esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to the State Explosion Problem: SOPC Case Study
## 3 Analysis of SOPC Software  
SOPC software is the product of the development of FPGA to the system era, from the early programmable logic gate array to the on-chip system composed of interconnected IP through an on-chip high-speed bus. In SOPC software based on Advanced eXtensible Interface (AXI) bus protocol, data exchange is often realized between multiple master devices and multiple slave devices through a bus interconnection module. As shown in Figure 2, the master and slave devices can be IP core, programmable logic, and CPU softcore or hardcore. SOPC software has a large number of multi-form IP cores, such as netlist IP core, black-box IP core, encryption IP core, CPU IP soft core, CPU IP hardcore, and traditional register transfer level (RTL) IP core.  
When constructing software using SOPC architecture, a high-level language is generally used to describe the software function. After synthesis, place, and route, the code is converted into a logic circuit constructed from clock resources, place and route resources, registers, lookup tables, etc. For example, we use the Xilinx xq7k325t chip integrated with MicroBlaze softcore to realize the SOPC software, which includ  
Figure 2: Multi-master and multi-slave interconnections.  
FC-AE-1553 protocol, peripheral component interconnect express (PCIE) protocol, and other complex communication protocols, as well as high-speed and large-quantity data transmission and processing. FPGA's EDA tool (Vivado from Xilinx, San Jose, CA, USA, Libero from Microsemi, Irvine, CA, USA and so on) synthesizes RTL code [28] to a synchronous sequential software net. The resources occupied by the software are as follows (Figure 3):  
Advanced high-performance seven-series FPGA logic is based on real six-input lookup table technology. It includes a flip flop (FF), a lookup table(LUT), input-output (IO), and other components. From the synthesis result, we can see that the FPGA software has 115,500 flip flops and 112,099 \(\times\) 6 logic lookup tables. In the worst case, a complete state diagram of the FPGA software is a complete graph with \(2^{115500}\) States and \(2^{115500}\times 2^{672594}\) transition relationships.  
On the one hand, SOPC software faces multiform IPs that cannot be recognized using formal tools. On the other hand, SOPC software also faces the problem of state space explosion. Therefore, formal verification technology cannot be well applied to SOPC software at present.
## 2 Granulation Techniques
### Concept Dependent Granulation  
A concept-dependent (cd) granule \(g_{gram}^{cd}(u)\) of the radius \(r_{gran}\) about \(u\) is defined as follows:  
\[v\in g_{gram}^{cd}(u)\text{ if and only if }\mu(v,u,r_{gran})\text{ and }(d(u)=d(v)) \tag{3}\]  
Toy Example  
For the decision system from Table 1, we have found concept-dependent granules. For the granulation radius \(r_{gran}=0.25\), the granular concept-dependent indiscernibility matrix (gcdm)--see Table 4--is  
\[c_{ij}^{cd}=\left\{\begin{array}{cccccccccccccccc}1,&if&\frac{|IND(u_{i},u_{ j})|}{|A|}\geq 0.25,d(u_{i})=d(u_{j}),i<j\\ &0,&\text{otherwise}\end{array}\right.\]  
