esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
###### Abstract  
Image classification usually requires connectivity and access to the cloud, which is often limited in many parts of the world, including hard-to-reach rural areas. Tiny machine learning (tinyML) aims to solve this problem by hosting artificial intelligence (AI) assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without Internet or cloud access. This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without Internet access. It was found that the developed prototype achieved a test accuracy of 78% when trained on the HAM10000 dataset, and a test accuracy of 85% when trained on the ISIC 2020 Challenge dataset.  
tinyML computing offloading; artificial intelligence; machine learning; computer vision; image classification
# Optimizing IoT Web Fuzzing by Firmware Infomation Mining
## 4 Evaluation
### Component Validity Testing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CVE** & **Vender** & **Device** & **Vuln** & **IoTParser-null** & **IoTParser-cov** & **IoTParser** & **Speed Improve** \\ \hline CVE-2019-20082 & asus & RT-N53 & BO & (114, NA) & (233, 5581, 10,875) & (233, 1576, 10,875) & 71\% \\ unassigned-0 & asus & RT-N53 & CI & (114, 6756, 12,580) & (233, 9136, 24,650) & (233, 3639, 24,650) & 60\% \\ CVE-2021-31655 & Trendnet & ip110 wn & XSS & (15, 61, 120) & (117, 211, 4590) & (117, 121, 4590) & 42\% \\ CVE-2019-11417 & Trendnet & ip110 wn & BO & (15, NA) & (117, 601, 2295) & (117, 91, 2295) & 84\% \\ CVE-2018-19241 & Trendnet & ip110 wn & BO & (15, NA) & (117, 1921, 2295) & (117, 211, 2295) & 89\% \\ unassigned-1 & Trendnet & ip110 wn & XSS & (15, NA) & (117, 2191, 4590) & (117, 1231, 4590) & 43\% \\ unassigned-2 & Trendnet & ip110 wn & XSS & (15, NA) & (117, 1, 4590) & (117, 1, 4590) & 0\% \\ CVE-2016-1555 & netgear & wmap320 & CI & (41, NA) & (76, 1123, 2686) & (76, 1, 2686) & 100\% \\ unassigned-3 & netgear & wmap320 & unauth & (41, NA) & (76, 76, 476) & (76, 4, 76) & 0\% \\ unassigned-4 & netgear & wmap320 & CI & (41, 953, 1938) & (76, 1021, 2686) & (76, 783, 2686) & 23\% \\ unassigned-5 & netgear & wndr3700 & CI & (181, 6495, 28,050) & (312, 14,629, 43,656) & (312, 16,525, 43,656) & \(-12\%\) \\ unassigned-6 & netgear & wndr3700 & CI & (181, NA) & (312, 23,869, 43,656) & (312, 15,403, 43,656) & 35\% \\ CVE-2019-9126 & dlink & dir-825 & unauth & (69, NA) & (186, 33, 186) & (186, 33, 186) & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of IoTParser-null, IoTParser-cov and IoTParser. Each experimental result is represented by a triple (a, b, c), a: the number of initial seeds; b: the serial number of the request for which the vulnerability was found in the fuzz process; and c: the number of all requests sent by fuzzer.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 4 SIF Retrieval Methods in Space Scale
### The Statistical Methods
#### 4.3.2 Principal Component Analysis (PCA)  
Based on Equation (9), it is assumed that the effects of atmospheric scattering are neglected in the limited window (\(\rho_{s0}\simeq 0\) and \(S\cdot\rho\ll 1\)). The reflectance is expressed as follows [37]:  
\[\rho_{tot}=\tau_{\uparrow}\cdot\rho\cdot\tau_{\downarrow}+\frac{\pi\cdot SIF \cdot\tau_{\uparrow}}{E_{0}\cdot\cos\theta} \tag{24}\]  
In Equation (24), \(\tau_{\uparrow}\), \(\tau_{\downarrow}\), and SIF are the unknown parameters, and the target parameter is SIF. To remove the unknown parameters, sun-to-satellite (two-way) atmospheric transmittance \(\tau(\lambda)\) is used and defined as the production of \(\tau_{\uparrow}\) and \(\tau_{\downarrow}\) (\(\tau(\lambda)=\tau_{\uparrow}\ast\tau_{\downarrow}\)). Through the mathematic relationship between them, the upward transmittance \(\tau_{\uparrow}\) is written as follows (\(\theta\): sun zenith angle, \(\theta_{0}\): view zenith angle) [37]:  
\[\tau_{\uparrow}(\lambda)=\exp\left[\ln\tau(\lambda)\frac{\sec\theta_{0}}{ \sec\theta+\sec\theta_{0}}\right] \tag{25}\]  
With the help of several simplifications, the reflectance equation is as follows [37]:  
\[\rho_{tot}=\rho\cdot\tau+\frac{\pi\cdot SIF}{E_{0}\cos\theta}\cdot\exp\left[ \ln\tau(\lambda)\frac{\sec\theta_{0}}{\sec\theta+\sec\theta_{0}}\right] \tag{26}\]  
