esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Years have passed, making it difficult to differentiate between the island's genuine landscape due to the natural occurrence of geological formations and anthropogenic coastal landforms for which data is incomplete. The aims of this study are therefore: (a) to differentiate between natural and anthropogenic coastal landforms using the GPR technique and the sediment analysis method; and (b) to discover how ground penetrating radar (GPR) works on sediments influenced by saltwater content and sedimentary structure compaction along the coastlines. Ground penetrating radar (GPR) is primarily designed to investigate the shallow subsurface of the Earth, buildings, bridges, discontinuities and heterogeneity using high frequency electromagnetic waves [13; 14; 15]. An antenna frequency of 500 MHz to 100 MHz gives good GPR penetration in shallow areas (less than 5 to 10 m) and provides clear images of sedimentary structures within sand [16; 17]. Signal attenuation is caused by paedogenic silts, clays, carbonate, and iron oxides [17]. GPR is a fast, non-invasive method that does not require excavation [18]. GPR provides good results in highly electrically resistive sediments such as well sorted and clean sand, gravel, peat, and limestone [19; 20]. Different studies have used GPR ranging from 100 MHz to 300 MHz showing good results in a sandy environment [21; 22; 23]. In this study, the GPR profiles were obtained using 250 MHz antennas at two sites (Figure 1). The results were then validated with sediment sampling and sieving analysis, as practiced in some other studies [24; 25]. The alluvium at the first site in Batu Feringghi is a sandy bay with a rocky headland. In contrast, the alluvium found in site two near Persiaran Bayan Indah is mud, and there was medium and low density urban development interspersed with grassland and coconut plantations before reclamation started [9]. Studies on the geological characteristics of land reclamation or coastline degradation have received more attention lately [26; 27; 28; 29].
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 1 Introduction
### Contribution of the Research  
This study proposes an innovative approach by researching the potential of bifacial panels to meet the energy demands of buildings in Sanlurfa, Turkey. What distinguishes this study is the comparison of bifacial and monofacial panels with the same production potential under the same conditions in the BIPV system. In addition, to enhance the production performance of the back surface of the bifacial panel, a reflector was placed on the wall surface by leaving a gap between the wall and the panel. This study, which compares the performance of the bifacial panel with the monofacial panel under the same conditions, provides valuable insights into the potential benefits of bifacial panels for meeting the electricity energy needs of residential and industrial buildings in Sanlurfa conditions.
## 4 Kinematic Simulation of the Fertilization Mechanism  
### Motion Analysis of Disc Hole-Pricking Fertilizer Applicator  
This section discusses the simulation analysis based on the fertilization mechanism of the vehicle, specifically the disc device, which is illustrated in Figure 19. The central component of this mechanism is the duckbill opening and closing controller. This controller consists of fixed parts and movable parts; the movable parts rotate around the lower end of the fixed parts to control the opening and closing of the duckbill. The fixed components include an upper Z-shaped steel plate, while the movable parts are assembled with a lower rolling bearing. The use of the rolling bearing group can effectively reduce the contact friction with the lever and alleviate the wear of components.  
The action schematic is presented in Figure 17. When the fertilizer applicator rotates forward, the duckbill lever moves along trajectory 1 and contacts the rolling bearing assembly. This contact makes the bearing assembly rotate along trajectory 2 until it stops against the Z-shaped steel plate (see the enlarged section in Figure 20). During this process, the lever rotates due to the block of the bearing assembly and drives the movable duckbill assembly to rotate around the fixed shaft, opening the duckbill for fertilization. After the lever disengages from the bearing assembly, the spring inside the movable duckbill returns the lever to the starting position and closes the duckbill, and the bearing assembly rotates back to the initial position by its own gravity, thus completing one fertilization cycle.  
The working vehicle may reverse in the field, causing the fertilizer applicator to experience reverse rotation. The reverse motion diagram is shown in Figure 21. When the fertilizer applicator is in reverse, the duckbill lever moves along the red trajectory 3. Once it contacts the rolling bearing, the rolling bearing rotates in the same direction along trajectory 4 (as shown on the enlarged view in Figure 21). When reaching the groove of the Z-shaped fixed steel plate, the lever passes through, but the movable duckbill remains in its original position in the meantime because of the blocking effect of the fixed duckbill.  
