esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Jitterless Execution--Test Case  
In order to illustrate the jitterless execution of a task set scheduled with P_FENP_MC and to compare the task execution with that under other scheduling algorithms, we provide an example of three mixed criticality tasks scheduled on a dual-criticality system with one processor. Table 6 contains the timing parameters of the tasks.  
Scheduling for both the Hi- and Lo-criticality modes is illustrated in Figure 10.  
The jitter of a task is calculated as the difference between the maximum and minimum separation between two consecutive jobs of the same task \(M_{i}\)[33] and is given by (18):  
\[Jitter(M_{i})=\max_{k\geq 1}\left\{\big{|}\ J_{i,k}-J_{i,k+1}\big{|}\right\}- \min_{k\geq 1}\left\{\big{|}\ J_{i,k}-J_{i,k+1}\big{|}\right\} \tag{18}\]  
where \(J_{i,k}\) is the \(k\)th job of task \(M_{i}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & \(T_{i}\) & \(D_{i}\) & \(L_{i}\) & \(C_{i,L_{i,o}}\) & \(C_{i,L_{i,u}}\) \\ \hline \(M_{1}\) & 8 & 8 & Hi & 2 & 5 \\ \(M_{2}\) & 12 & 12 & Lo & 1 & - \\ \(M_{3}\) & 16 & 16 & Lo & 2 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Task set example.  
Figure 9: Success ratio by varying the number of processors. \(U_{L}=0.05,\ U_{U}=0.75,\ Z_{L}=1,\ Z_{U}=4\).  
Table 7 contains the jitter values for the task set example scheduled by P_FENP_MC, P-EDF-VD [13] (non-preemptive variant), TT-Merge, and Energy-efficient TT-Merge.  
As can be seen from the table above, three of the algorithms (P_FENP_MC, P-EDF-VD, and Energy-efficient TT-Merge) provided jitterless execution for the first task (\(M_{1}\)), but only P_FENP_MC delivered a scheduling table for jitterless execution of all the tasks in the system.
## 1 Introduction
To clarify the effects of the online sentiment data and the CNN-LSTM model on car sales predictions, three models were created for forecasting car sales movement directions in Taiwan. The first \"classical\" model, using the historical sales data as predictor variables and MLR as the prediction model, was created as the performance baseline of forecasting of car sales movement directions in Taiwan. The MLR is a generalized logistic regression for solving problems with more than two classes [22; 30]. Then a \"sentimental\" model was created by adding the multi-channel online sentiment data as the predictor variables to the classical model so as to verify the effects of online sentiment data on prediction performance. Finally, a \"CNN-LSTM\" model was created by replacing the MLR method in the sentiment model with the CNN-LSTM method proposed in this research to verify the effects of the latter method on prediction performance. The performance comparison of the three prediction models showed that the forecasting accuracy of car sales movement directions in Taiwan was effectively improved by the use of online sentiment data and the CNN-LSTM model. This paper is organized as follows. Section 1 states the relevant topics of this research and reviews the literature related to the research problem. Section 2 elaborates the creation process of the three prediction models for forecasting car sales movement directions in Taiwan. In Section 3, the results of the three prediction models are compared and analyzed to verify the effects of the online sentiment data and the CNN-LSTM model on the forecasting of car sales movement directions in Taiwan. Finally, the important findings, discussions, and suggestions for further research are summarized in Section 4.
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
###### Abstract  
With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing process. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data.  
intelligent coal mine communication system; mine information recovery; mine intelligent receiver; deep learning; end-to-end signal processing Article
## 4 Discussion
### Practical Application  
Use of the GNSS system is needed in monitoring the key performance indicators in team sports of the disabled, similar to able-bodied players.  
KPIs of match load are demands specific to a sport discipline, which should be reflected in training load and individualization.  
Using the KPIs and pain parameters monitoring may be helpful to determine the time a player should stay on the pitch, presenting the best performance and avoiding consequences in the form of over-normal pain sensations.  
Using the data based on the KPIs and pain monitoring, optimal time on the pitch for each player may increase overall performance of the team.  
