esa-sceva/satcom-chunk-collection huggingface dataset:
# Accuracy of Code GNSS Receivers under Various Conditions
## 1 Introduction  
The GNSS (Global Navigation Satellite System) can determine a person's position anywhere in the world in navigation mode, while more precise measurements require additional input data (PPP--Precise Point Positioning) or additional reference stations (RTK--Real Time Kinematic). Currently operating GNSS systems include the American GPS system (Global Positioning System), the Russian GLONASS system (Tologalphasa), the Chinese BeiDou system, the Japanese QZSS (Quasi Zenit Satellite System), and the European Galileo system [1, 2]. The development of satellite systems is ongoing, which influences the continuous improvement of measurement techniques and accuracy of measurements, including in navigation solutions that use code receivers, in areas such as tourism or car navigation [3, 4, 5, 6].  
This study aimed to investigate the accuracy of GNSS code receivers under low-visibility conditions (tree-bounded terrain), in one session on the roof of a building with good sky visibility conditions, and the reliability and accuracy of solutions. This kind of research has not been conducted so far in such a complex and innovative way. To date, there has been no research analyzing the accuracy of the same receivers on three different fields:point, line, and circle. In addition to testing smartphone solutions, this study also assessed handheld GNSS receivers in GPS and GPS+GLONASS mode. Today, due to the widespread availability of smartphones and the growing popularity of watches with GNSS receivers, handheld receivers enjoy less and less interest. Another innovative aspect of the study was the use of a dual-system receiver, which is rare. This type of research can answer questions for professionals and laymen in many fields where the use of such receivers is important, such as land navigation, tourism, or forestry. For example, the study has great innovative potential to be applied to measurements in open-pit mining. In open-pit mines, there is a limitation on the horizon of the sky visibility zone. Therefore, the developed methodology should also be tested in the mining industry. Moreover, the majority of literature studies are based on the geographic/geodetic coordinates \(\varphi\), \(\lambda\), \(h\), which is not justified in practical use. The \(\varphi\), \(\lambda\), \(h\) system refers to geocentric coordinates, which are not natural and, in the field, difficult for the user to define. In this paper, the authors used the NEU system, which is directly related to the user's location and is easy to define in the field.  
For this purpose, a synchronous satellite measurement was performed using a precision geodetic receiver (in RTK mode) and 5 code receivers (3 smartphones, 2 handheld receivers, Figure 1).
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 2 Materials and Methods
### Samples Preparation and Experimentations  
CFRP UD lamina of thickness 0.125 mm was selected for preparing composite laminate for mechanical testing. Zund 1600 cutting plotter was used to cut laminae at 45\({}^{\circ}\) and 0\({}^{\circ}\) orientations, as shown in Figure 2. For good strength along the axial direction of a test coupon, stacking sequence \([0/+45/0/0/0/-45/0]_{3}\) was selected to prepare laminate for the tensile test coupon, whereas \([0/+45/0/0/0/-45/0]_{12}\) laminate was prepared for impact toughness and hardness test coupons. Both these laminates were prepared in the ISO class 8 cleanroom.  
The laminates were subjected to eight different CCs according to 23 full factorial design as shown in Table 2. It shows all eight combinations of three curing parameters. Vacuum bagging of laminates was carried out to avoid void formation during curing and exposure of epoxy to air, as shown in Figure 3. The curing was performed in an Irop Parma autoclave, as shown in Figure 4a,b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Symbols (Units)**} & \multicolumn{2}{c}{**Levels**} \\ \cline{3-4}  & & \(\mathbf{-1}\) & \(\mathbf{1}\) \\ \hline Curing temperature & \(T_{c}\) (\({}^{\circ}\)C) & 120 & 140 \\ Curing time & \(t_{c}\) (min) & 90 & 120 \\ Autoclave pressure & \(P_{au}\) (bar) & 3 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of designed curing parameters levels for experimentation.  
