esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Nomadic Mobility Model (NCM)  
#### 6.3.1 Packet Delivery Ratio  
Figure 12 depicts the PDR as a percentage across diverse scenarios distinguished by different node quantities and the inclusion of mobile and attacker nodes. In the initial scenario, the network operates under mobile conditions, utilizing the NCM without the Hello Flood attack. Conversely, the second scenario mirrors the same mobility conditions but incorporates the Hello Flood attack. As the total number of nodes increases from 10 to 60, there is a noticeable downward trend in the PDR, highlighting the negative impact of higher node density on delivery reliability. The inclusion of attacker nodes, particularly at higher percentages (30% and 40%), exacerbates this decline, emphasizing the detrimental effects of malicious activity on network reliability. The graph clearly shows how the presence of the Hello Flood attack in the mobile environment leads to a decrease in the PDR compared to scenarios without the attack. Specifically, scenarios with the Hello Flood  
Figure 11: APC without and with attack using RPGM mobility model.  
attack exhibit significantly lower PDR values, indicating a substantial decline in packet delivery reliability.  
The integration of the NCM model with the Hello Flood attack leads to a decrease in the PDR through various interrelated mechanisms. Firstly, the NCM model, which simulates group-based node movements, can create congestion hotspots within the network as nodes transition collectively from one location to another. This congestion can lead to packet collisions and delays, consequently reducing the PDR. Additionally, the Hello Flood attack floods the network with excessive packets, exacerbating congestion and further congesting network channels, which can significantly impair packet delivery and lower the PDR. Secondly, the structured movements of nodes in the NCM may introduce routing instabilities, resulting in suboptimal routing decisions and increased packet loss. When combined with the Hello Flood attack, which disrupts routing protocols and increases packet loss, the overall impact on the PDR is amplified. Moreover, the increased network activity caused by both the NCM and the Hello Flood attack can lead to heightened contention for network resources, such as bandwidth and processing power. This contention increases the likelihood of packet loss and degradation of packet delivery performance, contributing to a decrease in the PDR. Additionally, the Hello Flood attack may introduce additional processing overhead and network congestion, further reducing the efficiency of packet delivery and lowering the PDR. When the NCM mobility model and the Hello Flood attack are used together, they lower the PDR by causing congestion, routing instabilities, resource contention, and extra processing overhead. All of these things make it harder for packets to be delivered within the network.
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 1 Introduction
### Previous Work  
Previous work on comparing CoAP and MQTT has been done in the form of small scale trials over an unnamed radio technology [3] and by analytical means [4]. In [3], a healthcare use case was realized with prototype hardware. The performance of the two protocols was then measured when a set of medical sensor data was transferred over an unspecified wireless link. It was found that unpredictable packet loss is handled poorly by CoAP but better by MQTT due to use of TCP. In [4], the performance of the two protocols was studied over very lossy links. It was found that by tuning CoAP parameters, the performance can be greatly improved and CoAP often performs better than MQTT due to the lower overhead, although some level of packet loss needs to be accepted when UDP is used.  
The applicability of TCP (which MQTT uses for transport) for IoT has been analyzed in [5]. The analysis show that traditional TCP has several significant drawbacks for IoT traffic, such as increased overhead and lack of flexibility for loss-tolerant applications. A more lightweight implementation TCP is introduced.  
In addition, the two lightweight protocols have been compared in [6], where MQTT and CoAP are adopted in smartphone applications. The study shows that CoAP can be a valid alternative to MQTT in certain scenarios. However, smartphone radio connectivity is much less demanding and doesn't introduce similar challenges as NB-IoT in terms of throughput, availability, coverage and battery lifetime.  
Neither of these papers consider in detail the radio layer, and the implications of constrained radio technology on higher layers. Our contribution to the discussion is to study the performance over realistically modeled actual physical layer and medium access control (MAC) protocols and see how the combination of the protocols all the way from the application layer down to the physical layer impacts the application performance.  