The analysis of subjective parameters such as RPE, OPL and digital pain drawing can provide important knowledge for coaching staff which results in team management being more substantiated and based on evidence.
## 3 Results and Discussion
### AGREEprep vs. GREEN Principle of WAC Assessment  
It was verified whether the greenness assessment of the methods performed using the AGREEprep tool was consistent with the green principles of the WAC assessment. The methods assessed are presented in Table 2 in ascending order of score (from the AGREEprep assessment), and this order was in most cases confirmed after the assessment of the green principles of WAC. The only discrepancy was observed in the greenness assessment for the MEPS (fully automated) method, which obtained a higher result in the AGREprep assessment. This higher score was related to criterion 7, where the automated method receives additional points in the AGREprep assessment. However, in the RGB 12 algorithm, the automation of methods is assessed in the principle blue: 4 (operational simplicity). Despite this, a high degree of convergence was observed in the greenness assessments of both metrics used to evaluate the ten methods for preparing water samples for the analysis of UV filters using the GC-MS technique. This convergence is shown in Figure 3, which shows that the correlation of both assessments is high and amounts to 0.877.
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 2 System Model and Signal Processing  
### System Model  
Figure 1 shows the two-hop hybrid communication system model based on NOMA. The system consists of source end S, destination end D, and a DF relay node R with wireless and PLC dual interface. The source S contains \(n\) stations \(\mathrm{S=}\{\mathrm{S_{1},S_{2},\ldots,S_{n}}\}\), and the destination D contains \(n\) stations \(\mathrm{D=}\{\mathrm{D_{1},D_{2},\ldots,D_{n}}\}\). First of all, the sites in the listening range of the source end S compete based on the CSMA access scheme of the IEEE 802.11 standard. The successful sites send packets to the relay R with different power levels by using superposition coding technology. After ideal SIC decoding at R, R re-encodes and multiplexes them and sends them to \(n\) destination terminals equipped with PLC interfaces, respectively. SIC is applied at the destination terminal to remove part of the inter-user interference by first decoding the stronger signal and treating other signals as interference until its own signal is decoded.  
The above wireless and power line communication hybrid relay system model can be applied to scenarios such as power Internet of Things and smart grids, where terminals S and D refer to intelligent meters and sensors on factory equipment or independent. In order to solve problems such as the PLC not having mobile access, the penetration ability of high-frequency radio waves being limited, and the fading being large, the hybrid cooperation mode of wireless access and PLC relay can be adopted between the intelligent instrument or sensor (S) and the destination end (D) to achieve mobile access and long-distance communication, so as to ensure the coverage effect of indoor communication. At the same time, NOMA technology has the characteristic of high spectral efficiency, allowing large-scale terminal equipment access. The hybrid communication method of wireless and power line based on NOMA can improve the reliability and flexibility of communication in high load scenarios, which has important research value.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
###### Abstract
The major contributions of this research can be summarized as follows:  
* A closed-form probabilistic description of the \(\alpha\)-Fluctuating Nakagami-\(m\) fading channel model suitable for WSN channel description is introduced, and its first-order statistical characteristics of the instantaneous signal-to-noise ratio (SNR) (i.e., probability density function (PDF), cumulative distribution function (CDF), moment generating function (MGF) and raw moments) are derived. * Exact expressions for the high-SNR asymptotics are derived for all the assumed channel characteristics. * A broad comparison of the proposed \(\alpha\)-Fluctuating Nakagami-\(m\) model with the most widely used model in wireless communications is performed. The results obtained revealed its ability to account for both heavy and light fading in terms of the specific channel parameters. * The exact expressions are derived for _(a)_ amount of fading (AoF); _(b)_ outage probability (OP); _(c)_ average bit error rate (ABER) for both coherent and non-coherent modulations; _(d)_ ergodic channel capacity (EC). * All evaluated channel performance metrics are appended with the closed-form expressions for their high-SNR asymptotics. * The derived