esa-sceva/satcom-chunk-collection huggingface dataset:
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 5 Experimentation and Evaluation
### Random Forest Experiments
This model performed the worst in the city center case, with the majority of grids being classified as False Positives. Overall, the model did not perform well and seems sensitive to changes in the landscape, making it inconsistent and generally unable to detect abandoned buildings. The **third experiment** was performed using the _NDVI_ and _NDBI_ index combination. In all three areas, and with the three different fishnet sizes, the model was unable to not only detect abandoned houses but also identify the nonvacant ones correctly, a behavior not shared by previous models. Parts of the seaside area were identified as not-abandoned, while the nearby shops were. Moreover, the rest of the True Negative results seem random, with the only common part being the inclusion of roads. In the **fourth experiment**, we evaluated the performance of _NDVI_ and _NDBI_ along with the average radiance. The model predicted the majority of split polygons in the various areas of interest as False Positives, as depicted in Figure 7. While in most cases the Random Forest models using spectral index and nighttime light radiance from Chicago seem to be capable of detecting the majority of not-abandoned buildings, they fail dramatically to identify the abandoned ones and thus are not suitable for our needs. This can be due to various reasons, such as our wrong assumptions regarding the index selection and errors during the computation of said indices. Apart from bias generated by our decisions and assessment of the results, the data themselves may vary significantly compared with the ones we are trying to test our methods on. To test this, we performed some statistical tests. The indices, derived from Chicago and Volos, were computed for the dates between 1 January 2021 and 1 May 2022 to counter possible outliers related to the pandemic and because of data availability. Firstly, we performed a Levene test to check the variance in both cases. Since the returned \(p\)-value was equal to zero in all our cases, we rejected the Null Hypothesis and assumed that the two populations had significantly different variances. Next, we performed a \(t\)-test to assess the means of the two independent samples. Since this method assumes by default that the samples have equal variance, something not relevant in our case, we used [PERSON]'s implementation. The calculated \(p\)-values were equal to zero at a 0.05 level of significance (\(a=0.05\)), and thus the samples have a statistically significant mean difference. Spectral data derived from Chicago were considerably different from the ones regarding our area of interest and thus not suitable for accurate classification tasks. Figure 6: Experimentation results on central area using all spectral indices in Random Forest.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results  
### Experimental Setup  
Embedded systems such as Raspberry Pi have smaller capacities and lower processing speeds than general PCs in terms of memory and CPU. They are used as a video frame in an edge device of a data creator. Therefore, memory savings and processing efficiency in embedded systems are major essential considerations in application development, and this should be reflected in the implementation of the integrity verification function. Jetson Nano Orin is one of the embedded computing platforms developed by NVIDIA. The constructed hash tree is used to efficiently detect integrity violations in a data verifier. The Jetson Nano serves as an AI accelerator for object detection, employing the OpenSSL library for key generation, signing, and decryption. It utilizes the curl library to transmit video data via REST API. Figure 7 illustrates the physical hardware configuration.  
The experimental setup is shown in Table 3. Jetson Nano Orin is a compact, system-on-module (SOM) from NVIDIA, designed for edge computing and AI applications. It is based on the NVIDIA Orin architecture with an ARM Cortex-A78 AE CPU and Ampere GPU. It is running on Jetpack 5.1.4-b17. The YOLOv8 model is used to detect objects in a video frame. To run the object detector using YOLOv8, Jetson Nano Orin is applied to run it, which is CUDA-supported. The Raspberry Pi is a series of small, affordable, single-board computers (SBCs) developed by the Raspberry Pi Foundation; Raspberry PI 4 is used for running a logger, which uses a webcam connected to a USB.
## 4 Results and Discussion
### Sensitivity of Parameters and Model Calibration  
In addition to the optimization, the parametric study helped quantify the influence of the number of SPs used in the flow channel, \({actual}_{NSP}\) and the interaction diameter, \(D_{IA}\) on the charging efficiency of the EFC circular-geometry design. The change in the number of positive ions per carbon particle, \(\beta_{Na}\), was calculated as a function of the changes \({actual}_{NSP}\) and the interaction diameter.