Figure 3: Vacuum bagging of the laminate before curing.  
Figure 2: CFRP laminae at 45\({}^{\circ}\) and 0\({}^{\circ}\) orientation on Zund 1600 cutting plotter.  
A total of 3 coupons were prepared from each of the laminate cured at 8 different CCs. Thus, 24 coupons for mechanical tests, i.e., tensile test (ASTM D3039), Charpy impact test (ASTM D6110), and Rockwell hardness test (ASTM D785), were prepared. The tensile test was conducted on a 300 kN UTM at a strain rate of 2 mm/min (Figures 5a and 6a), impact toughness was conducted on Charpy impact testing machine model JBS300 (Figures 5b and 6b), whereas hardness tests were conducted on a Rockwell hardness testing machine model TH300 (Figures 5c and 6c). The mechanical test results are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**CC**} & \(\mathbf{T_{c}}\) & \(\mathbf{t_{c}}\) & \(\mathbf{P_{au}}\) & \multicolumn{2}{c}{**Tensile Strength**} & \multicolumn{2}{c}{**Impact Toughness**} & \multicolumn{1}{c}{**Hardness**} \\  & **(\(\mathbf{{}^{\circ}}\)C)** & **(min)** & **(bar)** & **(MPa)** & **(MPa)** & **(\(\mathbf{j}\))** & **(HRB)** \\ \hline  
\hline \end{tabular}  
* MRCC/CC7.  
\end{table}
Table 2: 2\({}^{3}\) full factorial design and mechanical tests results. MRCC parameters and corresponding properties are represented by bold numbers.  
Figure 4: (a) Irop Parma autodave used for polymerization/ curing of CFRP laminates; (b) close-up of vacuum-bagged laminates after curing.
## 3 Results and Discussion  
### Protein Fractions of Jackfruit Leaf Flour and Protein Concentrate  
The protein fractionation of jackfruit leaf flour revealed that prolamin had the highest protein concentration and yield, followed by glutelin, globulin, and albumin (Table 1). The protein composition of jackfruit leaf flour differs from that of other plant leaves. For instance, _Moringa oleifera_ leaves are rich in glutelin [16], whereas leaves from alfalfa [15], cauliflower, broccoli, cabbage, and beetroot have a more abundant protein fraction of albumin [21]. However, the protein content and recovery of jackfruit flour fractions were greater than those of _Moringa oleifera_ flour [16]. The distribution of proteins in leaves and their contents may vary due to the crop, growth, and environmental conditions [22].  
In contrast, for the LPC fractions, the highest protein concentration and yield were obtained for glutelin \(>\) prolamin \(>\) globulin \(>\) albumin (Table 1). However, the protein fractions in LPC had significantly (\(p<0.05\)) higher protein content than flour. These findings suggest that obtaining protein concentrate followed by fractionation can improve the protein content. Leaf flour contains abundant material fibre (approximately 39% of carbohydrates), and proteins (about 24%) are retained within the fibre structure [11]. Furthermore, protein is difficult to separate from the leaf fibres during fractionation, which explains the low protein content in the flour fractions. On the contrary, a significant amount of fibrous material was removed during ultrasound-assisted extraction, resulting in higher protein content in the fractions in LPC.
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## 1 Introduction
### Possible Uses of the rCB  
Options for Recycling Pyrolysis Carbon Black:  
1. [wide=0.5 em,width=  
In the Composition of Mortars and Construction Mixtures:  
Activated carbon black, when added at a ratio of 2-5% of the mortar's mass, significantly enhances compressive strength and structural uniformity. Microcement, a popular binder globally, benefits from the activated carbon black, offering advantageous properties such as self-leveling and a high compressive strength. It finds applications in underwater concrete casting, tunnel walls' strengthening, and the production of thin-walled construction elements [20; 21; 22; 23; 24].  
