esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The olive tree is considered one of the most important cultivated crops in Lebanon. It represents approximately 43% of the total area cultivated with fruit trees [1]. This crop has been threatened by several pests, especially the olive fruit fly (OFF), _Bactrocera oleae_ Rossi (1790) (Diptera: Tephritidae). This pest is widely distributed in the Mediterranean basin, Middle East, and North Africa and poses a severe economic threat for commercial olive growers, causing economic losses [1, 2, 3, 4]. In fact, in areas where the olive fruit fly is well established, such as in the Mediterranean region, it has been responsible for approximately 30% of crop losses, especially in countries such as Greece, Italy, Spain, and Tunisia, where a large amount of commercial production occurs. If not treated, it is capable of destroying 100% of table cultivars and up to 80% of the oil value [2, 3, 4]. In Lebanon, the damage losses are not yet reported. The adult females deposit their eggs (50-400 eggs in one lifetime) under the skin of the fruit [5]. The larvae develop and feed on the pulp of the fruit. Infested fruit sometimes drop prematurely. The secondary infestation of bacteria and fungi accumulated in the tunnels created by the larva inside the fruit reduces the oil quality [6, 7]. Thus, in years with large fly populations, damaged fruit cannot be processed as table olives, and, in the case of olive oil production, _B. oleae_ directly affects the amount and the quality of the oil by increasing the acidity degree [6, 7]. The flies are very mobile and have the ability to seek out cooler areas of the orchard and urban trees. Their mobility and the fact that generations overlap make the treatment of this insect a complicated task [8; 9; 10]. Moreover, the activity of _B. oleae_ is influenced by several factors, including the latitude, altitude, tree load, irrigation [11], temperature [12], relative humidity [13], variety [14], physicochemical characteristics of the drupe [15], and parasite activity [16]. In Lebanon, chemical spraying is the main option used to control _B. oleae_[4]. Given its aggressive mode of behavior, the control of the olive fruit fly is highly complex, as conventional chemical control remains ineffective and has a negative impact on the environment and human health, with associated problems of resistance and side effects for beneficial insects [17; 18]. There are alternative control measures for this important pest that are more environmentally friendly, such as Spinosal bait spraying, based on the monitoring of the pest population through several types of traps, especially yellow sticky (YS) traps [19; 20; 21; 22]. Thus, the objectives of this study were to evaluate the critical parameters involved in determining when, where, and how to spray, especially the number of adults captured by yellow sticky traps equipped with the attractant ammonium bicarbonate; we also performed observations of the infested fruits, coupled with the collection of climatic data (temperature, relative humidity, etc.) and the phenological characteristics of the tree, especially the fruit load rate.
# Environmental Impact of Massive Food Services and Homemade Meals: A Case Study on Steved Chickpeas
## 2 Materials and Methods  
This study focuses on comparing the various environmental impacts associated with food preparation. Specifically, a massive food service at a University in Chile and two recipes for homemade meals were examined (see Supplementary Materials). As a case study, the recipe for stewed chickpeas was selected since it represents a traditional and popular dish, offering a rich source of essential nutrients suitable for vegetarians and vegans, including protein for muscle repair, dietary fiber for digestive health, healthy fats for heart health, and vitamins/minerals for overall well-being [24]. The study's system boundaries encompass ingredient acquisition, processing for cooking, transportation, waste treatment, and wastewater treatment.  
For the analysis, the life-cycle assessment (LCA) approach allows us to quantify, compare, and evaluate the environmental impacts associated with different production processes [25]. It includes GHG emissions, water footprint, and overall environmental footprint generated by the production systems. The data obtained provide a comprehensive view of the environmental effects of each scenario, offering relevant information for decision-making that contributes to reducing the environmental impact associated with the production, preparation, and consumption of these food preparations.  
LCA emerges as a tool to assess which processes or materials contribute most significantly to the environmental impacts generated by a product, thereby enabling informed decision-making to reduce these environmental impacts [26]. This research adheres to the principles and structure of life-cycle assessment, as described in the ISO 14040:2006 and ISO 14044:2006 standards, encompassing the following phases: goal and scope definition, life-cycle inventory analysis, impact assessment, and interpretation of results. This study assessed the intermediate and final impacts of a typical Chilean recipe, stewed chickpeas, across various scenarios.  