hence, the granules in this case are  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \(u_{1}\) & \(u_{2}\) & \(u_{3}\) & \(u_{4}\) & \(u_{5}\) & \(u_{6}\) & \(u_{7}\) & \(u_{8}\) & \(u_{9}\) & \(u_{10}\) & \(u_{11}\) & \(u_{12}\) & \(u_{13}\) & \(u_{14}\) \\ \hline \(u_{1}\) & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 \\ \(u_{2}\) & & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\ \(u_{3}\) & & & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 0 \\ \(u_{4}\) & & & & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 0 \\ \(u_{5}\) & & & & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 0 & 1 & 0 \\ \(u_{6}\) & & & & & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ \(u_{7}\) & & & & & & 1 & 0 & 1 & 1 & 1 & 1 & 0 \\ \(u_{8}\) & & & & & & & 1 & 0 & 0 & 0 & 0 & 1 \\ \(u_{9}\) & & & & & & & 1 & 1 & 1 & 0 & 1 & 0 \\ \(u_{10}\) & & & & & & & & 1 & 1 & 1 & 1 & 0 \\ \(u_{11}\) & & & & & & & & & 1 & 1 & 0 \\ \(u_{12}\) & & & & & & & & & & 1 & 0 \\ \(u_{13}\) & & & & & & & & & & & 1 & 0 \\ \(u_{14}\) & & & & & & & & & & & & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Triangular indiscernibility matrix for concept-dependent granule generation (\(i<j\)), derived from Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Day** & **Outlook** & **Temperature** & **Humidity** & **Wind** & **Play.golf** \\ \hline \(MV(g_{0.5}(u_{1}))\) & Sunny & Hot & High & Weak & Yes \\ \(MV(g_{0.5}(u_{4}))\) & Rainy & Mild & High & Weak & Yes \\ \(MV(g_{0.5}(u_{5}))\) & Rainy & Cool & Normal & Weak & Yes \\ \(MV(g_{0.5}(u_{14}))\) & Rainy & Mild & High & Strong & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard granular reflection of the exemplary training system from Table 1, in radius 0.5, 5 attributes, 4 objects; MV is Majority Voting procedure (the most frequent descriptors create a granular reflection).
## 2 High Data Rate Modulator Design Description  
The developed ACM transmitter structure is divided into four parts: mode adaptation; serial concatenated convolutional codes; physical layer framing; and baseband filtering. The detailed block diagram is shown in Figure 1. An ACM Command input to a given block will reconfigure its parameters and functionality according to one of the 27 available operational modes [2]. The different modes have a different configuration for the parameters and include different options and blocks in Figure 1. The different modes are used depending on the needs, the channel conditions and the status of the transmitter.  
The parameters that can be changed according to the selected ACM format are given in Figure 1 located in the appendix:  
* ACM_format denotes the operational mode for the current frame;
* \(m\) denotes the modulation order of constellation symbols;
* \(S_{sur}\) denotes the number of surviving bits after CC2 puncturing in each input 300-bit segment of systematic bits;
* \(K\) is the slicer output block length;
* \(I\) is the interleaver block length;
* \(S\) is the total number of transmitted systematic bits;
* \(P\) is the total number of transmitted parity-check bits;
* \(N\) is the row/column interleaver total block size;\(\Delta\) is the number of deleted parity bits in CC2 puncturing.
## 2 Materials and Methods
### Hoechst 33258 Staining  
LoVo cells were grown on 12-well plate for 24 h, and then treated with vehicle as control or with _D. innovxia_ ethanolic extract at (5 and 10 ug/mL). After rinsing with PBS, cells were fixed in 4% paraformaldehyde followed by permeabilized in cold methanol. Cells were then washed with PBS, and then incubated with Hoechst 33258 (Sigma, St. Louis, MO, USA) which was prepared in PBS (0.5 ug/mL) and added to LoVo cells for 15 min at the dark. Finally, cells were examined under Zeiss fluorescence microscope a (Zeiss, Wetzlar, Germany).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gene Name** & **Forward Primer Sequences** & **Reverse Primer Sequences** \\ \hline
1 & P53 & 5\({}^{\prime}\)-TGGCCTCTGACTGTACCACCATCC-3\({}^{\prime}\) & 5\({}^{\prime}\)-CAGCTCTCGGAACATCTCGAAGC-3\({}^{\prime}\) \\ \hline