## 5 Simulation Results
However, subject to the intrinsic properties of the given topologies, the placement of gateways derived from implementing PKM was fixed. It is noted that the performance, such as network latency and network reliability, depends on characteristics of the topologies. As depicted in Figure 3, PKM showed a better latency performance than that of SAA in Nsfnet, Aarnet, ATT, and Chinnet, where the opposite results occurred in Agis and Geant. Thus, we further investigated the effect of the number of gateways on the average network latency. Figures 5 and 7 show the comparison results of average network latency in the topology of Agis and Chinnet among the above algorithms when the number of gateways varied from 1-5. Obviously, the network latency presented a monotone decreasing tendency as the number of gateways increased. However, PKM always had better performance than SAA in Chinnet, while the opposite results occurred in Agis. It is noted that the gap of the performance between OEA and the other two algorithms gradually narrowed as the number of gateways increased.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 5 Conclusions  
This paper presented the results of an analysis of large-scale path loss models in an indoor corridor environment at 28 and 38 GHz frequency bands with two different antenna polarizations, in both LOS and NLOS scenarios. For all of the frequency bands investigated, measurements were taken using a channel sounder based on a Rohde & Schwarz SMB 100A radio frequency signal generator as the transmitter and a Rohde & Schwarz FSIQ 40 signal analyzer as the receiver. In this environment, two vertically polarized high-gain directional broadband horn antennas (for both vertical-to-vertical and vertical-to-horizontal polarizations_ were adopted, analyzed, and contrasted for LOS and NLOS communication scenarios. The single-frequency CI and single-frequency FI path loss models were used in this investigation. The measurement evaluations in an indoor corridor environment at 28 and 38 GHz frequency bands were the focus of this work.  
The LOS study results showed that when changing the antenna polarization from V-V to V-H at 28 GHz, the path loss values increased, but only slightly at 38 GHz, indicating that signal degradation was not too noticeable when changing the antenna polarization at 38 GHz. However, in the NLOS scenario, the PLE was higher at 38 GHz when compared to 28 GHz for both antenna polarizations. This was due to greater dispersion and penetration losses at higher frequencies. The minimum standard deviation values for CI and FI were quite near to one another for 28 GHz at both antenna polarizations. However, the minimum standard deviation for the 38 GHz frequency increased from 2.7 in the V-V polarization to 3.59 in the V-H polarization. The V-V antenna polarizations had better accuracy and path loss efficiencies than the V-H polarizations in both scenarios and frequencies, according to the results in both antenna polarizations. The proximity of the walls, the materials used for the walls, the floor, and other irregular items, such as wooden doors, concretes, and elevator doors along the hallway, all have an impact on how radio waves propagate indoors. Both of the models (CI and FI) employed had good overall performances. In order to achieve a better outcome, it will be a good idea to develop an improved CI-FI model. Moreover, the high reflection rate of power and penetration losses in millimeter wave propagation will require researchers to devise path loss models that are unique to each building's structure in the future.  
Conceptualization, T.T.O.; methodology, T.T.O., P.K. and M.K.E.; formal analysis, T.T.O. and M.K.E.; measurements, T.T.O.; writing--original draft preparation, T.T.O.; writing--review and editing, P.K.; supervision, P.K.; project administration, P.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON], for his assistance during the measurement campaign.  
The authors declare no conflict of interest.
# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 2 Modular Hardware Platform for the Extreme Edge of IoT
### Low-Power Strategies  
#### 2.2.1 EFR32 Low-Power Modes  
According to [PERSON] et al. (2018), although the idle current of each hardware component is provided by its datasheet, the idle current of a sensor node may be impacted by many additional factors, making it significantly greater than the sum of the datasheet values ([PERSON] et al., 2018). The MCU of the Cookie, as part of the EFR32 MG12 family, has a variety of low-power modes available. These modes allow the SoC to save energy by reducing the power consumption of the processor when it is not required to display its complete functionality. These modes are depicted as follows ([PERSON] et al., 2018):  
* Active/Run Mode: the normal running mode, everything is active.
* Sleep Mode: the CPU clock is disabled. The memory can still be accessed through Direct Memory Access (DMA) and the peripherals can be handled using the Peripheral Reflex System (PRS).
* Deep Sleep Mode: not only the CPU clock, but the high frequency oscillators are also disabled. The 32 kHz low frequency oscillator is still enabled.
* Stop Mode: all high and low frequency oscillators are disabled except for the ultra-low frequency and, optionally, the auxiliary ones.
* Shutoff Mode: all oscillators are disabled, there is no RAM retention and the MCU is shut down except for the recovery logic. The only way to wake up is through an external reset.
* Hibernate Mode: similar to the EM4S mode, providing more options for the wake-up call. Mode EM4H can have RTCC running with the ultra-low frequency oscillator, while EM4S cannot. EM4H does also provide some RAM retention, which EM4S does not.