Within the limited spectral fitting window, SIF is considered a Gaussian function of wavelength centered at 736.8 nm with a standard deviation of 21.2 nm, \(\rho\) is expressed as a low-degree polynomial wavelength, and \(\tau(\lambda)\) is expressed using principal components (PCs), which are trained from the satellite data. Previous work suggests that different descriptions of the fluorescence spectrum have very little effect on the retrieval results [51; 98].  
In this method, the atmospheric effects were considered, and solved through the PCA method. GOME-2 and SCIAMACHY data were used to retrieve the fluorescence using the PCA methods [37; 39]. This method can be used in the total fluorescence spectrum including both red and far-red features.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks  
Statistics are indispensable in science and engineering, underpinning studies, measurements, characterization, analysis, modeling, and applications [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. As networks and systems become increasingly complex, statistical methods offer crucial tools for understanding their intricate behavior. Statistical modeling of networks, particularly for characterization and practical estimation, are vital across diverse fields like science, engineering, and social science [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. However, the specific motivations for statistical analysis and modeling vary depending on the context and application. The application of statistical methods in communication systems has a long-standing history, beginning alongside the development of communication engineering itself [59].  
Networks are closely related to the graphs and their physical parameters [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Mathematicians solve graph-related problems using graph theory, which originated accidentally when [PERSON] attempted to solve the famous 'Seven Bridges of Konigsberg' puzzle [34]. Today, graph theory plays a crucial role in network analysis, often incorporating statistical modeling for problem-solving. Physicists study the statistical mechanics of networks, focusing on their origin, growth, and time-dependent degree distributions, along with structural changes like percolation, which has applications in physics, chemistry, and material science [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. In computational biology, statistical network analysis helps model the spread of epidemics, bacterial growth, viral infections (such as HIV, COVID-19, and Ebola), and protein-protein interactions in human metabolism [10; 17]. Communication networks also rely heavily on network statistical models, from early telegraph systems to modern Internet advancements, helping engineers understand network complexities [29]. Sociology and psychology were among the first disciplines to apply network concepts, with studies on'small-world' behavior predating modern social networks like LinkedIn and Facebook [8; 35; 36]. The'six degrees of separation' theory, popular in the 1990s, emerged from small-world research, which also explains how brain cells are interconnected. In business and international trade, small-world models enhance trade efficiency and logistics, demonstrating the widespread influence of network analysis across multiple fields, from biology and physics to communication, social sciences, and global commerce [36]. In optical networks, statistical analysis and modeling has several applications, from network planning and dimensioning to design [3; 4].
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis  
Performance comparisons of our proposed clustering algorithms named EECP-MFO with GWO-based clustering, ACO-based clustering, and PSO-based clustering are presented in this part. For the assessment and evaluation process, we employ critical vital criteria, including the number of clusters, the probability of success, the time it takes to form a cluster, the longevity of the cluster, and the amount of energy consumed. The experiments are carried out using the MATLAB programming language. A variety of parameters are included to perform the simulation, as shown in Table 3. We used ten simulation runs in each set to develop the final results.
## 2 Materials and Methods
### Experimental Design
#### 2.5.3 Evaluating Indicator  
This research evaluates the algorithm's quality in two ways: the accuracy of the identification and the performance of the algorithm.  
