esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 2 Research Background
However, most of these studies divide the terrain model into grid-based maps for path generation, which creates challenges in generating paths with varyingroad widths or curved sections. Additionally, when grid cells are formed in curved sections, misalignment between cell orientation and road direction results in unnatural equipment movement. High volumes of data can lead to issues such as poor quality, schedule delays, and cost overrun [22]. In research focused on real-time path generation, issues include high costs due to numerous sensors, limitations in communication technology due to large amounts of data, and processing speed constraints. This study aimed to address these problems by using initial site data, 3D target models, and non-variable equipment specifications to develop a path-generation algorithm for graders in road construction. This approach seeks to overcome cost, data overload, and processing speed issues and to develop a practical path generation algorithm that can be easily commercialized in real-world applications.
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 4 Results and Discussion
### Experimental Results
#### 4.1.2 Load Resistance Analysis  
In this section, the optimal load resistance is analyzed with the acceleration \(a\) = 4 m/s\({}^{2}\) and the beams spacing \(D\) = 10, 15, 20 mm as an example. The effect of changing load resistance on output power is investigated in Figure 7; it has been verified that this optimum resistance is valid for the frequency range by simulation and experiment. With an increasing load resistance, the output power reaches an optimal point and then drops down. Experiments and simulations have the same trend. Through the impedance sweep, it can be seen that the maximum output power is reached near 120 k\(\Omega\) at different spacing. Therefore, 120 k\(\Omega\) is used as the experimental load resistance later.  
Figure 6: Static analysis curves: (**a**) Magnets Spacing; (**b**) Deflection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Material** & **Size (mm)** & **Weight (g)** \\ \hline \hline \begin{tabular}{c} Cantilever beam1 \\ \end{tabular} & Al-6061T & 130 \(\times\) 20 \(\times\) 0.5 & 3.55 \\ \begin{tabular}{c} Cantilever beam2 \\ \end{tabular} & Al-6061T & 130 \(\times\) 20 \(\times\) 0.75 & 5.32 \\ \begin{tabular}{c} Piezoelectric pitch \\ \end{tabular} & MFC-M8514-P2 & 101 \(\times\) 20 \(\times\) 0.21 & 2.01 \\ \begin{tabular}{c} Magnet \\ \end{tabular} & Nd\(-\)Fe\(-\)B & R10 \(\times\) 2 & 4.72 \\ \
\begin{tabular}{c} Mass \\ Frame \\ \end{tabular} & Al & / & 1.02 \\
\begin{tabular}{c} Frame \\ \end{tabular} & Acrylic & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the prototype.
## 3 Non-Thermal Technologies in Food Processing
### UV Radiation
#### 3.2.4 Application in Dairy Products  
Used for milk treatments, UV light reduces considerably the vitamin C content. [PERSON] et al. analyzed the modifications made on milk by continuous monochromatic low-pressure (LPM) and medium-pressure polychromatic (MPM) mercury UV lamps. Vitamin C reduced by \(-\)35.13 \(\pm\) 1.56% with an LPM UV lamp after 234 min and by \(-\)61.67 \(\pm\) 3.08% with an MPM UV lamp after 11 min. The pH, of value 6.68, was not affected [119]. Vitamin C is not the only one lowered by UV light. A study on cow and goat milk highlights that also vitamins A, B2, and E are damaged by UV irradiation. The most affected after vitamin C is vitamin E, then A, and at last vitamin B2. In cow milk, vitamin A, B2, and E decrease