{\mathbf{Xs}}(\tau_{0},\varphi_{0}) \tag{8}\]  
with \(\mathbf{r}_{\mathbf{Xs}}(\tau,\varphi)\doteq\mathbb{E}[\mathbf{Xs}^{*}(\tau, \varphi)]\in\mathbb{C}^{L\times 1}\) being the cross-correlation between the array output and reference signals. The beauty of this approach is that it does not require knowledge on the DoA to be pointed at. On the downside, and as a result of the latter, severe LOSS degradation may be incurred.
## 3 System Model  
As shown in Figure 1, we present a multi-user with multi-UAV-enabled MEC system model. In this system, we consider \(\mathcal{M}\) as a set of small UAVs that are equipped with an edge computing server and connected to centralized cloud computing through a backbone router. In addition, we consider \(\mathcal{N}\) as a set of mobile devices that are associated with UAVs via a wireless channel. Furthermore, each mobile device is involved in highly intensive computation tasks that need to be processed locally or be transmitted and processed by the nearby UAV or cloud server via the wireless channel. In our simulation settings, we assumed a quasi-static scenario where the UAV does not move during the computation offloading period, though it can be moved to another location across different periods [35; 36; 37].  
In the next subsections, the communication and computation models are firstly discussed in more detail. Afterward, our problem formulation is presented.  
Table 2 summarizes the main notations of this study.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\mathcal{M}\) & The set of small UAVs. \\ \(\mathcal{N}\) & The set of device users. \\ \(i\) & The \(ith\) UAV. \\ \(j\) & The \(jth\) device user. \\ \(b_{i,j}\) & Size of data for task of mobile device \(j\) which is connected with UAV \(i\). \\ \(c_{i,j}\) & CPU cycles to accomplish the task of mobile device \(j\) which is connected with UAV \(i\). \\ \(\tau_{i,j}\) & Deadline requirement for task of mobile device \(j\) which is connected with UAV \(i\). \\ \(\varphi_{i,j,a}\) & Offloading decision of mobile device. \\ \(r_{i,j}\) & Uplink data rate for mobile device \(j\) which is connected with UAV \(i\). \\ \(B_{i}\) & System bandwidth of UAV \(i\) \\ \(p_{i,j}\) & Transmission power of mobile device \(j\) which is connected with UAV \(i\). \\ \(G_{i}\) & Channel gain of UAV \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.  
Figure 1: System model.
## 3 Results
### Assessment Methods  
Assessment tools, such as assessment scales, have consistently demonstrated subjective enhancements in outcomes following targeted treatments. In the selected studies, numerous assessment scales were used to identify and quantify the degree of motor deficit, providing valuable insights into the effectiveness of treatment interventions. Except for one study, all used the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), developed by [PERSON] and his colleagues in 1975 [36]. This standardized method assesses stroke recovery through direct performance observation, determining disease severity, describing motor deficits, and planning and evaluating treatment. The FMA-UE is a widely accepted and validated tool in the field of stroke rehabilitation, making its consistent use in studies a significant factor in the reliability of the findings [37].  
[PERSON] et al.'s [32], 2018, study involved 13 post-stroke patients in 16 therapy sessions with multi-channel FES and immersive VR exercises. The intervention significantly improved upper limb motor function, with FMA scores increasing by an average of 15 points (\(p<0.05\)). Despite some limitations, such as a small sample size and a short follow-up period, which hindered the assessment of long-term effects, the study's findings are valuable. Similarly, the study by [PERSON] et al. [33], 2018, showed that VR-FES led to a more significant increase in FMA-distal score (\(p=0.011\)) compared to FES alone. The other two studies also significantly increased upper limb motor function, expanding our knowledge about using the right assessment tool in stroke rehabilitation.  
[PERSON] et al. [33], 2018, used the RAPAEL Smart Glove system and manual FES to demonstrate improvements in daily living activities by assessing the dexterity and coordination of the upper limb. Similar assessment tools were also used in studies conducted by [PERSON] et al. [35], 2020, and [PERSON] et al. [34], 2021. The studies presented a wide array of assessment tools, including the WMFT for upper extremity function, BBT for gross manual dexterity, the JTT for hand function, 9-HPT for finger dexterity, the Barthel Index for activities of daily living, the Abihand Questionnaire for hand function, and the MAL for quality of movement, to develop the efficiency of the rehabilitation program. These diverse and comprehensive assessment tools across the reviewed studies have proven effective in evaluating the outcomes of VR and FES interventions. Each tool demonstrated significant statistical improvements, providing robust evidence of the efficacy of these combined rehabilitation approaches.  
All these assessments aim to highlight the progression of motor and functional status and to correlate the results obtained based on the evaluation of motor deficits while performing tasks related to daily activities. Although the presented studies vary in the number of patients, period, type, and intervention method, they all share a common goal: integrating VR and FES in upper limb rehabilitation for stroke patients.
## 4 Experiment
### Mechanism Stiffness Evaluation  
The theoretical method has derived the input and output stiffness of the mechanism in Equations (21) and (22). This is validated using the FEM method in Section 3.3. Figure 20 is an experimental model for measuring the output force and input/output displacement of the mechanism. A force gauge (NK-500) records the applied force, and the laser displacement sensor (LK-G30, Keyence Corp., Osaka, Japan) measures the corresponding displacement. The experimental stiffness is calculated based on Hooke's law, and is listed in Table 6.  
