esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Interaction Technique Design
### Bimanual Scaling  
The \"Bimanual Scaling\" interaction model aims to address accuracy issues stemming from gesture pointers or proximity interactions, employing multimodal interactions to enhance the interaction's overall fluidity. Building upon traditional gesture interactions, \"Biasmanual Scaling\" incorporates eye-tracking selection in the \"selection\" phase, uses hand distance for data magnification during the \"adjustment\" phase, and the termination of a pinching gesture with both hands as the signal for \"confirmation\". The user interaction process with \"Bimanual Scaling\" is illustrated in Figure 1. Interaction steps include the following: (1) The user looks at the numerical panel that needs to be operated. (2) The user pinches and moves forearms to alter the inter-hand distance. To reduce the value of the data, users decrease the distance between their hands, and to enlarge, they do the opposite. This gesture is similar to the HoloLens2 default zoom action, facilitating user understanding. (3) By maintaining the pinch gesture, users can achieve the continuous adjustment of values. Upon completion of adjustments, users can release their hands to signify the confirmation of the input result. Implementation-wise, reflecting user tendencies for broad initial adjustments followed by fine-tuning to approach the target value, Bimanual Scaling's hand-distance magnification function is non-linear, accommodating coarse-to-fine input refinement. The function is as follows:  
\[y=\left\{\begin{array}{ccc}(1+C\cdot SmoothStep(0,0.5,x^{\prime}))\cdot x^ {\prime}&if&x^{\prime}\leq 0.5\\ (1+C\cdot SmoothStep(1,0.5,x^{\prime}))\cdot x^{\prime}&if&0.5<x^{\prime} \leq 1\\ x^{0.5}&if&x^{\prime}>1\end{array}\right. \tag{1}\]  
In the above function, \(x^{\prime}\) is defined as \(x^{\prime}=\sqrt{\frac{x}{X_{0}+0.0001}}\); \(x\) is the current distance between two hands and \(x_{0}\) is the original distance between two hands. The constant value C was designed to adjust the sensitivity of this operation and, on our current device, the value C was set at 0.45. The values 0.5 and 1 were chosen as thresholds to create distinct scaling modes or sensitivity ranges within the interaction. For \(x^{\prime}\leq 0.5\), the system entered a \"fine-tuning\" mode where smaller hand movements resulted in more precise adjustments. For distances \(x^{\prime}>1\), the system shifted to a \"coarse adjustment\" mode where hand movements lead to larger, more significant changes. The function part \(0.5<x^{\prime}\leq 1\) allowed for a seamless transition between precise and broad adjustments based on the scale of their hand movements, enhancing the interaction's intuitiveness and efficiency.  
The SmoothStep(a,b,t) function is defined as follows:  
\[SmoothStep(a,b,t)=\left\{\begin{array}{ccc}0&if&t\leq a\\ \frac{(t-a)^{2}(3b-2t-a)}{(b-a)^{3}}&if&a<t<b\\ 1&if&t\geq b\end{array}\right. \tag{2}\]
# Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers
## 2 Current Status of Research on Precision Fertilizer Dischargers
### Research Status of Disc Fertilizer Discharge
The above research primarily focused on optimizing parameters such as the angle of the fertilizer disc blades, the effective working length of the fertilizer groove, the size of the curved trough opening, rotation speed (angular velocity, frequency, period), fertilizer discharge opening, fertilizer spreading height, cutting position angle, disc inclination angle, and forward speed through discrete element simulation, bench, and field trials. These methods effectively enhanced the stability and uniformity of fertilizer distribution, offering a foundation and valuable references for further investigations into disc fertilizer discharges. Figure 22.5 Structural diagram of the turntable variable fertilizer discharger. Note: 1. stepper motor; 2. fertilizer injection port; 3. rotating shaft; 4. fertilizer box; 5. vertical screw conveyor; 6. fixed square plate; 7. rotating disc; 8. frame; 9. soil covering suppression mechanism; 10. conical fertilizer bucket; 11. fertilizer discharge pipe; 12. arrow shovel trencher. Figure 22.6 Schematic diagram of the force of granular fertilizer on the conical disc: (**a**) top view; (**b**) xOz face view. Note: \(\delta\) is the horizontal inclination angle of the conical disk delivery section; \(\gamma\) is the radial deflection angle of the push plate.