by 8 to 13%, 3 to 10%, and 16 to 33%, respectively, while in goat milk, they were lowered by 1 to 9%, 1 to 2%, and 1 to 48%, respectively [130]. [PERSON] and [PERSON] investigated UV light irradiation on the surface of kashar cheese, a kind of pasta-filata cheese, before the packaging step in order to eliminate or control contamination that can happen during the post-processing steps without compromising chemical and sensorial quality. The cheese's surfaces underwent different intensities in a batch UV cabinet system, and the researchers obtained log\({}_{10}\) pathogens reductions of 0.34, 0.69, and 2.49 in samples treated with different dosages of radiation. No significant differences in hardness or composition have been found in any of the treated cheeses. In addition, during sensory analysis, no color modifications have been perceived. Unfortunately, lipid oxidation, accelerated by light enhancement, has caused a perception of off-flavor [131]. Ricotta is another cheese potentially exploitable by a wide range of microorganisms as a growth medium. It has been artificially inoculated with _Pseudomonas fluorescens_ by [PERSON] et al. and then irradiated with UV-C light. After less than 5 days, the control samples were no more acceptable from the microbiological side, whereas the UV-C irradiated ricotta persisted for more than 6 days without any alteration in sensory properties [132]. Sliced cheese has been inoculated with _E. coli_, _S. typhimurium_, and _L. monocytogenes_ and then packaged in order to analyze the effectiveness of UV light irradiation in inctivating these food-borne pathogens living under the plastic packaging. Among the materials used for the packaging (polyethylene terephthalate (PET), polyvinylchloride (PVC), polypropylene (PP), and polyethylene (PE)), the ones that reduced the levels of the pathogens the most are PP and PE films. A thickness of 0.07 mm allows equal reduction in the three bacteria compared to non-packaged UV-treated samples [133].  
In Table 3, it is possible to find the studies where UV is investigated and utilized, which are schematized according to the food type, together with process parameters and effects.
## 2 Mathematical Definitions  
The characteristic function of the vector \(X=(x_{1},x_{2},\ldots,x_{k})^{T}\) composed of \(k\) real random variables \(x_{i}\) is defined by  
\[\Phi_{X}(V)=E[exp(jV^{T}X)], \tag{1}\]  
where \(V^{T}=(v_{1},v_{2},\ldots,v_{k})\) is vector composed of \(k\) random variables. \(V^{T}X=v_{1}x_{1}+v_{2}x_{2}+\cdots+v_{k}x_{k}\) represents the scalar product of \(V\) and \(X\) and \(E[.]\) is the mathematical expectation operator.  
The coefficients of the \(k\)th-order moments are defined in the Taylor series development of the characteristic function \(\Phi_{X}(V)\) as  
\[\Phi_{X}(V) = \sum_{0\leq m\leq N}\frac{j^{m}}{n_{1}!n_{2}!\ldots n_{k}!}Mom(x_{ 1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}}) \tag{2}\] \[\times v_{1}^{n_{1}}v_{2}^{n_{2}}\ldots v_{k}^{n_{k}}+o(|V|^{s}),\]  
where  
\[Mom(x_{1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}}) = j^{-m}\Big{[}\Big{(}\frac{\partial}{\partial v_{1}}\Big{)}^{n_{1 }}\Big{(}\frac{\partial}{\partial v_{2}}\Big{)}^{n_{2}} \tag{3}\] \[\ldots \Big{(}\frac{\partial}{\partial v_{k}}\Big{)}^{n_{k}}\Phi_{X}(V) \Big{]}_{V=0}\]  
The \(k\)th-order moments of these random variables are given by  
\[Mom(x_{1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}})=E[x_{1}^{n_{1}}x_{2}^{n_ {2}}\ldots x_{k}^{n_{k}}], \tag{4}\]  
The cumulant-generating function is defined as logarithm of the first characteristic function  
