esa-sceva/satcom-chunk-collection huggingface dataset:
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 3 Results
### Setting Up Rainfall in View of Disaster Reduction Relevant to Agricultural Droughts
In this study, the monthly rainfall was analyzed for municipality and the whole country, as a criterion for judging disaster reduction of agricultural drought at the time of disaster management. In South Korea, the agricultural drought ends within four months, due to monsoon season, but the timing of the end of the drought is unclear. Therefore, based on the end time of the agricultural drought, the monthly rainfall before and after a month was analyzed, and the policy manager proposed a criterion for determining when the agricultural drought would end. In this study, we intend to set the standard as the case where the monthly rainfall amount for disasters reduction in agricultural drought occurs more than 150 mm.
## 2 Materials and Methods
After the design of a total of 33 inlay restorations was completed, the restorations were milled from feldspathic ceramic blocks (CEREC B locs; Sirona Dental Systems GmbH, Bensheim, Germany) using a 4-axis milling unit (CEREC InLab MC XL; Sirona Dental Systems), following the manufacturer's standard milling protocol. All inlay restorations were milled by a single experienced operator using the same milling unit. The milling device was calibrated before each session in accordance with the manufacturer's instructions to ensure operational accuracy and consistency. The restorations were not randomly assigned but followed a fixed and consistent sequence for all typodont teeth to ensure standardization. Each tooth received one inlay restoration per group (CBCT, intraoral scanner, model scanner), enabling within-sample comparisons and eliminating anatomical variability. Inlay restorations were not adjusted after they were milled. Inlay restorations were not cemented to teeth because measurements would be made by placing differently manufactured restorations on the same prepared tooth; easily removable transparent paraffin was used to fix the restorations. Marginal gap and internal adaptation values were measured using micro-CT, a non-destructive method. The Skyscan 1275 (Skycan, Kontich, Belgium) device, equipped with high-resolution scanning capacity, was utilized for micro-CT scans. Scanning parameters were set as follows: 125 kVp, 80 mA, 20 \(\upmu\)m/pixel, and a rotation step of 0.2. To prevent radiological artifacts during scans, a 1 mm thick aluminum filter was used. Subsequently, each scanned sample was individually reconstructed using Skyscan's NRecon (version 1.7.4.6 Skyscan, Kontich, Belgium) software. This software also facilitated the elimination of other potential radiological artifacts that could arise during scanning. Two-dimensional (2D) axial projections were obtained from the reconstruction samples. These 2D axial projections were then transferred to CTan (version 1.23.0.2 Skyscan, Kontich, Belgium) software for mathematical analysis. Substance volumes, to be included in the Region of Interest (ROI) based on adaptive interpolation of the substructures in contact with inlays, were determined using gray-color values within the ROIs. The substance quantities in the ROIs indicated the solid volume and the gap size, which were subsequently subjected to statistical comparison. DataViewer software (version 1.7.0.1 Skyscan, Kontich, Belgium) was utilized for preparing 2D measurements. This software enabled examination of axially reconstructed images in transversal, coronal, and sagittal 2D views. Thereafter, these images were again uploaded into the CTan program, and 2D linear measurements were performed. To ensure measurement reliability, 20% of the restorations (n = 7) were randomly selected and re-analyzed by the same operator after a two-week interval using identical micro-CT and analysis protocols. The intraclass correlation coefficient (ICC) was calculated for the repeated measurements and yielded a value of 0.92, indicating excellent intra-observer agreement.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 5 Conclusions and Future Work  
In this paper, we present a new decentralized, data rate-based congestion control (DRCC) algorithm that dynamically adjusts the data rate used for each BSM transmission based on the current channel loads. The performance of the proposed approach was compared to both constant bitrates as well as some existing data rate control approaches. The simulation results indicate that DRCC is able to maintain the channel loads at or near the desired levels. One advantage of the proposed approach is that the upper and lower threshold values can be selected based on the current channel load and the desired CBR level. The proposed approach also achieved the highest packet reception ratio, compared to the other approaches, for medium to high traffic loads. For future work, we are planning to adaptively select the transmission power and data rate for BSM transmissions based on each vehicle's current context, e.g., speed, acceleration, surrounding vehicle density, etc., in addition to the channel load. One important assumption of our approach is that all vehicles are operating in an \"honest\" manner, i.e., if it observes a high CBR, it will try to update its bitrate accordingly, and will not continue to use a lower bitrate. Detection of \"malicious\" vehicles that do not follow this principle and try to claim an unfair portion of the bandwidth is out of the scope of this paper, but is another interesting direction for future work.  