This study aims to analyze and compare the environmental impacts of a dish of stewed chickpeas in three scenarios: a massive food service operation in a university cafeteria and cooking at home using two different recipes. Scenario 1 corresponds to the food preparation at the massive food service, while Scenario 2 and Scenario 3 correspond to two homemade meal recipes. Therefore, the functional unit in all three scenarios described is 100 g of stewed chickpeas. In this regard, the quantification of the impacts for each scenario was established, indicating which processes and ingredients contribute most significantly to the environmental impacts generated in each scenario. This information helps to take appropriate measures to reduce the environmental impacts.  
Furthermore, the scenario with the lowest environmental impact was identified, providing consumers with information on whether it is more environmentally friendly to cook at home or dine at a massive food service.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 2 Related Work
### Satellite Navigation  
The Global Navigation Satellite System (GNSS) plays a pivotal role in delivering positioning services that are essential for a wide range of applications, from personal navigation to complex industrial operations. In recent years, there have been two research hotspots: satellite signal processing methods and navigation algorithms in severe climate situations.  
In Signal Processing, the focus of research is often on how to improve the positioning accuracy by changing the transmission sequence or modulation mode. In [29], the author proposed an optimization-based tightly-coupled precise point positioning (PPP) inertial navigation system (INS) vision integration method to achieve precise and continuous state estimation. In [30], the author presented a new broadband multi-carrier navigation modulation, namely orthogonal frequency division multiplexing with binary offset carriers (OFDM-BOC) in order to improve spectrum efficiency, tracking performance and anti-interference ability. In [31], a novel M-estimation-based robust iterated cubature Kalman filter (ICKF) was developed to minimize the impact of GNSS outliers while improving the correction effect of high-quality line-of-sight (LOS).  
In Severe Climate Algorithms, the focus of research is the algorithm design to reduce the interference in remote areas with severe climate. In [32], the author implemented the tightly coupled integration of navigation satellites with a low-cost micro-electromechanical system's (MEMS) inertial measurement unit (IMU) to improve vertical accuracy and applied nonholonomic constraints to improve the standalone MEMS inertial navigation system (INS) performance during an outage. In [33], the author developed an adaptive-robust fusion strategy for low-cost GNSS systems which can provide reliable fusion positioning solutions when the GNSS signal is challenged.  
In summary, navigation systems used to be studied as separate modules. Other navigation-related services simply invoke the navigation module as clients. Such a fragmented design approach obviously has a large room for improvement in resource utilization and system reliability, especially in many emerging scenarios that require navigation system assistance.
## 2 Method  
### Design of the Proposed Double-Mirror System  
In the present study, we first proposed the application of a double-mirror system for extending viewing distance. The system consisted of two mirrors, one concave and one convex. The convex mirror first reduces the image and enlarges the field of view, and the concave mirror then enlarges the image. Finally, the image is viewed by the human eye.  
The diotpters of the two mirrors were +2.83 D and \(-\)2.83 D, respectively. The distance between the human eye and the concave mirror was 400 mm; the distance between the concave mirror and the convex mirror was 145 mm; and the distance between the convex mirror and the object was 280 mm.  
The imaging of the double-mirror system can be illustrated through the example of thin lens imaging. A concave mirror is equivalent to a convex lens, and a convex mirror is equivalent to a concave lens for simulation. Based on this, the distance from the eye to the image can be up to 2.285 m, and the paraxial magnification of the image is 3.386 X, as shown in Figure 1.  
Figure 1: Design of the double-mirror system and simulation of the image position.  
