esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Scenario Analysis
#### 4.3.2 Scenario 2: Grid-tied System Considering the FiT Scheme  
On the basis of this scenario, the total revenue from deploying the proposed HRES can be derived from two ways: (1) by replacing the total purchased electricity that is needed to meet the load requirement with the electricity generated by the HRES, and (2) selling back the surplus electricity generated by the PV panel to the grid, considering the FiT scheme. The optimal configuration of the HRES in this scenario is reported in Table 5. The model estimates the annualized total cost of the system at 262,100 JPY/year. The LCOE was calculated at 59.93 JPY/kWh, which can guarantee a net profit of 15,140 JPY/year for the system.  
In order to provide a sufficient amount of hydrogen for the fuel cell, both sizes of the SCWG reactor and PV panel will increase. To obtain the maximum profit, the first priority is given to the  
Figure 12: Particle positions (PV vs. fuel cell vs. SCWG) in different iterations: (**a**) = 0, (**b**) = 3, (**c**) = 9, (**d**) = 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PV Panel (kW)** & **FC (kW)** & **Electrolyzer (kW)** & **Hydrogen Tank (kg)** & **SCWG (kg/h)** \\ \hline
10 & 1.5 & 1.5 & 5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal configuration of the proposed HRES in scenario 2.  
The results of the techno-economic analysis model for the two scenarios are summarized in Table 6. The comparison between scenario 1 and scenario 2 highlighted the role of the PV module in increasing the profit through selling back the surplus electricity to the grid, using the FiT mechanism. The estimated value of LCOE in scenario 1 was less than its value in scenario 2, which indicates the superiority of the cost minimization problem over the profit maximization problem for this particular case study in which access to biomass feedstock was limited.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 3 Data and Methodology  
### Data  
#### 3.1.1 Source of Data Sets  
The study area addressed in this paper is situated in the Xinjiang Uyghur Autonomous Region of China, including some territories beyond its borders, and extends to countries such as Kyrygxstan, Pakistan, and India. Initially, the debris flow sample data's range within the study area was defined as (72\({}^{\circ}\), 38\({}^{\circ}\)), (73.5\({}^{\circ}\), 38\({}^{\circ}\)), (72\({}^{\circ}\), 37\({}^{\circ}\)), and (73.5\({}^{\circ}\), 37\({}^{\circ}\)). The data utilized in this study primarily originate from two sources: high-resolution, non-offset Google remote sensing imagery data with a resolution of 0.80 m provided by the Xi'ning Center for Comprehensive Survey of Natural Resources under the China Geological Survey, and geological disaster vector data acquired through field investigations conducted by professionals.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 5 Conclusion and Future Work  
In this paper, we introduce a cable-driven hyper-redundant snake-like manipulator (CHSM), then build up a rigorous forward kinematics model base on coordinate transformation and a simplified inverse kinematics model via geometry relationships which has a low time-consuming for real-time control. The correlation filter technique is introduced to get on well with the tracking task, optimized for the adaptive scale variation, and improved on the robustness under the visual servo. The experimental result shows that CHSM has a good trajectory tracking performance and is endowed with some intelligence by the visual tracking technique. This work brings an advanced computer vision technique into the cable-driven snake-like manipulator to form an intelligent scheme. This scheme can be conveniently expanded for more advanced computer vision techniques involved on comprehensive tasks.  
The future work will be carried out as follows. The weight of one tubular unit is a little beyond our expectation which restricts the extension for several units and impacts loading capacity. We have considered two aspects for optimizing it, reducing the radius of the tubular structure or finding an advanced composite material (e.g., carbon fiber reinforced polymer, etc.) instead of steel. The study of this paper is launched on the assumption that the end-effector is restricted to keeping horizontal orientation so that the camera's field of view is limited to some degree. In future work, this restriction will be relieved, and the end-effector can orientate toward any direction to assure that the manipulator can respond with minimal translation or rotation which is critical under the high-speed tracking circumstance. Meanwhile, the inverse kinematics and control loop should be redesigned.  
Conceptualization, J.T. and Z.C.; methodology, J.T. and Z.C.; software, Q.Z. and L.Q.; validation, Q.Z. and L.Q.; formal analysis, Y.N.; investigation, Q.Z. and L.Q.; resources, Z.C. and Y.N.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, J.T. and Z.C.; visualization, Y.N.; supervision, J.T. and Y.N.; project administration, Y.N., J.T. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This paper reports no data.  