###### Abstract
Compared with the technologies of the shorted via, slotting, and stacked structure, the composite right/left-handed transmission line can not only increase the resonant band, but also has the advantages of reducing cross-polarization and enhanced gain, as shown in [15; 16]. However, for the antennas in [14; 15; 16], the characteristics of miniaturization, low profile, multi-band, and high gain are not concentrated in a particular antenna. In this study, a patch antenna with the characteristics of tri-band, enhanced bandwidth, high gain, and a diverse radiation pattern is investigated. Two circular substrates are placed up and down to form a double-layer structure. A circular patch printed on the upper surface of Substrate 1 is directly excited by an off-center probe. Meanwhile, four metal cambered strips surround the outer side of the circular patch to adjust the impedance matching. Then, TM\({}_{01}\), TM\({}_{02}\), and TM\({}_{03}\) resonance modes with acceptable reflection coefficients are obtained. Furthermore, a parasitic patch located on the upper surface of Substrate 2 widens the impedance bandwidth of the patch antenna by coupling with the circular patch. The measured results show that the double-layer patch antenna has differentiated radiation patterns in three frequency bands, with impedance bandwidths of 1.1%, 6.7%, and 9.8%. Moreover, the advantages of the single-probe back-feeding method, simple structure, and easy processing all indicate that the antenna is suitable for multi-service applications.
# Changes in the Cell Division of Chang Liver Cells Induced by Simulated Microgravity
## 3 Results
### Effects of SMG on Morphological Changes during Cell Division  
Microfilament and microtubule staining were applied to assess the effects of SMG conditions on two structures related to cell division: the contractile ring and spindle. As shown in Figure 5a, the morphology of CCL-13 cells from the control group changed in an apical shape during mitosis and exposed the abundant microvilli. In the last step of cell division, the CCL-13 cells exhibited contractile ring formation, which generates a constricting force to separate one cell into two sister cells (Figure 5b). The microvilli were also detected in the CCL-13 cells from the SMG group in mitosis (Figure 5c), and so was contractile ring formation during cytokinesis (Figure 5d). However, the SMG-treated CCL-13 cells showed a lower intensity of microfilaments in these structures compared to the control cells. The CCL-13 cells from both groups showed similar normal morphologies without fragmentation and condensation, which are characteristics of apoptosis.  
The microtubule staining indicated that the CCL-13 cells in the control group and SMG group showed normal spindle formation during mitosis (Figure 6a-c,g-i). During cytokinesis, the control CCL-13 cells displayed a normal structure of polar spindle microtubules (Figure 6d-f), while an irregular form of this structure was discovered in the SMG CCL-13 cells (Figure 6j-l). The results showed that SMG affected the structure formation of polar spindle microtubules during sister cell separation.  
Figure 4: Comparing the relative transcript gene expression associated with the cell cycle in the SMG and control groups using real-time RT-PCR (**A**) and Western blot (**B**). ** denotes a significant difference between the SMG group compared to the control group (_p_ < 0.01);  denotes a significant difference between the SMG group compared to the control group (_p_ < 0.05).  
Figure 5: Microfilament staining of CCL-13 cells: (**a,b**) the appearance of the contractile ring and microvilli in the control CCL-13 cells (white arrows); (**c,d**) the appearance of the contractile ring and microvilli in CCL-13 cells under SMG conditions (white arrows). Microfilaments and nuclei were counterstained using Phalloidin (green) and H3342, respectively.
## 2 Robust Design Optimization Methods and Challenges
### Multi-Objective Optimization Techniques
For instance, the PSO algorithm, which uses the DBP technique, needs the best individual's information to generate the new position of the particle. Meanwhile, the genetic algorithm does not care about which individual in the examined population has the best solution. This problem of selecting the best individual can lead to a complex question if we want to use PSO--or similar metaheuristics--for multiobjective optimization, where it is not possible to define a single best solution, only a leader set of the Pareto-optimal solutions. This problem can highlight why there exist many different multiobjective optimization variants (e.g. Adaptive Multi-Objective PSO (AMOPSO) [132], Multi-Objective PSO (MOPSO) [133], Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) [134], Optimized Multi-Objective PSO (OMOPSO) [135], Vector Evaluated PSO (VEPSO) [136], etc.) of the PSO algorithm in the literature (Table 1). These algorithms do not only use different approaches to handle the leader set with Pareto-dominance or \(\epsilon\)-dominance based archives [137] or a method that randomly selects an arbitrary element for the current best element, like in the multiobjective firefly algorithm [39]; however, the PSO algorithm--as many other single objective metaheuristics--has greatly developed from its first publication. The first and still widely used modification is the introduction of the constriction factor by [PERSON] and [PERSON] [40]. However, there are many modifications and improvements that have been introduced in the last two decades, from the time-varying inertia weight and velocity constraints and the gradient based [138] speed components to the multi-level, hierarchical PSOs [139]. In the field of the electrical machine design optimization, PSO represents an umbrella term, which hides the information about the PSO variant used. This information is usually not highlighted in the research nor review papers [3; 5; 8; 19; 51; 140]. Figure 7: Some selected metaheuristic methods and their categorization. The Multiobjective Evolutionary Algorithms (MOEA) can generally be divided into three main paradigms: the Pareto-dominance based MOEAs (like NSGA- II [141]), Indicator Based Evolutionary Algorithms (IBEAs, [149]), and decomposition based MOEAs. The Pareto-dominance based algorithms work on the principle of non-dominated sorting where each solution is assigned a rank based on its Pareto-dominance. Usually, the best solutions are given preference during the mating and/or the selection process (elitist strategy) to increase the convergence. Moreover, these methods employ strategies such as crowding distance (NSGA-II) [141] and weighted distance [151] to increase diversity. In contrast to these algorithms, the indicator based strategies (IBEAs) directly contain performance indicators to select the most appropriate offspring [149, 152]. These performance indicators, like the hypervolume [152] and inverted generational distance [153, 154], were originally designed to
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 2 Motivation
### Middleboxes Cost  
The cost of middleboxes is not limited to the hardware cost. In addition, the management cost of middleboxes is also high. It includes the number of assigned employees, the cost of training, the number of middlebox failures during the year, and the estimated mean cost to repair (MCTR). Table 1 lists the average middlebox price based on the network size. Additionally, the estimated MCTRs per year for the participating enterprises are illustrated in Figure 2. To be able to estimate MCTR, we asked the participating enterprises the following questions:  
1. What is the average network engineer's salary?
2. How many engineers are assigned to fix a single failure?
3. What is a failure's mean time to repair?
4. What is the number of failures within a year?  
From these questions, we built our MCTR estimation. Since the majority of failures are due to human-based errors, such as misconfiguration or traffic overload [1; 10], we  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Small** & **Medium** & **Large** & **Very Large** \\ \hline Firewall & 1.4–3.1 & 4.3–14.8 & 20–44 & 50–104 \\ IPS & 0.56–1.1 & 5–21 & 35–50 & 84–200 \\ WAN Opt. & 1.7–10.5 & 12.5–24 & 26–59 & 114–235 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Middleboxes’ prices (thousand $) for various network sizes.  
Figure 1: Number of middleboxes that are deployed in enterprise networks of various sizes. (**a**) Middleboxes in small enterprises. (**b**) Middleboxes in medium enterprises. (**c**) Middleboxes in large enterprises.  
Figure 2: MCTR of all middlebox failures during a year.  
did not include the cost of spare parts in our estimation. In fact, MCTR would have been higher had the cost of the spare parts been included. It is clear from Figure 2 that the cost is positively proportional to the number of middleboxes deployed in the network. Hence, a key factor in reducing the cost of middleboxes is reducing the number of deployed middleboxes; any reduction in the number of deployed middleboxes is going to affect other costs directly. Instead of trying to reduce the cost of management by providing a sophisticated management console, or by increasing the utilization of middleboxes, it is easier to just simply lower the number of middleboxes deployed.
## 3 Experiment Results  
### Training and Test Data Preparation  
For each burr wear level, the coffee experts use different burrs of the same wear level and adjust all five grind settings of the coffee grinder to produce coffee granules. For every burr level, 200 images were collected. Therefore, 600 images were collected for all three burr levels. Among them, 480 (80%) images were used for image processing to extract their granule size distribution. A total of 480 training data were then obtained by unifying the number of inputs and normalizing the range of the input data. Using the same processes, the remaining 120 images generated 120 test data. It was observed that when a grinding burr was using the extra-fine grind setting, the coffee granule size distribution was more concentrated than those using fine, medium, coarse, and extra-coarse grind settings. Basically, the coarser the grind setting, the less concentrated the coffee granule distribution and the lower the granule count in every interval, as shown in Figures 11-13. This is because even if a coarse grind setting is used, some fine coffee granules are still inevitably produced to enlarge the scope of the coffee granule size distribution.  
Figure 11: Initial wear burr granule size distributions for five grinding settings.  
Figure 12: Normal wear burr granule size distributions for five grinding settings.  
It was found in experiments that grinder burrs with different wear levels could produce similar coffee granule size distributions when using different grind settings. For example, the coffee granule size distribution of a severely worn burr with the extra-coarse grind setting are very similar to that of an initial wear burr with the extra-fine grinding setting, as shown in Figure 14. This phenomenon can easily lead to people misjudging the wear level of the burrs, and that is why the deep learning model is needed for more accurate burr wear level classifications.