The image observed by a user is a target of 10 mm \(\times\) 10 mm, which is at the right-bottom side of the double-mirror system and can be analyzed using the optical software, LightTools, illustrated in Figure 2[21]. In the simulation, the concave mirror is of 140 mm wide and 70 mm high, with a curvature radius of 706.7 mm in the y-z plane and 745 mm in the x-z plane. The convex mirror is also 140 mm wide and 70 mm high, with a spherical curvature radius of 706.7 mm. When viewing the target through the double-mirror system, both eyes rotate to a vergence angle of 1.62\({}^{\circ}\) and can observe an enlarged image at 2285 mm with a size of 0.254 mm wide and 0.195 mm high, as shown in Figure 3a,b for the left and right eyes, respectively. A directly viewed image of the same target at 2285 mm is 0.075 mm wide and is shown in Figure 3c for comparison. The simulations indicate that the magnification of the double system is around 3.386 X along the horizontal direction and 2.6 X along the vertical direction. In fact, these values are obtained by assuming the pupil distance to be 65 mm and can vary significantly for different pupil distances. Besides the magnification difference between the horizontal and the vertical axes, directions needs to be improved for further applications.
## 2 Physical and Mathematical Models
### Assumptions and Boundary Conditions  
For ease of calculation, the following assumptions were made:  
1. Airflow in the carriage is a three-dimensional steady-state turbulent flow and the air is an incompressible gas. The change in air density adopts the Boussinesq assumption, and the air pressure in the carriage is approximately equal to the ambient pressure [5].
2. Air is a transparent radiation medium. The inner walls of the carriage, tables, chairs, and passenger surfaces are diffuse radiation gray bodies. Boundary Conditions: 1. Inlet boundary: The air inlet is the speed inlet boundary; \(u=0\), \(v=0\), \(w=-1\) m/s; the parameters of the adjustment working conditions are calculated based on the meteorological conditions along the railway line and different FAV. Turbulent kinetic energy and turbulent energy dissipation rate [24] were \(k=0.004\), \(\varepsilon=0.0008\).
3. Outlet boundary: The front and rear doors of the carriage were used as the outlet boundaries, \(P=P_{\text{out}}\), \(\partial k/\partial x=0\), and \(\partial\varepsilon/\partial x=0\).
4. Wall boundary: The middle section, luggage rack, and front and rear ends of the carriage were adiabatic boundaries, and the air-solid contact surface was a speed non-slip condition. In this study, the z264 train with the longest running period in summer was selected as the research object. The stations, altitudes, and arrival times (GMT + 8) are listed in Table 2. Considering the attenuation and delay effects of the train enclosure structure on the process of solar radiation heat transfer, the unsteady heat transfer calculation method [25] was used to obtain the inner wall temperatures of the train body and window at different times as temperature boundary conditions for  
Figure 3: Comparisons between CFD modeling values and reference values [23].  
Figure 2: Physical model of classroom.  
the numerical solution. The train windows were subject to solar radiation transmission effect based on the solar azimuth at different moments. The temperatures of the inner walls of the carriage when the train passed through different stations are shown in Figure 4.  
1. Heat source boundary: Human body heat dissipation is an important part of the cooling load of a train and has a significant impact on the flow and temperature fields in the train. The heat dissipation was 116 W per person [24] and the heat was evenly distributed on the surface of the human body model.
2. Human breathing boundaries: Human respiration affects the concentrations of pollutants in carriages, thereby influencing the quality of fresh air. The amount of CO2 produced by passengers was 18 L/(h-person) [26].
## 3 Results and Discussion
### Bio-Physico-Chemical Properties
#### 3.2.4 Ca2+-ATPase Activity, Reactive SH Content, and Surface Hydrophobicity  
Ca2+-ATPase activities and reactive SH contents of NAM extracted from unwashed mimee and surimi samples are shown in Table 1. Both biochemical indicators are related to myosin integrity as a result of the washing cycle [27]. The greatest of Ca2+-ATPase activity was found in unwashed mimee (\(p<0.05\)). A marginal decrease in the Ca2+-ATPase activities was observed in all surimi (\(p<0.05\)), regardless of the number of washing cycles. [PERSON] et al. [20] reported that a gradual decrease in the Ca2+-ATPase activities was found in sandstone, mackerel, pink perch, and croaker surimi, when compared to unwashed mimee. The loss of myosin integrity caused by denaturation and/or aggregation was suggested by a decrease in Ca2+-ATPase activity of NAM [27]. Loss of Ca2+-ATPase activity can be attributed to the structural changes of the myofibrillar proteins during washing process [20].  