## 2 Scheduling Strategy of Source-Network-Load in the ADN  
The ADN has a source-network-load ternary structure: \"source\" refers to all kinds of DGs and ESSs in the ADN, with DGs divided into controllable and intermittent types. Common controllable DGs include microturbines (MTs), diesel generators, fuel cells, etc., and intermittent DGs include WTs, PV cells, etc.; the \"network\" mainly includes transformers, lines, switches, and other power equipment, whose important function is to manage the power flow through a flexible network topology; \"load\" refers to various types of load resources on the demand side, including conventional loads, interruptible loads, etc. The control elements of ADN optimization scheduling in this paper include controllable DGs, ESSs, switches, and load resources. From the perspective of source-network-load, the ADN is a distribution system that can coordinate various types of DGs and ESSs, optimize the power flow based on the flexible topology, actively manage demand-side resources, promote the absorption of renewable energy generation, and ensure the safe and economic operation of the distribution network on the basis of meeting users' electricity demands.  
The specific scheduling strategy is shown in Figure 1. The ADN dispatching center uses the corresponding communication devices to collect information of source-network-load three-terminal equipment and then figures out the corresponding dispatching scheme based on the proposed strategy. Finally, the control system controls the three source-network-load aspects to achieve the scheduling goals.  
Figure 1: The scheduling strategy of source-network-load. DG: distributed generation; ESS: energy storage system.
## 2 Materials and Methods
### Sentinel-2
#### 2.4.2 Proximal  
At the LIRF and IIC research sites, proximal surface reflectance and nadir-looking T\({}_{\rm s}\) data was obtained using a handheld multispectral radiometer (MSR5, CropScan Inc., Rochester, MN, USA). The MSR5 radiometer is a compact device consisting of a quasi-cubic radiometer measuring 0.80 \(\times\) 0.80 \(\times\) 0.10 m. It features an integrated IRT sensor from Expergen Corporation in Watertown, Massachusetts. The radiometer has a field of view (FOV) of 28 degrees and captures readings at an altitude of 2.2 m above ground level (AGS). This measurement setup covers an area on the ground equivalent to a 1-meter-diameter circle, with a 2V:1H aspect ratio (Table 6). The attached Expergen IRT, with a FOV ratio of 3V:1H, results in a spatial footprint of 0.80 m in diameter. The MSR5 is a passive sensor, relying on natural sunlight for data collection of surface temperature. It replicates the spectral characteristics of Landsat-5, obtaining data in the visible, NIR, and MIR (mid-infrared) light spectrum. For this study, we deployed MSR5 units at both the LIRF and IIC sites. The multispectral data from these MSR5 devices were sampled about once a week, with four readings at each measurement location across all research sites: more specifically, two readings within the crop rows and two readings in the maize inter-row spaces per site. These readings around solar noon (11:30 a.m. to 1:30 p.m.) were then averaged to obtain the final measurements per sampling location in each research site per field visit. The MSR5 measurements' locations were the reference for the selection of pixels from the different images/platforms or scales, considering areas with homogenous soil texture and canopy cover.
###### Abstract  
A novel concrete beam-column connection utilizing L-shaped steel bars is proposed to address the growing demand for prefabricated buildings and to ensure good seismic performance in such beam-column structures. After positioning two prefabricated beams with L-shaped tendons into the designated connection points at the top and bottom of the columns, concrete is poured into the post-cast section of the joint and the composite beam area, realizing a connection between the beams and columns. Quasi-static tests were performed on four combined backbone curves and one cast-in-place joint to investigate their failure modes and stress mechanisms. Through low-cycle repeated loading tests, it is found that measures such as increasing the area of the post-cast concrete in the joint area, the length of the L-shape, and the concrete strength in the composite beam area can effectively improve the bonding ability between the post-cast area of the joint specimens and the precast members, to improve the ductility performance, energy dissipation capacity, and bearing capacity of the joint specimens. The initial stiffness of the joint can be effectively improved by presenting the steel pipe in the column. Concurrently, the finite element method (FEM) was employed for parameter analysis. By integrating the test and FEM results, an equation for calculating the shear capacity of the connection was derived. The findings demonstrate that the hysteresis curve of the newly assembled joints is full, and its overall performance index is roughly the same as that of the cast-in-place joints. Additionally, enhancing the post-casting area of concrete, the length of the L-shaped bars, the concrete strength in the composite beam region, the axial compression ratio, or the steel tube dimensions can effectively improve the overall performance. The derived equation for the shear-bearing capacity of the connection satisfies design and application requirements.  
s +
Footnote †: journal: Applied sciences  
11
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 1 Introduction
The Monte Carlo method serves as the core simulation technique for modeling photon transport. The rest of the paper is organized in the following manner: Section 2 details the optical parameters of the underwater channel, the selection of an appropriate scattering phase function and the process of the Monte Carlo numerical simulation. Section 3 presents the simulation results and provides a discussion on the normalized power loss and time delay spread as functions of the Transmitted full divergence angle, Received aperture, FOV, and attenuation length (AL). Section 4 concludes the paper with a summary of the findings. This theoretical calculation offers a dependable technical reference for the engineering design of UWOC systems operating in highly turbid waters.