The authors declare no conflict of interest.
## 4 Experimental Validation  
### Configuration of the Test Bed  
Experiments were carried out using a test table that has been designed as an ECB test tool. Figures 3 and 4 show a schematic of the experimental setup and a picture of the actual setup, respectively. Essentially, the test table had the same specifications as those in the numerical calculation. The current source used was a voltage rectifier (Lakoni, Falcon 120 E 900 W 120 E inverter, China). The given current was measured using AC/DC clamp meters (Krisbow, KW 0600491, Kawan Lama Grup, Indonesia), while braking force was identified by using a single point load cell. The brushless dc motor (qs motor, 3000 W 138 70H, China) was used as the driving source. The influence of the temperature was disregarded in this experimental work. Table 3 shows the additional conditions used during the experimental test.  
The electric motor is set to operate at a given speed. The experimental test is carried out by activating the ECB with a current of 20 A. After the current is given, the braking test is conducted.  
Figure 3: Experimental setup: (1) pulley; (2) disk; (3) bracket; (4) stopper; (5) load cell; (6) electromagnet; (7) power; (8) magnet.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Unit** & **Value** \\ \hline Electrical current & A & 20 \\ Number of coil windings & - & 360 \\ Number of magnet & - & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Additional conditions used during the experimental test.  
During the braking process, the speed is kept constant at a certain value. It is intended that the load measured by the load cell matches the desired speed.  
Experiments were conducted using a relatively simple principle. Figure 5 provides a schematic flow of the experimental process. The rotation speed is set at the designated speed (150-750 rpm), and the motor torque is delivered to the disk brake through the shaft. During the braking, an electric current is passed to the winding coil in the electromagnet. The generated magnetic field will interact with the disc and create the braking force. The resulting braking force is measured using the load cell, and its obtained data are then displayed on the LED display.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.3 Acquisition of Aerial Images by Drone  
Aerial images were acquired at a flying altitude of 200 m with a ground resolution of 6 cm/pixel and a footprint of 240 \(\times\) 180 m per image. These images were acquired in the visible light spectrum (red, green, and blue) and have already been georeferenced (the geographical location of the center of each image is known). A rate of one image every 42 m allowed us to obtain a longitudinal coverage of 65%. A spacing of 95 m between the flight lines allowed for a lateral overlap of 75%. These significant overlaps ensure proper image stitching in the production of an orthomosaic according to photogrammetric and stereoscopic principles [28; 29]. The entire set of 11 flight plans, each constituting nine parallel flight lines or transects in a north-south direction, allowed us to fly over an area of between 130 and 132 ha (Figure 4).  
Figure 3: The DJI Mavic Pro Drone and its accessories. (**a**) DJI Mavic Pro Drone. (**b**) Holster. (**c**) Radio control. (**d**) Battery. (**e**) Charging hub. (**f**) Car charger. (**g**) Propellers.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.6 Multi-Criteria RS
be the similarities between users \(u\) and \(v\), where \(m=0\), \(1\), \(2\),..., \(n\), then \(sim_{0}(u,v)\) is the similarity between \(u\) and \(v\) with respect to the overall rating \(r_{0}\), and \(sim_{1}(u,v)\) is the similarity between \(u\) and \(v\) with respect to rating \(r_{1}\), up to \(sim_{n}(u,v)\), which is the similarity between \(u\) and \(v\) with respect to the last rating \(r_{n}\). The overall similarity is computed by taking the average of the \(n\) similarities or by considering the slightest (worst-case) similarity, as in Equations (10) and (11), and then finally by going back to our rating prediction algorithms in Equation (2) or Equations (3) and (4) on page 4. Note that for any item \(i\), the utility function \(f\) for \(u\) and \(v\) with respect to \(i\) is \(f(u,i)=r_{0}\), \(r_{1},r_{2},\ldots,r_{n}\) and \(f(v,i)=r_{0}^{*}\), \(r_{1}^{*},r_{2}^{*},\ldots,r_{n}^{*}\), respectively. \[sim_{average}(u,v)=\frac{1}{n+1}\sum_{m=0}^{n}sim_{m}(u,v) \tag{10}\]  