6. As an Additive to Asphalt Concrete:  
Carbon black, including rCB, has been effectively employed to enhance the temperature stability of bitumen in asphalt concrete. This addition increases bitumen stiffness, durability against cracking, and resistance to rut formation at varying temperatures. Previous studies have indicated that incorporating 5-15% carbon black into asphalt concrete can boost wear resistance by up to 30%, particularly at higher temperatures [25; 26].  
In terms of properties, rCB from tire pyrolysis closely resembles the carbon black produced from oil or natural gas through conventional methods. The demonstrated benefits in various applications underscore the potential for sustainable recycling and reutilization of rCB in diverse industries.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 6 Generalized Analytical Method for Directed Acyclic Graphs
### Applying Our Method to Other Scheduling Algorithms  
Thus far, we assumed the EDF scheduling algorithm for the underlying RTOS scheduling. However, other scheduling algorithms such as RM are also widely used in the automotive industry.  
With this motivation, this subsection explains how we can apply our method to different scheduling algorithms. Fortunately, most real-time scheduling algorithms provide a schedulability analysis method based on the L&L utilization bound, where if the system utilization is less than or equal to a specific threshold value called a utilization bound, denoted by \(U_{B}\), the system is guaranteed to be schedulable. As noted earlier, \(U_{B}\) for EDF is 100%, whereas \(U_{B}\) for RM is 69.3%. Then, the schedulability condition is formally expressed as follows:  
\[U(p_{1},p_{2},\cdots,p_{n})=\sum_{i=1}^{n}\frac{e_{i}}{p_{i}}\leq U_{B}. \tag{63}\]  
Then, our optimization problem is slightly changed from Equation (54) to the following using \(U_{B}\) in the schedulability constraint:  
\[\begin{split}\underset{p_{1},p_{c},p_{n}}{\text{minimize}}& J(p_{1},p_{c},p_{n})=2\alpha p_{n}+2\beta(p_{1}+p_{c}+p_{n})\\ \text{subject to}& U(p_{1},p_{c},p_{n})=\frac{e_{1}} {p_{1}}+(n-2)\frac{e_{c}}{p_{c}}+\frac{e_{n}}{p_{n}}\leq U_{B}.\end{split} \tag{64}\]  
Then, its Lagrange function is also modified as follows:  
\[\begin{split}\mathcal{L}&=J(p_{1},p_{c},p_{n})- \lambda(U(p_{1},p_{c},p_{n})-U_{B})\\ &=2\alpha p_{n}+2\beta(p_{1}+p_{c}+p_{n})-\lambda\left(\frac{e_{ 1}}{p_{1}}+(n-2)\frac{e_{c}}{p_{c}}+\frac{e_{n}}{p_{n}}-U_{B}\right).\end{split} \tag{65}\]  
Solving the above Lagrange function yields the following solution:  
\[\begin{split} p_{1}&=\frac{e_{1}+\sqrt{(n-2)e_{1}e _{c}}+\sqrt{\frac{(\alpha+\beta)e_{1}e_{n}}{\beta}}}{U_{B}}\\ p_{c}&=p_{1}\sqrt{\frac{(n-2)e_{c}}{e_{1}}}\\ p_{n}&=p_{1}\sqrt{\frac{\beta e_{n}}{(\alpha+\beta )e_{1}}}.\end{split} \tag{66}\]  
From the above, (\(p_{2}\), \(p_{3}\), \(\cdots\), \(p_{n-1}\)) are decided by the assignment rule in Equation (50). Note that we can apply our method to any scheduling algorithm whose schedulability analysis can be conducted by the L&L utilization bound method.