\[\Psi_{X}(V)=\ln(\Phi_{X}(V)). \tag{5}\]The \(m\)th-order cumulant of these random variables are defined as the coefficient of \((v_{1},v_{2},\ldots,v_{m})\) in the Taylor series development of the cumulant-generating function  
\[\Psi_{X}(V) = \sum\limits_{0\leq m\leq s}\frac{j^{m}}{n_{1}!n_{2}!\ldots n_{k}!} Cum(x_{1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}}) \tag{6}\] \[\times v_{1}^{n_{1}}v_{2}^{n_{2}}\ldots v_{k}^{n_{k}}+o(|V|^{s}),\]  
where  
\[Cum(x_{1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}}) = j^{-m}\Big{[}\Big{(}\frac{\partial}{\partial v_{1}}\Big{)}^{n_{1 }}\Big{(}\frac{\partial}{\partial v_{2}}\Big{)}^{n_{2}} \tag{7}\] \[\ldots \Big{(}\frac{\partial}{\partial v_{k}}\Big{)}^{n_{k}}\Psi_{X}(V) \Big{]}_{V=0}\]  
The operator \(Cum[\cdot]\) stands for the nth-order joint cumulant of the random variables \(x_{1}^{n_{1}},x_{2}^{n_{2}},\ldots,x_{k}^{n_{k}}\). Thus, considering a zero-mean random process \(\{y(t)\}\), we have  
\[C_{n,\Psi}=Cum\Big{[}y(t),y(t+\tau_{1}),\ldots,y(t+\tau_{n-1})\Big{]} \tag{8}\]  
where, \(\Psi=\Big{\{}y(t),y(t+\tau_{1}),\ldots,y(t+\tau_{n-1})\Big{\}}\), and, hence, the involved real random variables are time-shifted samples of the process \(\{y(t)\}\). Notice that, for stationary processes, the nth-order statistics depend only on the \(n-1\) time lags \(\tau_{1}=t_{1}-t_{0},\ldots,\tau_{n-1}=t_{n-1}-t_{0}\). This allows us to introduce the following notations:  
\[C_{n,y}(\tau_{1},\tau_{2},\ldots,\tau_{n-1})=Cum\Big{[}y(t),y(t+\tau_{1}), \ldots,y(t+\tau_{n-1})\Big{]} \tag{9}\]  
where \(\{y(t)\}\) is a zero-mean stationary random process.
## 5 Results and Discussion
### Particle Removal Efficiency
Increasing the airflow rate at the pickup head suction port can increase the inlet pressure in the system, thereby increasing the efficiency of particle removal. Figure 11: Experimental result of particle suction efficiency with different inclination angles of the suction port. Figure 12: Experimental results of the effect of pressure drop on particle suction efficiency.
# Smart Roads for Autonomous Accident Detection and Warnings
## 4 Hardware and Experimental Setup
### GPS 8M Module  
A GPS receiver receives radio frequency (RF) signals at a very high frequency, as shown in Figure 6a. A GPS receives static data broadcast from different geo positioning satellites. These data contain information about the location, time, altitude, etc., in the form of National Marine Electronics Association (NMEA) sentences. These sentences are difficult to understand, and are therefore parsed by the website (www.freenmea.com) (accessed on 21 April 2021). To obtain the latitude and longitude, the parse button is pressed after copying a sentence that starts with SGNGLL to the website. These coordinates are transferred to the Google website to determine the physical location.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 4 Conclusions  
We have proposed the APD-TDMA that enables concurrent data packet transmissions without collisions at the SN. The channel utilization and the channel access delay of the APD-TDMA have been verified using numerical examples, concluding that the proposed APD-TDMA can significantly improve both the channel utilization and the channel access delay of the BTB-TDMA. Multi-hop extension of the APD-TDMA would be one of our future works.  
Conceptualization: A.-R.C.; formal analysis: A.-R.C.; investigation: A.-R.C.; data curation: A.-R.C.; writing-original draft: A.-R.C. and C.Y.; writing-review & editing: Y.C.; visualization: A.-R.C.; supervision: Y.C.; project administration: Y.-K.L.  