\[sim_{Min}(u,v)=Min\underbrace{sim_{m}(u,v)}_{m=0,1,n} \tag{11}\]  
This method applies only to algorithms that require similarities between users or items to make predictions, such as collaborative filtering. However, other methods work by assuming a solid relationship between criteria ratings and the overall rating as used in [20, 29, 30, 31, 32, 33]. For instance, in Table 1, one can observe that the overall rating \(r_{0}\) has a well-defined relationship with the rating of their corresponding criteria \(r_{1},r_{2},r_{3}\), and \(r_{4}\). This relationship can be formally represented in Equation (12). \[r_{0}=\varphi(r_{1},r_{2},r_{3},r_{4}) \tag{12}\]  
Generally, for any number of criteria \(n\), the relationship function can be extended to account for all \(n\), as in Equation (13). \[r_{0}=\varphi(r_{1},r_{2},\ldots,r_{n}) \tag{13}\]  
The function \(\varphi\) is called the aggregation function. If we can define how to find ratings for each criterion and define the function \(\varphi\), then \(r_{0}\) will be computed easily, as outlined by [PERSON] et al. in [27], who demonstrated how to arrive at \(r_{0}\) pictorially. The following steps summarize the necessary procedures needed to compute \(r_{0}\) using the aggregation function approach:  
* Break down the multi-criteria ratings into single ratings and use any traditional recommendation technique to find the values. I.e., instead of \(f:u\times i\to r_{0}\), \(r_{1},r_{2},\ldots,r_{n}\), use \(f:u\times i\to r_{k}\), where \((k=1,2,\ldots,n)\);
* Define the function \(\varphi\) that can take multi-criteria ratings to produce \(r_{0}\), just as in Table 1, where the function was defined as the average of multiple ratings;
* Lastly, Equation (13) will be used to predict \(r_{0}\).
## 3 Generation Model Effect Analysis
### Control Effect of the Constraint Parameter  
The new algorithm can control the morphology of the pore-throat network model based on parameters such as porosity, coordination number, pore radius, and throat radius, resulting in a more realistic pore-throat network model under parameter constraints. For example, the pore-throat network models generated under different porosities are shown in Figure 13. Within the same range, and under the condition that other parameters remain unchanged, the particle size decreases as porosity increases. As can be seen from Figure 13, the higher the porosity, the more particles and pores, which is in accordance with the physical laws observed in real-world situations.  
It needs to be pointed out that this algorithm is mainly applicable to medium and high-permeability clastic sedimentary rocks. Tight sandstones, shales, and other rocks with multiple media types of pores, throats, and fractures are often not applicable.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 2 Problem Formulation  
### Measurement Model  
Consider that \(M\) time-synchronized UAVs are applied to receive the emitted signals and measure the TOAs with the state vector of each UAV \(\chi_{i}(k)=(x_{i}(k),y_{i}(k))^{T}\), \(i=1,2,\cdots M\). Let \(\mathbf{x}_{t}=(x_{t},y_{t})\in\mathbb{R}^{2}\) be the location of an unknown emitter. The TDOA measurement can be obtained by the difference between any two TOA measurements, eliminating the unknown time of emission. By multiplication of the TDOA measurements by the electromagnetic wave transmission speed, the measurement function in the range domain is obtained:  
\[z_{ij}=r_{i}-r_{j},\,i,j\in\{1,\ldots,M\}\wedge j\
eq i, \tag{1}\]  
with \(r_{i}=\sqrt{\left(x_{t}-x_{i}\right)^{2}+\left(y_{t}-y_{i}\right)^{2}}\) being the distance between the emitter and receiver. Let \(v_{i}\) denote the TOA estimation error, which is assumed to be Gaussian. Then the TDOA measurement equation can be expressed as  
\[\mathbf{\hat{z}}_{ij}=\mathbf{z}_{ij}(\mathbf{x}_{t})+v_{ij},i,j\in\{1,\ldots,M\}\wedge i\
eq j,\,v_{ij}\sim\mathcal{N}(0,\sigma_{i}^{2}+\sigma_{j}^{2}), \tag{2}\]  
where \(\sigma_{i}^{2}\) is the measurement variance of the \(i\)-th receiver of the UAV platform, the measurement noise \(v_{ij}=v_{i}+v_{j}\) is composed of the noise at the two associated receivers and has the covariance \(\sigma_{i}^{2}+\sigma_{j}^{2}\).  