2 & Bax & 5\({}^{\prime}\)-GGA TGC GTC CAC CAA GAA G-3\({}^{\prime}\) & 5\({}^{\prime}\)-CCT CTG CAG CTC CAT GIT AC-3\({}^{\prime}\) \\ \hline
3 & Bcl-2 & 5\({}^{\prime}\)-GTG GAT GAC TGA CTA CCT CAA C-3\({}^{\prime}\) & 5\({}^{\prime}\)-GCC AGG AAT CTA ACA GAG G-3\({}^{\prime}\) \\ \hline
4 & Caspase 9 & 5\({}^{\prime}\)-CAG GCC CCA TAT GAT GGA GG-3\({}^{\prime}\) & 5\({}^{\prime}\)-TCG ACA ACT TTG CTG CTT GC-3\({}^{\prime}\) \\ \hline
5 & Caspase 8 & 5\({}^{\prime}\)-CTG GTC TGA AGG CTG GIT GT-3\({}^{\prime}\) & 5\({}^{\prime}\)-CAG GCT CAG GAA CTTG GAG GG-3\({}^{\prime}\) \\ \hline
6 & Caspase 3 & 5\({}^{\prime}\)-CTG GTT TTC GGT GGG TGT G-3\({}^{\prime}\) & 5\({}^{\prime}\)-ACG GCA GGC CTC GAT AAT GAA \\ \hline
7 & GAPDH & 5\({}^{\prime}\)-GGT ATC GTG GAA GGA CTC ATG AC-3\({}^{\prime}\) & 5\({}^{\prime}\)-ATG CCA GTG AGC TTC CCG TTC AGC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences for quantitative real-time polymerase chain reaction (qRT-PCR).
## 2 Literature Review
The authors achieved an accuracy rate of 76.29% on the training dataset and 51.44% on the testing dataset. Table 1 illustrates the overview summary of the literature review section. After a thorough investigation of the literature, we discovered that there are no works that incorporate TinyML or model optimization in BSL detection or recognition. Additionally, there is no work related to quantizing the Yolov4-Tiny model, and very few works implement sentence generation in their research.
## 2 Materials and Methods
### Estimation of Total Antioxidant Capacity
#### 2.4.2 Ferric Reducing Antioxidant Power Assay (FRAP)  
Frozen tissue powder (200 mg) was extracted on ice with the addition of cold 80% EtOH for 15 min. After the extraction, samples were placed in a hot bath at 84 \({}^{\circ}\)C for 30 min. The homogenate was centrifuged at 22,000\(\times\)\(g\) and 4 \({}^{\circ}\)C for 15 min. The supernatant was used for further analyses of antioxidant capacity by FRAP. The FRAP assay is based on antioxidants as electron-donors in reaction with a yellow ferric 2,4,6-tripyridyl-s-triazine (Fe III TPTZ) complex, resulting in a blue-colored ferrous form (Fe II TPTZ). The intensity of the blue color, which is proportional to the reducing power of the antioxidants, was monitored spectrophotometrically at 593 nm. The reaction mixture for the FRAP assay consisted of 0.5 mM TPTZ and 1 mM FeCl\({}_{3}\):6H\({}_{2}\)O in acetate buffer (pH 3.6). The sample (10 \(\mu\)L) and reaction mixture (290 \(\mu\)L) were added to the wells of microtiter plates, mixed, and incubated at room temperature. After incubation, absorbance at 593 nm was measured using a microplate reader (Tecan, Spark, Mannedorf, Switzerland). Total antioxidant capacity was determined using a standard curve in which Trolox was used as a standard at a concentration range from 0.25 to 2 mM. The results are expressed in \(\mu\)mol Trolox equivalents per 100 g of fresh weight (FW).
## 2 Materials and Methods  
### Participants Recruitment and Ethical Approval  
Forty-two volunteers participated in the experimentation (Table 1).  
The population consisted of active men aged between 18 and 35 years old with no declared neurological or motor deficits. Participants were recruited through posters and communication among well-trained physical students and multisport practitioners. Volunteers were included in the experimental group and remunerated for their participation. This study was approved by the Leon Berard Centre's Research and Ethics Committee under the number A 13-160.  