expressions helped to evaluate the amount of fading (AoF) and the diversity/coding gain of the channel. * The practical validation of the applicability of the \(\alpha\)-Fluctuating Nakagami-\(m\) model for wireless communications was performed on a set of D2D experimental data. * A numerical study of the derived expressions demonstrated their high computational efficiency, that is, high computational speedup and low relative error compared to brute-force numerical integration. * An intensive analysis of the joint dependence of all the derived channel performance metrics on the parameters of the \(\alpha\)-Fluctuating Nakagami-\(m\) is performed for various fading scenarios (including strong and light fading). The remainder of the article is organized as follows. Section 2 presents a brief introduction into the underlying Fluctuating Nakagami-\(m\) model; Section 3 presents the physical model for the proposed \(\alpha\)-Fluctuating Nakagami-\(m\) channel and derives its statistical representation (in terms of PDF, CDF, and MGF), as well as their high-SNR asymptotics, raw moments, and AoF; Section 4 presents the derived results for the specific application of the \(\alpha\)-Fluctuating Nakagami-\(m\) model in wireless fading communications (that is, the closed-form expressions for outage probability, average bit error rate, and ergodic capacity); Section 5 presents the results of an in-depth numerical analysis of the evaluated expressions for all specific channel and system parameter values; and concluding remarks are drawn in Section 6.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 3 Results
### Spacecraft Case
#### 3.1.2 Results  
All the thermal images presented are produced using a circular orbit at 3 m from the center of TANGO with the camera in a center pointing attitude. The first thing to highlight in the results is the effect of the different components' emissivities. As is clearly visible for a given camera pose reported in Figure 9, the solar cells are isothermal with the support panel, making them invisible in the temperature field image (left image in Figure 9), but due to the difference between the emissivity of the solar cells and of the panels, respectively 0.86 and 0.40, the heat flux received by the camera is completely different, thus creating a sharp contrast in the final thermal image (right image in Figure 9), where the solar cells shapes are indeed visible.  
The same goes for the patch antenna, completely invisible as isothermal with the lateral panel (left image in Figure 9); it stands out clearly in the thermal image (right image in Figure 9). Furthermore, due to the different orientations of the mesh faces of the patch antenna with respect to the camera, given its curved shape, it is possible to appreciate the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Part Name** & **Emissivity** \\ \hline Antennas & 0.86 \\ Patch antennas & 0.90 \\ Coil & 0.86 \\ Support coil & 0.86 \\ Panels & 0.40 \\ Solar cell & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emissivity values.  
Figure 8: Tango geometry used for thermal simulation.  
effects of the view factors on the amount of heat flux intercepted by the camera. As the face normal is aligned closer to the camera normal, the color is brighter, and vice versa. The same consideration goes for other curved shapes, such as the cylindrical antennas of TANGO (top right, right image in Figure 9).  
The effects of the emissivities and view factors are even more visible for an arbitrary sequence of images taken from an orbit around the spacecraft pointing its center (Figure 10). As the solar panel normal gets closer to being perpendicular to the camera normal, the intercepted heat flux lowers down as reported in the second and fourth rows of Figure 10. To show the different orientations of the panels, the frames are not taken at equal intervals; thus, the second and fourth rows of the frames have different angles with respect to the camera, even though they look symmetric. This also highlights how small-angle differences in the camera-object system can affect the results. To better clarify the last point, two different orientations are reported in Figure 11, where we can see the drop in the received heat flux due to the different orientation of the solar panel; the same considerations go for all the panels of the spacecraft. In conclusion, the actual output of the pipeline is a black and white image that is sent to the navigation chain, and thus, the final grayscale image is reported in Figure 12.
## 2 Preliminaries  
This section presents the necessary background of our proposed method. Complementary ensemble empirical mode decomposition (CEEMD), is applied to process the raw signals of multiple components in this paper. Its principle is introduced as follows.  