The presence of SH groups in surimi can promote the formation of disulfide bonds, which is necessary for gel strengthening upon gelation [28]. From results, reactive SH  
Figure 1: Changes in absorption spectra in the Soret region (380–450 nm) of myoglobin extracted from unwashed mimee (U) and surimi from Indian mackerel prepared by one washing cycle (W1), two washing cycles (W2), and three washing cycles (W3).  
content of surimi tended to increase after washing for one cycle (W1), and the significant increase was found in W2 or W3 treatment (\(p<0.05\)). Thus, the washing can enhance the unfolding of protein structure leading to expose of buried SH. According to [PERSON] [29], the head and tail sections of the myosin molecule included several reactive SH groups. The reactive SH groups in myosin molecules may become increasingly exposed when their conformation changes [30]. A non-significant difference in SH content with a longer washing cycle could be related to the limited number of SH or the creation of disulfide linkages during washing, resulting in a constant SH content. SH residues are required for disulfide bonding to enhance the gel structure [27; 28].  
The surface hydrophobicity values of unwashed mince and surimi samples are shown in Table 1. Generally, the hydrophobic amino acid residues are situated internally in the protein molecules [11], and the augmentation in surface hydrophobicity is related to the exposed hydrophobic groups of protein molecules [31]. From the results, the lowest surface hydrophobicity was observed in unwashed mince (\(p<0.05\)). For washed treatments, the increasing of washing cycles markedly increased surface hydrophobicity of surimi (\(p<0.05\)). Protein unfolding exposes hydrophobic amino acids, changing the hydrophobicity of the protein's surface [32]. However, a good gel-forming ability of protein needed a proper change on surface hydrophobicity [9]. During thermal gelation, exposed hydrophobic residues can form interactions and strengthen the gel network [7; 15].
## 3 Experimental Results by Unit Testing
### Anechoic Chamber Test  
Lastly, the measurement test was conducted in an anechoic chamber. As shown in Figure 7, four anchors were installed at a 5 m interval in the length, width, and height directions, and the position of tags were repeatedly measured from four locations in the chamber.  
The results of the stopping test in anechoic chamber are presented in Table 2. The tags were installed to be fixed on a tripod. Measurement errors occurred on average 55.75 mm and 57 mm in the x- and y-axes directions, but on average 144.25 mm in the z-axis direction, slightly more than the x- and y-axes direction.
## 2 Methods
### Filter Design
#### 2.5.3 Beam Area  
The area around the steering angle is the beam area, which defines the main lobe of the directivity pattern:  
\[\begin{split}\phi_{beam}&=\{\phi_{m}|\phi_{steer}- \phi_{b}\leq\phi_{m}\leq\phi_{steer-1}\ \wedge\phi_{steer+1}\leq\phi_{m}\leq\phi_{ steer}+\phi_{b}\}\\ &\text{with }\phi_{b}\in\mathbb{R}^{\geq 0}\end{split} \tag{17}\]  
Figure 4: Different spatial areas in the directivity pattern optimization problem. The steering angle \(\phi_{steer}\), the beam area \(\phi_{beam}\) (indicated by horizontal hash lines), an area without any constraints \(\phi_{unconstrained}\) (indicated by crossed hash lines) and the stop area \(\phi_{stop}\) (indicated by vertical hash lines).  
