esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Results of the Bibliometric Analysis
### Social Structure Analysis
#### 3.2.2 Institutions Analysis
The most cited or influential work of these institutions in the dataset is approaching a decade old. This could suggest that they made significant contributions to the field around that time. However, the older APY might also indicate a potential decrease in either the quantity or the impact of recent publications. It is importantly conduced that the institutions may have maintained their research momentums until 2014 or 2015, and then shifted its focus to other areas. In addition, it should be noted that the table clearly demonstrates China's leading role in the field, as evidenced by the significant number of publications from the country. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Institution** & **Country** & **TP** & **TC** & **AC** & **TLS** & **APY** \\ \hline
1 & Dalian Maritime University & China & 31 & 428 & 13.81 & 18 & 2020.2 \\
2 & Harbin Engineering University & China & 21 & 160 & 7.62 & 17 & 2019.0 \\
3 & Chinese Academy of Science & China & 15 & 91 & 5.69 & 14 & 2015.8 \\
4 & University College London & England & 13 & 338 & 26.00 & 13 & 2019.2 \\
5 & Shenyang Institute of Autonomous CAS & China & 12 & 57 & 4.75 & 12 & 2015.7 \\
6 & Wuhan University of technology & China & 12 & 266 & 22.17 & 17 & 2019.4 \\
7 & University of Ljubljana & Slovenia & 7 & 101 & 14.43 & 3 & 2018.7 \\ Norwegian University of Science & & & & & & \\
8 & Technology NTNU & Norway & 9 & 141 & 15.67 & 11 & 2020.2 \\
9 & Gdynia Maritime University & Poland & 8 & 262 & 32.75 & 4 & 2014.1 \\
10 & Jiangsu University of science technology & China & 8 & 5 & 0.63 & 8 & 2019.2 \\
11 & Korea Maritime Ocean University & Korea & 8 & 100 & 12.50 & 14 & 2021.1 \\
12 & Shanghai University & China & 8 & 53 & 6.63 & 11 & 2019.7 \\
13 & National Oceanic Atmospheric Admin & USA & 7 & 48 & 6.86 & 12 & 2016.3 \\ NOAA USA & & & & & & \\
14 & Shanghai Maritime University & China & 7 & 29 & 4.14 & 9 & 2021.7 \\
15 & Harbin Institute of Technology & China & 6 & 41 & 6.83 & 4 & 2019.8 \\
16 & Shanghai Jiaotong University & China & 5 & 27 & 5.40 & 3 & 2021.8 \\ \hline \hline \end{tabular}  
Notes: TP = total publications; TC = times cited; AC = average number of citations per publication; TLS = total link strengths; APY = average publication year.  
\end{table} 
Table 2: Bibliometric network and citation information of the top 16 most productive institutions. Specifically, 10 out of the top 16 institutions are based in China, with the country dominating even more impressively among the top 6 institutions, 5 of which are affiliated with China.
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges  
Although FPAs offer significant potential for a wide range of advanced applications, their benefits do not come without critical challenges that must be addressed to ensure the performance of the final device. This section reviews the main issues identified across relevant studies.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 4 Simulations  
In this section, we first use MATLAB R2016b to simulate and verify the convergence of the improved ant colony optimization algorithm. Then, the IEMACO protocol designed in this paper is simulated by OPNET. The simulation scenario is set to a rectangular area of 100 m \(\times\) 100 m. The source node coordinates are (0, 0) and the destination node coordinates are (100, 100). Other 40 nodes are uniformly distributed within the rectangular.
## 2 Methodology
### Damage Identification  
Usually, when working with autoencoders, the damage or novelty index is identified by means of the reconstruction or loss error computed from an autoencoder model previously trained with healthy data. This error measures the residual between the input data and the reconstructed data. When the trained model is fed with test data, a residual higher than a threshold, defined from the training data, can be considered as the symptom of an anomaly or damage, and the more the anomalies, the greater the residual.  
However, although this is a standard procedure, as noted in Section 2.2, this way of proceeding does not take full advantage of the main strengths of autoencoders, i.e., hierarchical dimensionality reductions and high feature extraction capabilities. One of the main features of autoencoder models is that by means of successive layers of encoding-decoding hierarchical, low-dimension representations of both input and reconstructed data are learned; this might be exploited for damage identification purposes by defining damage indicators directly from the hidden spaces instead of from the output layer.  
