esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device
## Materials and Methods
### Three Dimensional Culture of NHBE in the Gel Cube Device  
Normal human bronchial epithelial (NHBE) cells were purchased from Lonza (CC-2541; Walkersville, MD, USA). The growth factor-reduced reconstituted basement membrane Matrigel (#356230; Corning, NY, USA) was used as a culture gel for 3D culture experiments. Eight microliters of Matrigel was added to the gel cube device from the top, open surface. After centrifugation, 0.5 \(\mu\)L of concentrated NHBE (2.5 \(\times\) 10\({}^{4}\) cells/\(\mu\)L) was injected into the center of the Matrigel and incubated for 25 min at 37 \({}^{\circ}\)C to facilitate gelatinization. Agarose was dropped onto the top surface to confine the culture gel and sample, and the cube was incubated with medium in a well plate. The cell culture medium consisted of a one-to-one mixture of bronchial epithelial growth medium (BEGM, CC-3170; Lonza; Walkersville, MD, USA) and endothelial growth medium (EGM-2, CC-3162; Lonza; Walkersville, MD, USA) supplemented with 50 IU/mL penicillin and 50 \(\mu\)g/mL streptomycin. Two milliliters of this medium were added to each well, and the medium was changed every day.
## 3 Preliminary and Problem Statement
### Formal Concept Analysis  
Formal concept analysis [1] uses a formal context to construct a hierarchy of concepts, more exactly, a concept lattice. A formal context is a triple \(K=(G,M,I)\), where \(G\) indicates a set of objects, \(M\) represents a set of attributes, and \(I\subseteq G\times M\) is a binary relation.  
Each pair \((g,m)\in I\) expresses that the object \(g\in G\) contains the attribute \(m\in M.\) Given a subset of objects \(A\subseteq G\) and a subset of attributes \(B\subseteq M\), the following derivation operators are defined:  
\[f(A)=\{m\in M|\forall g\in A,(g,m)\in I\}\]  
\[g(B)=\{g\in G|\forall m\in B,(g,m)\in I\}\]  
where \(f(A)\) is the set of attributes common to all objects of \(A\) and \(g(B)\) is the set of objects sharing all attributes from \(B\). A formal concept is a pair \((A,B)\), where \(A\subseteq G,B\subseteq M\) and \(f(A)=B\), \(g(B)=A\). \(A\) is called the extent and \(B\) is called the intent of the concept \((A,B)\). Such concepts are then gathered in a hierarchical, lattice-based structure, namely concept lattice. In a concept lattice, a partial order exists between two concepts \((A,B)\leq(C,D)\) if \(A\subseteq C(D\subseteq B)\), a pair \((A,B)\) is a subconcept of \((C,D)\) and \((C,D)\) is a superconcept of \((A,B)\). If \((A,B)\leq(C,D)\) and there is no \((X,Y)\) satisfies \((A,B)\leq(X,Y)\leq(C,D)\), \((A,B)\) is a lower neighbor of \((C,D)\) and \((C,D)\) is an upper neighbor of \((A,B)\). Each finite lattice has the topmost element with \(A=G\), called the top concept, and the lowest element with \(B=M\), called the bottom concept.  
**Example 1**: _Table 1 shows a formal context \(K\) with \(G=\{1,2,3,4\}\) and \(M=\{a,b,c,d,e\}\), in which \(\times\) denotes that there exists the binary relation between an object and an attribute. Since the objects 1, 2 and 3 share the common attributes \(\{a,b\}\) and the attributes a and \(b\) have the common objects \(\{1,2,3\}\). Thus, \((\{1,2,3\},\{a,b\})\) is a concept. \(\{1,2,3\}\) is the extent of the concept, \(\{a,b\}\) is the intent of the concept. The corresponding concept lattice is sketched by Figure 2. Each blue node represents a concept. The upper labels and lower labels of the nodes represent intentsand extents of the concepts, respectively. The subconcepts of \((\{3,4\},\{c\})\) are \((\{4\},\{c,d,e\})\), \((\{3\},\{a,b,c\})\) and \((\{\emptyset\},\{a,b,c,d,e\})\). The lower neighbor concepts of \((\{3,4\},\{c\})\) are \((\{4\},\{c,d,e\})\) and \((\{3\},\{a,b,c\})\).