## 7 Taking One Step Further down the Road on the Hybrid Computational Platform
Along these lines, two possible implementations immediately come to mind. One implementation is the use of the ANN-based relative permeability and capillary pressure models in conventional simulators. In other words, during the computations at any reservoir block, at any time step, at any iteration level rather than calling the same subroutine that will return the relative permeability values only as a function of saturation, it will be much more representatively accurate to receive the response from an ANN-based property model that is capable of tracing and updating the requested petrophysical property not only as a function of saturation but also as a function of other independent spatial and temporal properties such as permeability, porosity, interfacial tension or capillary number (the latter two collectively determine the final saturation values). As another example of integration of ANN-based proxy models with numerical models, multi-component, multiphase simulation of coalbed methane (CBM) reservoirs can be considered. For example, consider multi-purpose CO\({}_{2}\) injection into a CBM reservoir for enhanced methane gas recovery and CO\({}_{2}\) sequestration. In compositional modeling of such an implementation, one of the challenges that surfaces may be the construction of multi-component Langmuir adsorption isotherms from individual pure adsorption isotherms. The thermodynamics of multi-component isotherms is analogous to vapor-liquid-equilibria calculations as established by [PERSON] (1999). However, the use of ideal adsorbate solution (IAS) theory still requires expensive flash calculations. In deploying the IAS theory again at any time step in any reservoir block at any iteration level, the computationally expensive flash calculations need to be executed to construct and reconstruct the multi-component adsorption isotherms. Accordingly, a relatively simple two-component (CH\({}_{4}\) and CO\({}_{2}\)) ideal adsorbate construction for multi-component adsorption isotherms can be accomplished with the help of an ANN-based model. Then, such a proxy model can construct and update the multi-component adsorption isotherms in an extremely fast manner as requested by the transport equations. Obviously, implementation of the VLE computations at the reservoir block level with the help of the same proxy model will even accelerate the overall solution much further. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & Full Numerical- & Numerical-ANN Coupled & Full ANN-Based \\  & & Model 1 & Model 2 & Model 3 \\ \cline{2-5} \ \begin{tabular}{c} Gas lift Infection \\ rate (MSE 5D) \\ \end{tabular}  &
\begin{tabular}{c} Well \\ Abandonment \\ Time (days) \\
## 3 Proposed Identification Methods
### Cost Function  
The objective of the parameter identification process is to minimize the mean square error between the actual and estimated angular velocities of the 2 MM system. This is achieved through a cost function (\(Fcost\)) defined as follows:  
\[Fcost=\frac{1}{N}\sum_{i=1}^{N}\left((\Omega_{1}-\hat{\Omega}_{1})^{2}+(\Omega _{2}-\hat{\Omega}_{2})^{2}\right) \tag{8}\]  
where \(\hat{\Omega}_{1}\) and \(\hat{\Omega}_{2}\) represent the estimated normalized motor and load angular velocities, respectively. \(N\) denotes the number of data samples used for parameter identification.
## 4 Analysis of Experimental Results
### Analysis of Slurry Pressure Distribution Law by Grouting Pressure  
Figure 12(a),b represent the plots of slurry pressure variations with diffusion distance at different grouting pressures for fissure openness of 2 mm and fissure roughness of 5.7 and for fissure openness of 2 mm and fissure roughness of 5.7, respectively. The greater the grouting pressure, the larger the external driving force acting on the slurry, resulting in a higher slurry pressure. Overall, the evolution pattern of slurry pressure along the diffusion path is similar, showing a non-linear distribution, and the rate of slurry pressure decay exhibits an 'initial increase and subsequent decrease' trend along the diffusion path. This is because, as the slurry flows from the grouting pipe into the rock fractures, slurry pressure is rapidly released, leading to a faster rate of decay. However, with an increase in diffusion distance, slurry pressure decreases, resulting in smaller pressure differences per unit distance. This phenomenon is due to the fact that the pressure at the discharge orifice is equal to the external atmospheric pressure, and the greater the grouting pressure, the larger the decrease in slurry pressure. Conversely, a smaller decrease in grouting pressure leads to a smaller decrease in slurry pressure.  
Figure 12: Variations in slurry pressure with diffusion distance for different fracture openings.  
Figure 13: Variations in slurry pressure with diffusion distance under different grouting pressures.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Node Deployment  
Clustering in CR-WSNs brings several deployment challenges, such as ensuring PU access rights, SU connectivity, selection of the optimal channel, CH selection (under CR-WSN-all), CH deployment (under CR-WSN-few), and optimal cluster sizes. For measuring an accurate degree of optimum coverage, sensing PU activities, and maintaining connectivity, an optimal strategy for node deployment is essential. Node deployment could be different based on the objectives, such as: (1) PU protection; (2) coverage and spectrum information collection; (3) minimal energy consumption configuration; (4) reduction of event-sensing delay; and (5) reduction of the control channel saturation problem.