We studied the performance of CoAP and MQTT over two different last hop wireless links, Bluetooth and Wi-Fi in [7]. These links can be considered to be not narrow band, as the bandwidths are larger than in NB-IoT. The smartphone links studied in [6] are also not narrow band. To best of our knowledge, application level performance has not been studied over NB-IoT before. In [8], a NB-IoT technology overview and full stack was experimented in a cloud-RAN implementation but impact of higher layer on performance was not studied.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 2 PEMFC System Model and Verification
### PEMFC Model
#### 2.1.3 Humidifier  
Humidifiers in fuel cell systems provide water vapor to prevent too much water (which is produced from the electrochemical reaction) from being absorbed into the dry air. It prevents the cathode from drying out and the anode from suffering from low humidity because of electroosmotic drag. Meanwhile, the electrolyte membrane of the fuel cell system must also be sufficiently hydrated to maintain hydrogen ion conductivity. When the moisture in the electrolyte membrane is insufficient, the hydrogen ion conductivity decreases as it dries and causes the membrane to contract. The contraction of the membrane leads to the separation of the electrode and the membrane, and the gap between them will eventually increase the contact resistance. On the other hand, when an excess of water is supplied, it becomes difficult for the reactive gas to access the surface of the porous catalyst, and the stack performance is greatly reduced. Furthermore, since excessive humidification causes flooding in cells, it must be adequately controlled. The performances of fuel cells in different humidification conditions were studied by [PERSON] et al. [20] They showed that both the anode and cathode had the best performance at relative humidity values of 100 percent compared to other conditions.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 4 Experimental Environment and Results
### Evaluation Method  
In the experiment, in addition to the performance evaluation of the proposed method, the amount of change before and after the rearrangement and the simplification performance comparison were additionally analyzed. First, the RMSE value are calculated and the amount of loss depending on voxel size is checked. Additionally, checks are made to verify if the RMSE value has a large image quality difference within the actual rendering environment. The simplification performance confirms the difference in compression ratio and image quality when compared in an environment similar to commercial software. In the simplification comparison experiment, MeshLab's Simplification function and MatLab's Sampling function, which are well known as point-cloud editing tools, were used [42]. In this experiment, we obtained 300 point-cloud data, thus there is a limit to how much of the experimental data can be represented. Some evaluation factors were measured from all point-clouds, the maximum value was denoted as Max, the minimum value Min, and the average value of the whole point-cloud Avg. These measurement to calculate the range of error that each point-cloud result can be displayed by the (Max-Min)/Avg calculation. The range of the voxel size used in the experiment records the changes that occur when the voxel size is increased from the minimum distance between the points constituting a point-cloud to twice the maximum distance.  
This is the RMSE expression for evaluating the rearrangement performance:  
\[RMSE=\sqrt{\frac{1}{s}\sum_{v\in\:frame}{(F(v)-v)^{2}}} \tag{5}\]  
The RMSE value uses a commonly known formula. The source vertex of each vertex of the frame corresponding to the point-cloud frame and the amount of spatial change of the changed vertex are squared and added up. Then, after dividing by the number of vertex, the value of the square root is calculated.  
This is the rearrangement cost expression to evaluate the rearrangement performance:  
\[Re-arrangement\:Cost=\frac{1}{s}\sum_{v\in\:frame}{\sqrt{(F(v)-v)^{2}}} \tag{6}\]  
RC (rearrangement cost) is a formula for performance evaluation of the proposed method. It is the value obtained by reconstructing the point-cloud and dividing the total distance traveled by the moving vertex by the number of vertices. This is the average movement of the vertex. In other words, it represents the movement of the point-cloud.  
Table 2 shows the rearrangement results by voxel size in terms of the simplification rate. Due to the limitations of representing all experimental data, representative comparison values of 1.26, 1.55, and 2.0 were used, which correspond to levels of 30, 50, and 70% of the text.
## 3 Experimental Setup and Tests
### The Collection of Experimental Data  
In this experiment, which is similar with the paper [30], 160,800 contact voltage data were collected. Then the micro-switches were failed, and the resistance remained constant in 5.34 M\(\Omega\), which is a normally open state, thus the micro-switch was determined to fail. The failure threshold was set to 1.80 V. On the basis of not losing the monitoring information, we processed the degradeded data in order to represent the degradation process of the whole data. The average value of the dynamic contact voltage drop of each 600 cycles was recorded until the end of life (Figure 4).  
Figure 3: The test rig of micro-switches.  
Figure 2: The working period of the typical drive controller micro-switch (S826).  
From the degraded data, it can be seen that the initial phase of degradation was similar to the linear stochastic degradation process. However, the last 20 points are growing rapidly.  
Results  
Our approach in this paper was used to simulate the degradation path of the dynamic contact voltage drop, as shown in Figure 5. As seen from this approach, whether it is in the initial and final monitoring phase, our approach fits very well. As considered less previously, for initial parameters, we used an inverse Kalman filter for the initial data update. It can be seen that an inverse Kalman filter is still sensitive to the drift parameter \(\theta\) in the case of less initial parameters. Furthermore, in order to enhance the robustness in the process of estimation, we added the fading factor based on the STF.  