## 3 Concepts and Methods  
This paper presents a task allocation-based ensemble fuzzing framework aimed at reducing redundant work. This framework first divides the callgraph of the target program into multiple subgraphs. Then, it divides the global seed corpus into subcorpora based on the association between the subgraphs obtained above and the execution trace of each seed. These subcorpora are allocated as fuzzing tasks to different heterogeneous fuzzers. More importantly, the framework introduces strategies for synchronizing bitmaps and seeds between the heterogeneous fuzzers, as well as a task scheduling method. As shown in Figure 3, the framework consists of three main parts:  
_Callgraph division_ (Section 3.1): The callgraph of the program under test is obtained through static analysis, updated with seeds execution traces, and then transformed into a minimum spanning tree. After that, the callgraph is divided into subgraphs by using the minimum spanning tree partitioning algorithm [35]. Finally, the large number of subgraphs obtained through partitioning are merged into a given number of subtasks, aiming to achieve a balanced expected workload for each subtask and ensure that every subtask is easily accessible.  
_Subgraph association and seed mapping_ (Section 3.2): The tested program is executed using each seed from the global seed corpus one by one as input. Then, based on the association between the execution paths of the tested program when using each seed as input and the subgraphs of the callgraph corresponding to each subtask, the global seed corpus is divided into subcorpora, each corresponding to a subtask.  
_Bitmaps and seeds synchronization and task scheduling_ (Section 3.3): During the process of fuzzing, the global bitmap is synchronized with the current bitmap of each paralleled fuzzer at fixed intervals. The updated global bitmap is then used to replace the bitmap of each fuzzer, allowing each paralleled fuzzer to access the progress of other subtasks. When a new seed is discovered by any fuzzer, it will not be added to the local seed queue of that fuzzer immediately. Instead, the new seed is sent to the global seed division program. The global seed division program then checks the seed's trace to determine which subtask it is related closest to and synchronizes it with the corresponding fuzzer that is responsible for fuzzing the identified subtask. In addition, the divided subtasks are cyclically allocated to each fuzzer, enabling every subtask to be tested by all fuzzers. This ensures that the advantages of ensemble fuzzing can be utilized on each subtask.
## 2 Materials and Methods
### Preparation of Sample and Standard Solutions for UPLC-DAD-MS/MS Analysis  
JGTW solution was prepared at a concentration of 0.05, 0.1, 0.5, and 20 mg/mL in methanol and filtered using a 0.2-\(\upmu\)m syringe filter. The 24 reference standards (purity > 95%), namely catechin, vicenin-2, schaftoside, isoschaftoside, neoliquiritin, liquiritin, luteolin, liquiritigenin, calycosin, naringenin, kaempfer, formononetin, licochalcone B, echinatin, isoliquiritingenin, albiforin, paeoniflorin, mudanpioside C, benzoylpaenoinflorin, glycyrrhizin, hederagenin, and glycyrrheinic acid (ChemFaces Biochemical Co.,Ltd., Wuhan, China), taxifolin (ICN Biochemical, Aurora, OH, USA), and 1,2,3,4,6-O-pentagalloyglucose (Biopurify Phytochemicals, Chengdu, China) were used to identify and quantify the phytochemicals in JGTW. LC-MS grade solvents were purchased from Thermo Fisher Scientific (Waltham, MA, USA). The 24 reference standards were prepared in methanol at final concentrations ranging from 0.02-10 ug/mL, except for vicenin-2 (0.001-0.595 ug/mL) and echinatin (0.001-0.27 ug/mL).
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Scheduling Solution Inspection
#### 4.1.2 Example 2  
The features of the second example are shown in Figure 10:  
* Five-node network, where Node 2 connects all other nodes. Furthermore, Nodes 3 and 4 have a direct link between them.
