esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 2 Datasets and Methods
### Datasets  
By analyzing the growth situation of crops, the growth trend in crops can be determined to the greatest extent, providing timely and reliable growth information for crop production management personnel and accurately estimating crop yields [23]. At the same time, there are many types, and a high density, of crop diseases, which can easily cause a large reduction in crop yields and seriously restrict agricultural production [24]. Therefore, the precise recognition of crop growth stages and crop diseases plays an important role in improving crop yields.  
In order to verify the effectiveness of the AgriVLM model on crop growth stages, disease recognition, and poultry recognition, this paper collected five datasets to conduct experiments on the model.  
The first is cabbage growth stage data, which includes four growth stages, namely the germination stage, seedling stage, rosette stage, and dormancy stage, with a total of 400 images. The second is strawberry growth stage data. It contains 787 images and has been divided into four growth stages: the flourishing stage, flowering stage, fruiting stage,and maturity stage. The third is chili pepper disease data, which includes both healthy and bacterial spot data and contains 2475 images. Then, there is tomato disease data, including health, as well as septoria leaf spot, late blight, target spot, and yellow leaf curl virus--four kinds of disease data. There are a total of 5500 images, including five categories. The last is poultry recognition data, which contains 1213 images of chickens and pigs. We will conduct experiments on these five datasets.  
Finally, in order to realize the agricultural data analysis service process, we learned agricultural knowledge by learning a large amount of agricultural conversation data. The agricultural conversation data comes from the \"China Agricultural Technology Extension\" APP, which contains a large number of answers from agricultural experts and technicians to questions raised by users. Here, we collected a total of 15,000 pieces of conversation data from the APP to conduct the experiments.
## 3 Results
### Composition and Changes of cbbL-Containing Bacteria  
Proteobacteria was the dominant phyla among the _cbbL_-carrying bacteria, with mean values of 93.49% (Figure 6a). The proportions of \(\beta\)-, \(\delta\)-, and \(\alpha\)-_Proteobacteria_ reached up to 43.71%, 27.00%, and 18.65% in class level, respectively, and _Burkholderiales_ was the identified dominant order with a mean value of 37.74%. Orders of _Rhizobiales_ (4.18%) and _Nitrosomondadles_ (1.98%) were also very abundant. We found that the composition differences of the _cbbL_-containing bacteria between the rhizosphere sediments and bulk sediments were notably higher than those between sediments with and without glucose or those during the glucose degradation in levels of phyla to genus. The proportions of _Proteobacteria_ in rhizosphere sediments (95.68%) was significantly higher than that in bulk sediments (91.30%) at the phylum level. _Proteobacteria_ presented a slightly increased trend from the 8 th h to 24 th h in the RS and RSG pots, but gently decreased in the BS and BSG pots.  
At the class level, _Acidithiobacillus_ and \(\beta\)-_proteobacteria_ were significantly higher in the rhizospheric sediments than in the bulk sediments, while \(\alpha\)- and \(\delta\)-_proteobacteria_ were the opposite. In addition, \(\beta\)-_proteobacteria_ showed a decreasing trend in the BS pots, but increased in the RS pots from the 8 th h to 24 th h, while \(\delta\)-_proteobacteria_ was the opposite. At the genus level, _Acidithiobacillus_, _Cupiravidus_, _Variovorax_, and _Vitroscilla_ were typical and dominant, with the proportions being notably or relatively higher in the RS pots than in the BS pots (Figure 6b). With respect to microbial changes during the experimental process, the RS groups and BS groups presented significantly different clusters, but not the result of glucose degradation (Figure 6c). The principal component analysis (PCA) suggested that the first two axes explained 66.6% of the distribution of the main microbial orders and soil properties, among which the variance of 45.3% was attributed to PC 1 (Figure 6d). In addition, the Pearson correlation analysis showed non-significant relations between all of the bacteria and glucose, but a significant association between pH and the dominant _Proteobacteria_ (\(p=-0.900\)), which may suggest that the principal factor affecting the bacterial composition was pH, not the glucose content. Interestingly, the proportions of _rhizobiales_ presented a notably opposite trend with an absorbance near 1600-1650 cm\({}^{-1}\), and _nitrosomondales_ presented similar trend with an absorbance near 3150 cm\({}^{-1}\); this finding may indicate that there were internal relations.  
