esa-sceva/satcom-chunk-collection huggingface dataset:
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
###### Abstract  
The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.  
yield; estimation; machine vision; remote sensing; correlative; models; fruit; tree; review
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 4 Simulation Results and Analysis  
### Simulation Setup and Parameters  
In this section, the BER performance of the traditional _LDM_ scheme was evaluated by physical layer simulations. The transceiver chain complied with the ATSC 3.0 specification [21]. The traditional SIC detector with a minimum mean square error (MMSE) equalizer was used to evaluate performance. Two symbol puncturing algorithms and the DIC detector described in Section 3 were implemented. The scattered pilot (SP) scheme shown in Figure 11 was utilized in the simulation. The proposed LDM-Ex-FDM scheme was compared with the traditional ATSC 3.0 scheme in terms of BER.  
The channel models used in the simulator included the AWGN channel and multipath fading channel for both mobile and fixed reception scenarios. The multipath fading channel was a fast-fading model composed of six taps shown in Table 1. Both the ideal and least square (LS) channel estimation methods were adopted. Moreover, the ideal time and frequency synchronization was assumed in the simulation for the sake of simplicity.  
The basic transmission parameters for simulations are shown in Table 2. To facilitate the development of the practical system with low complexity and low memory usage, the LDPC code length was 16,200 and the FFT size was 16K for both the _CL_ and the _EL_ signals. In addition, in terms of modulation and code rate, the LDPC code rate used by _CL_ is 5/15 and the constellation was QPSK because _CL_ is typically designed to deliver robust services to mobile receivers at a low SNR, while the LDPC code rate for the _EL_ was 11/15 and the constellations were NU\(-\)16 QAM and NU\(-\)64 QAM instead due to the fact that _EL_ is typically designed to provide high data rate services to fixed receivers at a high SNR. The puncturing rate was chosen from 1/12 to 1/6, while the injection level varies from 10 to 15 dB. The symbol-level SNR was used in the following part. The simulations were performed on the MATLAB 2017b platform with Intel(R) Xeon(R) E5-2667 central processing unit (CPU) under 2.9 GHz and the 64-bit Windows 7 operating system. All results were obtained on the basis of 10,000 frames of Monte Carlo simulation per SNR.  
Figure 10: Capacities of mobile service and fixed service in ATSC 3.0 system with and without LDM-Ex-FDM scheme over the Rayleigh channel, where the parameter of ATSC 3.0 system was set to \(\Delta=10\) dB, the _CL_ constellation was set to QPSK, the _EL_ constellation was set to NU\(-\)64 QAM, and the puncturing rate of the LDM-Ex-FDM scheme was set to \(1/12\).
## 2 Formulation of the Direct and Inverse Problems
### The Inverse Problems  
Consider the _inverse problem_ (**IP1**), in which, simultaneously, the coefficient \(\beta(t)\) and the source term \(f(t)\) are _unknowns_ and will be reconstructed upon the _additional_ conditions  
\[\int_{\Omega}u(x,t)\eta_{1}(x)dx=\varphi(t),\ \ \int_{\Omega}B(x,t)\eta_{2}(x)dx= \psi(t), \tag{5}\]  
where \(\eta_{1}(x)\) and \(\eta_{2}(x)\) are some weight functions. In particular, if some of the weight functions are Dirac-Delta functions  
\[\eta_{1}(x)=\delta(x-x_{*}),\ \eta_{2}(x)=\delta(x-x^{*}),\ (x_{*},\ x^{*})\in\Omega,\]  
then the conditions (5) take the form  
\[u(x_{*},t)=\varphi(t),\ \ \ B(x^{*},t)=\psi(t). \tag{6}\]  
In this work, we use \(\eta_{1}(x)=\eta_{2}(x)=1\) and then the total energy takes the simple form  
\[\int_{0}^{X}u(x,t)dx = E(t), \tag{7}\] \[\int_{0}^{X}B(x,t)dx = F(t). \tag{8}\]  
Here, \(E(t)\) and \(F(t)\) are known functions.  