Without loss of generality, let the 1 st receiver be the reference receiver and the others be auxiliary receivers. The variance matrix of measurement matrix \(\hat{\mathbf{z}}_{1j}\) consisting of \(M-1\) measurements can be represented as:  
\[\mathbf{\Sigma}_{r1}=\left[\begin{array}{cccc}\sigma_{1}^{2}+\sigma_{2}^{2}& \sigma_{1}^{2}&\cdots&\sigma_{1}^{2}\\ \sigma_{1}^{2}&\sigma_{1}^{2}+\sigma_{3}^{2}&\cdots&\sigma_{1}^{2}\\ \vdots&\vdots&\ddots&\vdots\\ \sigma_{1}^{2}&\sigma_{1}^{2}&\cdots&\sigma_{1}^{2}+\sigma_{M}^{2}\\ \end{array}\right]. \tag{3}\]  
Therefore, the measurement vector is given by  
\[\hat{\mathbf{z}}=\mathbf{z}(\mathbf{x}_{t})+\mathbf{w},\mathbf{w}\sim\mathcal{ N}(0,\mathbf{\Sigma}_{r1}). \tag{4}\]
## Materials and Methods
### Regression Models  
We use two types of regression models to calculate correlations between SIF emission and WUEs. One is the linear regression (LR) model and the other is the adaptive, nonlinear regression called the Gaussian process regression (GPR) model [38]. The regression analyses were implemented on the hourly and daily time series. The coefficient of determination (\(R^{2}\)) was used to quantify the strength of SIF emission in predicting WUEs. For all analysis, statistically significant differences were set at \(p<0.05\). All the statistical and regression analyses are made using Matlab Statistics Toolbox 2016b (The Math Works, Inc., Natick, MA, USA).  
In addition to these single SIF bands retrieved at the three absorption lines (SIF\({}_{687}\), SIF\({}_{720}\) and SIF\({}_{761}\)), both recent model-based [39] and field-based studies [32; 40] showed that combinations of SIF bands tend to enhance SIF-GPP/\(T\) correlations. Therefore, we added the following two SIF band combinations to the analysis: (1) SIF emission at the O\({}_{2}\)-B and O\({}_{2}\)-A absorption lines (hereinafter referred to as SIF\({}_{687}\) and SIF\({}_{761}\)), and (2) SIF emission at the three main absorption lines including O\({}_{2}\)-B, water vapor, and O\({}_{2}\)-A (hereinafter SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\)).
## 5 New Opportunities for Design and Development of Innovative Last Mile Logistics
### Innovative LML Applications Using New Technologies and Systems for Environmental Sustainability  
Although many extant studies addressed the significant effects of new LML delivery technologies on environmental sustainability (see Section 4.3), adverse effects of new technologies on the environment should be also investigated. For example, using crowdsourcing may have a negative impact on the environment in the road owing to an increase in traffic flow [59]. In particular, greenhouse gas emissions may increase due to open-loop routes of delivery vehicles, and a resultant low per-kilometer emission rate of crowdsourcedvehicles can be offset in that the aggregated car trip distance is over 35% longer than that of trucks due to cars' much smaller capacity [54]. In this regard, the role of future LML delivery services in terms of environmental sustainability should be investigated in more detail to facilitate a transition to environmentally sustainable LML as follows:  
* Management of negative impacts on the environment through technology enabled LML services: From the perspective of environmental sustainability in LML systems, a major challenge is to significantly decrease greenhouse gas emissions that affect climate changes. Replacing existing light delivery vehicles that are powered by an internal combustion engine to EVs for LML services can be more sustainable than traditional delivery trucks or vans [1; 12; 102]. New technologies to achieve sustainable LML can be performed with innovative management strategies for LML deliveries such as LML delivery services during the night avoiding high traffic congestion [103] and using EVs [78] for CO2 emission reduction with fuel saving.
* Sustainable LML system design in urban areas: Collaborative urban logistics services should be considered for better consolidation of existing infrastructure and resources to leverage the environmental sustainability of LML deliveries in urban areas. An environmentally-friendly LML system can be operated by fewer delivery vehicles and light-weight vehicles to lessen emissions [104]. Moreover, the innovative digitalization of LML systems is required for the successful development of a sustainable smart city. Future LML delivery services are expected to integrate automation technologies and digitization into operational strategies to facilitate real-time decisions [88; 105]. Thus, an intelligent system that can monitor and analyze environmental impacts of deliveries in LML in real-time will be necessary to enhance environmental sustainability in urban areas. Similarly, integrating drone- and/or robot-assisted last mile deliveries should be evaluated from both cost and environmental perspectives to suggest sustainable LML operations. For LML innovations through new technologies, policies and regulations for different stakeholders can affect successful implementation of LML [10]. Development of novel methodologies from different perspectives and that of tools to assess the viability of sustainable LML should be also addressed in future LML studies.