Figure 5: (**a**) Changes of RubisCO (ribulose-1,5-biphosphate carboxylase/oxygenase) activity and (**b**) the power function curves between _cbbL_ gene abundance and RubisCO activity based on the _cbbL_ gene abundance at the 8 th, 16 th, 24 th, and 45 th h of glucose degradation in the four pots.
## 2 Materials and Methods
### SAR Imagery  
Compared with visible remote sensing and near-infrared remote sensing, the SAR images acquired by Sentinel-1 had the advantage of being unaffected by atmospheric and solar radiation; compared with passive microwave remote sensing, this approach has higher spatial resolution and can provide more details on sea ice boundaries. Further, compared with other SAR image types, it yields far more data, which are open-source and more accessible [15], and have short satellite revisit cycles. These characteristics make it widely popular among polar researchers, and it is the top choice for the extraction of sea ice classification information.  
The Sentinel-1 satellite constellation is an Earth observation satellite within the European Copernicus program, consisting of two radar satellites set 180\({}^{\circ}\) apart. It is a two-satellite system containing Sentinel-1A and Sentinel-1B satellites, with a single satellite revisit cycle of 12 days and a shortened double satellite revisit cycle of 6 days. One of the Sentinel-1B satellites was officially determined to be offline on 6 August 2022, after it was rendered unable to transmit radar data due to anomalies in the power supply of its instruments, along with other reasons, beginning on 23 December 2021. We selected 2020 data for this study. The Sentinel-1A/B data used in this study were obtained from [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 3 May 2023) and comprise L1-level medium-resolution ground data detection product (GRDM) data, from which images had multi-look and thermal noise removed to enhance their quality.  
The SWATH mode of the Sentinel-1 dual-polarization (HH/HV) product, the medium-resolution ground data detection product (GRDM), was used in this study. The product has an image width of 400 km per scene, a resolution of 100 m in terms of both distance and orientation, and a uniform pixel scale of 50 m \(\times\) 50 m after resampling. All the data were dual-polarized, i.e., they underwent horizontal emission and reception (co-polarization, HH) and horizontal emission and vertical reception (cross-polarization, HV). The data were pre-processed, including via orbit correction, refined Lee denoising, radiometric calibration, and terrain correction. All data were projected into a coordinate system with a spatial resolution of 50 m.  
Following filtration and space-time matching, data with spatial distributions overlapping with the weekly ice charts published by the CIS, as well as data with a time difference of less than 1 day from the ice charts, were obtained and used to extract sea ice features. Finally, more than 60 views of Sentinel-1A/B data between July and November 2020 were obtained as the training dataset. To reduce the incidence angle dependence of Sentinel-1 SAR images in the HH band, angle of incidence normalization correction was first carried out using Python software, and the images were then sliced to obtain the 256 \(\times\) 256 pixel training datasets.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 2 System Model  
The present paper primarily investigates a UAVs-assisted model for disaster emergency communication networks, as depicted in Figure 1. We consider a disaster scenario with a square area of \(S\). The system comprises \(l_{UAV}\) of UAVs, \(J_{person}\) of ground users awaiting rescue, and \(L\) target deployment points are located, the sets \(l_{UAV}=\{1,2,\ldots i\}\), \(J_{person}=\{1,2\ldots j\}\) and \(L=\{1,2\ldots l\}\). In the disaster scenario described in this paper, the target deployment point represents the damaged ground communication base station, and the UAV base stations replace these damaged ground communication base stations to provide emergency communications services for the rescue ground users. The communication links depicted in Figure 1 include UAV-to-UAV communication (U2U) and UAV-to-groundcommunication (U2G) for interaction between unmanned aerial vehicles and ground users in need of rescue. In addition, Table 1 summarizes the main notations used in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(I_{UIAV}\) & UAVs’ set \\ \(J_{person}\) & Sets of ground users awaiting rescue \\ \(D\) & The horizontal distance between the user’s equipment antenna on the rescue surface and the UAV’s station antenna \\ \(\mathbb{Z}(h_{msta})\) & The antenna parameter calibration factor \\ \(\mathbb{k}\) & The scenario type calibration factor \\ \(R_{u_{i},l_{i}}\) & Euclid distance between UAVs and target deployment points \\ \(R_{u_{i},Z_{i}}\) & Euclid distance between a UAV and any point \\ \(u_{i}\) & The horizontal coordinates of a UAV \\ \(l_{i}\) & The horizontal coordinates of the target deployment points \\ \(Z_{i}\) & The coordinates of any point in the rectangular area \\ \(f_{1}\) & The connectivity fitness function \\ \(f_{2}\) & The coverage fitness function \\ \(fit_{final}\) & Fitness function \\ \(P_{loss}\) & Pathloss \\ \(\theta\) & Random variable \\ \(a_{k}\) & Step size \\ \(d_{k}\) & The search direction \\ \(N\) & Number of UAVs \\ \(L\) & Number of target deployment points \\ \(M\) & Number of the ground users awaiting rescue \\ \(d_{min}\) & UAVs safe distance \\ \(d_{max}\) & Maximum communication distance of UAVs \\ \(f_{0}\) & Carrier frequency \\ \(T_{1}\) & Path loss threshold \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of notations.  