We also will study the following inverse problems:  
**IP2.** The convection coefficient \(\beta(t)\) is known, but the source \(f(t)\) is unknown and the overdetermination (7) is given.  
**IP3.** The convection coefficient \(\beta(t)\) is unknown, but the source \(f(t)\) is known and the overdetermination (8) is given.  
Least squares is often used to define the closeness between the computed solutions and the solution measured data objective functional. The resulting methods are referred to as least squares output error criteria procedures; see, e.g., [1; 3; 6]. Such procedures, along with many advantages, also have their disadvantages. For example, they are computationally consuming because iterative procedures (gradient-like search) are used. In addition, through each step in the iteration, they will reduce to the solution of some forward problem which involves the numerical solution of partial differential equations. Thus, there is a clear need to minimize the number of steps in the iteration as well as reduce the range of the global search. One way this can be achieved is to obtain an independent initial estimate of the structure of the control parameters.  
Our goal here is to construct a direct approach to solve the problems IP1, IP2, and IP3. We transform the time-dependent functions identification problems into a nonclassical forward problem. For this, we use the obvious idea of differentiating the relations (7) and (8) with respect to time \(t\) and then to insert the time derivative of the flow velocity \(u\) and the magnetic field \(B\) into the Equations (1) and (2), respectively. As a result, we obtain a new direct initial boundary value problem for the solution \(\{u,B\}\). Then, we concentrate on the construction of efficient finite difference algorithms for solving the nonstandard direct differential problems.
## 3 Results
### LUE and GPP Modeling  
We investigated using PRI and SIF to model two important photosynthetic parameters, LUE and GPP. The statistics of modeling LUE using PRI and SIF are summarized in Table 3. In general, SIF by itself did not generate strong relationships with LUE when examined over time. By itself, however, the PRI exhibited the best correlation with LUE (r\({}^{2}\) = 0.45, RMSE = 0.000324 mg CO\({}_{2}\)/\(\upmu\)mol PAR).  
Figure 3: Cornfield seasonal dynamics of the daily average over four growing seasons for: (**a**) GPP, (**b**) PRI and Normalized Difference Vegetation Index (NDVI), (**c**) SIF yield (\(\times\)100%), and (**d**) SIF (W/m\({}^{2}\)/sr/nm).  
Nevertheless, as shown in Figure 4, the relationship between PRI and LUE was not consistent among the four years (grey lines). Linear fit with r\({}^{2}\) of 0.56, 0.69, 0.50 was found for 2008, 2011, 2012, respectively with different slopes and offsets (Figure 4). By applying a logarithm fit, a 9% improvement was observed for the PRI model over the four-years (r\({}^{2}=0.54\), RMSE = 0.000322 mg CO\({}_{2}\)/\(\upmu\)mol PAR, Figure 4, black line). By combining the PRI with any SIF variable, substantial improvements in relationships to LUE and GPP were achieved. For LUE, a 16% improvement over the linear PRI model, and a 7% further improvement over the log PRI model, was obtained for the model that combined the PRI with the SIF (red) yield (r\({}^{2}=0.61\), RMSE = 0.000275 mg CO\({}_{2}\)/\(\upmu\)mol PAR; Table 3). The statistics of modeling GPP using PRI and SIF are summarized in Table 4. For GPP, \(\geq\)24% improvements over the linear PRI model (r\({}^{2}=0.54\), RMSE = 0.2770 mg CO\({}_{2}\)/m\({}^{2}\)/s) were obtained for the models that combined the PRI with either the SIF (far-red) (r\({}^{2}=0.78\), RMSE = 0.1894 mg CO\({}_{2}\)/m\({}^{2}\)/s) or with the SIF (red). The SIF (red) retrieval provided the strongest performance overall (r\({}^{2}=0.80\), RSME = 0.1994 mg CO\({}_{2}\)/m\({}^{2}\)/s; Figure 5; Table 4).