## 2 The Mathematical Modeling of OPF Objective Function
### Constraints  
As was previously established, the OPF issue involves both equality and inequality constraints where both of them depend on vectors \(x\) and \(u\) as shown in Equations (4) and (5):  
\[g(x,u)=0 \tag{4}\]  
\[h(x,u)\leq 0 \tag{5}\]  
where the function \(g(x,u)\) represents the equality constraints, and the function \(h(x,u)\) represents the inequality constraints.
## 3 Methodology and Database
### Spatial Analysis  
Spatial analysis is based on the integration of spatial databases, representing the digital and alphanumeric elevation model, represented by the values that outline the unitary drainage hydrograph through HEC-RAS software v. 5.0.7, this being one of the best hydraulic analysis software. The software allows the realization of 2D computation simulation models based on the 2D fully dynamic equations and the 2D diffusion wave equations [46; 47].  
The hydraulic modelling based on the 2D component of the HEC-RAS software v. 5.0.7 is based on spatial and alphanumeric databases that highlight the morphometric and hydraulic characteristics of the analysed spatial system. The database used can be divided into two general categories: the spatial database and the numerical database. The spatial database is represented by the DEM with a resolution of 3.86 cm, a value used to create simulation cells (mesh cells) in obtaining 2D flow area. Additionally, in the category of spatial databases is the upstream runoff profile, which represents the input database in terms of runoff represented by the simulated hydrograph. The numerical database is represented primarily by the Manning's coefficient, which is considered unitary over the entire runoff surface, in this case, the coefficient of 0.045. One of the important parameters in terms of the drainage modelling is the slope of the drain, which is introduced in the simulation model as a numerical database with a value of 0.32 and is obtained based on the operation of the digital elevation model. The flow hydrograph has a total duration of 60 min with an observed flow period of 1 min being identical to the shape with the flood simulated during the analysis stage of the model input databases.  
The spatial analysis models used in the simulations were performed in four simulations, in order to obtain spatial databases that highlight stream power and shear stress, which analysed correlatively highlight the specifics of each simulations based on the 2D component of the HEC-RAS program. The related spatial databases resulting from running the simulation models of velocity, arrival time and maximum depth are used for the general state analysis of the ravine as a result of each simulation.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods
### Preparation of Models  
Irfan view (version 5.54) software was used to crop each image to 1280 \(\times\) 720 pixels, tag it with the YOLO Mark tool, and then enlarge it to that size. The YOLO technique for object detection operates in real time. It is a clever CNN that processes the entire image using a single neural network, before dividing it into regions to predict bounding boxes and probabilities for each zone. It is a cutting-edge CNN that processes the full image using a neural network before segmenting it into sections and forecasting bounding boxes and probabilities. The most recent iteration of YOLO, version 5, often known as YOLOv5, was employed. Compared to another detection model with equivalent performance, the YOLOv5 runs much quicker. The reduced version of YOLOv5, known as Tiny-YOLOv5, is crucial in lowering the convolutional layer. For both the weed and wheat crop picture datasets, the ratio of training to testing validation photos was 70:30. Images utilized for training were not used for testing. On Ubuntu 16.04, GPU was used for all training experiments. Using the torch framework, the model was trained using the YOLO and small YOLO algorithms established by [PERSON] et al. [26]. YOLOv5 outperforms the small, reduced YOLO in terms of speed [24]. The pictures were chosen to be 224 \(\times\) 224 pixels in size, with a base learning rate of 0.001 and a decay learning rate policy (Table 2) and hyper parameter of YOLOv3-Tiny and YOLOv4-Tiny is shown in Table 3. However, 0.95 was chosen as the value of momentum. We utilized the same dataset for all versions of YOLO; thus, we did not need to perform training and validation several times with various different image orderings. To guarantee the right image shuffle and a minimal number of runs for each version of the model, we did utilize certain software and testing.