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 2 Background  
This section reviews the existing work on energy-efficient clustering and UAV-based data aggregation approaches. A UAV-based data aggregation network architecture differs significantly from a traditional wireless sensor network. For these reasons, existing algorithms for stationary or low-mobility WSNs are not feasible [13]. A data aggregation algorithm based on UAVs needs to be able to adapt to networks with high mobility, sudden changes in topology, and sporadic communication links. WSNs require different levels of quality of service, including delay, packet loss, and reliability on the underlying networks. Furthermore, WSNs are limited in terms of energy, computation power, and network resources. An appropriate data aggregation technique is essential for meeting requirements while respecting WSN limitations. WSNs require time synchronization to coordinate data, energy, and localization. To address the sensor time synchronization problem, the authors of [14] proposed a pairwise broadcast synchronization (PBS) protocol for multi-cluster sensor networks that reduces overall energy consumption while maintaining synchronization accuracy. Reference [15] proposed a distributed heuristic algorithm for selecting appropriate sensors in a multi-hop sensor network to reduce the number of message exchanges needed for network-wide synchronization using PBS.
## 4 Discussion
### Value of Collaborative Features  
The collaborative search feature allows annotators to more efficiently find search results that have previously been used for annotation by making previous annotation choices immediately transparent. Additionally, it allows annotators to compare the already annotated data items to the data item that is currently being annotated and thereby supports annotators in their judgment on whether the search results semantically fit the data item to be annotated. We believe that compared to approaches where a user actively needs to search pre-existing annotation databases to identify possibly relevant prior work, this seamless and automatic integration of pre-existing knowledge generated by collaboration partners into the annotation workflow realizes a collaboration modality adapted well to high throughput annotation workflows by implementing the communication fundamental to all collaboration implicitly and seamlessly as an integral part of the workflow itself. Nevertheless, additional support for explicit communication with collaborators may be useful and is discussed below (see Future Work).  
When annotating data with codes from multiple terminologies, users have to identify (ideally) semantically equivalent codes in these different terminologies. First-time annotation for a medical data item can be challenging since precise identification of the optimal mapping of source item content in its documentation context to a terminology or possibly complex hierarchical ontology requires both medical and technical knowledge--the relationship between the medical data item and the annotation in effect corresponds to creating new knowledge by the annotator.  
However, annotating other semantically equivalent codes from other terminologies after a primary annotation choice has been made in one terminology or ontology could be much easier as the relationships between codes from different terminologies stay the same, are possibly already available, and therefore may allow the user to annotate these equivalent codes. This cross-mapping feature should speed up the annotation process as it reduces the time needed to search for equivalent codes and improve annotation quality.  
The collaborative features were found a welcome addition to the tool by annotators and show promise with regard to improved annotation quality, annotation consistency across sites, and decreased annotation time, but they require further evaluation.
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 3 Experiment
### Evaluation Metrics  
This study employed several metrics to assess the performance and complexity of the model. Firstly, the training accuracy of the model was measured by the following indicators: Precision (abbreviated as P), Recall (abbreviated as R), and mean Average Precision (abbreviated as mAP). Secondly, to evaluate the complexity of the model, the number of parameters (parameters) was considered. These metrics reflect the model's demand for hardware resources and its efficiency. Precision P refers to the proportion of correctly predicted samples among all predicted samples. The formula is as follows:  
\[P=\frac{T_{P}}{T_{P}+F_{P}}\times 100\% \tag{7}\]  
The recall rate (R) serves as a pivotal performance metric, quantifying the proportion of samples correctly identified by the model among all actual positive samples. This metric reflects the model's ability to identify positive samples, indicating its capacity to detect all true positive samples. Typically, a higher recall rate signifies fewer positive samples missed by the model. The formula is as follows:  
\[R=\frac{T_{P}}{T_{P}+F_{N}}\times 100\% \tag{8}\]  
The mean Average Precision (mAP) is a critical metric obtained by calculating the mean of Average Precision (AP). Average Precision (AP) essentially represents the area under the Precision-Recall curve. Across different recall levels, AP measures the average precision, thereby providing a comprehensive assessment of model performance. The formula is as follows:  
\[mAP=\frac{\sum_{i=1}^{N}\int_{0}^{1}P(R)dR}{N}\times 100\% \tag{9}\]  
In this formula, T\({}_{\text{P}}\) (True Positives) represents the number of samples correctly identified as positive, while F\({}_{\text{P}}\) (False Positives) denotes the number incorrectly labeled as positive. F\({}_{\text{N}}\) (False Negatives) refers to the number of samples incorrectly classified as negative. N denotes the total number of classes. In the context of this study, as only the sugarcane species were of interest, the value of N was set to 1.