**Author Contributions:** Conceptualization, A.J.; Methodology, B.S.A. and A.J.; Software, B.S.A.; Writing--original draft, A.J.; Writing--review & editing, B.S.A.; Supervision, A.J.; Funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by NSERC DG, Grant #RGPIN-2015-05641.  
Figure 7: Comparison of received BSMs from vehicles at different distances.  
Data Availability Statement: The data used to support the findings of this study are included within the article.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Master-Slave Separate Parallel Intelligent Mobile Robot Used for Autonomous Pallet Transportation
## 3 Synchronization Nonlinear Control
### Input of Nonlinear Control System  
Separate parallel intelligent mobile robot uses master-slave robot structure. In order to achieve high performance of real-time control and response, the chip with over 600 MHZ main frequency should be selected as main control chip. What is more, RTOS system should be embedded for using time slices efficiently. And infrared data transmission module is used for real-time communication between master robot and slave robot. In this way, the operation cycle time of the control system can be within 5 milliseconds, which is an important factor of affecting master-slave robot real-time synchronization control.  
In order to reduce the following errors and achieve high repetitive position accuracy, high-precision sensors should be used, for example distance sensors. In this way, sensors will get relative distance data, which are also the input parameters of slave robot nonlinear control system. Here are the input parameters of slave robot nonlinear control system:  
(1) Distance between master robot and slave robot on X-axis: the precise distance between slave robot and master robot can be obtained by two sets of high-precision point laser distance sensors which are installed on the relative side of the front and rear of slave robot.  
(2) Distance between master robot and slave robot on Y-axis: position error on Y-axis can be roughly measured by tree groups of mirror reflection photoelectric switches, which return a set of SIGN function signal to the control system. That is to say, if the difference between slave robot and master robot is greater than a preset value, the Y-axis distance sensor returns +1 signal to the control system. If the difference is within a preset range, the Y-axis distance sensor returns 0 signal to the control system. Otherwise, the Y-axis distance sensor returns -1 signal.  
Obviously, the procedure of slave robot following master robot is a nonlinear control process. The input parameters of the nonlinear control system include the distance on X-axis and position error SIGN signal on Y-axis. And the output is the moving speed of each driving motor. The goal of the slave robot control system is to make the following process stable and keep the following errors between master robot on X-axis and Y-axis within a preset value.
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 4 Venus Device
### Acoustical Modem  
A general description of the acoustic modem in reported in [15]. In the current implementation of the Venus vessel, the acoustic transmitter adopts the 8-FSK modulation scheme and uses the following frequency vector: [220; 240; 260; 280; 300; 320; 340; 360] (kHz). It can transmit simultaneously any combination of the 8 available frequencies that assumes a binary weight meaning: a given frequency \(f_{k}\) is associated with the bit \(k\) of an 8-bit codeword. A line coding is used with the capability of detecting single errors through a parity bit. In particular, 7 bits are dedicated to the transmission of the data (dataword) and 1 bit is for the parity check (odd-numbered parity). This involves \(2^{7}=128\) usable configurations where 8 out of 128 are used for signaling and the remaining 120 for the payload.  
The modulated signal waveforms are generated from samples that are stored in a ROM memory that the transmitter uses as a Look-Up-Table for the generation of the tones starting for a given codeword. All the logic for addressing and buffering data is implemented in a FPGA. The receiver performs the complementary decoding operations to recognize the transmitted codeword from the received tones.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline length & 1 m \\ diameter & 20 cm \\ min distance & 3 m \\ average speed & 1 m/s \\ engine power & 70 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAUV Venus main features.  
Figure 2: The Venus AUV—realized by ENEA [15].  
The modulated signal is transmitted by a quasi omnidirectional electro-acoustic transducer Reson TC4034. The technical characteristics are: Receiving Voltage Sensitivity (RVS) of \(-218\) dB re \(1\) V/\(1\) uPa (at 250 Hz), Transmit Voltage Response (TVR) exceeding 122 dB re 1 uPa/v@1m (at 100 kHz). This broadband spherical hydrophone model provides uniform omnidirectional characteristics over a wide frequency range of 1 Hz to 470 kHz.  