# Dynamic Analysis of Modified Duffing System via Intermittent External Force and Its Application
## 2 Duffing Oscillator Description  
The Duffing equation has a wide range of practical applications, such as the mass-spring-damper system model, i.e.,  
\[m\ddot{x}+\beta^{\prime}\dot{x}-\left(k_{1}^{\prime}x-k_{2}^{ \prime}x^{3}\right)=F^{\prime}. \tag{1}\]  
If Equation (1) is divided by \(m\) on both sides, one has  
\[\ddot{x}+\beta\dot{x}-\left(k_{1}x-k_{2}x^{3}\right)=F, \tag{2}\]  
where \(\beta=\frac{\beta^{\prime}}{m}\), \(k_{1}=\frac{k_{1}^{\prime}}{m}\), \(k_{2}=\frac{k_{2}^{\prime}}{m}\), \(F=\frac{F^{\prime}}{m}\), \(m\) is the mass, \(\beta^{\prime}\) is the damping coefficient, \(k_{1}^{\prime}\) and \(k_{2}^{\prime}\) are the spring force coefficients, and \(F^{\prime}\) is the external force.  
Meanwhile, let \(y=\dot{x}+\beta x\); Equation (2) can be rewritten as  
\[\begin{cases}\dot{x}=-\beta x+y,\\ \dot{y}=k_{1}x-k_{2}x^{3}+F.\end{cases} \tag{3}\]  
If the external force is a periodic function \(F=A\cos\omega t\) (\(A\) is the amplitude, and \(\omega\) is the frequency.), and let \(m=1,\beta^{\prime}=0.2,\ k_{1}^{\prime}=0.6,\ k_{2}^{\prime}=0.1\), i.e.,  
\[\beta=0.2,\ k_{1}=0.6,\ k_{2}=0.1,\ A=1.8,\ \omega=1.6; \tag{4}\]  
then, System (3) can generate chaotic attractor, as is shown in Figure 1.  
Furthermore, without external force \(F\), System (3) is given by  
\[\begin{cases}\dot{x}=-\beta x+y,\\ \dot{y}=k_{1}x-k_{2}x^{3}.\end{cases} \tag{5}\]  
Obviously, by solving the following equations:  
\[\begin{cases}-\beta x+y=0,\\ k_{1}x-k_{2}x^{3}=0,\end{cases} \tag{6}\]  
the equilibrium points of System (5) are \((0,0)\), \(\left(\pm\sqrt{\frac{k_{1}}{k_{2}}},\pm\beta\sqrt{\frac{k_{1}}{k_{2}}}\right)\). Then, the Jacobi matrix at equilibrium point \((0,0)\) is  
\[J_{(0,0)}=\left[\begin{array}{cc}-\beta&1\\ k_{1}&0\end{array}\right], \tag{7}\]  
and the corresponding eigenvalues of Jacobi matrix \(J_{(0,0)}\) are  
\[\lambda_{1,2}=\frac{-\beta\pm\sqrt{\beta^{2}+4k_{1}}}{2}. \tag{8}\]  
If \(\text{Re}\left(-\beta\pm\sqrt{\beta^{2}+4k_{1}}\right)<0\), then System (5) is asymptotically stable at equilibrium point \((0,0)\).  
Similarly, for equilibrium points \(\left(\pm\sqrt{\frac{k_{1}}{k_{2}}},\pm\beta\sqrt{\frac{k_{1}}{k_{2}}}\right)\), if \(\text{Re}\left(-\beta\pm\sqrt{\beta^{2}-8k_{1}}\right)\)\(<0\), then System (5) is asymptotically stable.  
Figure 1: Chaotic attractor of System (3) with parameters in Equation (4).  
However, with external force \(F\), there is no equilibrium point in System (3), and it may behave with periodicity or chaos. The next section discusses the dynamic characteristics for this case.
## 2 Characteristics of RS  
RS is produced when harvesting rice crop, and it can be collected from the field once it contains a dry matter of more than 75%.  
RS looks like flat fibers having approximate dimensions of 20-60 cm in length and 0.5 cm in width [4].  
