esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 2 Background  
### System Model  
We consider the preemptive scheduling of periodic task sets \(\tau=\{\tau_{1},\tau_{2},\ldots,\tau_{n}\}\) with unrestricted task migration on \(m\) identical processors (or cores). Task \(\tau_{i}\) is a periodic task denoted by \(\tau_{i}=(c_{i},p_{i})\), where \(c_{i}\) is an execution time and \(p_{i}\) is a period. Task \(\tau_{i}\) periodically releases a job \(\tau_{i,j}\) at time \((j-1)p_{i}\) and the job requires the execution of, at most, \(c_{i}\) by its deadline \(d_{i,j}(=jp_{i})\) for \(j=1,2,\ldots\) The utilization of \(\tau_{i}\) is defined as \(u_{i}=c_{i}/p_{i}\). The total utilization of \(\tau\) is \(U(\tau)=\sum u_{i}\). The tasks are assumed to be indexed in the order of non-increasing utilization, i.e., \(u_{i}\geq u_{i+1}\). A task set \(\tau^{(i)}\) is defined as a subset of \(\tau\), consisting of \((n-i+1)\) tasks of lower utilization, that is, \(\tau^{(i)}=\{\tau_{i},\tau_{i+1},\ldots,\tau_{n}\}\). The amount of remaining execution of job \(\tau_{i,j}\) at time \(t\) is \(r_{i,j}(t)\). Laxity is the amount of time for which a job can idle safely without missing its deadline. We denote the laxity of \(\tau_{i,j}\) at time \(t\) by \(lx_{i,j}(t)=d_{i,j}-t-r_{i,j}(t)\). The notations are summarized in Table 1.  
We assume preemptive scheduling, i.e., any job can be preempted by a higher priority job at any time. We also assume that task migration is allowed, i.e., a preempted job can be resumed later on any available processor and the cost is negligible. A job cannot be executed on more than one processor at the same time and each processor cannot execute more than one job at the same time.
# IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures
## 4 Use Cases
### Fan Control Scenario  
We deployed this app for a small factory that produces extractor chimneys. The final goal was to turn their traditional chimneys into smart appliances by allowing remote control from a mobile application. The deployed architecture is shown in Figure 4.  
Since the product was designed to be used by final users, we had several conditions to meet:  
Figure 4: Fan control scenario architecture.  
Figure 3: Support Library UML class diagram.  
* The additional hardware cannot be expensive in order to avoid a significant increase in the market price of the chimney;
* The chimney needs to be accessible both at home, i.e., from the local WiFi network, or from outside, i.e., from the internet;
* Configuration and installation steps need to be as easy as possible, without the need of an IT expert.  
Since the chimney has six fan speeds, we represented them as six actuators on the Embedded System. In addition, we connected two sensors to monitor temperature and humidity in the proximity of the chimney. This allows us to create simple rules to change the speed of the fan based on the chimney status. For example, \"turn on the chimney when the temperature is higher than 28 \({}^{\circ}\)C.\"  
For the Embedded System, we used Arduino Mega 2560 because it has a good balance between RAM size (256 KB) and price. To enable system discovery over the network, we used the Zero Configuration Network protocol by using a Bonjour implementation for Arduino ([[https://github.com/adafruit/Adafruit_CC3000_Library](https://github.com/adafruit/Adafruit_CC3000_Library)]([https://github.com/adafruit/Adafruit_CC3000_Library](https://github.com/adafruit/Adafruit_CC3000_Library)), accessed on 15 October 2021).  
For the Message Broker, we opted for Software-as-a-Service (SaaS) solution, PubNub. This provides two important benefits: (a) since it is based on cloud architecture, it offers potentially unlimited scalability, and (b) it reduces the costs of dedicated hardware and maintenance.  
Finally, for the remote control, we implemented different client versions: a Java desktop application and two mobile versions (one for Android devices ([[https://play.google.com/store/apps/details?id=it.unibas.ardomotica](https://play.google.com/store/apps/details?id=it.unibas.ardomotica)]([https://play.google.com/store/apps/details?id=it.unibas.ardomotica](https://play.google.com/store/apps/details?id=it.unibas.ardomotica)), accessed on 15 October 2021) and one for iOS devices). All of them were developed by using the supporting library, allowing developers to send commands to the Embedded System without knowing any technical details of the board and its physical connections or on the message format.