## 4 Discussion
Another application of RSLDE in waste recovery concerns the extraction of bioactive compounds from the shells of sea urchins _Paracentrotus lividus_[40]. All plants, through their metabolic processes, elaborate and accumulate more or less complex chemical substances that are defined as \"secondary metabolites\" and have different functions for vegetables: essential oils, volatile compounds, and so on. Among these, those with a positive physiological action on the human body are defined as active ingredients, bioactives or phytocompounds. To obtain these phytocompounds, it is necessary to choose the most suitable extraction procedure and the best extractive conditions. As previously mentioned, currently, there are conventional and innovative extraction methods [41; 42]. The first ones can be defined as more traditional methods with typical disadvantages such as the need for high amounts of solvent, low extraction selectivity, the need for long process times and thermal decomposition of thermolable compounds (in case that high temperatures are required). As for the innovative extraction techniques, introduced relatively recently in the industrial field, these have numerous advantages, such as the reduction or elimination of the solvent, the reduction of energy consumption, the satisfaction of legal requirements in emissions, greater safety and control of the extraction technique, cost reduction and improvement of the quality and functionality of the entire process [43]. Among the innovative techniques, the RSLDE used in this work allows for extraction at room or lower temperatures, by exploiting the increase of the operating pressure due to the extracting liquid acting on the solid matrix. The principle on which the RSLDE is based, in addition to making the extraction process fast and efficient, makes the RSLDE a valid alternative to the innovative extraction processes currently existing [2]. In a global context in which the concept of productivity and income often conflicts with environmental sustainability, the use of new technologies more performant and environmentally friendly can resolve this dualism. According to our results, RSLDE could be considered as a valid alternative to conventional solid-liquid extractive processes such as maceration and percolation for the simplicity of its use, the low operative costs and for low environmental impact.
## 3 Results
### Comparison of the Performance with the Other Segmentation Models  
To validate the efficiency and advantages of the BFFDC-YOLOv8-seg weed segmentation model in this study, we compared it with mainstream segmentation networks such as Mask-RCNN [31], YOLOv5-seg [32], and YOLOv7-seg [33]. The same dataset and training parameters were used for training the models, and tests were conducted on the test set. The complete experiment assessed the accuracy (P), recall (R), mean average precision (mAP), inference speed (FPS), and model size for each segmentation network, as shown in Table 5.  
Figure 10: Groups A, B, and C demonstrate the model’s ability to segment slender stems of plants, edges of weed leaves, and detect small objects, respectively.  
From a comprehensive comparison of tests, it is evident that the BFFDC-YOLOv8-seg model proposed in this paper exceeds the original YOLOv8-seg model by 2.8% in mAP50 for segmenting small weed targets, and surpasses the Mask-RCNN, YOLOv5-seg, and YOLOv7-seg models by 10.8%, 13.4%, and 1.3% respectively, showing the highest precision in detecting and segmenting weeds in complex agricultural environments. In terms of real-time detection capabilities, Mask-RCNN, with its complex model and high-density computations, and YOLOv7-seg's large-scale network require high computational costs, resulting in inference speeds of only 34 and 18.3 FPS, respectively. After increasing the sampling scale, this paper's network has a reduced inference speed compared to YOLOv8-seg's 270 FPS, but at 101 FPS, it fully meets the real-time detection needs of slow-moving laser weeding device, while maintaining optimal segmentation accuracy. Regarding model size, the network retains its lightweight characteristics, making the 6.8 MB model more suitable for deployment on resource-limited automatic laser weeders compared to the Mask-RCNN and YOLOv7-seg models.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 6 The Potential Applications of Voxel-Based Navigation
### Voxel-Based Indoor Navigation for First Responders  
In disaster response scenarios, quickly providing safe and reliable navigation routes for emergency responders is critical. This becomes particularly challenging in complex public buildings, such as train stations, hospitals, airports, or shopping centers, where dynamic hazards like fires and toxic smoke significantly increase the difficulty of navigation. Voxel-based modeling and navigation techniques demonstrate unique advantages in handling the uncertainties in such environments. These models not only provide detailed spatial layout information but also track real-time environmental changes, such as fire spread, smoke diffusion, and structural damage, to support optimal path planning for rescue efforts.  