* Four streams: Stream 0 consists of three frames, while the others have only one frame. The first stream has a period of 2000 ms, while the other streams have periods of 5000 ms, which makes the hyper-period 10,000 ms.
* The stream distribution is as follows:
* Stream 0: origin at Node 3 and destination at Node 0, passing through Node 2.
* Stream 1: origin at Node 0 and destination at Node 2.
* Stream 2: origin at Node 3 and destination at Node 1, passing through Node 2.
* Stream 3: origin at Node 2 and destination at Node 4.  
Figure 10: Second scheduler problem.  
This example is noticeably more complicated than the previous one. It involves a larger number of frames, a larger network, and more conditions to fulfill for the ILP due to the multiplicity of frames of some streams within the hyper-period. However, when looking at the image, we can appreciate that there is no overlapping of streams in any of the links simultaneously, even considering the repetitions in the hyper-period. In addition, the solver foresees when the successive frames of a stream will require an additional transmission time slot in a link, so it will prevent collisions between streams with multiple frames.  
It is interesting to note that Stream 0 never interrupts Stream 1, even though they both use Link 0. However, the most crucial moment occurs with Streams 0 and 2 on Link 2. Although the frames of Stream 0 could have been transmitted consecutively on Link 2, this option would interfere when Stream 2 transmits over that link. Therefore, the system decides to leave a time slot free for Stream 2 to use, even if this is necessary only in the third repetition period.
## 4 ML Performance Evaluation
### Multi-Class Classification Metric  
In this scenario, the models are trained to classify all specific types of attacks present in the given dataset. The performance evaluation follows the same principles as in the binary case. However, certain clarifications are necessary when defining performance metrics in the multi-class case. When extending a binary metric to multi-class problems, the data is treated as a collection of binary problems using the One-Vs-All (OVA) approach. In OVA classification, a binary classifier model is created for each class in the dataset.  
Based on these new classification labels, the same performance metrics used in the binary case are computed for each class in the dataset [52; 54]. After obtaining the binary results for each class, there are two different approaches that can be taken:  
* Macro-average: This method simply calculates the average of the binary metrics, assigning equal weight to each class. It can be particularly useful when infrequent classes hold significance, as it highlights their performance. With the _Macro-average_, the effect of the most frequent classes is considered equally important as that of the least frequent ones.
* Micro-average: In this approach, each sample-class pair contributes equally to the overall metric. Instead of summing the metrics by class, it sums the numerators and denominators that constitute the metrics by class to calculate an overall quotient. The _Micro-average_ is often preferred in multi-label or multi-class classification settings, where the majority class must not dominate the evaluation.  
The difference between these methods lies in how they weigh each class or sample. _Macro-average_ assigns equal weight to each class, while _Micro-average_ assigns equal weight to each sample. If the dataset is perfectly balanced, both _Macro_ and _Micro_ averages yield the same result. In this work, the performance of all attack classes in the two multi-class datasets under analysis will be evaluated. Hence, the contribution of each class's performance value is equally important compared to the others. In the upcoming sections, balanced datasets will be used, eliminating any distinction between _Macro_ and _Micro_ indices. The following metrics will be employed as indicators of multi-class performance: _M-Sensitivity_ (M-SE), _M-Specificity_ (M-SP), _M-Precision_ (M-PR), and _M-Accuracy_ (M-AC). The \"M\" prefix indicates the term _Mean_, encompassing both _Micro_ and _Macro_ without distinction (see Figure 2).  
Figure 2: Graphical interpretation of the definition of True-Positive (**a**), False-Positive (**b**), False-Negative (**c**), and True-Negative (**d**), referring to class 1 out of 3.