# Study of the Preparation and Dielectric Property of PP/SMA/PVDF Blend Material
## 3 Result and Discussion  
### Structure Analysis  
Figure 1 shows the XRD pattern of PP/PVDF blends. The diffraction peaks of pure PVDF are at 20 = 18.3\({}^{\circ}\), 19.9\({}^{\circ}\), and 26.5\({}^{\circ}\), respectively, while that of PP are at 20 = 14\({}^{\circ}\), 19.9\({}^{\circ}\), and 18.5\({}^{\circ}\), respectively [8; 17]. It can be seen from Figure 1 that the diffraction peaks of PP and PVDF coexist after their blending. The diffraction peak of PP was enhanced with the increase of PP, and the \(\beta\)-crystal form diffraction peak of PVDF gradually weakened. The diffraction peak of PP started to emerge  
since PP/PVDF blends contained 40% PP. New phases did not appear among the above blending proportions (20/80, 40/60, 60/40, 80/20), which means that the crystal structure remained constant.  
Figure 2 shows the XRD pattern when PVDF and PP are mixed at same volume proportion and the SMA content is 0%, 3%, and 5%. It can be seen from the figure that adding a small amount of SMA compatibilizer increases the voltage of PVDF applied to blends, facilitates the electric domain diversion of PP, and thus improves the polarizability of PVDF in blend material and finally achieves a good polarization effect under the condition of low polarized voltage. With the continuous addition of SMA, it can be observed that when the content was 5%, the composite material could hardly apply voltage and the leak current showed excellent polarizability.  
Figure 3 is the SEM image of PP/PVDF blends. It can be seen from the figure that the PP particles are uniformly distributed in PVDF, and the structure of the blends is relatively compact. When PP content was relatively high, PVDF particles were completely wrapped by PP. However, the distinction between PP and PVDF was obvious.  
Figure 1: X-ray diffraction (XRD) pattern of polypropylene/polyvinylidene fluoride (PP/PVDF) blends.  
Figure 2: XRD pattern of PP/SMA/PVDF blends. SMA: styrene maleic anhydride copolymer.  
Figure 4 is the SEM image of PP/SMA/PVDF blends with 3 wt % SMA. As indicated in Figure 4, the interface of the blend systems is not visible. This can be explained by the in situ reaction at the interface between PP and PVDF phases, and this reaction creates PP-g-SMA, which reduces the interfacial tension and the size of dispersed phase, facilitates diffusion, promotes the interaction and permeation between phases, and improves the structure form. Meanwhile, PP-g-SMA improves the interface bonding between components.
## 5 Energy Consumption on Active Tilting System
### Energy Consumption in Real World Condition
#### 5.3.2 Energy Consumption in AMS Magazine Evaluation Course  
As shown on the left of Figure 36, the energy consumption rate has sharp peaks at intersections and at tight corners, and the accumulated consumed energy is less than 6 kJ in 5000 s. This amount is fairly small.  
Figure 34: Consumed and saved energy on slalom course (ccc = 0.7).  
Figure 35: AMS evaluation course as typical European driving route.  
As shown on the right of Figure 36, energy saving caused by avoidance of cornering drag occurs much more frequently than energy consumption for tilting. This means that the steering wheel holding time on cornering sections is much longer than the steering input and return.  
There is a difference in both energy rates and a larger difference in accumulated energy amounts. The accumulated energy saving against cornering drag reaches 76 kJ in 5000 s.  
As shown in Figure 37, there is about 13 times difference between the two accumulated energies, and it shows no need to worry about energy consumption for active tilting mechanisms. High precision in the simulation model is not required at all for this definite result.  
As shown in Table 2, the total mass of the vehicle including one passenger of this PMV is about 370 kg, and the total vertical load is about 3600 N. Assuming the tire rolling resistance coefficient (RRC) is approximately \(80\times 10^{-4}\), tire rolling resistance is approximately 29 N. With this rolling resistance, 2700 kJ of energy is consumed on the 92.5 km course.  
This energy, 2700 kJ, is about 36 times the energy consumption by cornering drag and 450 times the energy consumption for the tilting mechanism, as shown in Figure 37. Energy consumption for driving PMVs is overwhelmingly dominated by tire rolling resistance if acceleration/deceleration and air drag are not considered.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 5 Conclusions  
This study presents a comprehensive evaluation of the linearity and repeatability of thin film force-sensitive resistor (FSR) sensors, specifically focusing on the Flexiforce A201-1 and Interlink FSR-402 models. Through rigorous static load testing, we confirmed their reliability and suitability for precise force measurement applications.  