Voxel models, with their discrete and regularized representation, can effectively adapt to dynamic changes in complex scenarios, such as corridor planning, obstacle recognition, and emergency evacuation route selection. By continuously updating environmental data, these models can capture emerging hazards, including fire sources, collapsed walls, or  
Figure 10: Voxel-based classification results: dynamic obstacles are shown in green, and static obstacles are shown in red (from [52]).  
expanding smoke areas, ensuring that navigation paths are adjusted in real time to avoid dangers and safeguard emergency personnel. In addition to representing static structures, voxel models are adept at handling dynamic obstacles like falling debris or the spread of toxic gases, integrating these changes into path planning to generate safe, adaptable routes. Furthermore, voxel models excel in emergency simulations by recreating dynamic scenarios, such as fire and smoke spread [96; 98; 113], allowing for real-time evaluation of the safety and feasibility of evacuation routes. This capability enables responders to make faster, more informed decisions, thereby improving the efficiency and effectiveness of disaster response efforts.
## 3 KGs Learning
### Transformer-Based Learning
#### 3.4.1 Text Integration and Finetuning  
Research such as MHAVGAE [130] combines weakly supervised learning with a Multi-head Attention Variational Graph Auto-Encoder, integrating Transformer-style multi-head attention into a VAE to automatically learn prerequisite relationships in large-scale conceptual graphs. Metrics like Resource-Prerequisite Reference Distance (RPRD) are used to augment incomplete labels, significantly reducing the need for manual labeling.  
Pretrained text-based models (e.g., BERT) have inspired dual attention structures in graph layers, assigning different attention channels based on the importance of the relations [131]. For instance, the Dual-AEN model independently updates entities and relations while facilitating their interactions.  
Methods like SATrans [132] inject structural information from tables and graphs directly into the attention mask of a Transformer encoder, mitigating structural loss caused by \"flattening\" data into plain text. The lower layers of self-attention focus on local information, while the upper layers emphasize global information, achieving high accuracy in tasks that reflect local-global structures, such as validation and question answering. Fine-tuning these models on domain-specific KG data enables the capture of nuanced semantic features that facilitate tasks like relation extraction and entity linking [133].  
Ref. [134] proposed a technique to expand knowledge graphs (KGs) using pretrained language models (e.g., BERT, GPT). This approach extracts entities and relationships from text data and integrates them into the KG, thereby enriching its representation. By leveraging the strong language understanding capabilities of pretrained models, this method adds new information that existing KGs may lack and enhances the semantics of the existing relationships. The synergy between NLP and KG in this approach improves performance across various applications, including question answering and recommendation systems.  
Transformer architectures (e.g., BERT) have become the dominant paradigm in knowledge graph construction, particularly due to their ability to capture long-range dependencies. However, traditional recurrent neural networks (RNNs), such as LSTM or GRU models, can still offer advantages in terms of computational efficiency for shorter sequences. Table 1 summarizes the key differences between these methods in terms of accuracy, efficiency, and practical applicability.
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
## 4 Scheduling Solution
### Inner Layer Optimization Model Solving  
When solving the inner optimization model, the maximum and minimum values of each objective are obtained by analyzing the direction of the action of each uncertain element fluctuation on each objective and determining the corresponding extreme scenarios.  
1. Economic goals According to the economic target function, when the residential load, gas load, interactive power with the grid, and interactive power with the gas network take the upper boundary of the interval, and the photovoltaic power and wind power take the lower boundary of the interval, the economic target takes the maximum value; conversely, when the residential load, gas load, interactive power with the grid, and interactive power with the gas network take the lower boundary of the interval, and the photovoltaic power and wind power take the upper boundary of the interval, the economic target takes the minimum value.
2. Environmental Goals According to the environmental protection objective function, the fluctuation of the power exchange between the virtual power plant and the grid and the gas network directly affects the environmental protection objective.