\(\phi_{steer-1}\) and \(\phi_{steer+1}\) indicate one discrete angle before and after the steering angle, respectively. The constant \(\phi_{b}\) can be chosen freely and defines the width of the beam area. Fitting the directivity pattern to the desired one, an angle-dependent upper bound \(\epsilon_{beam}\) is set to the error (cf. Equation (14)) in this area:  
\[\begin{split}&\text{abs}(\mathbf{error}_{beam})\leq\epsilon_{beam }\\ &\text{with }\epsilon_{beam}\in\mathbb{R}^{\geq 0}\end{split} \tag{18}\]  
where \(\text{abs}()\) denotes the absolute value of every entry of the vector argument. In this case, \(\epsilon_{beam}\) is a column vector with as many entries as the directivity pattern in the beam area.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Raff Culture Zone Fusion Evaluation
#### 3.2.1 GF-1 Fusion Evaluation  
As shown in Table 5, the average gradient of the rafts in each band of the fusion images (Figure 8) is higher than that of the original multi-spectral data, indicating the definition of each fused image has been improved to some degree. The standard deviation, information entropy, and average gradient of the B1, B2, and B3 bands in the NNDiffuse results are higher, indicating clear detail and diverse textural information. The standard deviation and information entropy in the B4 band of the PCA fusion results are higher than with other techniques. The average gradient in the B4 band of the GS fusion results is also higher than with other techniques. Correlation coefficients in the B3 and B4 bands of the NNDiffuse results are higher, indicating the gray levels of each band are scattered and the information is uniform. The spectral distortion and the deviation index were the smallest in each NNDiffuse band, indicating lower spectral information loss and higher spectral fidelity. The spectral retention produced by GS fusion is lower than with NNDiffuse and the gray mean value of each NNDiffuse band is closest to the gray mean value in the original multispectral images. This suggests the NNDiffuse method is capable of maintaining spectral characteristics, producing the best overall effect.  
The NNDiffuse technique produces the best fusion results for GF-1 data in floating raft culture areas, as measured by qualitative and quantitative comprehensive evaluations.  
Figure 10: Comparison of fusion effects for WV-2 data for floating raft culture.
## 3 Results
### Comparison between BDS Single-System and Joint POD
#### 3.3.2 Comparison with GBM Orbit  
Strategies 1 (Table 5) and 3 (Table 9) are compared with the GBM orbit products, and the results are shown in Figure 6. The figure shows that the results compared with the GBM orbit are different from the results of the overlapping arcs precision. Among them, for GEO and MEO satellites, the difference between strategy 1 and GBM is less than that of strategy 3. On the contrary, for IGSO satellites, the difference between strategy 1 and the GBM orbit is larger than that of strategy 3.  
Figure 5: Comparison of overlapping arcs precision between strategies 1 and 3. (**a**) 3 DRMS; (**b**) Along-track RMS; (**c**) Cross-track RMS; (**d**) Radial RMS.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Forecasts Combination
#### 2.3.2 Linear Quantile Regression Averaging  
Linear quantile regression averaging (LQRA) is a specific type of QR averaging defined in Section 2.3. It contains a model involving the response variable and the independent variables of the combined forecasts. Let \(y_{iq}\) be the wind speed forecast for the next two days, with \(K\) total number of methods for predicting the next observation, \(y_{i+1},y_{i+2\cdots}\) of \(y_{i+K_{q}}\); the combined forecasts are expressed by Equation (9), as given in [12].  
\[y_{iq}^{LQRA}=\beta_{0}+\sum_{k=1}^{K}\beta_{k}\hat{y}_{ik}+\varepsilon_{iq}, \tag{9}\]  
where \(\hat{y}_{ik}\) represents the forecast from \(k\)th method, \(\hat{y}_{iq}^{LQRA}\) is the combined forecast and \(\varepsilon_{iq}\) is the error term. We seek to minimise Equation (10)\[\arg\min_{\beta}\big{(}\sum_{i=1}^{N}\rho_{q}(y_{i}^{LQRA}-\beta_{0}-\sum_{k=1}^{ K}\beta_{i}\beta_{ik})\big{)}. \tag{10}\]  
We express Equation (10) in reduced matrix form [40] analogous to Equation (8), as seen in Equation (11):  
\[\arg\min_{\beta\in A^{p}}\big{[}\sum_{i:y_{i}^{LQRA}>x_{i}^{T}\beta}q(y_{i}^{ LQRA}-x_{i}^{T}\beta)\sum_{i:y_{i}^{LQRA}<x_{i}^{T}\beta}(1-q)(y_{i}^{LQRA}-x_{i}^{T} \beta)\big{]}. \tag{11}\]