Figure 3 shows the error probability \(P_{b}\) as a function of the SNR, for an 8-FSK modulation in the case of an Additive White Gaussian Noise channel and a Rayleigh fading channel.  
According to [PERSON] et al. (2016), the error probability is given by:  
\[P_{b}=\frac{2^{k-1}}{2^{k}-1}P_{M} \tag{1}\]  
In the case of Gaussian channel, \(P_{M}\) is equal to:  
\[P_{M}=\sum_{n=1}^{M-1}(-1)^{n+1}\binom{M-1}{n}\frac{1}{n+1}e^{-\left(\frac{n+ SNR}{n+1}\right)} \tag{2}\]  
while in case of Rayleigh channel, \(P_{M}\) is:  
\[P_{M}=\sum_{n=1}^{M-1}(-1)^{n+1}\binom{M-1}{n}\frac{1}{n+1+n\cdot SNR} \tag{3}\]  
where \(M=8\) (for the 8-FSK), \(SNR\) is the signal to noise ratio and \(k=\log_{2}M\).  
Figure 4 shows how the \(SNR\) of an acoustic communication varies according to the distance between the transmitter and the receiver. The expected value of the \(SNR\) is estimated using the methodology reported in (Han  
Figure 3: Error probability as a function of SNR in case of Gaussian and Rayleigh channels and 8-FSK modulation.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 3 The Proposed Algorithm
### Adaptive Scale Factor F and M-Matrix Evolution Scheme  
The scale factor \(F\) obeys Cauchy distribution \(C\) (\(X_{F}\), \(\gamma_{F}\)), and during the initial stage, the position parameter \(X_{F}=0.5\), while \(\gamma=0.2\). \(nfe\) means the number of function evaluations. The dynamic rule is in Equation (6):  
\[\left\{\begin{array}{c}\omega_{\mathrm{s}}=\frac{std(\Delta\omega_{c})}{ \sum_{s=1}^{|S_{F}|}std(\Delta\omega_{c})}\\ \Delta\omega_{c}=loc(U_{i,G}-X_{i,G})\\ mean_{WL}(S_{F})=\frac{\sum_{s=1}^{|S_{F}|}\omega_{s}\times S_{F}^{2}(s)}{ \sum_{s=1}^{|S_{F}|}\omega_{s}\times S_{F}(s)}\\ X_{F,G+1}=\left\{\begin{array}{c}mean_{WL}(S_{F}),S_{F}\
eq\varnothing\\ X_{F,G},otherwise\end{array}\right.\end{array}\right. \tag{8}\]  
where \(S_{F}\) represents a collection of \(F\) values associated with success individuals and \(loc(U_{i,G}-X_{i,G})\) indicates the parameter of the positioning position difference \(U_{i,G}-X_{i,G}\) and std() represents the operation of calculating standard deviation.  
The key point of M-matrix generation lies in the number of kth vectors incorporated into the initialization matrix in each generation of the lower triangular matrix. The number of the kth vector can be calculated by probability \(P(A=K)\). The update formula of \(P\) is given in Equation (9):  
\[\left\{\begin{array}{c}ns=\sum\limits_{d=1}^{D}ns_{d}\\ P(A=k)=\left\{\begin{array}{c}\frac{ns_{k}^{2}}{ns\times(ns_{k}+nf_{k})},ns _{k}>0\\ \varepsilon\end{array}\right.\end{array}\right. \tag{9}\]  
where \(ns\) is the size of the successful individuals of the recorded population, \(ns_{k}\) is the number of successful individuals inherited from the donor vector by k parameter and \(\varepsilon=0.01\) is to avoid zero probability [28].  