Figure 1: Communication network model of UAV base stations.
## 3 Experiment and Results
### Sample Construction and Network Parameters  
In the extraction of PolSAR buildings, the use of CNN to achieve the extraction of buildings, the construction of training samples, and the initialization of convolutional neural network parameters are essential for the effective acquisition of the model for PolSAR building extraction.  
In the construction of the sample, we used the real surface map to select the building as a positive sample and the non-building as a negative sample. For the selection of positive samples, we selected 15% of the building area pixels randomly as the center point of the sample. For the selection of negative samples in the non-building area, we randomly selected the same number of pixels as the positive sample as the center point of the sample. We used these pixels as the center point of the sample. After that, a window of size N \(\times\) N was constructed, centering on the selected pixel point, and the data in the window were taken as a sample, and the size of the sample is N \(\times\) N \(\times\) C, where C is the number of  
Figure 2: The SAR and optical images, and the true ground map. (**a1,b1,c1**) are the PauliRGB images from E-SAR, GF-3, and RADARSAT-2, respectively. (**a2,b2,c2**) are the optical images. (**a3,b3,c3**) are the true ground map. Red is the building and green is the non-building in (**a3,b3,c3**).  
channels. The label value corresponds to the category of the central pixel point. After an experimental analysis, the sample map size of ESAR data were selected as \(25\times 25\) pixels, and the sample map size of RADARSAT-2 and GF-3 data were selected as \(29\times 29\) pixels. See Section 4.2.2 for details.  
To learn the higher-level features of the feature set in the sample while extracting the building, we use a CNN. The CNN that we construct is a Lenet5-like network, shown in Figure 3, which is a relatively simple convolutional neural network. The structure includes two convolutional layers, two pooling layers, and two fully connected layers, wherein the convolutional layer has several convolution kernels of 500 and 100, respectively. The number of fully connected layers is 200 and 84, respectively. The size of the convolution kernel is \(3\times 3\) pixels, and the size of the pooled layer is \(2\times 2\) pixels. The stride size is 1 pixel. The initialization of all parameters in the network selects randomly from a Gaussian distribution with a variance of 1. In addition to the bias parameter, the initialization of the bias parameter is zero. In the convolutional neural network training, we used 500 training samples as one batch, and the learning rate during training was 0.01. In training, the loss value is less than 0.005 as a complete fit, and the model can be acquired for subsequent building extraction.
## 2 Materials and Methods
### Data Collection
#### 2.3.2 Isokinetic Strength Test  
An isokinetic dynamometer (CMSi Cybex Humac Norm Dynamometer, Lumex, Ronkonkoma, NY, USA) was used to assess participants' maximum strength in ISO knee-extension movement. Calibration and correction of the force of gravity were carried out following the standard procedures of the instrument. Setting up of the seat and mechanical arm was carried out in accordance with the participants' anthropometric characteristics to allow the alignment between the center of rotation of the knee and the fulcrum of the dynamometer. Participants were asked to sit on the isokinetic dynamometer seat, keeping the trunk straight and the thighs parallel to the ground in order to for the hip joint to be at 90\({}^{\circ}\). Subsequently, the trunk, hips, and dominant thigh were fixed with straps to the seat; the dominant ankle was fixed with straps to the mechanical arm. Once the participant was positioned correctly, the lever arm was measured with a tape between the fulcrum of the dynamometer and the point of application of the force to be used for data analysis. Finally, to keep the test safe, electronic and mechanical locks based on individual maximal knee extension and flexion were set to limit the range of motion of the machine.  