## 3 Results and Discussion  
### Loading Displacement Caused by MOT  
Using the method outlined in Section 2.1, we obtained the loading time series caused by MOT for the selected 109 stations. Figure 2 shows an example of the averaged weekly MOT height time series for station KOKB (Hawaii, USA) and POTS (Potsdam, Germany). We observe that for inland stations, e.g., POTS, the MOT effect is quite small, with most of the vertical displacement smaller than 0.5 mm. However, for coastal or island stations, such as KOKB, the peak-to-peak vertical displacement caused by MOT reaches 8 mm.  
To have a better understanding of the global MOT effect, Figure 3 describes the standard deviation (STD, left panels) and the maximum displacement (right panels) of the MOT loading time series for the 109 stations. From top to bottom are the up (U), east (E), and north (N) components. We can see that MOT could only cause small variations in stations' horizontal displacement, with most of the STD and absolute maximum displacement smaller than 1 mm and 3 mm respectively. For the U component, however, it's quite a different story. In general, the vertical displacement induced by MOT becomes smaller with the increasing distance between station and the sea. This is obvious due to the characteristics of the ocean tidal loading itself. Coastal and island stations suffer the most MOT effects, with the biggest STD and the absolute maximum height reaching more than 3.5 mm and 10 mm respectively. For inland stations, the MOT effects are much smaller, with most of the height variation smaller than 1.5 mm, in particular for those in the Eurasia plate, which have almost the same magnitude as that of the E component.  
Figure 3: Spatial distribution of the STD (**left**) and the maximum displacement (**right**) of the MOT loading time series. From top to bottom are the U, E, and N components.
# Valorization of Waste Tires by Pyrolysis and Activation Processes
## 3 Results and Discussion
### Solid Fraction Characterization
S1 has the lowest conductivity value, while S4 has the highest conductivity value from all of the solid samples evaluated. S2 reported the second highest conductivity--higher even than that obtained for S3. This suggests that chemical activation favors nanopore size formation, which increases its conductivity. On the other hand, physical activation causes a clear increase in surface area, but the pores formed are not necessarily nanosized. Therefore, despite S3 having a high surface area, its conductivity is lower than S2, which has a lower surface area, but experiences nanopore formation. Figure 7 compares the effect of the surface area and the pore size on the electrical conductivity. As can be seen in Figure 7a, there is not a clear effect of the surface area on the conductivity; despite S3 have a high surface area, its conductivity is lower than S2, which has a lower surface area, but experiences nanopore formation. Figure 7b shows that pore size has a stronger effect on conductivity, which in turn is heavily influenced by the presence of chemical activation. Figure 6: (**a**) Boxplots of conductivity results and (**b**) plot of [PERSON]’s test. Notes: Boxplots show the median, mean, interquartile range, and range from each solid fraction; if an interval in [PERSON]’s test plot does not contain zero, the corresponding means are significantly different. Similar results were obtained by [PERSON] et al. [45], who successfully synthetized nitrogen-enriched biochar-based electrode material for supercapacitors from lignocellulosic, graphene oxide, and p-phenylenediamine, with 0.3 Sm\({}^{-1}\) (0.3 \(\Omega^{-1}\) m\({}^{-1}\)) being the highest conductivity value obtained. [PERSON] et al. [46] studied the behavior of carbon black particles in a water suspension for their use in flow batteries, concluding that their carbon black sample preparation demonstrates good conductivity between 0.01 Sm\({}^{-1}\) and 0.5 Sm\({}^{-1}\). The four solid samples obtained in our study presented higher conductivity values. As electrical conductivity is a crucial factor in terms of electrochemical performance [45], the higher conductivity values from solids obtained from processes involving chemical activation demonstrate the material potential in electric energy storage applications.
## 4 System Architecture and Performance Analysis
### Performance Analysis  
#### 4.3.1 Blurred Image--Principle and Simulation Method  
On the basis of the findings in [30; 31; 32; 33; 34; 35; 36], the received image of LEDs which is contaminated by the blur process and noise can be obtained by a convolutional process of the blur kernel with a clear image of LEDs, followed by the addition of noise, as follows:  
\[\mathrm{y}=\mathrm{h}\star\mathrm{x}+\mathrm{n} \tag{9}\]  
where y is the captured image matrix, h is the blur kernel matrix, x is the original image matrix, and n is the noise matrix. Note that all of the matrices here are two-dimensional, as the LEDs image will be converted to a grayscale format.  