The findings of this research have significant practical implications. When precisely calibrated, thin film FSR sensors can be highly reliable for use in medical devices, robotics, and consumer electronics. Their cost-effectiveness, flexibility, and ease of installation make them an attractive option for force and pressure sensing across various fields. However, our study also underscores the necessity for further research to optimize calibration techniques and explore new materials and sensor designs to enhance linearity and repeatability. Additionally, investigating the performance of these sensors under dynamic loading conditions could broaden their applicability to more complex scenarios.  
Key points from our study include the following:  
* High Linearity and Repeatability: Both the Flexiforce A201-1 and Interlink FSR-402 sensors demonstrated excellent linearity and repeatability, ensuring reliable and accurate force measurements.
* Comparison of Sensors: Interlink FSR-402 exhibited slightly higher conductivity compared with Flexiforce A201-1, highlighting differences in material composition and design.
* Calibration Methodology: Utilizing a voltage divider circuit and multiple fixed resistances, we derived calibration equations that significantly enhance measurement precision.
* Practical Applications: Precisely calibrated thin film FSR sensors are suitable for use in various applications, including medical devices, robotics, and consumer electronics, because of their cost-effectiveness and flexibility.  
In conclusion, thin film FSR sensors, when accurately calibrated, offer a reliable and precise solution for force measurement. This study provides valuable insights and methodologies that contribute to improving the performance of FSR sensors, facilitating their broader adoption in technological and industrial applications.  
Conceptualization, B.K. and K.L.; methodology, J.J.; software, K.L.; validation, K.L. and B.K.; formal analysis, K.L.; investigation, B.K.; writing--original draft preparation, B.K.; writing--review and editing, B.K.; supervision, B.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by research grants from Daegu Catholic University in 2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 4 Trends and Prospects of Carbon Emission Research on Biomass Combustion
### Detailed Regional Studies and Applications on a Global Scale  
(1) Regional-scale research: In light of the findings of global-scale research, regional-scale research has gradually become a focal point in its own right. For example, studies on biomass burning emissions in the Amazon rainforest and Sub-Saharan Africa have not only revealed the characteristics of burning emissions in different ecosystems but also provided a scientific basis for environmental protection and policy formulation in these regions [3]. [PERSON] et al. employed the regional model NCAR to simulate fire activity in Sub-Saharan Africa and indicated that these fires were responsible for 25% of global atmospheric CO\({}_{2}\) concentrations [44]. (2) Application and extension at the global scale: While regional studies are indispensable, the prevailing emphasis in the context of global climate change research remains on the application of these findings to the monitoring of carbon emissions on a global scale. Global biomass burning emission inventories, such as GFED and FINN, have been implemented globally and provide fundamental data supportfor global carbon cycle research. It is imperative that future research endeavors to enhance the precision of these inventories and integrate a greater quantity of real-time observational data [39].
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 2 Related Works
### The AMBTC Method  
The AMBTC method [8] compresses image blocks into two quantized values and a bitmap. The detailed approaches are as follows. Let \(I\) be the original image of size \(w\times h\) and partition \(I\) into non-overlapping blocks \(\{l_{i}\}_{i=0}^{N-1}\) of size \(n\times n\), where \(N=(w/n)\times(h/n)\) is the total number of blocks. Let \(I_{i,j}\) be the \(j-\)th pixel of \(i-\)th block. Therefore, \(I_{i}=\left\{I_{i,j}\right\}_{j=0}^{n\times n-1}\). For block \(I_{i}\), the averaged value \(m_{i}\) can be calculated by:  
\[m_{i}=\frac{1}{n\times n}\sum_{j=0}^{n\times n-1}I_{i,j}. \tag{1}\]The \(j-\)th bit of bitmap \(B_{i}\), indicated by \(B_{i,j}\), is used to indicate the relationship between \(I_{i,j}\) and \(m_{i}\). \(B_{i,j}\) can be obtained by:  
\[B_{i,j}=\begin{cases}0,&I_{i,j}<m_{i};\\ 1,&I_{i,j}\geq m_{i}.\end{cases} \tag{2}\]  
The lower quantized value \(a_{i}\) and higher quantized value \(b_{i}\) are obtained by averaging the pixels in \(I_{i}\) with values smaller than and larger than or equal to \(m_{i}\), respectively. This can be implemented by sequentially visiting pixels in \(I_{i}\). The lower quantized value \(a_{i}\) is obtained by calculating the averaged value of visited pixels with values smaller than \(m_{i}\). Similarly, the higher quantized value \(b_{i}\) is the averaged values of the other pixels. Therefore, the compressed code \(\Phi_{i}\) of \(I_{i}\) is \(\{a_{i},b_{i},B_{i}\}\). Each block is processed using the same manner, and the AMBTC compressed codes \(\{\Phi_{i}\}_{i=0}^{N-1}=\{a_{i},b_{i},B_{i}\}_{i=0}^{N-1}\) of image \(I\) are then obtained.  