In addition, to take into account the types of sports practiced by the subjects, we divided them into two categories: sports with endurance (END) characteristics (triathlon, running, cycling and trail running) and those without (climbing, volleyball, basketball, judo, water polo, etc.). We had 22 athletes in the NONEND group and 20 in the END group.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subjects** & **Mean** & **Standard Deviation (SD)** \\ \hline Age (years) & 25.81 & 4.92 \\ Height (cm) & 180.55 & 6.78 \\ Weight (kg) & 73.90 & 10.71 \\ Body mass index (kg/m\({}^{2}\)) & 22.59 & 2.27 \\ VO2 max (mL\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\)) & 55.74 & 7.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subjects’ characteristics for the incremental exercise test (\(n=42\)).  
### Participants Recruitment and Ethical Approval  
Each participant completed an IET. The purpose of the IET was to assess maximal aerobic power (MAP) and maximal oxygen consumption (\(\dot{\mathrm{V}}\)O\({}_{2}\)max).  
Prior to the test, a standardized warm-up allowed participants to familiarize themselves with the protocol and equipment. Each participant was asked to pedal constantly for 8 min: 2 min and 45 s at 50 W, and 5 min and 15 s, including six stages of 15 s each from 90 to 240 W (increasing by 30 W per stage), interspersed with 45 s intervals at 50 W basal power output. During the 5 min recovery period that followed, the participants were fully equipped before beginning the IET. This test was preceded by a 30 s period at 50 W before performing an incremental test until exhaustion. The test itself began at the power of 90 W and consisted of a series of two-minute increments of 30 W, with participants pedaling at their own frequency. No information on power or time was given during the test.  
No verbal encouragement was given during the test. The experimental session is presented in Figure 1.
###### Abstract  
The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OFSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OFSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OFSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OFSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.  
radiometric calibration; Zhuhai-1; micro-nano satellite constellation; hyperspectral; RadCalNet; vicarious calibration 2022
## 3 Results and Discussion
### Data Analysis of Sensors Calibration  
The relationship between the readings of air temperature from the developed device and the Testo 625 device is shown in Figure 3, and the relationship between the readings of air relative humidity from the developed device and the Testo 625 device is shown in Figure 4. It was found that the relationship between the readings was strong and linear, with coefficients of determination (R\({}^{2}\)) equal to 0.963 for air temperature and 0.987 for air relative humidity. Therefore, it became clear that it was possible to rely on the readings produced by the developed device to know the temperature and relative humidity of the atmosphere.  
Figure 3: Relationship between the readings of air temperature from the developed device and the Testo 625 device.  
To verify the readings of the weather sensors placed on the Arduino board, a comparison was made with what was available on the web pages of the measurements of weather conditions, such as air temperature, air relative humidity, and atmospheric pressure (Table 15) in Appendix A; it was noted that there were differences in the heights at which the measuring devices were placed; these devices included both the developed device and the standard devices in meteorological stations; the measurement locations also differed as they could be indoors or outdoors. The average values from the meteorological stations on the web pages for the values of air temperature, atmospheric pressure, and air relative humidity were compared with the general average of the actual measurements. The relative error (RE) between the readings was calculated, and the computed average relative error was about \(-13.22\%\) for atmospheric pressure, \(10.04\%\) for air temperature, and \(21.01\%\) for air relative humidity from data shown in Table 14 in Appendix A.
## 2 Materials and Methods
### Image Processing and 2D-KeyPoints Computing  
Once the videos for each participant in the test campaign were collected and the scores on the MDS-UPDRS scale noted for each of them, the locations of the KeyPoints for each subject in the acquired images had to be found to extract the subjects' gait features.  
Before being able to proceed to KeyPoint finding, it was necessary to perform pre-processing of input images to minimize mismatch and other sources of error in the following process. A combination of image enhancement filters, such as contrast enhancements, histogram equalization, sharpness and so on, was applied to the input images to increase the accuracy of OpenPose, improving the perceptibility of people in the scene by amplifying the difference between them and the backgrounds. Once the images were properly treated, we were able to proceed and detect the people in the images and recover their poses.  