With these premises, in this work and unlike most traditional approaches, the latent space was used for damage monitoring purposes by means of a procedure applied on hybrid data, constituted by the original experimental and synthetic data, and based on three damage indicators: density, volume of the convex hull, and ellipse overlap. These indicators provide comprehensive insights into the structural integrity and potential damage within the monitored beam. A qualitative description of each one of these indicators is presented next.
## 3 DSPOT-Enhanced Self-Evolving Neural Networks
### Short-Term Wind Farm Generation Forecast  
The proposed DSPOT-enhanced self-evolving neural networks (DSN) will train multiple NNs, which capture different dynamics of wind farm generation. When forecasting wind farm generation, we will first leverage the DSPOT-based ramp classifier to determine whether the current state of wind farm generation is in ramp up, ramp down, or non-ramp. Based on the classified state, we choose the corresponding NNs to forecast the wind farm generation.  
Specifically, let the function \(H^{class}_{\theta_{i}}(\cdot)\) represent the neural network with parameters \(\theta_{i}\) (i.e., the best gene) trained using the datasets: \(X^{class}_{i}(t)=\{W_{s}(t),W_{dir}(t),P_{class}(t),P_{class}(t-1),\ldots,P_{ class}(t-Lag)\}\), the output of the neural network is  
\[\hat{P}_{class}(t+1)=H^{class}_{\theta_{i}}(X^{class}_{i}(t)). \tag{15}\]  
Based on the results of the ramp classifier, we pick the corresponding NNs (i.e., the best gene) for each class of wind turbines. Therefore, the wind farm generation forecast \(\hat{P}_{ag}(t+1)\) can be achieved by:  
\[\hat{P}_{ag}(t+1)=\hat{P}_{mit}(t+1)+\hat{P}_{ge}(t+1). \tag{16}\]  
Equation (16) is the point forecast of wind farm generation.  
Distributional forecasts are often needed to manage the uncertainty [49]. To this end, we leverage the collection of genes generated in NEAT and use the forecasts by these genes to develop distributional forecasts. Let \(\{\hat{P}^{(j)}_{ag}(t)\}\) represent the set of forecasts offered by each gene \(j\). It is assumed that the forecast error of the point forecasts follows the standard normal distribution with the mean \(\mu_{t}\) and the variance \(\sigma_{t}^{2}\) as follows:  
\[\mu_{t}=\frac{1}{J}\sum_{j=1}^{J}\hat{P}^{(j)}_{ag}(t), \tag{17}\] \[\sigma_{t}^{2}=\frac{1}{J}\sum_{j=1}^{J}(\hat{P}^{(j)}_{ag}(t)- \mu_{t})^{2}, \tag{18}\]  
where \(J\) is the number of genes. Under such assumption, we calculate the \((1-\alpha)\) confidence interval of the point forecasts (16) as follows:  
\[[\hat{P}_{ag}(t+1)-Z(1-\frac{\alpha}{2})\sigma_{t+1},\hat{P}_{ag}(t+1)+Z(1- \frac{\alpha}{2})\sigma_{t+1}], \tag{19}\]  
where \(Z(1-\frac{\alpha}{2})\) represents the point where the cumulative distribution function of the standard normal distribution is equivalent to \(1-\frac{\alpha}{2}\).  
**Remark 1**: _The proposed SENs can be trained offline. As the learning process of each SEN is based on different datasets, we can train these SENs on parallel. This can significantly reduce the training time of these SENs. Furthermore, the learning of SENs needs no AI experts to manually tune the topology and the hyperparameters; SENs can automatically adapt to the changing dynamics ofwind farm generation purely based on the data. This can greatly facilitate the implementation of the proposed method in reality._
# Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers
## 1 Introduction  
The growth of agricultural products is inseparable from the nutrients provided by fertilizers. According to statistics from the Food and Agriculture Organization of the United Nations (UNFAO), the rational use of fertilizers can increase crop yields by 40% to 60% [1]. Fertilization is the main means of increasing the production of agricultural products [2], but long-term insufficient fertilization will affect growth, while excessive fertilization will cause serious damage to the soil environment. Therefore, precise fertilization is of great significance. Precision fertilization devices are the key to precise fertilization, and it is necessary to conduct in-depth research on them [3].  