As an adaptive decomposition algorithm, CEEMD [22] can decompose non-stationary signals into several approximately stationary intrinsic mode functions (IMFs) and non-stationary residual terms. The brief process of CEEMD is described as follows:  
1. Given a raw signal \(x(t)\). First, add the particular white noise into the signal \(x(t)\) and decompose it. Repeat the decomposition under different noise realizations and average these results as the first sub-signal \(IMF_{1}\): \[IMF_{1}=\frac{1}{N}\int_{i=1}^{N}E_{1}[x(t)+\sigma_{1}\omega_{i}],\] (1) where \(N\) is the average time, \(E_{1}[]\) is the defined operator which produces the 1 th mode obtained from the given signal using the EMD, \(\sigma_{1}\) is the ratio coefficient and \(\omega_{i}\) is the particular white noise in each time.
2. Compute the first-order residual as follows: \[r_{1}=x(t)-IMF_{1},\] (2)Set \(r_{1}+\sigma_{1}E_{1}[\omega_{i}],i=1,\ldots,N\) as the new signal and compute the \(IMF_{2}\): \[IMF_{2}=\frac{1}{N}\int_{i=1}^{N}E_{1}[x(t)+\sigma_{1}E_{1}[\omega_{i}]],\] (3)
4. Repeating step (1) to step (3) until the \(IMF_{n+1}\) is obtained, and the raw signal is finally decomposed into \(S\) sub-signals \(IMF_{s}\) and a residual \(R(t)\): \[IMF_{n+1}=\frac{1}{N}\int_{i=1}^{N}E_{1}[x(t)+\sigma_{n}E_{n}[\omega_{i}],\] (4) \[x(t)=\sum_{s=1}^{S}IMF_{s}+R(t).\] (5)  
Through the CEEMD, the non-stationary signal is decomposed into several approximately stationary sub-signals, and these sub-signals will be support for the subsequent feature extraction.
## 5 Results and Discussion
### Investigating Error Sources  
Three potential error sources were identified when validating the methodology. One is a missing factor in the current version of the model, the second is a characteristic of GNSS surveys in urban areas and the third is a potential limitation of the obstructions used in the model. Each was assessed in turn to estimate their contribution to the errors in the satellite prediction tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **Time** & \(Total_{\text{sat}}\) & \(Predicted_{\text{obs}}\) & \(Measured_{\text{obs}}\) & \(Error\) & **Location** & **Shadowing Objects** \\ \hline
1 & 10:24 & 9 & 9 & 8 & 1 & Pitches & Buildings \\
2 & 10:26 & 9 & 9 & 8 & 1 & Pitches & Buildings, Trees \\
3 & 10:31 & 10 & 9 & 7 & 2 & Open Space & Trees \\
4 & 10:39 & 10 & 8 & 8 & 0 & Courtyard & Buildings, Spire \\
5 & 10:41 & 9 & 2 & 2 & 0 & Courtyard & Buildings, Spire \\
6 & 10:45 & 9 & 9 & 9 & 0 & Square & Buildings, Spire \\
7 & 10:47 & 9 & 4 & 4 & 0 & Square & Buildings \\
8 & 10:55 & 10 & 2 & 2 & 0 & Junction & Buildings, Spire \\
9 & 11:03 & 10 & 5 & 4 & 1 & Car Park & Buildings, Trees \\
10 & 11:17 & 11 & 1 & 1 & 0 & Open Space & Building, Trees \\ \hline \hline \end{tabular}
\end{table}
Table 2: GNSS prediction validation test results, description of site and shadowing objects.  
\begin{table}
\begin{tabular}{c c|c c c|c c c} \hline \hline
**Observations** & \multicolumn{3}{c}{**Shadow Object 1**} & \multicolumn{3}{c}{**Shadow Object 2**} \\ \hline
**Point** & **Error** & **Azimuth** & **Height** & **Range** & **Azimuth** & **Height** & **Range** \\ \hline
1 & 1 & 195\({}^{\circ}\) & 13.8 m & 30.6 m & 283\({}^{\circ}\) & 12.7 m & 45 m \\
2 & 1 & 287\({}^{\circ}\) & 9.6 m & 23.5 m & 110\({}^{\circ}\) & Veg & 33.9 m \\
3 & 2 & 65\({}^{\circ}\
## 2 Background and Scope  
### Geographical Situation  
In an area near Guaramales county (Azuay province, Ecuador), the CELEC Public Company South-Demarcation has an unpaved road that connects the main national route E-40 with the facilities buildings. It is the only way to access the powerhouse of the Sopladora Hydroelectric Power Plant (SHPP).  