In order to show the superiority of this method, the initial parameters of the model were set as \(\Theta_{0}=[2,0.001,0.4]\), and the updated process of the parameter is shown in Figure 6. The results show that the accumulation of model parameters can converge quickly and can adjust slightly with the change of the degradation tendency.  
Figure 4: Dynamic contact voltage drop of S826.  
Figure 5: Our approach simulates the degradation path.
# Selected Antioxidants in Organic vs. Conventionally Grown Apple Fruits
## 2 Materials and Methods
### Phenolic Acids and Flavonols Extraction and Identification  
Polyphenols (phenolic acids and flavonoids) were analysed using High Performance Liquid Chromatography (HPLC) method, as previously described by [PERSON] et al. (2019) [22]. The analyses were done with Shimadzu equipment (USA Manufacturing Inc, USA, two LC-20 AD pumps, CBM-20A controller, a CTD-20 AC oven, SIL-20 AC autosampler, UV/Vis SPD-20 AV detector). A 100-mg freeze-dried apple fruit sample was mixed with 5 mL of 80% methanol, shaken on a Micro-Shaker 326 M (Poland), and incubated in an ultrasonic bath at 30 \({}^{\circ}\)C for 10 min. Then the sample was centrifuged (3450 g, 12 min, 2 \({}^{\circ}\)C), supernatant was collected, and 500 ul of the supernatant was injected onto the HPLC Synergi Fusion-RP 801 Phenomenex column (250 mm \(\times\) 4.60 mm). Polyphenols were separated under gradient conditions with a flow rate of 1 mL min\(-1\) by applying an aqueous solution of 10% (_v/v_) acetonitrile (phase A) and 55% (_v/v_) acetonitrile (phase B), both acidified by ortho-phosphoric acid to pH 3.0. Time of the analysis was 38 min. The phases changed as follows: 1.00-22.99 min 95% phase A and 5% phase B, 23.00-27.99 min 50% phase A and 50% phase B, 28.00-35.99 min 80% phase A and 20% phase B, and 36.00-38.00 min 95% phase A and 5% phase B. The wavelengths used for detection were as follows: 250 nm for flavonoids and 370 nm for phenolic acids. Identification of individual phenolics was based on Sigma-Aldrich and Fluka external standards with a purity of 99.00-99.99%. HPLC chromatograms presenting time of retention and profiles of the identified phenolic acids and flavonoids are presented in Figures 1 and 2, respectively. Concentration of compounds was calculated based on standard curves and applied dilution coefficients.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 3 Results and Discussion  
The strength, efficiency, and accuracy of the algorithm are major factors in the growth and evolution of green energy sources. The extraction of exact parameters from photovoltaic cells and panels is a powerful resource that may help advance the renewable energy sector and lead us to a more sustainable future for the world.  
To achieve accurate parameter extraction for PV cells and modules, this section determines how successfully the suggested PSA works. RTC France, amorphous silicon (aSi) and monocrystalline silicon (mSi) photovoltaic cells and panels, multijunction cells (MJS), PVM 752 GaAs, and STM6-40 photovoltaic panels were all used in the experiment. Based on a measured dataset, these PV devices were evaluated using the PSA for SDMs and DDMs. The PSA shows better or at least equivalent results compared to twenty-six of the best-performing algorithms.  
Four metrics, including RMSE, MBE, STD, and AE, have been evaluated by comparing them with other top metaheuristic algorithms that have been used to extract the parameters of PV cells and panels to evaluate the performance of the PSA. In terms of improving the manufacturing process for manufacturers, it is crucial to achieve ideal solutions for each device while decreasing computation time. The performance results of the twenty-six comparison algorithms were taken directly from their original studies, using the same datasets and evaluation metrics. While our PSA was fine-tuned for optimal performance, the benchmark results also reflect tuning performed in their respective original papers.  
The iteration and population size used for all the tests are 1100 iterations and 100 population sizes. It is significant to know that the PSA leads to better RMSE solutions as the number of iterations increases. This, however, leads to increased computational time. As a result, there is a trade-off between producing better outcomes and requiring more processing resources. In the end, the best combination that led to finding the optimal RMSE value from the first run was 100 population and 1100 iterations. With this iteration and the population size, the PSA provided positive results from its first run. The algorithm's exploration step was much improved and effectively prevented convergence to local minima, which would otherwise result in less-than-ideal results. All execution times reported in this study include both the initialization phase and the full optimization loop to reflect the complete computational cost of the PSA.  
The parameters of the PV cells are provided with their upper and lower bounds in Table 1, and in Table 2, the lower and upper bounds applied to the parameters of the PV modules are shown.