The general characteristics of RS such as total solids (TS), volatile solids (VS), total carbon (TC), total nitrogen (TN), and C/N ratio are usually reported as a single value or mean values with a variation in the literature. The original data is further processed to generate median, maximum, minimum, and quartiles as shown in Figure 2. Box plots of TS and VS are smaller, and the whiskers do not extend as far, indicating that these properties do not differ much over the various analyzed studies, while the C/N ratio is different for the RS used in these studies with spread-out data points [10; 26; 28; 36; 37; 38; 39; 40; 41; 42].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **[PERSON] et al. [11]** & **This Review** \\ \hline Environmental concerns & \multicolumn{3}{c}{Emissions of greenhouse gas from} \\  & \multicolumn{3}{c}{rice fields} \\ \hline Product & & & Mass balance of nutrients and their distribution into liquid digestate (LD) and solid digestate (SD) in the AD of RS \\ \hline Potential & \multicolumn{3}{c}{Biogas production potential of RS} & AD of RS at various scales \\ \hline  & \multicolumn{3}{c}{Effect of the following parameters} & Effect of the following factors and methods on the enhancement of energy \\  & \(\bullet\) & Temperature & recovery from the AD of RS \\  & \(\bullet\) & pH & \(\bullet\) & Temperature \\  & \(\bullet\) & Nutrients balance & \(\bullet\) & Co-digestion \\ Influence & \(\bullet\) & Total solids & \(\bullet\) & Inoculum \\  & & & \(\bullet\) & Mixing \\  & & & \(\bullet\) & Organic loading rate (OLR) \\  & & & \(\bullet\) & Recycling LD \\  & & & \(\bullet\) & Addition of trace elements (TEs) \\  & & & \(\bullet\) & Bioavailability of TEs \\ \hline  & \multicolumn{3}{c}{Effect of the following} \\  & \multicolumn{3}{c}{pretreatment alternatives to} \\  & \multicolumn{3}{c}{enhance biogas potential of RS} \\ Pretreatment & \(\bullet\) & Mechanical size reduction & \\  & \(\bullet\) & Biological treatment & \\  & \(\bullet\) & Acid treatment & \\  & \(\bullet\) & Alkaline treatment & \\ \hline \hline Microorganisms & \multicolumn{3}{c}{Effect of various microorganisms} \\  & \multicolumn{3}{c}{on the AD of RS} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aspects of anaerobic digestion (AD) of rice straw (RS) discussed in both studies.  
Other nutrients and TEs, like Phosphorus (P), Calcium (Ca), Magnesium (Mg), Potassium (K), Sodium (Na), Nickel (Ni), Copper (Cu), Zinc (Zn), Iron (Fe), etc., are usually not analyzed in the RS, except in two studies (Table 2).
# Interactive Part Segmentation Using Edge Images
## 1 Introduction
The edge image, which consists of a single channel, has one disadvantage in that it is difficult to distinguish the class of object in the selected area because there is no texture information, but it can be suitable as training data for the interactive (object) segmentation models. The ability to distinguish between the area of the target object and background is important because it is important for an interactive segmentation model to accurately segment the target area rather than to recognize what it is. Thus, instead of texture information that can recognize the classes of objects, the edge images can be more suitable for an interactive segmentation model than color images. Edge images generated by the classic edge detectors such as Canny Edge [4] are difficult to use in real applications due to a large amount of noise, but with the advent of edge detectors using CNN models, relatively accurate edge images can be obtained. Existing deep-learning models are trained the feature of the color texture of the image in the training dataset. These models show good performance in the evaluation dataset because they are evaluated with color images obtained under similar environment conditions, but when they are applied in the actual industry, the desired performance \begin{table}
\begin{tabular}{c c} \hline \hline  & **f-BRS [2]** & **IOG [3]** \\ \hline Object Segmentation (Motor) & **f-BRS [2]** & **IOG [3]** \\ \hline Part Segmentation (Wheel) & **f-BRS [2]** & **IOG [3]** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of estimated masks for segmenting motorcycle (object) and wheel (part). sometimes is not achieved because the environment is different. On the other hand, an edge image is an image in which the same characteristics are refined even in color images taken under different environments. In this paper, we will examine whether similar performance can be achieved only with edge images instead of color images in interactive object or parts segmentation, which is one of the tasks sensitive to color texture characteristics. One of the contributions of this paper is to find an edge image suitable for an input image of an interactive segmentation model. In this paper, suitable edge images are found through experiments to accurately segment the target object. We conducted the second experiment to compare with the proposed model trained with the edge images which were selected based on the results from the first experiment and the existing interactive object segmentation model. The proposed models were trained with an object segmentation dataset (e.g., PASCAL VOC 2012 [5] or SBD [6]), and these were evaluated by a part segmentation dataset. Here we used PASCAL VOC Part [7] dataset for evaluation. Experimental results showed that the proposed model trained with edge images could segment the target part more accurately compared to the existing segmentation models trained with color images.