This road has a sinuous S-shaped descending path along a significant slope from an elevation of 1780 m above sea level (m.a.s.l.) at the top intersection point and with the buildings, facilities, and installations located at an elevation of approximately 1020 m.a.s.l, at the same level of the Paute river course.  
The area is close to the community and population of Amaluza (central coordinates UTM-datum WGS-84 zone 17M: 784,104 E/9,711,897 N), where all surrounding landscapes have pronounced slopes. It is a complex geomorphology area dominated by V-shape snaked-type valleys with slopes sides of more than 45\({}^{\circ}\) on average (Figure 1).  
No previous investigations were conducted in the area by owners, and only a mitigation action was applied to the damaged road by the slide of materials that affected the circulation of people and vehicles to the facilities area (Figure 2).
## 3 NB-IoT Frame Structure  
The downlink transmission of NB-IoT is similar to legacy LTE with 15 KHz subcarrier bandwidth. However, only one physical resource block (PRB) [2] is occupied by NB-IoT. Since 12 subcarriers form one PRB, the downlink bandwidth of NB-IoT is 200 KHz, including 20 KHz for the guard band. In the time domain, the length of one radio frame is 10 ms, and a radio frame consists of 10 subframes. Then, a subframe is subdivided into two slots and each slot contains seven OFDM symbols. Besides, only normal cyclic prefix (CP) is supported by NB-IoT. There are three deployment modes for NB-IoT, namely in-band, guard-band and standalone. In-band operation means that the NB-IoT band locates within legacy LTE, and the time and frequency resources reserved for LTE will not transmit NB-IoT signals. The guard-band mode indicates that the NB-IoT carrier is deployed in the guard band of the LTE carrier. As for standalone operation, it is realized by replacing GSM carriers with NB-IoT carriers. There are three physical channels and three physical signals defined in [2]. They are listed as follows:  
* Narrowband physical downlink shared channel (NPDSCH).
* Narrowband physical broadcast channel (NPBCH).
* Narrowband physical downlink control channel (NPDCCH).
* Narrowband reference signal (NRS).
* Narrowband synchronization signal: NPSS and NSSS.
* Narrowband positioning reference signal (NPRS).  
Figure 1 illustrates the distribution of the physical channels and signals in NB-IoT radio frames. It is worth mentioning that NSSS only appears on the 9 th subframe of even radio frames.
###### Abstract  
Two years of field experiments were conducted at the National Field Observation Experiment Station for Efficient Agricultural Water Use in the Wuwei Oasis, Gansu Province. Based on the eddy correlation system, the evapotranspiration (_ET_) of the cabbage agroecosystem during the growth period was obtained and the main driving factors of _ET_ changes were determined. The Root Zone Water Quality Model 2.0 version (RZWQM2 model) was used to simulate _ET_ during the growth period. The results showed the following: (1) The _ET_ of cabbage during the growth period was 260. \(1\pm 24.2\) mm, which was basically lower than other crops planted in this area. (2) Through partial correlation analysis and principal component analysis, it can be found that environmental and physiological factors jointly drive changes in _ET_. The main driving factors include gross primary productivity, net radiation, and water use efficiency. (3) The RZWQM2 model can simulate the _ET_ of the cabbage agroecosystem well, especially in simulating the total _ET_ value and its trend. The growth period _ET_s were 7.3% lower than the _ET_m. Cabbage is an important cash crop in Northwest China, and _ET_ is an important component of the water cycle in the agroecosystem. Determining the main driving factors of _ET_ is of great significance for the sustainable utilization of agricultural water resources in Northwest China. Our results can provide a scientific basis for the cultivation of cabbage as a cash crop and the development of water saving agriculture.  
evapotranspiration; agroecosystem; cabbage; eddy correlation system; driving factor Article