The mAP (mean average precision) is commonly used to assess the algorithm's accuracy. To begin, you must first determine the precision and recall, which are determined as follows:  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\,, \tag{2}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\,, \tag{3}\]  
In the formula, TP (true positive) means that it is divided into positive samples and correct. FP (false positive) means that it is positive but wrong (in fact, this sample is a negative sample). FN (false negative) means that it is divided into negative samples but wrong (in fact, this sample is this). AP is calculated for n samples of a certain classification; assuming that it has m positive cases, each positive case corresponds to a recall R value (1/m, 2/m,..., 1). Maximum accuracy P was calculated for each recall, and then averaged for m \(p\)-values. The expression for calculating the AP is as follows:  
\[\text{AP}=\frac{1}{\text{m}}\sum_{i}^{\text{m}}\text{P}_{i}=\frac{1}{\text{m} }\times\text{P}_{1}+\frac{1}{\text{m}}\times\text{P}_{2}+\dots+\frac{1}{\text {m}}\times\text{P}_{\text{m}}\,. \tag{4}\]  
While the AP is particular to a single class, a dataset often comprises a large number of classifications, and the mAP is calculated by averaging the AP of all classes in the datasets. The following is the formula for determining the mAP:  
\[\text{mAP}=\frac{1}{\text{C}}\sum_{\text{C}}^{\text{j}}\text{P}_{\text{j}}\,. \tag{5}\]  
A boundary regression task is included in the object detection task, and its accuracy is commonly tested by IoU. The IoU is calculated using the following formula:  
\[\text{IoU}=\frac{\text{S}_{1}}{\text{S}_{2}}\,. \tag{6}\]  
S\({}_{1}\) is the area of overlap between the predicted boxes and actual boxes; S\({}_{2}\) is the total area occupied by the predicted boxes and actual boxes. Thus, mAP (0.5) refers to the value of mAP at IoU = 0.5. The mAP (0.75) is the value of the mAP at IoU = 0.75.  
The F1-score is a measure of a classification problem and is often used as the final metric for multi-classification problems; it is the summed average of precision and recall. F1-score can be calculated using the following formula:  
\[\text{F1}=2\times\frac{\text{recall}\times\text{precision}}{\text{recall}+ \text{precision}}. \tag{7}\]  
We set the performance metric to the time it takes to collect the mAP values in order to better discover the algorithm's time performance because the time it takes to calculate the mAP comprises both the time it takes to infer the image and the time it takes to calculate the aforementioned series of numbers. It can correctly display the algorithm's performance on a variety of devices. We also use model size, number of parameters, and flops as a measure of the performance of the algorithm on hardware.
## 1 Introduction
Another approach involves least-squares reverse time migration (L8 RTM) for separately extracted multiples. L8 RTM with controlled-order multiples migration (L8 RTM-CM) [36] utilizes surface-related multiple elimination (SRME) to separate different-order multiples and then applies least-squares reverse time migration for direct multiple imaging. Subsequently, phase-encoded reverse time migration and L8 RTM multiple imaging were developed [37; 38]. In recent years, viscoacoustic LSRTM of separated multiples has also been investigated [39]. Another approach is the Marchenko imaging algorithm [40; 41; 42], which can be implemented by applying a reconstructed reference surface, directly circumventing crosstalk generated by internal multiples in the overburden. However, this method relies on dense sampling conditions, restricting its application to field data. In this paper, we target full-wavefield imaging (surface-related multiples and internal multiples) and modify the global normalization conditions for all shots to consider the illumination of surface-related and internal multiple downward wavefields, which is further combined with least-squares migration. The remainder of this paper is organized as follows. First, we analyze the crosstalk artifacts and downward illumination wavefields of surface-related and internal multiples of different orders, and then we derive the global normalization conditions for multi-order wavefields in combination with amplitude normalization conditions and apply them to full-wavefield least-squares migration. Finally, we use synthetic data to verify our algorithm.