As a key component of fertilization devices, the performance of fertilizer dischargers directly affects the uniformity of fertilization and operation stability of the entire machine. The precise dosing and distribution of the fertilizer by fertilizer dischargers are the keys to precise fertilization. In order to achieve precise fertilization, the requirements for the performance of fertilizer dischargers are increasing [4]. In addition to achieving high accuracy, stability, and uniformity of fertilizer distribution, it is also necessary to ensure that they can flexibly and precisely adjust the dosing of the fertilizer so that it can be applied to more crops and will not be affected by external factors such as working conditions and terrain when performing fertilization tasks [5, 6].  
This review first explains the current research on precision fertilizer dischargers and analyzes the operating principle and performance characteristics of the four types of fertilizer dischargers (spiral fertilizer dischargers, groove wheel fertilizer discharges, disc fertilizer dischargers, and air-feed dischargers). Then, we discuss and summarize how researchers use precision fertilization-related technologies and optimization methods for fertilizer discharges' structure and working parameters to achieve precise fertilizer dosing and distribution. On this basis, the problems in the existing research on fertilizer dischargers are pointed out. Finally, the development trend of fertilizer dischargers in the precision fertilizer dosing and distribution is predicted based on previous articles.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.5 NOMA--Future Challenges and Research Issues
Nowadays, the demand for connected devices such as IoT, V2V, and massive machine types is increasing, which requires them to pair a large number of users to one RB. Therefore, NOMA requires some new pairing techniques to fulfil the requirements of future wireless networks. Along with this, there is another challenge to be addressed, which is the practical implementation of user pairing in NOMA systems;
* Receiver Complexity in SIC implementation: In NOMA, the users are multiplexed at the transmitter side using pairing techniques and decoded at the receiver using a SIC receiver. As the number of users increases, the allocation of transmission power to pair the users becomes very complex, and at the same time, decoding the strong user to the next strong user and up to a weak user is a very difficult task at the receiver end. This process increases the latency and also introduces interference with the increased number of multiplexed users. Therefore, reducing the latency and interference and providing an efficient and dynamic SIC receiver for the NOMA system is a future challenge;
* Multi-cell NOMA system: In NOMA systems, most of the researchers considered a single cell, and very few addressed the multi-cell concept because the multi-cell system causes inter-cell interference. The interference will affect the performance of the weak users who are at the cell edge. Therefore, to solve the interference issues along with the pairing and decoding issues, a small cell concept of 5G is included in the multi-cell NOMA system. This is the future challenge in the multi-cell NOMA systems to be addressed;
* Mobility in NOMA: NOMA is used to enable 5G and beyond 5G technologies. In IoT, V2V, and M2M communications, mobility is the key parameter. Most of the research in NOMA systems is based on static systems. The PA, pairing, and SIC receiver algorithms are proposed based on the static behaviour of the users. However, in future communications, dynamic PA, pairing, and SIC algorithms will be required. As the user moves from one location to another, the channel gains vary with respect to the user's location. Therefore, proposing dynamic algorithms for NOMA systems is one of the challenges of the future;
* CSI in NOMA: Most of the research is carried out assuming perfect CSI in NOMA systems, but fewer users consider imperfect CSI. The CSI plays an important role in user pairing and decoding for users with SIC receivers in the NOMA system. To increase the system performance and spectrum efficiency of a dynamic system, the estimation of CSI is very important. For a dynamic channel, the estimation of CSI using machine learning and game theory algorithms is one of the important future challenges in NOMA systems.
## 2 Dataset and Methods
### GNSS-R Airborne Data
#### 2.1.2 Airborne Campaigns  
Five scientific flights were made above the studied site (Figure 1), over a period of two weeks (22 June-6 July 2015). The aircraft flew at a speed of \(\sim\)100 m/s, and an altitude of \(\sim\)600 m above the ground. The antenna radiation patterns allowed measurements to be made at all elevations between 30\({}^{\circ}\) and 90\({}^{\circ}\). Three synchronized streams of raw data, centered on the GPS L1 frequency (1.57 GHz), were continuously acquired at 10 MSPS: one from the zenith-pointing RHCP antenna and two from the nadir-pointing antenna, in the RHCP and LHCP polarizations. The data were then processed offline to extract the GPS L1 signal waveforms, with a coherent integration time of 20 ms. After correcting for the antenna radiation pattern and receiver noise [30; 31], the individual waveform maxima were averaged incoherently for 240 ms, allowing the GNSS-R observables to be computed.  