This research was supported by a grant from National R&D Project \"Development of the wide-area underwater mobile communication systems\" funded by the Ministry of Oceans and Fisheries, Korea (PMS3930).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Phytotoxicity Evaluation in Grapes  
The phytotoxicity associated with cold-temperature treatment after PH3 fumigation of grapes was evaluated in a 0.65 m\({}^{3}\) fumigation chamber. Grapes were fumigated with 3 g/m\({}^{3}\) PH3 with a 15% loading ratio for 4 h and then cold treated at 1 \({}^{\circ}\)C for 24 h. After the combined treatment, the tested grapes were incubated at 5 \({}^{\circ}\)C to maintain freshness. Fifteen grape clusters were randomly selected and 5 quality criteria (weight loss, berry abscission, decay rate, sugar content, and surface color) were evaluated after 3, 7, 10, and 14 d. The weight-loss rate (%) was calculated as the weight change in each grape cluster. The berry abscission rate (%) was calculated using the number of abscised berries after shaking in a shaker (N-Biotec Inc., Bucheon, Korea) at 150 rpm for 1 min. The decay rate (%) was calculated as a percentage by dividing the number of decayed berries by the total number of berries. Measurement of sugar content (%, brix) was performed using a refractometer (Atago Co. Ltd., Tokyo, Japan). Surface color (brightness, redness, and yellowness) was measured using a chromateter (CR-400, Minolta Inc., Osaka, Japan). The control group consisted of grape clusters stored at 5 \({}^{\circ}\)C without any treatment.
## 8 Application Challenges and Strategic Directions for Digital Twin Integration
### Barriers to Deployment: Organizational, Technical, and Cultural Factors
#### 8.2.2 Tool Fragmentation and Lack of Standardization  
Digital twin integration is further complicated by the heterogeneity of simulation tools and the lack of standardized modeling formats [8]. Inconsistent model exchange formats, time synchronization mechanisms, and interface semantics impede interoperability between tools used in SiL, HiL, and DT workflows.  
Despite efforts by standardization bodies, such as AUTOSAR, ASAM, and the Modelica Association [57; 58], practical interoperability remains limited. Test artifacts developed in SiL environments often cannot be reused seamlessly in HiL or DT settings, which leads to redundant engineering effort and reduced efficiency in regression testing and requirement verification.
# Can Women Maintain Their Strength Performance Along the Mestrual Cycle?
## 3 Results
### CR-10 and Muscle Soreness  
There were no significant differences between MC phases for the CR-10 (\(\chi^{2}\)(2, N = 12) = 4.47; \(p\) = 0.107) and muscle soreness (\(\chi^{2}\)(2, N = 12) = 2.88; \(p\) = 0.237) perception.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**MC Phase**} & \multirow{2}{*}{**Pre**} & \multirow{2}{*}{**Post**} & \multirow{2}{*}{**\(\Delta\) (95\% CI)**} & \multicolumn{3}{c}{**ANOVA**} \\ \cline{5-8}  & & & & & **MC Phase** & **Time** & **Interaction** \\ \hline \multirow{3}{*}{CMJ (cm)} & EFP & 24.1 (4.7) & 23.2 (4.9) & \(-\)0.86 (\(-\)1.71 to \(-\)0.01) & F\({}_{(2,22)}\) = 0.30; & F\({}_{(1,15)}\) = 6.8; & F\({}_{(2,30)}\) = 1.4; \\  & LFP & 23.9 (4.6) & 23.9 (4.7) & 0.01 (\(-\)0.57 to 0.58) & \(p\) = 0.743 & \(p\) = 0.024 & \(p\) = 0.271 \\ \cline{1-1}  & MLP & 24.0 (5.2) & 23.6 (5.1) & \(-\)0.36 (\(-\)1.15 to 0.43) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the countermovement jump (CMJ) heights performed previously and after the back squat set to momentary failure in different menstrual cycle (MC) phases.  