Figure 19: Schematic diagram of disc hole-pricking fertilizer applicator. 1—Feed mouth. 2—Duckbill opening and closing controller. 3—Fixed duckbill. 4—Movable duckbill and lever. 5—Disc fertilizer temporary storage box. 6—Open and close controller moving parts. 7—Rolling bearing.  
During the actual fertilization process, the duckbill controller is vulnerable to moisture, rust, and debris. Moisture causes rust, increasing component friction and impeding bearing group return; debris adhesion disrupts bearing rotation, affecting fertilization cycle accuracy and stability. After each operation, comprehensive inspection and maintenance are needed. Key parts should be checked for rust, rust removed promptly and an anti-rust agent applied, cleaning tools used to remove debris, and the bearing flexibility checked and replaced if necessary to ensure efficient and accurate fertilization.
###### Abstract  
The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complementary tunnel information encoding method. The FDNN model utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to extract features related to tunnel structural deformation. FDNN model is used to predict deformations in the Capital Ring Expressway, and the predictions align well with monitoring results. To demonstrate the superiority of the proposed model, we use four different performance evaluation metrics to analyze the predictive performance of FDNN, DNN, XGBoost, Decision Tree Regression (DTR), and Random Forest Regression (RFR) methods. The results indicate that FDNN exhibits high precision and robustness. To assess the impact of different data types on the predictive results, we use tunnel geometry data as the base and combine geological, support, and construction data. The analysis reveals that models trained on datasets comprising all four data types perform the best. Geological parameters have the most significant impact on the predictive performance of all models. The findings of this research guide predicting tunnel construction parameters, particularly in the dynamic design of support parameters.  
tunnel; deformation prediction; deep learning; performance metrics +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Classification Accuracy Evaluation Method  
In this study, a sample was selected by combining a field survey and visual interpretation. Forty percent of all samples were chosen as validation samples, and the accuracy was validated using the confusion matrix, which is a commonly used method for quantifying classification accuracy. The metrics used to measure the accuracy included the overall accuracy (OA), the producer's accuracy (PA), the user's accuracy (UA), and the kappa coefficient. The calculation formulas and descriptions of the indicators are shown in Table 6.
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 2 PV Prosumers in Distribution Network
### Prosumer Definition and Configuration  
Historically, the term \"prosumer\" was first mentioned in 1980 in [PERSON]'s book _The Third Wave_[17]. In this book, the author discusses the transition of society from an Industrial Era in which production and consumption were separated to the Information Era. In this new concept of society, the term prosumer is created by combining a producer and a consumer. Nowadays, with the increase of integration of PVs in DNs, the notion of prosumer and prosumerism has become increasingly significant in electrical engineering [18]. Therefore, many authors propose the definition of a prosumer. The authors in [19; 20; 21] define a prosumer as an end-user that consumes electrical energy, acts as an energy producer, and shares surplus energy with utility networks and other consumers. In [22], prosumers are described as energy consumers or energy producers in different periods depending on their electricity demand and price. The focus is on the prosumer that only generates electricity. In [23; 24], the authors include a facility for electricity generation and energy storage systems in the prosumer definition. The definition of prosumer was expanded in [25; 26] and includes consumers that generate electricity and/or contain in their configuration home energy management systems (EMS), ESS, electric vehicles (EVs), and electric vehicle-to-grid (V2G) systems. In [27], a prosumer has been deemed as a consumer or an electricity producer and can be actively managed.  
The European Union defined an active consumer uniform in its 2016 directive [28]. According to [28; 29], an active consumer is \"a customer or a group of jointly acting customers who consume, store or sell electricity generated on their premises, including through aggregators, or participate in demand response or energy efficiency schemes provided that these activities do not constitute their primary commercial or professional activity\".  
While a variety of definitions of the term prosumer have been suggested, this paper uses the following definition: a prosumer is an entity that not only withdraws/retracts energy from a network but also produces energy that can be consumed, stored or sold to the network and other consumers and actively participates in providing more flexibility such as voltage and reactive power control.  
