esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 2 Theoretical Background
### Radiation-Induced Error Types  
One of the errors from the entire SEE set is the Single Event Upset (SEU), which corresponds to a change in the state of a memory element as a result of the impact of a high-energy particle. In FPGAs, these changes can be at two different levels: in the memory elements of the implementation and in the memory elements of the configuration memory. The impact types with their causes and effects are summarized in Table 1. This article focuses on the impact type SEE, which results in an SEU.  
Single Event Effects are caused by a single, energetic particle such as protons, neutrons, or heavy ions, and can take on many forms [3; 39], i.e., if an ion particle transversing a device deposits sufficient charge, an event such as a memory bit flip or transient may occur, which may be destructive or not. The destructive (like Single-Event Latch-up (SEL), Single-Event Gate Rupture (SEGR), or Single-Event Burnout (SEB)) are not considered in this paper.  
The non-destructive errors are described as soft errors. The SEUs are characterized by a single particle strike in memory elements leading to bit flips and consequently data corruption. The downsizing of transistors, associated with smaller supply voltages and higher operating frequencies, has resulted in Single Event Transients (SETs) and is referred to combinatorial logic [40].  
Considering the FSM, both SET and SEU can have a significant impact on the performance of the FSM. A change of state in a state register DFF cell can result in an incorrect determination in the transition block \(\delta\) the excitation for the state register DFF. Of course, the output block \(\lambda\) can also give an incorrect logic state in response.  
The SET in the transition block \(\delta\) can have a similar effect. A change of logic state in a combinatorial circuit can cause the state register DFF to latch into a bad state or a metastability phenomenon to occur.  
If SET occurs in the output block \(\lambda\), the FSM response may be incorrect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Cause** & **Effect** \\ \hline \multirow{3}{*}{Dose effect} & Long-term exposure to ionising radiation & - Degradation of the crystal structure, - changes in threshold voltages - increase in leakage currents \\ \cline{1-1}  & & - Single Event Upset (SEU): \\ \multirow{3}{*}{Single Event Effects} & High-energy particle impact & - Single Event Transient (SET): \\ \cline{1-1}  & & brief electrical pulse - Single Event Latchup (SEL): \\ \cline{1-1}  & & power supply short circuit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radiation effects in electronics.  
The impact of SET on internal FSM state is presented in Figure 2. As an effect, the transition block \(\delta\) outputs with 1001 state instead of 1011. If an error is latched in the state register DFF, the FSM will be in a different state than it should be, potentially disrupting the whole system.
# Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder
## 5 Conclusions  
SSC is one of the important indicators in the processing of green plums. The hyperspectral technology is used for testing to achieve the nondestructive testing of SSC. On the basis of the newly developed hyperspectral imaging system, the spectroscopic data of green plum are collected. Experimental analysis has proven that the use of visible-infrared spectroscopy can effectively predict the SSC in plums. The spectral information of 400-1000 nm is collected, and the SSC prediction is performed using the PLSR model, which has \(R_{P}\) and RMSEP values of 0.895 and 0.807, respectively. The SAE-PLSR model is proposed on the basis of the traditional unsupervised learning models to improve the prediction accuracy of green plum SSC. The autoencoder is improved, and the multilayer SAEs are connected. Moreover, the PLSR module is used for regression prediction to back propagate the predicted error, adjust the model parameters, and improve the model prediction performance. The results of the experiment prove that compared with the traditional PLSR model, the SAE-PLSR model prediction set has a 4.8% increase in \(R_{P}\) and a 19.0% decrease in RMSEP. The model also has advantages compared with the traditional feature extraction and regression prediction methods. By comparison, the prediction set has a 2.2% increase in \(R_{P}\) and an 8.7% decrease in RMSEP. The SAE-PLSR model has good prediction performance on green plum SSC. However, the training time of the model has increased because of the multiple back-propagation processes. The model can still be improved later to reduce the training time of the model.  