In addition, in the process of evolution, our algorithm has two groups. The selection probability \(P(j)\) indicates the possibility of an individual being classified into the \(j\)th group. Then, all the current individuals can be divided into different groups through random universal selection. In the groups, \(P(j),j=1,2\) dynamically change according to the success rate of the individual in the evolution process, as in Equation (10):  
\[\left\{\begin{array}{c}r_{j}=\left\{\begin{array}{c}\frac{ns_{j}^{2}}{ns \times(ns_{j}+nf_{j})},ns_{j}>0\\ \varepsilon,\text{otherwise}\\ ns=\sum\limits_{j=1}^{2}ns_{j}\\ P(j)=\frac{r_{j}}{\sum_{j=1}^{|S_{F}|}(r_{j})}\end{array}\right.\end{array}\right. \tag{10}\]  
where \(ns_{j}\) represents the number of individuals who found a better solution in the \(j\)th group and \(nf_{j}\) refers to individuals who cannot find a better solution in the \(j\)th group. The upper and lower selection probability of each group is fixed, so \(P(j)\) should satisfy \(P(j)\in[0.3,0.7]\), \(j=1,2\), and only satisfy that the least selected group needs to update the probability for each generation.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 2 High-Throughput Satellite Communication System Model
### Zero-Forcing Precoding  
Precoding refers to the process of using channel state information to obtain the precoding matrix on the ground gateway side and processing the transmitted signal to reduce interference. The zero-forcing precoding algorithm is the most classical precoding algorithm, whose main idea is to multiply the modulated signal \(\mathbf{s}\) by the inverse matrix of the channel matrix at the transmitting end first. Secondly, the precoding processed signal is transmitted through the channel and finally multiplied by a channel matrix to eliminate the interference between signals. The idea is described in mathematical language and can be equivalent to \(\mathbf{HW}_{ZF}\)=\(\mathbf{I}\). The precoding matrix is obtained by calculating the pseudo-inverse of the channel matrix. Therefore, the expression of the zero-forcing precoding matrix is:  
\[\mathbf{W}_{ZF}=\left(\mathbf{H}^{H}\mathbf{H}\right)^{-1}\mathbf{H}^{H} \tag{7}\]  
In practical application, the precoding vector of a corresponding user can be obtained by normalizing each column vector in the zero-forcing precoding matrix. It can be seen from the above calculation that the zero-forcing precoding algorithm can eliminate the interference of users in the same frequency. However, without considering the influence of noise, the zero-forcing precoding algorithm is an optimal precoding scheme only when there is no additive noise, and the system performance will be poor in the case of low SNR.
## 3 Results and Discussion
### Clinker Characterization  
#### 3.2.1 f-CaO Content Analysis of Clinkers  
Free calcium oxide (f-CaO) denotes the unreacted or uncombined calcium oxide remaining after high-temperature calcination of cement raw materials and serves as a key indicator for assessing cement clinker quality [51]. Excessive free calcium oxide can compromise volume stability due to delayed hydration reactions, potentially causing expansion and cracking in hardened cement. According to the Chinese national standard GB/T 21372-2024 [52], the maximum permissible f-CaO content in cement clinker is set at 1.5 wt%.  
Figure 5 illustrates the f-CaO content of nine types of cement clinker synthesized at 1350 \({}^{\circ}\)C, 1400 \({}^{\circ}\)C, and 1450 \({}^{\circ}\)C, respectively. As depicted in Figure 5, the f-CaO content in cement clinker exhibits significant variation at 1350 \({}^{\circ}\)C, 1400 \({}^{\circ}\)C, and 1450 \({}^{\circ}\)C, decreasing substantially as the calcination temperature increases. At 1350 \({}^{\circ}\)C, with an increasing biomass fuel blending ratio, the f-CaO content in clinker samples shows a rising trend. At this temperature, the incomplete decomposition of CaCO\({}_{3}\), combined with the slow reaction rate of CaO with SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\), limits the formation of C\({}_{3}\)S and C\({}_{2}\)S minerals, leaving residual free calcium oxide [53]. Conversely, at calcination temperatures of 1400 \({}^{\circ}\)C and 1450 \({}^{\circ}\)C, the incorporation of rice straw, corncob, and sawdust significantly reduced the f-CaO content in the clinker. This effect can be attributed to the presence of alkali metal oxides (e.g., K\({}_{2}\)O and Na\({}_{2}\)O) in biomass fuel ash, which lowers the liquid phase formation temperature, facilitates the reaction between CaO and SiO\({}_{2}\), accelerates the formation of C\({}_{2}\)S and C\({}_{3}\)S mineral phases, and minimizes f-CaO residue. As observed in Figure 5, the f-CaO content of all clinker samples at 1450 \({}^{\circ}\)C remains below 1.5 wt%, confirming that complete calcination has been achieved, thereby meeting the specifications of the Chinese national standard.  