Participants performed a warm-up immediately before the ISO strength test consisting of 2 sets of 5 consecutive knee extensions at moderate angular velocity (2.09 rad \(\times\) s\({}^{-1}\)). The isokinetic strength test consisted of performing 3 maximal isokinetic contractions at 1.05, 1.57, 2.62, 3.14, and 3.67 rad \(\times\) s\({}^{-1}\)[5]. Each set was separated by a 3-min recovery. The order of the sets was randomized and counterbalanced. An additional trial was performed immediately before each set to familiarize the participants with the different velocities.
## 3 Results and Discussion
### Prediction of HRR
#### 3.3.2 Determination of \(r\)
Meanwhile, the relative amount of CO and CO\({}_{2}\) indicates the combustion efficiency of samples. A typical result of mass production rates of CO and CO\({}_{2}\) for 8 cm NC-D sample is depicted in Figure 10, where the concentration of CO\({}_{2}\) are almost two orders of magnitude than that of CO. Both NC-I and NC-E samples also present the similar results. Thus, the current tests are reasonably assumed as complete combustions, or the measured HRRs are slightly lower than the total HRRs, as shown in Figure 9.  
Figure 9: Comparisons of the modified and measured HRRs: **(a)** NC-I; **(b)** NC-E; and **(c)** NC-D. Moreover, the application range for Equations (1) and (3) is limited in \(7<\dot{Q}^{2/5}/D<700\) kW\({}^{2/5}\)m\({}^{-1}\)[36]. Given the flame spread at the beginning and the reduction in the effective burning area during the decay stage, the diameter \(D\) should be smaller than \(\frac{2}{\sqrt{n}}L\). The relatively smaller HRR at the beginning and the end together with the reduction of \(D\) leads to the uncertainty of the feasibility of Equations (1) and (3). It is also difficult to identify the change of effective burning area in the two stages. In the prediction model, the diameter \(D\) is roughly approximated as a constant \(\frac{2}{\sqrt{n}}L\). Thus, there are some errors in the calculation of HRR for the situations with smaller burning area and HRR.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 2 Visible Light Communication System
### Standardisation Efforts
#### 2.1.1 IEEE 802.15.7  
Due to increasing interest of researchers in VLC, the VLCC has introduced the first IEEE 802.15.7 standard in 2009 [33]. The standard defines the physical and media access control (MAC) layer parameters for short-range optical wireless communication. It covers topics such as network topologies, modulation domain spectrum, MAC protocol specification, collision avoidance, addressing, performance, quality indicators, dimming support, coloured status indication, and stabilisation. The standard proposes one-off keying (OOK), color shift keying (CSK) and variable pulse position modulation (VPPM) techniques for indoor and outdoor communication [34]. The highest achievable data rate for indoor communication can go up 96 Mb/s. However employing the multiple input-multiple output (MIMO) system and other modulations scheme, the data rates can be improved significantly.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 6 Conclusions  
This work further explored the support of multiple PHY layers in a single IEEE 802.15.4e TSCH slot bonding network. More specifically, the parent and PHY selection was investigated in such a multi-PHY network. First, a computationally efficient PHY and parent selection heuristic was proposed that can be integrated into a distributed routing protocol. The performance of this heuristic was initially evaluated via simulation and theoretical results, by comparing it against a near-optimal, but computationally complex, centralized scheduler. The results confirmed that the heuristic is capable of approximating the PDR results of the centralized scheduler. Second, an easy-to-configure TSCH slot bonding implementation was proposed in the latest version of the Contiki-NG IIoT operating system. Subsequently, the slot bonding implementation was used to confirm the PDR performance of the proposed heuristic in a real sensor testbed. In this testbed experiment, the proposed heuristic was at most 2.3% worse than the GA in terms of PDR. Both approaches outperformed the single-PHY solution, increasing PDR results by more than 160%.