Methodology, Validation, Writing-original draft, L.S.; Funding acquisition, Y.L. ([PERSON]); Software, L.S., H.W., Y.L. ([PERSON]), X.Z., and Y.F.; Supervision, Y.L. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
Jiangsu agricultural science and technology innovation fund project (Project#: CX (18) 3071): Research on key technologies of intelligent sorting for green plum.  
The authors would like to extend their sincere gratitude for the financial support from Jiangsu agricultural science and technology innovation fund project in China.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Triaxial Tests  
Triaxial tests were carried out on specimens 50 mm in diameter and 100 mm in height. The specimens were prepared using the moist-tamping method [52]. The back pressure method was adopted to saturate the specimens. A maximum back pressure of 8 MPa was used to ensure that the pore pressure parameter B could reach a minimum value of 0.95. It should be noted that a high pore water pressure is required to reach the stability conditions of gas hydrate. Hence, the test results of this study can be used to compare with those of hydrate-bearing soil mixtures that are not reported in this article. Thereafter, the specimens were isotropically compressed to an effective confining pressure ranging from 0.5 to 3 MPa. After the completion of consolidation, drained shear tests were conducted at an axial strain rate of 0.1%/min.  
According to past studies on sand-fine mixtures, the interfine void ratio is a potential density parameter for carbonate sand-fine mixtures in addition to the conventional void ratio [7,8]. Therefore, two series of triaxial tests were conducted by controlling the void ratio and interfine void ratio. A total of fifteen tests were carried out, and the test conditions are summarised in Table 1. In the first series of triaxial tests, nine specimens were controlled with a similar initial void ratio of around 0.580. The remaining six specimens were controlled with a similar initial interfine void ratio of around 0.720 in the other series of tests. For the specimens with a similar initial interfine void ratio, a target value (0.720) of the interfine void ratio was set, and the initial void ratio was back-calculated based on Equation (2). According to the calculated initial void ratio, the specimen was prepared and the real initial void ratio was measured after the specimen production was completed. Then, the real initial interfine void ratio was obtained by recalculation. The reference specimens of the mixtures are C5_V20, C10_V20 and C30_V20 under the effective confining pressures of 0.5, 1 and 3 MPa, respectively. The void ratio or interfine void ratio of other specimens are all referred to these three specimens under the condition of similar void ratios or interfine void ratios.  
Figure 2: Maximum and minimum void ratios of carbonate sand-fine mixtures.
## 2 Methods and Materials
### Experimental Program  
The concrete formulation (Table 1) used in this study was based on cement type I (CEM I) with a water-cement ratio of 0.59 and a porosity of 15.3 \(\pm\) 0.3%, and with a maximum aggregate size of 12.5 mm.  
Five PCB sensors were embedded in five cylindrical specimens (numbered from S1 to S5) of 11 cm diameter and 22 cm length to quantify the variability of the measurement between specimens obtained with the sensors and to measure the water content gradient over the entire depth. In each specimen, the sensor was placed along the axis and the grids were placed near the faces, embedded at a depth of 5 to 10 mm (Figure 2).  