# Characterization of Two Hydrogen Peroxide Resistant Peroxidases from _Rhodococcus apacus_ 1 Cp
## 3 Results  
### Identification of Dyp Genes  
The _dyp_ genes from _Rhodococcus apacus_ 1 CP were compared to the DypA and DypB from _Rhodococcus jostii_ RHA1 via a BLAST search. Results showed (data not shown) the DypA of _R. apacus_ 1 CP is 98% similar to that of _R. jostii_ DypA and shared only 33% similarity to the DyP enzyme of _Geobacillus geotrichum_. Meanwhile, DypB of _R. apacus_ 1 CP have 97% similarity to the DypB of _R. jostii_ RHA1 and only 29% to DyP of _G. geotrichum_.  
Dye-decolorizing peroxididases are often classified into four distinct classes based on their homologies [3]. Class A, B, and C generally include Dyps of bacteria belonging to the phyla Proteobacteria, Actinobacteria, Firmicutes, and Bacteroides, while Class D includes fungal Dyps. Thereby, Class A and B form a cluster distinguishable from Class C and D indicating that they share more sequence similarities than to the other two groups. As expected, DypA and DypB from _R. apacus_ 1 CP are assigned to Class A and B, respectively,  
Figure 1: Structures of the dyes used in the study. Reactive Blue 5 (RB5) and Reactive Blue 19 represent the anthraquinone dyes. Reactive Black 5, Reactive Red 23, Reactive Orange 16, and Reactive Yellow 37 represent the azo dyes. Poly R-478 represents an anthraquinone-based polymeric dye.  
and are the closest related to Dyps of other rhodococci like _R. jostii_ RHA1, _R. opacus_ PD630 or _R. opacus_ B4 (Figure A1).
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### Powering  
Continuous power delivery to the IBBDs is one of the foremost challenges for in-body antennas. The current battery technologies are an inefficient solution for such application due to their short lifetime [11; 14]. Furthermore, batteries contain hazardous ingredients and necessitate a surgical operation to be replaced. Additionally, the power system of IBBDs must be lightweight and easy to fabricate to facilitate an easy movement of patients. It is also necessary to maintain the energy level of the system in the design of a powering system for IBBDs.
# A Sketch-Based Fine-Grained Proportional Integral Queue Management Method
## 3 System Model
### Traffic Statistics
#### 3.1.1 Sketch Structure  
A Sketch consists of a two-dimensional array of counters and a set of hash functions that map items to the array, as shown in Figure 2, where the width of the two-dimensional array is \(w\), the depth is \(d\), and the \(d\) hash functions \(h_{1}\), \(h_{2}\),..., \(h_{d}\) are independent of each other [33]. An item is processed by mapping it to each row and incrementing the counters  
Figure 1: S-PIE algorithm architecture.  
to which it was mapped. When a packet arrives, it is mapped and stored in the form of (_key_, \(value\)), where the _key_ field can be the attribute information in the packet header, such as a five-tuple (source/destination IP, source/destination port, transport layer protocol), the \(value\) field can be the number of occurrences of the flow or the size of the flow.
## 4 Conclusions  
In this study, welding was performed using different magnesium and manganese contents of filler wire used for GMA welding of an aluminum 5083 alloy, and the mechanical properties of the weld were analyzed. The detailed conclusion is as follows:  
* When an aluminum alloy filler wire (W1) with a magnesium content of 7.33 wt.% and a wire (W2) with a magnesium content of 6.38 wt.% were used, respectively, the tensile strength was similar. As a result of the chemical composition analysis of the two welds, a similar amount of magnesium remained. When the two filler wires are melted during arc welding, the magnesium is vaporized by the high arc heat, and the amount remaining in the deposited metal zone is similar.
* When W2 and W3 were used with similar magnesium contents and different manganese contents of 0.004 wt.% and 0.46 wt.%, respectively, the tensile strength and hardness of the W3 weld were higher. This is for the reason that manganese, like magnesium, has a solid-solution strengthening effect in aluminum alloys because the  
Figure 12: Regression analysis of tensile strength and magnesium content of filler wire in aluminum 5083 alloy GMA welds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Base Metal**} & \multicolumn{2}{c}{**First Pass**} & \multicolumn{2}{c}{**Second Pass**} \\ \hline
**Element** & **Mg** & **Mn** & **Mg** & **Mn** & **Mg** & **Mn** \\ \hline W1 & 4.271 & 0.631 & 4.952 & 0.363 & 5.257 & 0.354 \\ \hline W2 & 4.264 & 0.639 & 4.793 & 0.360 & 5.070 & 0.355 \\ \hline W3 & 4.232 & 0.620 & 4.944 & 0.606 & 5.110 & 0.597 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of aluminum 5083 alloy GMA welds measured by ICP-OES (wt.%).  