Prosumer configuration and interconnection with DN is shown in Figure 1. The main representative of distributed generation is the PV system. It often includes an ESS such as a battery in its configuration and together forms a hybrid system. EVs, electric V2G vehicles, smart home EMS, and other flexible loads represent demand-side management (DSM).
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
## 2 Materials and Methods  
The data used in this work were collected from two different geographical regions as described:  
* near Cairo: Cairo International Airport, Egypt, time zone UT + 2 with latitude 30.13\({}^{\circ}\) N, longitude 31.40\({}^{\circ}\) E and altitude 36 m;
* near Riyadh: Al Ahsa, Saudi Arabia, time zone UT + 3 with latitude 22.0\({}^{\circ}\) N, longitude 51.0\({}^{\circ}\) E and altitude 138 m.  
Three experiments were run with both datasets at different settings of field orientation tilt angle \(\beta\) and azimuth angles \(\alpha_{z}\). These settings were determined to include the most common values of (\(\beta\) and \(\alpha_{z}\)) used in both regions as follows:  
1. Experiment 1 at (30\({}^{\circ}\) and 0\({}^{\circ}\));
2. Experiment 2 at (30\({}^{\circ}\) and 20\({}^{\circ}\));
3. Experiment 3 at (30\({}^{\circ}\) and 30\({}^{\circ}\)).  
The values of the meteorological data (\(T_{A}\), \(V_{2n}\), \(G_{i}\)) were collected from the available database of both regions. These data cover the twelve months of the year.  
The method proposed to predict the assessment measures and lifetime, or the time over which the system can work properly, is composed of the following three major steps, which are described in later subsections.  
1. Simulation of the PV system;
2. Factorial design experiments;
3. Reliability analysis.
## 3 Results and Discussion
### Plants Isotopic Values (C-N-S), Comparison Between Regions
#### 3.1.1 \(\delta^{13}\)C of Plants
This indicates that they do not differ significantly, likely because plant-based organic fertilizers have \(\delta^{13}\)C values similar to those of C3-C4 plants (\(-29\) to \(-9\%\)). In contrast, animal-based organic fertilizers have \(\delta^{13}\)C values that reflect different mixing processes, such as sedimentary organic carbon, soil CO\({}_{2}\), freshwater and marine carbonates, and animal-based atmospheric CO\({}_{2}\) compositions. In addition, \(\delta^{13}\)C values of cereal grain samples range between \(-23.2\) and \(-26.2\%\). According to a study conducted by [PERSON] et al. [60], the \(\delta^{13}\)C values of barley grain samples from bothconventional and organic farming systems vary from \(-\)28.8% to \(-\)27.5%. It also reveals that there was no significant difference in the grain samples from different crop management systems (\(p>0.05\)) [60]. These values are consistent with the C3 photosynthetic cycle, and the \(\delta^{13}\)C values for this type of plant can vary from \(-\)30% to \(-\)20%., mainly because of the climate regime. In fact, \(\delta^{13}\)C values of Greek barley grain samples fall within this range. Therefore, carbon isotopes do not appear to be affected by manure, in contrast to nitrogen isotopes, which are optimum tracers for manuring plants [61].
## 2 Problem Formulation and Preliminaries
### Prescribed Performance Control  
In this section, we will introduce the main idea for the PPC approach, which is used for linearizable MIMO nonlinear systems [13].  
First, the constraints are imposed on the controlled variable \(e(t)\), which are designed as upper and lower bound constraints. The mathematical expression of the prescribed performance specification can be formulated by the following inequality [13]:  
\[\begin{cases}-m_{\xi}(t)<e(t)<\varsigma(t),\;if\;e(0)>0\\ -\varsigma(t)<e(t)<m_{\xi}(t),\;if\;e(0)<0\end{cases} \tag{1}\]  
for all \(t\geq 0\), where \(0\leq m\leq 1\) and \(\varsigma(t)\) is the performance function satisfying that (i) \(\varsigma(t)\) is a smooth function, (ii) \(\varsigma(t)\) is positive and decreasing, and (iii) \(\lim_{t\rightarrow\infty}\varsigma(t)=\varsigma_{\infty}\), where \(\varsigma_{\infty}\) is a small positive constant.  