Figure 4: TG and DTG diagrams of mixed fuels at varying substitution ratios: (**a**) TG\(-\)10 wt%, (**b**) DTG\(-\)10 wt%, (**c**) TG\(-\)20 wt%, (**d**) DTG\(-\)20 wt%.
# Conditional Measurements with Silicon Photomultipliers
## 2 Materials and Methods
### Theoretical Description
#### 2.1.3 Nonclassicality  
Sub-Poissonianity is a well-known sufficient condition for nonclassicality [28; 29]. A direct and experimentally approachable estimator of sub-Poissonianity is the ratio between the variance and the mean value of the photon-number distribution, which is known as Fano factor [29]. In particular, in Section 3 we will evaluate the Fano factor for the number of detected events, i.e.,  
\[\text{F}\equiv\frac{\langle\Delta\hat{k}^{2}\rangle}{\langle\hat{k}\rangle} \tag{14}\]  
where \(\langle\Delta\hat{k}^{2}\rangle=\langle\hat{k}^{2}\rangle-\langle\hat{k} \rangle^{2}\) is the variance of the distribution. As already shown in Refs. [14; 16], in the presence of an OCT probability \(\varepsilon\), the mean value of the detected events can be written as \(\langle\hat{k}\rangle=(1+\varepsilon)\langle\hat{m}\rangle\), while the variance reads as \(\langle\Delta\hat{k}^{2}\rangle=(1+\varepsilon)^{2}\langle\Delta\hat{m}^{2} \rangle+\varepsilon(1-\varepsilon)\langle\hat{m}\rangle\). The nonclassicality condition is achieved if \(\text{F}<1\). Note that just the knowledge of the first and the second moments, provided by Equation (13), is required.  
As a last remark, we point out a well-known effect of the OCT which will be crucial for our considerations on the nonclassicality: by inspecting the definition of OCT, one may infer that both the mean value and the variance of the light distribution are increased by the OCT. It can be shown that this is actually the case. However, one may also ask whether this enhancement is the same for variance and mean value, i.e., if the Fano factor remains unchanged under the effect of the OCT. The answer is no [14; 16]: the OCT widens the variance with respect to the mean value and thus it heavily affects the statistics of light. This effect can be easily shown by retrieving the first and second moments of an OCT-affected distribution from Equation (9) and noting that  
\[\frac{\langle\Delta\hat{k}^{2}\rangle-\langle\hat{k}\rangle}{\langle\hat{k} \rangle}=(1+\varepsilon)\bigg{[}\frac{\langle\Delta\hat{m}^{2}\rangle-\langle \hat{m}\rangle}{\langle\hat{m}\rangle}+\frac{2\varepsilon}{(1+\varepsilon)^{2 }}\bigg{]}\geq\frac{\langle\Delta\hat{m}^{2}\rangle-\langle\hat{m}\rangle}{ \langle\hat{m}\rangle}\quad\forall\varepsilon\geq 0. \tag{15}\]
## 5 Numerical Results
### General Comparison
Additionally, the achieved accuracy is almost the same as the maximum accuracy that was achieved with regular RBF in Figure 4. It is also found that \(N_{e}\) has a negligible influence on accuracy. Subsequently, this model removes any dependency on \(\varepsilon\), which is considered to be an advantage. However, this model depends on \(\varepsilon_{k}\) through the range of \(\varepsilon_{k}\) and the constant (\(m\)) of the weight of partition of unity. The effect of these parameters is discussed in the next two subsections.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### The Transmission Range  
The transmission range is mainly determined by the transmission power, typically limited by regulations [11], the radio and propagation properties, as well as coding and modulation complexity. If a radio transmits at a constant power, lowering this complexity rate permits the correct decoding of a weaker or more distorted signal by a receiver, thus extending the transmission range. Also, higher frequency bands with more bandwidth available enable higher data rates and faster data transmission but they also have worse penetration capabilities which reduces the range in an industrial site full of obstacles. Range is largely determined by topology as well. Multi-hop topologies extend the range at the expense of latency, design complexity and energy consumption because of the need for synchronization of nodes, routing and so forth. In conclusion, low data rates at low frequencies and multi-hop topologies are prolonging the range but they all increase latency.