For this task, we used the software package OpenPose developed in 2017 by CMU-Perceptual Computing Lab [31], a well-known tool for multi-person 2D pose estimation.  
OpenPose works with a bottom-up approach and uses a feed-forward multi-stage Convolutional Neural Network (CNN) to simultaneously predict a set of 2D confidence maps of human body-part locations and a set of 2D vector fields of part affinities (PAFs), which encode the degree of association between the body parts. Architecture details of the multi-stage CNN can be found in [31]. Finally, the confidence maps and the affinity fields are parsed by greedy inference to output the locations in the 2D image reference of the KeyPoints for each subject present in the image. There are several different models used to approximate the parts of the human body and to detect people in an image [31]. In the presented application, we used the BODY-25 model, which describes the pose of a human body through 25 KeyPoints (see Figure 2) and 24 parts that are segments that connect some of them. The accuracy of this pose estimation method depends on acquisition system calibration and the considered KeyPoint. In particular, for the estimation of the speed and types of movements taken into account in the present study, the positioning error was always considered to be below the value of 30 mm [38].  
Of particular importance to determine the parameters of the gait of the subjects involved in the tests was the correct identification of the KeyPoints of their trunk, shoulders, hips and feet. OpenPose tools use NVIDIA libraries CUDA 10.0 and cuDNN 7.2 (CUDA Deep Neural Network) [39], a GPU-accelerated library of primitives for deep neural networks. Our system ran on an HP Z6 workstation with an Nvidia Quadro RTX 8000 and 48 GB of GDDR6 memory. The OpenPose package was pre-trained and ready to process input images. Using the OpenPose library, our system computed, for each frame, the image coordinates of the 25 KeyPoints for each person who appeared within the scene.
# Advantage of Steerable Catheter and Haptic Feedback for a 5-DOF Vascular Intervention Robot System
## 4 Results  
The task-time was reduced by using the steerable catheter in Scenario 2 compared with the case of the commercial curved catheter in Scenario 1. Additionally, by applying the haptic function in Scenario 3, the average measured force could be reduced by allowing the user to manipulate the catheter without exerting an excessive force on the vessel wall.  
The experimental results for the different targets, which are presented in Table 2 and Figures 15-17, also indicate the following. For Target 1, the task-time was reduced by 8.07% when the steering catheter was used and was reduced by 49.07% when the haptic function was added. Additionally, the average measured force per second was reduced by 32.56% for the steering catheter and was reduced by
## 3 Satellite Characteristic Fundamentals
### Satellite Orbit Classification  
The main satellite classifications regard the orbits and altitudes in which they are placed (see Table 1); they can classified as [1]:  
* Geostationary Earth orbit (GEO): these satellites are placed over the equator with the same angular speed of the Earth. The main characteristic of these satellites is that they appear in the sky as fixed points, so they are able to service the same constant area, also called a footprint (approximately 43% of the Earth's surface). They have an altitude of 35.786 km and the same rotation and direction of the Earth, with a propagation delay of 250-280 milliseconds (ms). GEO satellites are more suited for fixed communications, but recently, they have also been used for providing services to mobile users;
* Non-geostationary Earth orbit (NGEO): belonging to this category are satellites that, due to Van Allen radiation belts, are placed in three different orbits and altitudes. They are: low Earth orbit (LEO), at a height between 500 and 1500 km of altitude and with an end-to-end propagation delay of about 20-25 ms; medium Earth orbit (MEO), at a height between 7000 and 25,000 km of altitude and with a delay of about 110-130 ms; and highly elliptical orbit (HEO), at a height between 400 and 50,000 km. LEO and MEO satellite orbits have the advantages of being closer to the Earth and having lower latency, but have also the disadvantage of needing several satellites, normally called a constellation, in order to cover the entire Earth's surface, introducing handover procedures in the communication towards the users.