The specimens were cured for 28 days in a climatic chamber at a temperature T = 20 \(\pm\) 2 \({}^{\circ}\)C and at a relative humidity HR = 95 \(\pm\) 2%. Since the specimens were not saturated under vacuum, it was assumed that \(M_{95}\) (\(S_{r}\) = 95.0 \(\pm\) 2.0%) \(=M_{sat}\) with a 2% margin of error when considering this assumption. They were sealed with adhesive aluminum foil and placed in an oven at a temperature T = 45 \({}^{\circ}\)C for the homogenization of the amount of pore water during a period equal to the duration of drying [37; 38]. They were dried at 45 \({}^{\circ}\)C to reach various masses \(M_{t}\) and, therefore, different degrees of saturation (\(S_{r}\) = 95.0 \(\pm\) 2.0%, 72.2 \(\pm\) 1.0%, 53.3 \(\pm\) 0.3%, and 33.8 \(\pm\) 0.5%) to establish calibration curves connecting the resistivity to the degree of saturation. After all the resistivity measurements were taken, the specimens were dried at 105 \({}^{\circ}\)C (\(M_{dry}\)) to calculate the true degrees of saturation \(S_{r}\) from Equation (4):  
\[S_{r}=\frac{(M_{t}-M_{dry})}{(M_{sat}-M_{dry})} \tag{4}\]  
After homogenization, the specimens were placed at three different temperatures (20, 45, and 60 \({}^{\circ}\)C) and the resistivity measurements were carried out. As the specimens were still sealed, a constant degree of saturation was ensured during temperature increase and was controlled by weighing. This allowed us to obtain resistivity values for each degree of saturation at various temperatures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constituents** & **Origin** & **Quantity (kg/m\({}^{3}\))** \\ \hline Aggregates 4/12 & Boulonna & 984 \\ Sand 0/4 & Boulonna & 890 \\ Cement CEM I 52.5 N & Val d’Azergue & 350 \\ Effective water (_Weff_) & & 206 \\ \hline _Weff/C_ & & 0.59 \\ Compressive strength at 28 days & & 42.6 + 1.3 MPa \\ Porosity accessible to water at 28 days & & 15.3 + 0.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete mix design and characterization.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 4 The Predictable Time of Wind Speed Series
### Selection of Wind Speed Series  
In this paper, we selected wind speed series from three anemometer towers in China. The three anemometer towers are in three wind farms which are located in Hunan province, Henan province and Zhejiang province, respectively. The location, local terrain condition, selected time and measurement height of the three anemometer towers are shown in Table 1. These wind speed series were used to analyze the predictability of direct multistep forecasting. On the three anemometer towers, the calibrated NRG measuring instruments were used to automatically record the wind speed data, and the recorded time interval was 10 min. After processing the missing and invalid data of the anemometer towers, the integrity rate of the wind speed data can reach 100%. And these wind speed data have passed the reasonableness test.  
Figure 3: The flowchart of the EEMD-FuzzyEn-LSTMNN model.
## 6 Discussion and Results  
### Validation of Proposed HMFO Algorithm with the Experimental Instances  
To validate our approach toward optimization of makespan and energy consumption, we consider some experimental instances from the literature. Table 7 shows the results of the experimental instances with makespan and energy consumption values. We have calculated makespan and energy consumption for around a total of 35 experiments with the data available from (instances 1-32) mentioned in [51] and (instances 33-35) mentioned in [52]. A comparison of our proposed HMFO results with results carried out with a Simulated Annealing Genetic Algorithm (SA-GA) for instances 1-32. For most of the instances, the proposed HMFO gives better makespan and energy consumption values when compared with existing SA-GA makespan and energy consumption values. We also compared our proposed HMFO results with results carried out by [52], a Genetic Algorithm-based Memetic Algorithm (GA-MA) for instances 33-35. For most of the instances, the proposed HMFO gives better makespan and energy consumption values when compared with existing GA-MA makespan and energy consumption values. All the above results indicate the better performance of proposed HMFO with some of the experimental instances which were already proposed in the literature.
## 1 Introduction  
The COVID-19 pandemic period undoubtedly increased and accelerated interest in and use of telemedicine in healthcare [1; 2] in various medical specialties as a safe and effective way to deliver healthcare [1; 2; 3]. Telemedicine (\"tele\" means at a distance, and \"medicine\" refers to medicine, considered the art of healing) is a rapidly growing way of providing medical services remotely [4; 5; 6; 7; 8]. In its purest form, telemedicine refers to treatment by a physician who is professionally responsible for providing this service remotely to a patient without face-to-face contact [9; 10; 11]. Other medical services that do not necessarily include direct interaction with a physician are generally referred to as telehealth or eHealth [12; 13; 14]. Telehealth includes health services in the form of various applications [15]. An operational definition of telemedicine stimulates the discussion of its role in the healthcare system and the global economy [16]. Economic analysis of telemedicine, its basic approaches and requirements for evaluating and identifying core issues require further research around the world as healthcare delivery environments evolve due to rapid technological advances [16; 17]. For this paper, we adopted a research hypothesis that conference speeches were related to subsequent publication of scientific papers. This work aimed to look at telemedicine in Poland from two perspectives. On the one hand, it reviewed the conditions and progress in telemedicine, i.e., the development of the telecommunications system. On the other hand, the corpus of 45 presentations from telemedicine and eHealth conferences in Poland was analyzed to assess the dissemination of research and the impact of publications facilitated by the Polish Telemedicine Society.