The study sites were located in the southwest of France (Figure 1). This region is characterized by a temperate climate, with annual rainfall ranging from 600 mm to 900 mm and a mean maximum daily air temperature close to 19 \({}^{\circ}\)C in summer. Two agricultural sites, Lamasquere and Marcheprime, were considered. The reference fields were sampled among various crops (wheat, sunflower, corn, soybean, carrots, green bean) presenting a wide range of cover characteristics during the two weeks of the airborne campaign.  
* The Lamasquere site (43.49\({}^{\circ}\)N, 1.23\({}^{\circ}\)E, Figure 2a) included eight fields (four wheat, two soybean, one corn and one sunflower)
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods  
### Study Area  
Wudaoliang town is located in Yushu Tibetan Autonomous Prefecture, Qinghai Province, at an altitude of approximately 4000 m. Wudaoliang has an alpine climate, with lower temperatures throughout the season, more rainfall in summer, and an annual precipitation of 301.4 mm. Most of the surface in the study area (92.5\({}^{\circ}\) E-94.5\({}^{\circ}\) E, 34\({}^{\circ}\) N-35.5\({}^{\circ}\) N, an area of approximately 41,400 km\({}^{2}\)) is grassland mixed with sandy land, and the land surface coverage type is relatively simple, as shown in Figure 1a.  
As the source of the Luan River, the Shandian River is located in the North China Plain and originates from Guyuan County and Fengning County. The overall terrain of the basin slopes from southwest to northeast, with a river length of 877 km and an altitude between 1260 and 1680 m. The area has a temperate continental monsoon climate, with high temperatures and rainy summers, cold and dry winters, and an annual rainfall of approximately 400 mm. The study area (115.5\({}^{\circ}\) E-116.5\({}^{\circ}\) E, 41.5\({}^{\circ}\) N-42.5\({}^{\circ}\) N, an area of about 15,600 km\({}^{2}\)) is a small forest in the south, grassland in the north, small farmland in the middle and west, and mixed in the east and southeast corner of the middle. Shrubs, bare land, etc. [3; 9], as shown in Figure 1b.  
In order to improve the accuracy and spatial resolution of soil moisture mapping, the Academy of Aerospace Technology, the Information Center of the Ministry of Water Resources, and the Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences jointly conducted the \"Remote Sensing Comprehensive Experiment of Water Cycle and Energy Balance in River Basins\" with universities. The areas of this study are all experimental areas.  
Figure 1: Study area. (**a**) is the surface coverage type and sites distribution of the Wudaoliang area; (**b**) is the surface coverage type and site distribution of the Shandian River.
## 4 Channel Models  
To calculate the reliability of links on IoUT, we investigate the channel models for underwater environments. The aim of the models is to calculate the successful delivery ratio of each link over UWSNs. The successful delivery ratio (between a pair of sensor nodes) is equivalent to the reliability (of a link), and is one of the most advantageous information for designing reliable communication protocols on IoUT.  
The underwater channel models consist of several elements. We divide the complicated channel models into two main parts. In the first part, we investigate the relationship between the transmitter power and signal-to-noise ratio (SNR); then, in the second part, we further investigate the relationship between the SNR and successful delivery ratio. To sum up, the channel models can provide researchers with a systematic way to calculate the reliability (i.e., successful delivery ratio) of each link over IoUT.