Figure 3: Group and individual response for the countermovement jump (CMJ) height changes performed previously and after the back squat set performed to momentary failure in different menstrual cycle phases. EFP: early follicular phase, LFP: late follicular phase, and MLP: mid-luteal phase.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 6 Communications Technologies for Real-Time Home Services
### Wireless Technologies
* _6 loWPAN_ (6 Low-power Wireless Personal Area Network) was the first working group of the Internet Engineering Task Force (IETF) that investigated methods to allow the usage of IPv6 protocol over IEEE 802.15.4 (the same data link layer used by ZigBee), with the aim of supporting Internet connectivity among cheap and constrained WPAN devices (it requires only 30 kbytes of sensor memory). The 6 loWPAN standard supports networks of arbitrary topologies and includes packet fragmentation methods and header compression techniques to fit IPv6 and UDP datagrams \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**LoRa**} & \multicolumn{1}{c}{**SIGFOX**} & \multicolumn{1}{c}{**wM-Bus**} \\ \hline Frequency (MHz) & 433, 863–870 & 868, 902 & 169, 433, 868 \\ Bandwidth (MHz) & 0.125, 0250 & 0.0001 & 0.01 \\ DataRate Upload & 250–50000 bps & \textless{}100 bps & 24.4–48 kbps \\ DataRate Download & 250 bps–50 kbps (adaptive) & 256 b/day & 2.4–4.8 kbps \\ Duplex & yes & no & yes \\ Interference tolerance & Good (spread spectrum) & Scarre (narrow band B-PSK) & Scarre \\ Coverage range & 15–45 flat, 15–22 suburban, 3–8 urban & 50 km rural; 10 km urban & \textless{}300 m \\ Specs/Standard & LoRaWAN & SIGFOX & ISO/IEC 14543 \\ Transmission power & 14 dBm & 10 μW-100 mW & 1–100 mW \\ Licensed/unlicensed band & unlicensed & unlicensed & unlicensed \\ Standard \& commercial products & Available & Proprietary & Available \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of LoRa, SIGFOX, and wM-Bus technologies. in the (smaller) IEEE 802.15.4 frame size by means of cross-layer optimization approaches: for devices sharing the same network some portions of the IP header may be inferred from the Medium Access Control (MAC) header, thus reducing the IP/UDP headers by up to a few bytes.
# Motion Response of the Submersible Underwater Towed System as Cable Length Changes
## 4 Results and Discussion
### Composite Maneuvering Motion
#### 4.2.1 Accelerated Motion
Figure 16 displays the motion response values of the towed system when the submersible circular rotational motion and cable length are changed. Figure 16a illustrates that the submersible rotational speed is 8 kN. The maximum tension values during recovery and deployment are the largest, and the drag body depth variation values are closer to 0, which are 6.35 kN, 5.33 kN, \(-41\) m, and 41 m. The increase in maximum tension with increasing speed is well understood. The reason that the value of the depth variation of the towed body decreases at higher rotational speeds is that at high towed speeds, the lift and drag effects of the fluid surrounding the towed body and the inertial forces of the system are enhanced. These forces will balance the motion of the towed body to some extent and stabilize it in a certain depth range, thereby reducing the cyclic variation in depth. In Figure 16b, it can be observed that when the buoyancy-to-gravity ratio of the towed body is 1.0, the maximum tension values of the towed cable are at their minimum for both the retracting and deploying cables. Additionally, the depth change of the towed body is closer to 0 m, with values of \(-46\) m and 38 m. The corresponding tension values for these depths are 2.27 kN and 1.31 kN, respectively. In Figure 16c, it is evident that the maximum tension during deployment remains relatively constant despite variations in the towed body resistance coefficient. As depicted in Figure 16d, the added mass coefficient of the towed body solely impacts the maximum tension during the deployment maneuver. A smaller added mass coefficient results in a correspondingly smaller maximum tension. When the added mass coefficient is 0.5, the maximum tension is 1.76 kN. From Figure 16e, when the buoyancy force per unit length of the towed cable is equal to gravity, the maximum tension of the towed cable is the smallest, and the value of the depth change is closer to 0 m.  
Figure 15: The motion response values of the towed system when the rate of change of the length of the towed cable varies during the circular rotational motion of the submersible. (**a**) The maximum tension of the towed cable; (**b**) The difference in depth variation of the towed body.