## 3 Results
### Fuzzy Cognitive Model (FCM)  
Based on the application of the FCM Expert software and the application of a FIS, the FCM that interrelates the indicators identified in Table 3 was determined. The FCM illustrates the interaction weight of 16 indicators of the additives that contribute socially to sustainability in a national setting. The operational formulation of the FCM is configured through the FCM Expert software and Equations (4) and (5). In this case, the \(\lambda\) value of 0.75 was used, which is consistent with the proposals by [PERSON] et al. [28] and [PERSON] et al. [49] for equivalent situations. Based on the FCM interrelationships, two proposals are presented. The first is the full unconstrained interrelationship model. The second is derived from the first but includes constraints that limit the positive impact of some indicators based on the type of product evaluated. Figure 4 depicts the complete FCM without restrictions.  
Based on Equation (1) and Equation (2), Figure 5 represents the strategic positioning of the indicators of the cognitive assessment system. Figure 5 on the left represents the strategic positioning of the indicators of the complete model (Fityre), and the one on the right represents the model with restrictions (other fibers). In both cases, the indicators _Consumer interest_ (I16), _Innovation and Development_ (I11), _Knowledge transfer_ (I12), and _Association to the REP law_ (I10) of quadrant Z1 show a high degree of impact on and from other indicators. In this case, controlling them is challenging despite their significance, as they are the most affected by the system. This set includes the indicators (I7) _Amount of additive required_, (I9) _Degree of acceptance to change_, and (I2) _Extension of useful life of a reused product_ in the complete model without restrictions.  
Figure 4: Fuzzy cognitive map of the social contribution of unrestricted HMA additives (Domestic product and eco-friendly).  
In contrast, the indicators Technical Contribution (ID3) and Certifications (ID14) in quadrant Z4 are of greater strategic importance in both cases. In other words, these indicators are less influenced by the rest, making their intervention more effective and significantly impacting the rest of the system. Similarly, the indicators in quadrant Z2 are highly influenced by the rest of the system even though they do not have a major impact, and the indicators in quadrant Z3 have the least amount of influence on the system.
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 3 Materials and Methods
### Scenario  
At the time of writing, four LoRaWAN gateways are installed on Tereeira Island, with the setup described above covering approximately 90% of the island's territory. Table 1 shows the model, location, and altitude of each gateway. This, in turn, is far from being at maximum capacity.  
In the first step, we tried to understand if the service provided on land could be reliably extended to at least 100 km from the coast to track fishing vessels. To test the viability of the service over the ocean, the gateway located in the Serra de Santa Barbara was selected. Mapping the aggregated density of vessels over long periods can be helpful for the management of large Marine Protected Areas (MPAs) or the Exclusive Economic Zone (EEZ). This initial assessment investigates the potential to support use cases that are not critical and can handle incomplete data due to packet loss and other phenomena. Depending on the potential demonstrated in future tests, different use cases that extend to critical might emerge. However, those must be based on a better knowledge of network performance, including packet loss and the impact of weather conditions, among others.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Model** & **Coordinates 1** & **Altitude** \\ \hline Serra do Cume & Wirnet iStation & \(\begin{array}{c}38.70945,\\ -27.11188\end{array}\) & \(\begin{array}{c}563\,\mathrm{m}\\ \\ \end{array}\) \\ Altares & Wirnet iStation & \(\begin{array}{c}38.79919,\\ -27.29119\end{array}\) & \(\begin{array}{c}156\,\mathrm{m}\\ \\ \end{array}\) \\ Serra de Santa & Wirnet iStation & \(\begin{array}{c}38.73015,\\ -27.31887\end{array}\) & \(\begin{array}{c}1040\,\mathrm{m}\\ \\ \end{array}\) \\ Bárbara & Wirnet iStation & \(\begin{array}{c}38.64762,\\ -27.22546\end{array}\) & \(\begin{array}{c}166\,\mathrm{m}\\ \\ \end{array}\) \\ Pico das Cruzinhas & Wirnet iStation & \(\begin{array}{c}38.64762,\\ -27.22546\end{array}\) & \(\begin{array}{c}166\,\mathrm{m}\\ \\ \end{array}\) \\ \hline \end{tabular}
\end{table}
Table 1: LoRaWAN gateways on Tereeira Island.  