In our experiment, each LED occupies a \(50\times 50\) area on the image. Thus, the clear image of LEDs group is a 2D matrix with size = 100 x 200 pixels (rows x columns). The blur kernel is also a 2D square matrix, with a matrix size is 20 x 20, 40 x 40, 60 x 60 and 80 x 80 pixels. All cells in a blur kernel will take equal values and add to one, so each cell of the blur kernel will take value of \(\frac{1}{\mathrm{blur\_size}^{2}}\). The output matrix of the convolution between a blur kernel and a LEDs image will have a full size of: \((\mathrm{blur\_size}+100-1)\times(\mathrm{blur\_size}+200-1)\). To have the final blur image in our experiments with the same size as an original image, we make a crop on a product matrix C at the row and column index equal to blur_size/2.  
For clarity purposes, in our data simulation for training and performance testing on SER value, firstly we collected a basic set of clean data. These basic sets should be sufficient to represent all S_Phase values corresponding to each dimming level, which are mapped in Table 2 or Table 3. Observe that, to use Table 3 as the basic clean set of images, the values of the two \(x\) variables need to be from zero to one, and the sum value of the two \(x\) has to be one (to guarantee that the sum of all LED states is equal to the dimming level). Figure 8 illustrates the whole process of contaminating the clean image set with  
Figure 7: The principle of training a neural network (NN)-based decoder for blurry and noisy data.  
blur and noise using (7) in order to create a dataset for training the neural-network-based decoder as well as the test set for SER evaluation.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 2 Hybrid VLC/RF Networks in Indoor Scenarios under Ideal Conditions
### Hybrid Channel Model
In addition, if an orthogonal frequency division multiplexing with a multiple inputs-multiple outputs (OFDM-MIMO) system is used, spatial diversity must be taken into account, making it necessary to introduce parameters at one end related to the correlation between the different antennas [47]. Table 3 shows a comparison of the communication channel characteristics of VLC and RF networks. Although these two technologies have very different communication channel models, it is possible to design a hybrid model because there is no interference between the two networks. In [48], a robust channel model of a hybrid VLC/RF system was developed for optimal channel selection. Figure 4 shows a hybrid model of a VLC/RF communication channel composed of mobile terminals at each end equipped with hybrid VLC/RF frequency bands. The RF channel has a Hermitian model with WLAN frequencies (2.4 GHz/5 GHz), while the VLC channel approximates a Lambertian model with frequency bands on the order of THz. The wireless communication channels are shared simultaneously between the two technologies to transmit (T) and receive (R) information. The authors of [49] described a hybrid model of an imperfect VLC/RF channel, which they used to analyze the energy efficiency of these hybrid systems. Among the imperfections of the hybrid channel, they took into account the uncertainty caused by noise and quantization errors in the digital-analog process. Due to the presence of hybrid frequency bands and the dynamic conditions of the hybrid communication channel, optimal channel design is a very important and difficult challenge for VLC/RF systems. Optimizing the transmission channel of the VLC/RF network is essential for the development of IoT systems because smart devices often require a stable connection for proper operation. A reliable and robust connection can help extend the lifetime of IoT devices and their applicability. Figure 4: Hybrid model of a VLC/RF communication channel. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **VLC Channel Model** & **RF Channel Model** \\ \hline Model & Lambertian & Hermitian \\ Propagation mode & Visible light & Electromagnetic wave \\ Modulation technique & IM/DD, NOMA 1 & OFDM-MIMO, \\ Coverage & Minor & NoMA \\ Complexity & Simple & Major \\ Symmetry & No & Yes \\ Links & Download & Download/Upload \\ Doppler effect & No & Yes \\ Interference inter symbols & Minor & Major \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of VLC and RF channel models.