## 3 Results
### Expression of Stress-Related Proteins  
The induction of Hsp60 in the liver and the gills of gilthead sea bream during exposure to increasing salinity (at different times) for 45 days are depicted in Figure 2. Regarding the hepatic Hsp60 expression, two-way ANOVA revealed that there was a statistically significant interaction between the effects of time and salinity on Hsp60 expression (F(6, 24) = 211.88, \(p<0.001\)) (Figure 2a). Compared to the initial Hsp60 levels (T0), a significant induction was apparent after 15 days of exposure (T15) to all examined salinities, while further exposure (T30, T45) led to a significant decrease at salinities 8 and 14 ppt. However, the aforementioned decrease resulted in lower Hsp60 levels, compared to T0, only at the 8 ppt. Concerning Hsp60 levels at 20 ppt, prolonged exposure for 30 days led to a significant decrease compared to both T0 and T15, while a significant induction compared to T0 and T30 was apparent following further exposure (T45). In addition, exposure to increasing salinity at the same time mainly resulted in significantly (\(p<0.05\)) induced Hsp60 levels. Based on the two-way ANOVA, salinity, time, and the cumulative effect of these two variables significantly affected the Hsp60 levels in the gills (\(p<0.01\)). The exposure of gilthead sea bream to 8 ppt displayed no changes in the expression of Hsp60 among the different times (Figure 2b). In contrast, decreased levels were observed following exposure to 14 ppt for 15 days (T15) compared to T0, while further exposure (T30, T45) significantly induced the Hsp60 expression. However, compared to the initial Hsp60 levels (T0) at 20 ppt, a significant elevation was apparent during prolonged exposure, although a reduction was  
Figure 2: Induction of Hsp60 in the liver (**a**) and gills (**b**) of gilthead sea bream following exposure to a salinity of 8, 14, or 20 ppt for 0, 15, 30, and 45 days. Different bar colors represent the exposure time to the experimental salinities (T0: 0 days, T15: 15 days, T30: 30 days, and T45: 45 days). Data are expressed as mean values of three replicates \(\pm\) S.D.
## 3 Application of the Proposed Method to the Fruit Variety Classification
### Research Method
#### 3.2.2 You Only Look Once for Fruit Detection  
The YOLO V3 [18] architecture was used to generate the apple ROIs from the original images of [28]. The YOLO (You Only Look Once) family of models is a series of end-to-end deep learning models designed for fast object detection. Version 3 used in this research has 53 convolutional layers. The main difference from the previous version of this architecture is that it makes detections at three different scales, thus making it suitable for the smaller objects. Object features are extracted from these scales like the feature pyramid network.  
In the first step, YOLO divides the input image into an \(\text{S}\times\text{S}\) grid where S depends on the scale. For each cell, it predicts only one object using boundary boxes. The network predicts an objectless score for each bounding box using logistic regression. The score parameter was used to filter out weak predictions. The result prediction is a box described by the top left and bottom right corner.  
The original dataset consisted of folders for each fruit class, such as apple, banana, etc. Within the current class folder, additional classification was done for specific species. Apples in the images were located on a silver shiny plate that generated many false predictions. We used the weights pre-trained on a COCO dataset, containing 80 classes where one of them was the apple class. The COCO apple class consists of many different apple species; a desirable attribute in this case. We could run the object detection using YOLO and filter out just the apple class. To maximize predictive performance, we set the minimum score parameter to 0.8. The predictions were good, but many apples were not detected. This this reason, we set the minimum score to 0.3, which allowed almost all objects to be included regardless of the species.  
Model predictions were saved as separate files named according to the source sample to allow for later verification. Generated predictions could be used for ground truthing during the training process.
## 3 Results and Discussion
### Experimental Results
#### 3.2.2 Particle Concentration  
Various concentrations, expressed in weight per volume _(w/v)_, of 552 nm PS particle solutions (0.0625% _w/v_, 0.125% _w/v_, 0.25% _w/v_, 0.5% _w/v_) have been tested, always with a flow rate Q = 10 \(\upmu\)L/min. The resulting simulated and measured spectra are plotted in Figure 6a,b, respectively. From the simulation results shown in  
over the whole particle concentration range in particular at the expected maximum Doppler shift _fd_ = 27 kHz, while in the low frequency region (\(<\)20 kHz), the increase in concentrations enhanced slightly the signal level. This behavior can be explained that when the particle density increases, the laser beam cannot enter deeper in the channel, and thus only the particles passing through the top area of the channel with a lower flowing velocity can properly contribute to the optical feedback, resulting in such a slight signal increase in the low frequency region.