Figure 2: (a) Physical appearance of Kerlink Wirnet iStation [22]; (b) Kerlink ACCIOT-KAN01 antenna [23]; (c) Yabby from Digital Matter [24].  
The choice of the gateway was based solely on the height above sea level. Figure 3 shows the location of the Serra de Santa Barbara gateway on Terceira Island, as well as the terrain elevation of the entire island.  
The Azores is an archipelago composed of nine islands, five of which are relatively close to each other. This makes communication impossible in some cases because the islands themselves can act as barriers to communication, as shown in Figure 4, where the theoretical coverage of the gateway is represented at a maximum distance of 150 km.
# Study on the Flow Velocity of Safe and Energy-Saving Transportation of Light-Particle Slurry
## 4 Analysis and Discussion of Results
### Flow Analysis and Critical Flow Rate  
When observing the flow of polyethylene granule slurry in a highly transparent Plexiglas round tube under different working conditions, the following flow patterns can be found:  
(1) Static bed (Figure 12a): In the case of a very small flow rate (or static state), polyethylene particles are gathered above the pipe due to their small density, and, at this time, it is difficult to reach the state of mixing of the two phases of the solid-liquid flow. Moreover, the flow velocity of the carrier fluid is not enough to drive the flow of the solid-phase particles, and the solid phase is basically static. This state cannot achieve the purpose of transporting particles. In particular, for ice slurry fluid with ice particles in the solid phase, particle agglomeration is bound to occur, resulting in clogging.  
(2) Moving bed (Figure 12b): As the flow velocity increases, part of the particles gathered above the pipe converge into the carrier fluid and flow together, and the other part is gathered in the upper part of the pipe and flows slowly. In this flow state, the particles and the carrier fluid begin to mix and flow together, however, at this time, some of the particles will still float and agglomerate, resulting in high particle accumulation rates, slow flow velocity, low particle transport efficiency, and easy wear and clogging of the pipeline. In particular, for ice slurry fluids, an increase in the accumulation of ice particles still occurs, which in turn leads to ice blockage.  
Figure 12: Flow pattern of mixed slurry: (**a**) Static bed; (**b**) Moving bed; (**c**) Non-homogenized suspended bed; (**d**) Homogenized suspended bed.  
(3) Suspended bed (Figure 12c,d): With the further increase in flow velocity, the solid-phase particles gathered above the pipeline gradually converge into the entire area of the carrier fluid, according to whether the solid-phase particles are uniformly distributed can be divided into \"non-homogenized suspended bed (Figure 12c)\" and \"homogenized suspended bed (Figure 12d).\" In the suspended bed flow state, the particles are fully suspended, the solid phase follows the liquid phase together with the flow, which does not easily cause a clogging phenomenon, and is conducive to the safe, stable, and efficient transportation of slurry.  
By comparing and analyzing the above four flow regimes, it can be found that, in order to ensure the safe and stable transport of the slurry, we should keep the slurry in the state of suspended bed, and the flow velocity cannot be too small. Therefore, it is necessary to find the velocity of the solid-liquid two-phase flow from the moving bed exactly transformed into a non-homogeneous suspended bed and the velocity that is the critical flow velocity.