# Assessing the Seismic Demands on Non-Structural Components Attached to Reinforced Concrete Frames
## 6 Artificial Neural Network (ANN) Model
### Predictive Expression Using ANN Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hidden Neuron**} & \multicolumn{3}{c}{**Input-Hidden Weight**} & \multicolumn{3}{c}{**Hidden-Output Weight**} & \multicolumn{2}{c}{**Bias**} \\ \cline{2-7}  & \(\mathbf{T}\) & \(\mathbf{\xi_{\text{NS}}}\) & \(\mathbf{S}\) & \(\mathbf{Floor}\) & \(\mathbf{CDAF}\) & \(\mathbf{Hidden}\) & **Output** \\ \hline
1 & \(-\)1.823 & \(-\)7.929 & \(-\)0.136 & 0.233 & \(-\)2.726 & 4.917 & \\
2 & 1.926 & 13.893 & 0.032 & \(-\)0.192 & 1.251 & 3.018 & \\
3 & \(-\)1.796 & 0.169 & 0.061 & \(-\)0.003 & 2.886 & 0.085 & \\
4 & \(-\)5.367 & \(-\)0.440 & \(-\)6.494 & 2.704 & 0.118 & \(-\)1.311 & \\
5 & \(-\)6.863 & 6.238 & 0.071 & \(-\)0.298 & 2.718 & 2.136 & \\
6 & \(-\)6.562 & 5.712 & 0.081 & \(-\)0.301 & \(-\)2.957 & 1.885 & \\
7 & 8.947 & \(-\)1.633 & 0.050 & 7.035 & \(-\)0.100 & \(-\)3.219 & \\
8 & 20.749 & 2.468 & 0.057 & \(-\)0.922 & 0.482 & 20.907 & \\
9 & 26.644 & 0.166 & 0.027 & 0.403 & \(-\)4.378 & 21.303 & \(-\)3.109 \\
10 & \(-\)14.638 & 0.262 & 0.007 & \(-\)0.020 & \(-\)8.095 & \(-\)5.555 & \\
11 & 0.157 & 4.150 & 0.014 & \(-\)0.001 & 1.447 & 3.074 & \\
12 & 7.773 & 0.332 & 11.839 & \(-\)7.140 & \(-\)0.103 & 13.698 & \\
13 & \(-\)14.563 & 0.188 & 0.010 & \(-\)0.012 & 8.206 & \(-\)5.490 & \\
14 & \(-\)36.300 & \(-\)1.397 & 0.126 & \(-\)3.951 & \(-\)0.289 & \(-\)26.922 & \\
15 & 32.328 & 0.135 & 0.024 & 0.279 & 3.800 & 25.904 & \\
16 & \(-\)0.177 & 0.404 & \(-\)0.010 & \(-\)0.005 & \(-\)14.903 & \(-\)0.063 & \\
17 & 3.627 & \(-\)0.032 & 1.161 & \(-\)2.418 & \(-\)0.157 & \(-\)1.237 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Weights and biases of the ANN model for irregular building. where \(b_{0}=\) bias of the output layer; \(w_{k}=\) weight connection between hidden layer neuron (\(k\)) and single output neuron; \(b_{hk}=\)\(k\)th hidden neuron bias; \(w_{ik}=\) weight between the input and hidden layer neuron (\(k\)); \(X_{ni}=\) input parameter; \(f_{h}=\) transfer function of the hidden layer (Tan-sigmoid); and \(f_{o}=\) transfer function of the output layer (Tan-sigmoid):  
\[CDAF_{regular}=\left(10.25\times CDAF_{n,regular}\right)+10.29 \tag{13}\]  
\[CDAF_{regular}=\left(9.735\times CDAF_{n,irregular}\right)+9.7691 \tag{14}\]  
where \(CDAF_{n,regular}\) and \(CDAF_{n,irregular}\) are the normalized component dynamic amplification factors of regular and irregular building models, respectively. Only the dataset range for which the ANN model was developed should be utilized in Equations (13) and (14). The maximum and minimum input parameter constraints are shown in Table 9.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 6 Performance Study
### Partially Visible Picture Test  
In order to verify the capability of detecting the partially visible pictures, we designed the third experiment. The camera is placed in different positions to capture different percentages of visible picture. The comparison results on detecting partially visible picture is shown in Table 4. Neither ArUco nor ArPico can report any pose result since they need to see all four corners in the camera view. The visibility percentages on PicPose tests represent the ratio of visible parts of pictures appearing in the view. It can be seen in the table that only a few frames can be reported when the percentage of the visible part is 10%. The success rate of PicPose highly depends on SSD; if there are not enough features to be detected by SSD, the pose calculation will fail. The objects can be successfully detected when the visibility ratio increases to 50%. More than 92% of the frames are successfully used for pose calculation with the pictorial object visibility ratio of 90%.