To decode \(\{\Phi_{i}\}_{i=0}^{N-1}=\{a_{i},b_{i},B_{i}\}_{i=0}^{N-1}\), blocks \(\{I^{\prime}_{i}\}_{i=0}^{N-1}\) of size \(n\times n\) are prepared, where \(I^{\prime}_{i}=\left\{I^{\prime}_{i,j}\right\}_{j=0}^{n\times n-1}\). The \(j-\)th pixel of \(I^{\prime}_{i}\) can be decoded by:  
\[I^{\prime}_{i,j}=\left\{\begin{array}{ll}a_{i},&B_{i,j}=0;\\ b_{i},&B_{i,j}=1.\end{array}\right. \tag{3}\]  
After all of the image blocks are reconstructed, the image \(I^{\prime}\) can then be obtained.
## 2 Cloud Radar Prototype  
### Measurement Principle  
The radar developed in this work is of the Frequency-Modulated Continuous-Wave (FMCW) type, similar in principle to the cloud radar described in [12]. As in most linearly modulated radars, an up-chirp waveform is transmitted, while the receiver collects the echo signal (see Figure 2). It follows that the range (r) to a target is proportional to the frequency difference between transmitted and received signals following Equation (1):  
\[r=\frac{F_{d}T_{rep}c}{2B}, \tag{1}\]  
where \(F_{d}\) is the frequency difference, \(T_{rep}\) is called the repetition period, \(c\) is the speed of light, and \(B\) is the modulation bandwidth.  
For FMCW radars, the range resolution (\(\Delta r\)), the minimum discernible distance between targets, depends on the modulation bandwidth according to Equation (2). However, the actual range resolution can be degraded due to non-ideal factors in FMCW radars [15; 16]:  
\[\Delta r=\frac{c}{2B}. \tag{2}\]  
The wider the bandwidth, finer cloud structures could be resolved in the radar height profiles. On the other hand, the maximum unambiguous range (\(r_{a}=T_{rep}\cdot c/2\)) depends on the repetition period. Therefore, the modulation bandwidth and the transmitted power have to be selected properly to guarantee enough points inside the unambiguous range.  
For cloud radars operating within the Rayleigh Scattering Regime, the received power \(P_{r}\) from a target at range \(r\) is related to the target radar reflectivity (\(Z\)) by the meteorological radar equation as presented in [17]:  
\[10\cdot log(P_{r})=10\cdot log(Z)-20\cdot log(r)+C_{radar}, \tag{3}\]  
where the radar calibration constant (\(C_{radar}\)) depends on the radar parameters and the dielectric characteristics of the target. The calibration constant specific to this radar will be estimated in Section 3.2.