## 5 Results and Discussion
### Current Density Distribution Analysis
#### 5.5.1 Color-Coded Current Density Maps  
Figure 9a-c present color-coded maps of current density distribution for the 2D models, at 50 Hz. These maps use a color gradient from blue (lowest \(J\)) to red (highest \(J\)) to effectively illustrate the spatial distribution of current density across the conductor's cross-section. The  
Figure 8: Percentage error between 2D and 3D models across frequencies for different segmentations.  
numerical values displayed on the color scale correspond to the range of current density (\(J\)) measured in amperes per square meter (A/m\({}^{2}\)). The color-coded maps corroborate the findings from the line charts. The single-segment model shows a high concentration of red near the conductor's surface, highlighting the pronounced skin effect. The three-segment model exhibits a more uniform color distribution with fewer red regions, indicating a more balanced current flow due to its denser mesh. The five-segment model, while more segmented, still shows areas of high current density, particularly at the edges, which align with its higher discretization errors.  
Figure 9: Current density distribution across conductor cross-section at 50 Hz (2D models) of 1-segment (**a**), 3-segment (**b**) and 5-segment (**c**).
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 4 Results and Discussion
### Extending ELOOP's Current Area of Operation  
The overall goal of our study was the design of optimal expansion strategies for the electric car sharing company ELOOP. However, our previous algorithmic approaches never considered the layout of ELOOP's existing area of operation, which they also refer to as _home zone_.  
While starting from a blank slate certainly allows an operator more flexibility in the design of their system, existing customers would likely prefer a strict expansion of the AoO. We therefore present a variant of our algorithm that ensures that the current system's AoO is necessarily included in any feasible solution.
# Gradient Correlation Functions in Digital Image Correlation
## 3 Evaluation of Correlation Bases  
The different correlation functions described in Section 2 are evaluated using simulations and are demonstrated on real images using laser speckles. In the simulations, the average speckle size is varied between three and seven pixels, and the speckle motion can vary randomly between \(-5\) and \(5\) in both orthogonal directions, respectively. Simultaneously, the speckle correlation is varied between unity and \(0.7\). In all simulations the correlation window was chosen to be \(32\times 32\) pixels and \(225\) independent windows are evaluated for each set of parameters. The reason for ignoring the effect of the correlation window size on the performance of the different correlation functions is that according to Equation (2) the important parameter to consider is the quotient \(M/S\). Hence, it is sufficient to vary only the speckle size to capture the general behavior of the different correlation functions. The experiments were performed using laser speckles whose sizes were controlled by the objective aperture. In contrast to painted speckles, laser speckles are generated from random interference on the detector and do not exist on the object surface. Their extension thus depends on the numerical aperture of the imaging and on the wavelength of the laser [15]. In addition, for a well-focused system they will appear to follow the movement of the surface [16]. A set of ten rigid body translations were performed for each setting and the motion between the acquired images were analyzed in 225 independent regions with a correlation window size of \(32\times 32\) pixels using each of the described correlation functions, respectively. Details are provided in the subsections below.
## 4 Experimental Study
### Results Analysis and Discussion
#### 4.4.5 Time Processing Cost  
The time needed to process the proposed DB-CSA-II variant was computed to solve the 5 FDA and 3 dMOP test suites, as shown in Table 11. The DB-CSA-II algorithm was compared to six state-of-the-art transfer-learning-based approaches: (MMTL-MOEA/D, KF-MOEA/D, PPS-MOEA/D, SVR-MOEA/D, Tr-MOEA/D, DB-CSA-II). The run-time of the six transfer learning approaches was obtained from the original paper [15] where the important values are in bold. The PPS-MOEA/D algorithm was found to be fast for solving FDA1, FDA2, and FDA3 test beds. The SVR-MOEA/D approach has the fastest running time for FDA4, the MMTL-MOEA/D algorithm for both FDA5, dMOP1, and the  
Figure 14: The plots of OPF for WFG1-9 functions with 10 objectives using DB-CSA algorithm.  
Figure 15: The plots of OPF for DTLZ1-7 functions with 15 objectives using DB-CSA algorithm.  
KF-MOEA /D method for solving dMOP2 and dMOP3. We can conclude that the novel DB-CSA-II algorithm is not very fast in terms of computation time compared to the state of the art methods. However, the robust performance of the proposed DB-CSA-II algorithm is proved by the obtained means and standard deviation values based on the MIGD metric. The time comparison should be moderated, since it depends on the processor capacities as well as the hardware configurations used to conduct the tests.