Second, by means of spatial equivalent mapping, the nonlinear system under constraint (in the sense of (1)) can be transformed to the unconstrained system of \(\varepsilon\). More specifically, define  
\[\varrho(t)=\Gamma(\varepsilon) \tag{2}\]where \(\varrho(t)=\frac{e(t)}{\varsigma(t)}\), \(\varepsilon\) is the transformed error, and \(\Gamma\) satisfies  
(i) \(\Gamma(\varepsilon)\) is strictly increasing and smooth  
(ii) \(\begin{cases}-m<\Gamma(\varepsilon)<1,\ if\ e(0)>0\\ -1<\Gamma(\varepsilon)<m,\ if\ e(0)<0\\ \end{cases}\)  
(iii) if \(e(0)>0\)\(\begin{cases}\lim_{\varepsilon\rightarrow-\infty}\Gamma(\varepsilon)=-m\\ \lim_{\varepsilon\rightarrow+\infty}\Gamma(\varepsilon)=1\\ \end{cases}\)  
if \(e(0)<0\)\(\begin{cases}\lim_{\varepsilon\rightarrow-\infty}\Gamma(\varepsilon)=-1\\ \lim_{\varepsilon\rightarrow+\infty}\Gamma(\varepsilon)=m\\ \end{cases}\)  
A diagrammatic sketch of \(\varepsilon(t)\) is represented in Figure 1. The boundedness of \(\varepsilon\) leads to \(-m<\varrho(t)<1\) in the case of \(e(0)\geq 0\) and \(-1<\varrho(t)<m\) in the case of \(e(0)<0\). Hence, the evolution of \(e(t)\) with some constraint specification, namely, (1) holds if \(\varepsilon\) is bounded.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 3 Controller Design  
In this section, a sliding mode controller [31; 32; 33] is designed to track the command signal. The structure of the control system is given by Figure 6, wherein the block SMC refers to the Sliding Mode Controller, and the block RBF is employed to provide compensation induced by the input saturation constraint block named Sat.  
Take the control purpose as \(x\to x_{d}\), where \(x_{d}\) is the command signal and define the error as \(e=x-x_{d}\), then \(\dot{e}=\dot{x}-\dot{x}_{d}\). For the tracking problem, the sliding mode function is designed as  
\[s=\kappa e+\dot{e}, \tag{12}\]  
where \(\kappa>0\). According to the structure of the closed-loop system shown in Figure 6, one has  
\[\begin{array}{ll}\dot{s}&=\kappa e+\dot{e}=\kappa\dot{e}+\ddot{x}-\ddot{x}_{ d}\\ &=\kappa\dot{e}+Ax+Bu+dt-\ddot{x}_{d}\\ &=\kappa\dot{e}+Ax+B(\
u+\tau)+dt-\ddot{x}_{d}\end{array}, \tag{13}\]  
where \(\mathbf{d}t\) is the fluctuation vector, \(v\) is the output of the sliding mode controller, \(u\) is the input of the plant, and \(\tau\) is the error caused by the input saturation constraint.  