### Open Urban Platforms  
Open Urban Data Platforms are used to ease the use and analysis of urban data, which are mostly collected within an IoT network. The collected data are often combined with measurements and readings from other sources to produce valued services such as application or city management systems.  
According to the German pre-standard DIN SPEC 91357 [5], an Open Urban Platform (OUP) is characterized as follows:  
* They assist in the implementation of logical reference architecture following design principles of open APIs that supports data flows within and across city systems as well as enriching the raw data streams to generate smart data as required by the consuming entities;
* They exploit modern technologies to harvest, collect, and analyze urban data and provide the results to citizens and enterprises, e.g., sensor nodes and other IoT devices, cloud services, mobile connectivity, machine learning for analytics, and publishing and sharing via social media and APPs;
* They provide the building blocks that enable cities to rapidly shift from fragmented and isolated operation of individual infrastructures towards an integrated approach by connecting the systems via a platform, including cross-domain data analytics for predictions, forecasts, or better insight, and novel ways of engaging and serving city stakeholders offering smart services, both public and commercial.
# A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems
## 5 Comparison with the Literature  
Comparison with the current literature is not a trivial operation because there are no other systems which work well with both laying and sitting postures recognition. Moreover, custom HW implementations are tailored for a specific application or for a specific support, obviously, and require less physical resources than are proposed. This happens, for example, with the sitting posture recognition in [27], specifically designed to use six flexible sensors applied at the armrests, backrest and seat of a chair, in conjunction with a very compact two-layer Artificial NN (ANN) to classify seven sitting positions, representing the state-of-the-art solution for this specific problem. However, although the Artificial NN in [27] requires less physical resources than the FCN, (755 slice reg., 1822 FFs and 649 LUTs), it consumes more power (7.33 mW) with a much higher processing time of 267.5 us to classify one posture less than the proposed one with an average accuracy of 97.43%.  
With reference to laying posture recognition, the proposed FCN obtains an average accuracy value of 96.77% to classify 17 laying postures in real-time exploiting 108 sensors, and a throughput of 9.13 kHz. The state-of-the-art in this case is represented by the recent work in [36], dealing with in-bed posture recognition, exploiting a microcontroller unit to implement a very complex ResNet composed by 17 CONV layers, two MaxPool and three FC layers, in order to obtain an average classification accuracy of 95.08% by using 1024 force sensitive resistor sensors.  
In order to explore the possibility to embed the proposed accelerator for in-sensor circuitry, analysis with a conservative TSMC LP-HVT CMOS 130 nm technology has been done, which is compatible with that of the glue logic in modern MEMS. Synthesis results, by using the Cadence tool-chain of the larger configuration for lying posture recognition, return a power dissipation of 425 uW/MHz and 1.7 mW, respectively, at the maximum operating frequency of 40 MHz, and an area occupation 1.78 mm\({}^{2}\) and which support in real-time an ODR up to 8.7 kHz. All the above results overcome the state-of-the-art for this kind of system.