## 2 Research on UAV Path-Planning Problem
### Fitness Function
#### 2.4.4 Stability Cost  
The stability of the UAV's flight path is very important, as large fluctuations during flight can easily cause an unstable attitude and increase the wear and tear of the equipment, thus shortening the life of the UAV. The smoothness of a path largely reflects its stability, which can be measured by yaw and pitch angles; the smaller the angle, the more stable the path. Figure 3 shows the flight path from the path node \(W_{j-1}\big{(}x_{j-1},y_{j-1},z_{j-1}\big{)}\) to the path node \(W_{j+1}\big{(}x_{j+1},y_{j+1},z_{j+1}\big{)}\) in a 3D coordinate system, and \(W^{\prime}_{j-1},W^{\prime}_{j},W^{\prime\prime}_{j+1}\) are the projection points of \(W_{j-1},W_{j},W_{j+1}\) in the \(X-Y\) plane, respectively. The angle \(\psi_{j}\) formed by the intersection of the extended line of line segment \(W^{\prime}_{j-1}W^{\prime}_{j}\) and line segment \(W^{\prime}_{j}W^{\prime}_{j+1}\) is the yaw angle, which is calculated as follows:  
\[\psi_{j}=arctan\left(\frac{\left\|W^{\prime\rightarrow}_{j-1}W^{\prime}_{j} \times W^{\prime}_{j}W^{\prime}_{j+1}\right\|}{W^{\prime}_{j-1}W^{\prime}_{j} \cdot W^{\prime}_{j}W^{\prime}_{j+1}}\right) \tag{20}\]  
The path point \(W^{\prime\prime}_{j+1}\) is the projection of onto the plane where the line segment \(W_{j-1}W_{j}\) is located. The angle \(\gamma_{j}\) formed by line segment \(W_{j+1}\) and line segment \(W_{j}W^{\prime\prime}_{j+1}\) is the pitch angle, which is calculated as follows:  
\[\gamma_{j}=arctan\left(\frac{z_{j}-z_{j-1}}{\left\|W^{\prime^{\prime}}_{j-1}W ^{\prime}_{j}\right\|}\right) \tag{21}\]  
The smoothness of the paths in this paper is measured by computing the second-order difference in the angular change (the acceleration of the angular change):  
\[\begin{cases}f^{\prime}_{4}=\sum_{i=2}^{k-1}\left|\psi_{j+1}-2\psi_{j}+\psi_{j -1}\right|\\ f^{\prime\prime}_{4}=\sum_{j=2}^{k-1}\left|\gamma_{j+1}-2\gamma_{j}+\gamma_{j -1}\right|\end{cases} \tag{22}\]  
\(k\) is the total number of path nodes, \(f^{\prime}_{4}\) and \(f^{\prime\prime}_{4}\) and are the smoothing of yaw and pitch angles, respectively. The total smoothness is:  
\[f_{4}=f^{\prime}_{4}+f^{\prime\prime}_{4} \tag{23}\]  
To summarize, the expression for the fitness function \(F_{t}\) for each path is:  
\[F_{t}=\sum_{j=1}^{4}I_{i}f_{i} \tag{24}\]  
Figure 3: A schematic of the drone’s turning angle.  
\(l\) is the weight coefficient and satisfies \(l=\sum_{i=1}^{4}l_{i}=1\); in this paper, we take \(l_{1}=0.6\), \(l_{2}=0.2\), \(l_{3}=0.1\), \(l_{4}=0.1\).
## 3 Related Work
### Reliability in WSNs  
Various studies [22; 23; 24; 25] have been done in reliability analysis of homogeneous WSNs, but reliability analysis of backbone enabled WSNs is an unexplored domain by researchers so far. In [26] the authors have introduced the concept of hotlines to improve the reliability for WSNs. They have proved analytically that use of hotlines noticeably increases the reliability in WSNs. Most of the previous work, especially in the backbone approach area, focuses on energy efficiency in WSNs whereas we have combined versatile concepts, like energy aware routing, energy aware reactive routing, multipath routing and hotlines, to simultaneously enhance both reliability and energy efficiency in a WSN. Moreover, our paper suggests a brand new concept of WEAMR for WSNs to support the end-to-end delay requirements of different applications.
## 2 Methods and Principle  
### Random Signal Autoregressive Model  
An autoregressive model (abbreviated AR model) is analyzed by time series. The information within the system state can be reflected by identifying model parameters. The accurate ordering of descriptors in the AR model can express the system state intensively and accurately in the objective laws of a dynamic system. In addition, studies have shown that the autoregressive parameters of the AR model are very sensitive to the state change law.  
The AR model is a linear model. The model uses a difference equation to describe the random sequence. The relationship between the sequence points of different signals is different, so different models are obtained.  
\(\rm{AR(n)}\) model is  
\[\rm{x(k)}=\varphi_{1}x(k-1)+\varphi_{2}x(k-2)+\cdots+\varphi_{n}x(k-n)+a(k),\;a(k )\;\sim NID\Big{(}0,\sigma_{a}^{2}\Big{)} \tag{1}\]  
In formula (1), \(\rm{x(k)}\) is observation, \(\rm{k}\) is time series, \(\rm{k}=1,2,\cdots,N\), \(\rm{a(k)}\) is zero-mean Gaussian white noise, \(\rm{n}\) is the model order. The AR model describes the relationship between the value of the \(\rm{k}\)-th point in the random sequence \(\rm{\{x(k)\}}\) and the previous adjacent \(\rm{n}\) points, and uses a zero-mean Gaussian distribution error term \(\rm{a(k)}\) to represent the uncertainty of the relationship.