In this work, the input saturation constraint is considered and compensated for via the RBF neural network so as to approximate the nonlinear function in a compact set with an ideal precision [34; 35]. Let \(x^{\text{RBF}}\) be the input of the RBF neural network, i.e., \(x^{\text{RBF}}=v\). Then, based on the structure of the RBF neural network shown in Figure 7, one obtains  
\[\tau=W^{\text{\tiny{\text{\tiny{\text{\tiny{\text{\tiny{\text{\text{\
## 3 Results and Discussion
### Influence of Mix Ratio of Wastewaters on Nutrient Removal
For the wastewater with a high COD/TN ratio, the deficiency of nitrogen and phosphorus or the accumulation of toxic substances such as furfural sourced from the FW were possible reasons for hindering the microalgae growth at the late stage of the culture process. Toxic substances such as alcohol, formaldehyde and phenols can inhibit algal growth and decrease nutrient removal efficiency in the treatment of the anaerobic digestate of sludge combined with acidified starch wastewater ([PERSON] et al., 2016). At the end of the culture process, the mixed wastewater with a dilution rate of SW:DFW = 1:19 achieved the best water quality. The corresponding concentrations of COD, TN and TP were 2538, 18 and 0.4 mg L\({}^{-1}\), respectively. The TN and TP concentration reached the discharge threshold values of 80 and 8 mg L\({}^{-1}\). Unfortunately, the COD concentration exceeded 400 mg L\({}^{-1}\) which is a maximum discharge requirement of livestock and poultry pollutants in China ([PERSON] et al., 2016). For the FW discharge, the COD and NH\({}_{3}\)-N concentrations should be lower than 60 and 15 mg L\({}^{-1}\) according to discharge standards of water pollutants for the furfural industry of Heilongjiang province, China. However, the mixed wastewater after microalgae treatment did not achieve these discharge requirements. Additional work must be done to decrease the nutrient concentrations and meet the corresponding values of the discharge threshold. In terms of the carbon/nitrogen ratio, more mixture ratios of SW to DFW should be investigated, such as SW:DFW = 1:1, 1:2. Better mixture ratios of wastewater may be found to achieve high nutrient removal. On the other hand, improvement of gas-liquid mass transfer could be realized by increasing shaker speed or providing aeration. Thus, the oxygen or CO\({}_{2}\) concentration in the culture medium will be increased, which is critical to microalgae growing mixotrophically.
## 6 Conclusions  
Congestion control mechanisms play a prominent role for transmitting real time multimedia applications. TFRC aims to transfer multimedia applications by maintaining a smooth sending rate over the network. However TFRC is unable to produce better results in all possible environments. It needs some improvements in order to be used for multimedia traffic. This paper presents efficient traffic engineering strategies and is applied to TFRC to alleviate the problems of packet losses due to congestion and link errors. An enhanced TFRC includes adaptive RTT and RTO calculations, efficient average loss interval method for finding packet loss rate and adaptive TTL feature for reducing packet loss. Moreover, enhanced TFRC helps to improve the performance by reducing unnecessary retransmissions with the inclusion of enhanced RTT, RTO and TTL features. Simulation results have shown that enhanced TFRC produces increased throughput and less packet loss even with high network traffic, which is useful especially for transferring VOIP and online streaming video applications. As future work, we plan to measure TFRC throughput in adversities like measuring round trip time during dynamically changing bandwidth. Moreover, we plan to differentiate sensitive information by TFRC and establish efficient methods for sending variable lengths of data.  
[PERSON] created the main ideas and wrote the paper; [PERSON] and [PERSON] critically reviewed and revised the paper.  
Figure 6: Comparison of packet delivery ratio between classical and enhanced TFRC.  
Figure 7: Comparison of jitter between classical TFRC & enhanced TFRC.
# New Insights into the Biology of the Emerging Tembusu Virus
## 3 Epidemiology
### Geographic Distribution  
Following the first isolation of TMUV in mosquitoes in Malaysia in the mid-1950s, TMUV has been sporadically reported in SEA, mainly in wild and domestic birds and in trapped mosquitoes [2; 5; 25] (Figure 3). Subsequent entomological, serological and virus isolation studies pointed out a geographical distribution restricted to a few countries. While TMUV was discovered more than 70 years ago, the presence of the virus in human was not reported before 2000 with the finding of TMUV in natives and migrants in Borneo, Indonesia, and in farm workers in China [45; 46]. The first marked TMUV outbreak was reported in 2010 in China in egg-laying ducks [23]. The dramatic decrease in egg production (egg drop syndrome), associated with neurological manifestations in ducks, reaching up to 90% of animals, highlighted the potential nuisance to intensive duck farming. Since then, several epizooties were reported in China, Thailand, Malaysia and Taiwan [3; 26; 63]. Following the identification of new TMUV variants in Thailand in 2015, a retrospective study confirmed the presence of the virus as early as 2007 [30].  
Despite the lack of information about the virus distribution and its mode of transmission, human population movements and regional trade may provide a partial explanation for the distribution. The impact of bird migration may also explain the emergence of the virus in countries distant from its site of origin. Finally, as TMUV mainly affects birds, its presence may have been unreported or underestimated until farms with large numbers of fowls in close proximity were affected [46; 73].