3. Grid load smoothing target According to the grid load smoothing target function, the fluctuation of residential electricity load, photovoltaic power generation, and wind power generation affect the grid load smoothing target. When taking the upper boundary of residential electricity load and the lower boundary of photovoltaic power generation and wind power generation, the grid load smoothing target takes the maximum value; conversely, when taking the lower boundary of residential electricity load and the upper boundary of photovoltaic power generation and wind power generation, the grid load smoothing target takes the minimum value.
## 3 Experimental Design and Case Study
### Objectives  
The experiments aimed to showcase the framework's capability to  
* Generate large, labeled synthetic datasets representing various traffic conditions and sensor configurations.
* Train U-Net models using this synthetic data to perform tasks relevant to DAS traffic monitoring:
* Enhancing spatial resolution beyond the instrument's native gauge length.
* Filtering traffic signals based on direction of travel.
* Simulating the DAS response of alternative, potentially optimal, fiber-optic cable layouts.
* Evaluate the transferability of models trained purely on synthetic data to real DAS measurements.
# Healthy Effects of Pomegranate (_Punica granatum_ L.) in Internal Medicine and Dentistry
## 5 Pomegranate in Internal Medicine
### Cancer
#### 5.2.2 Pomegranate and Lung Cancer  
LC is the most common cancer and the leading cause of death worldwide, due to its diagnosis at an advanced stage [118]. In addition to a positive family history of LC, other important risk factors for this type of disease are tobacco smoking, unhealthy eating habits (such as high intake of fried or well-cooked red meat and alcohol consumption), chronic inflammation related to infections, exposure to ionizing radiation, and occupational exposures (such as asbestos and indoor air pollution) [119]. Thanks to their countless nutrients, such as vitamins, minerals, phytochemicals, and dietary fibers, fruits could play a pivotal role in the prevention of LC and in the risk reduction in current smokers, thanks to the ability of these micronutrients to exert antioxidant activities, repair DNA from oxidative damage, inhibit tumor cell proliferation, and induce tumor cell apoptosis, caused by smoking [120]. It was highlighted how pomegranate polyphenols have potent chemotherapeutic properties that exert anti-cancerous activities on lung carcinoma. In more detail, several in vitro studies have pointed out how pomegranate leaf extract (PLE) has potential anti-proliferative, antimigratory [81], and anti-metastasis properties against lung carcinoma, while pomegranate peel extract (PPE), PJ, and PSO, tested both in vitro and in vivo have shown strong anti-inflammatory activities, reducing proinflammatory cytokine levels (IL-1\(\beta\) and IL-6), and strong antioxidant properties inactivating cellular oxygen radicals in the lungs [82]. In addition to the effects described above, PFE seems to play a pivotal role against the LC. In fact, both in vitro and in vivo studies have shown how PFE is involved in the downregulation and inhibition of several signaling pathways against lung cancerous cells, including nuclear factor kappa-light-chain enhancer of activatedB cells (NF-\(\kappa\)B) expression, mitogen-activated protein kinase (MAPK) phosphorylation, phosphoinositide 3-kinases (PI3K), and mechanistic target of rapamycin (mTOR) pathway activity and Akt phosphorylation, leading to reduced cell proliferation and angiogenesis in lungs [83,84].
## 2 Materials and Methods
### Materials
#### 2.1.4 Modifiers  
The organophilic nanoclay used in the modification of CAP 50/70 is known commercially as Dellite 67G. It has a particle size (dry) of 7-9 \(\upmu\)m, a particle size after dispersion of \(1\times 500\) nm, and density of 1.7 g/cm\({}^{3}\). It has the following chemical composition: carbon (45.50%), silica (33.42%), aluminum (16.08%), iron (3.60%), chloride (0.80%), titanium (0.31%), potassium (0.27%), and strontium (0.02%). According to the results of thermogravimetry tests, this nanomaterial is thermally stable at temperatures below 262.4 \({}^{\circ}\)C [22].  
The polymer SBS used as a modifier was Kraton D1101. It has a linear structure, with a polystyrene content of between 30% and 32%, and it was supplied in granules.