## 3 Results and Discussion
### FTIR Spectroscopic Study  
FTIR presents the interaction between the raw materials of the novel films that may affect the characteristics of the films at 600-4000 cm\({}^{-1}\). FTIR spectra of the cross-linked CH film and nano/SiO\({}_{2}\) films are presented in Figure 2. FTIR patterns were relatively similar in all the novel films with several differences in CH film. When the nano-SiO\({}_{2}\) was added, the strong absorption band was transferred to the high wavenumbers. The intensity of nano/SiO\({}_{2}\) film peaks was higher than CH film only, which was perhaps due to the nano-material Si--O--Si resonance mode of vibrations [15]. Consequently, when the 1% nisin was added, the peak did not change except in the wavenumber (3150-3750 cm\({}^{-1}\)) due to the nisin chemical structure. According to Figure 2, it was noticed that the major peaks associated were at 1027.39, 1348, and 1558.2 cm\({}^{-1}\). The FTIR results preserved that the compatibility between 30 nm nano-SiO\({}_{2}\) and nisin would be scattered uniformly during the film formation and presented excellent miscibility.
## 2 Materials and Methods
### Identification of the Volatiles from EOs and Hydrolates by GC-MS  
The identification and quantification of volatiles in the EOs and hydrolates of sea fennel were conducted using GC-MS. A gas chromatograph (model 8890 GC) equipped with an automatic liquid injector (model 7693A) and a tandem mass spectrometer (MS, model 7000D GC/TO, Agilent Inc., Santa Clara, CA, USA) were used for the analysis. A non-polar HP-5 MS UI column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Inc., Santa Clara, CA, USA) was used for chromatographic separation. Helium served as the carrier gas with a flow rate of 1.0 mL/min, with a sample injection volume of 1 \(\upmu\)L. The analyses were performed in MS full-scan mode (33-350 \(m/z\)), the ion source temperature was set to 200 \({}^{\circ}\)C, the interface temperature to 250 \({}^{\circ}\)C, and the ionization voltage to 70 eV. For the EO analysis, the column temperature was held at 60 \({}^{\circ}\)C for 3 min and then increased at a rate of 3 \({}^{\circ}\)C/min to 246 \({}^{\circ}\)C, where it was maintained isothermally for 25 min. For the hydrolate analysis, the oven temperature was initially held at 40 \({}^{\circ}\)C for 3 min and then increased to 80 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, followed by a further increase to 220 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, where it was held for 5 min. Compound identification was based on comparing the retention indices with a series of \(n\)-hydrocarbons and matching the mass spectra with commercial databases (Wiley 7 MS library, Wiley, NY, USA, and NIST02, Gaithersburg, MD, USA) and the literature [28]. Quantification was performed by calculating the relative peak areas (%) using the normalization method (without correction factors). All the analyses were conducted in duplicate, and the percentages shown in Tables 1 and 2 represent the mean values.
## 2 Materials  
### Study Area  
The study area was at Hengshui City (37\({}^{\circ}\)03\({}^{\prime}\)N-38\({}^{\circ}\)23\({}^{\prime}\)N, 115\({}^{\circ}\)10\({}^{\prime}\)E-116\({}^{\circ}\)34\({}^{\prime}\)E) of Hebei Province and included 11 counties, such as the Shenzhou, Zaoqiang, Gucheng, Wuyi, Anping, Raoyang, Wuqiang, Fucheng, Jingxian, Jizhou, and Hengshui counties (Figure 1). The Hengshui City was a part of the North China Plain, which was an alluvial plain of the Yellow River, Huaihe River, and Haihe River [40]. In this region, winter wheat was the dominant crop and the main crop planting patterns were the winter wheat and summer maize rotations. Every year, the winter wheat was sowed in early October and harvested in early June. The climate was semi-humid and semi-arid with an abundant solar heat resource. The mean annual precipitation was about 510 mm, and \(>\)70% fell in the summer. Spring was a dry season. The annual mean temperature was 12.7 \({}^{\circ}\)C and groundwater depth was below 10 m. The soil was loam fluvo aquatic soil. The annual irrigation was about two or three times and the N fertilizer was about 240 kg N/ha for the winter wheat cultivation. The main factor affecting the winter wheat growth was a water deficit with a heavy exploitation of the deep groundwater [41]. In addition, the government conducted a strategy for reducing the fertilizer rate in recent years. Taking into consideration the influence of hydrothermal and fertilizer factors on the crop growth simultaneously, the production-estimation was practical and essential for the winter wheat cultivation in this region.