Figure 19: Experimental results of closed-loop control with impulse response: (**a**) sine, (**b**) triangle, (**c**) square, and (**d**) error when controlling.  
Figure 20: Experimental model to measure the structure stiffness.  
Table 6 presents the results of displacement amplification ratio, natural frequency, and stiffness calculated using different methods, along with the experimental results. The benchmarking among theory, simulation, and empirical models is determined using analytical results as a standard. The differences in results for the displacement amplification ratio and natural frequency are 3.46% and 1.26%, respectively. The input and output stiffness discrepancies between the experiment and theory are 4.7% and 4.03%, respectively. This difference might affect the practical cutting forces with a given cutting depth. However, in micromachining, cutting loads are relatively small, which moderately impacts the performance of the instrument. The reason for this variance is imperfections in the fabrication and assembly of the experiment and testing. Since this difference is modest, the proposed theoretical model, simulation, and corresponding empirical models are adequate.  
The results obtained for the CFDM design in this study are compared with some previously published works, as shown in Table 7. With an amplification ratio of 3.0, the current mechanism has a much more prominent first-natural frequency than previous studies, and the concentrated stress is relatively small. These properties are beneficial not only for high-precision feed drive mechanisms, but also for high-frequency vibration-assisted machining applications using CMs.
## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.5 Al-Enabled Networks
Awareness of known items (e.g., automobiles) [132] and techniques encouraged by biology (e.g., for learning neural controller structures [133]) are two areas in which such approaches are currently outperforming deep learning models. By locating calculations in energy-efficient data centers, we may lessen the environmental effect of every calculation. Many factors contribute to the efficiency of such data centers, including (i) their location in regions with typical cooling and (ii) their architecture, which allows for more efficient use of the cooling systems that are required. Methods like smart load allocation, flexible voltage expanding, and server downsizing are all examples of how server administration can improve data centre efficiency [134]. The carbon footprint of deep learning systems is further affected by the energy that powers servers. For instance, according to Google's 2021 sustainability reveal, the tech giant bought sufficient clean energy to power all of its workplaces and data centers [135]. This indicates that carbon production can vary substantially depending on the region of hosting [136]. All the challenges that were explained in this section are summarized in Table 3.  
Intelligent slice management solutions for 5G and beyond networks have been the subject of few research that have taken AI's potential into account. The concept of network slicing allows for the coexistence of numerous separate virtual networks, or slices, on top of a single physical network. Network slicing has many benefits [137]. First, network slicing allows for multiple tenancy, or the sharing of physical network infrastructure, via multiplexing virtual networks. Second, network slicing allows for the creation of customized slices for different service types with different quality of service (QoS) requirements. This allows for service differentiation and guarantees service level agreements (SLAs) for each service type. Third, network slicing increases the flexibility and adaptability in network management. It reduces capital expense in network deployment and operation. In order to keep network operation performance high, fulfil new key performance indicators (KPIs), such as dependability, energy consumption, and data quality, and support various and heterogeneous services, the study [138] suggested an efficient, intelligent network slicing framework.
## 8 Simulation Results
### Energy Consumption
Nevertheless, we can also see that the energy consumptions of the third and fourth application versions (a.k.a. SimpleTemperature and Multiple, respectively) are the closest to the SimpleCount in face of the first application version (a.k.a. Original). Thus, it appears as stated before that our solution  
Figure 15: _Cont._  
uses double the energy of the current solutions, but its energy consumption is still low when compared to the version without prediction (original version). The amount of energy spent to receive messages (E\({}_{\text{recp}}\)) from application broadcast on the transmission of neighbor sensor nodes (routing gossip) is observed in Figure 15. In some scenarios [Figure 15(c)], the E\({}_{\text{recp}}\) of our approach is about three times smaller than the original application, but still consuming more energy than the current approach. We can see more details of the percentage of energy saving from the three application versions that use simple or multiple linear regression in face of the original application version in Table 5.  
The results of spatial correlation (C\({}_{\text{spacial}}\)) showed no differences between our approach and current approaches, but it points to the fact that is essential to save energy. The amount of times that the correlation was detected is greater in the scenarios where there is fixed density of 0.25 sensor nodes per m\({}^{2}\), _i.e._, in scenarios #5 and #6. It shows that in higher density situations the packets will not be sent twice to the sink. Thus, we avoid overlapping and save more energy.
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 3 Data and Methods
### Drainage Basin Shape Index (Bs)  
The shape of basins is an indicator of tectonic activity. In active tectonic areas, relatively young basins have elongated shapes that are normal to topographic mountain slopes [6; 60; 61]. On the other hand, with less active tectonic signals, the basins have shapes that tend to be semi-circular to circular. The basin shape index may be expressed by the following equation:  
\[\text{Bs}=\text{Bl}/\text{Bw}\]  
where Bi indicates the basin length extended between its headwaters and mouth [9], while Bw describes the basin width at the basin widest strip. Regarding tectonic activity evaluation, high values of Bs index indicate elongation basins and indicate high tectonic activity. Bs lower values have a more circular-shaped basins with low levels of tectonic activity.