boiling point of manganese is relatively high; therefore, vaporization hardly occurs and it remains in the weld. As a result of microstructure analysis, the grain size of the W3 weld was the smallest. Since the weld zone, having fine grains, has a larger grain boundary area that hinders the movement of dislocations than the weld zone with coarse grains, the strength of the weld zone of W3 with the finest grains is excellent. When the magnesium content of the filler wire used for aluminum alloy GMA welding is high (6 wt.% or more), the manganese content, rather than the magnesium content, had a dominant effect on the strength improvement of the weld.  
Conceptualization, G.-G.K. and D.K.; methodology, G.-G.K. and D.-Y.K.; formal analysis, G.-G.K. and I.H.; investigation, G.-G.K. and D.K.; writing--original draft preparation, G.-G.K. and Y.-M.K.; writing--review and editing, G.-G.K. and Y.-M.K.; supervision, J.P. and Y.-M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Technology Innovation Program (No. 20011305, Development of arc welding gap compensation adaptive control technology and artificial intelligence-based welding process-control-integrated system for high-quality cowl cross of vehicle) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).  
Not applicable.  
The authors declare no conflict of interest.
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 1 Introduction
Therefore, this paper aims to propose an open source massive deployment system that includes system architecture as well as associated software for system deployment. This massive deployment system can handle single machine backup and recovery, the massive deployment of disks in a computer, and massive deployment in large-scale computers. In addition, this system can deploy most dominant OSs, including Linux, MacOS, MS Windows, and FreeBSD. Once verified, the proposed mass deployment system can be promoted and applied in various scenarios of computing, thereby enhancing the efficiency of system deployment. To achieve this goal, the program \"Clonezilla\" [10] was developed, which was based on the proposed architecture and following the open source developing model. In order to demonstrate the feasibility of the proposed massive deployment system, the authors validated the system by deploying massive disks in a single machine. Clonezilla live was used to deploy Ubuntu Linux to a large quantity of USB flash drives in parallel on a single x86-64 based personal computer and USB 3.0 hubs. Various factors influence the input/output (I/O) throughputs (e.g., the number of disks, the bandwidth of system I/O bus, and the size of each I/O transfer). A governing equation to determine the optimal performance of the massive deployment system was also proposed and verified. In addition, cost comparisons between the proposed novel massive deployment system versus other widely adopted commercial proprietary solutions are provided. The software was also validated by deploying a Linux system to massive computers in a computer classroom via the multicast mechanism. The experimental results demonstrate the feasibility of massively deploying disks in a computer, and the use of massive deployment in large-scale computers. Nevertheless, the well-verified mass deployment system can also be used for single machine backup and recovery. The rest of this paper is organized as follows. Section 2 reviews and discusses related works. Section 3 introduces the system architecture, software implementation, software packaging, massive deployment, and major changes and improvements of the developed software in the past decade. The experimental results are demonstrated in Section 4. The results and comparisons are discussed in Section 5. Conclusions and suggestions for future works are provided in Section 6.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.1 CTP-SEEC [24]  
In [24], the authors proposed a transmission power-based sparsity-conscious energy-efficient clustering (CTP-SEEC), a reactive layer-based protocol. Here, the nodes are divided into square layers, and the author considered a one-sink situation where all the data propagated are transferred to the sink, which then transfers the data to the base station. The adaptive nature of nodes is considered, and two different algorithms are proposed for dense and sparse networks, the density search algorithm (DSA) and the sparsity search algorithm(SSA), respectively. The cluster head (CH) is elected if the node is lower than other nodes in the cluster, the residual energy is greater than that of others in the cluster, and the probability calculated is greater than the threshold value. Only one CH is selected in each group of clusters. The network's lifetime is improved by applying a controlled transmission power strategy.