esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection and Dataset Construction  
The data in this study were collected by the research group from a pig farm in Guangxi Province, with a total of 48 gestating sows, including those in pre-gestation and mid-gestation. The sows were kept in single pens, and sows of different gestation periods were kept in different buildings. The video data of each sow were captured by an Azure Kinect camera. The camera was set up on a homemade adjustable mobile cart to capture video data of the sow back while standing at an overhead angle. The camera captured videos at 30 frames/second for 3 min, and the videos were parsed to obtain RGB images, which were used as the object of this study. The backfat thicknesses of 48 sows were measured using a Renco (LEAN-METER) backfat meter with a measuring range of 4-35 mm and an error of \(\pm\)1 mm. The backfat measuring point was chosen as the P2 measuring point commonly used in the international pig industry.  
Parsing the video data of the sow back to obtain the image data of the sow back and taking into account the similarity of the neighboring images, one frame of the image was taken every two seconds. Each sow's video was parsed to obtain 90 images with a size of 1280 \(\times\) 720, and a total of 4320 RGB images were obtained from the parsing of the video data of 48 sows. Some of the images are shown in Figure 1, and the captured information contains the background of the sows in the limiting pen, the sows in the limiting pen next to it, and the drainpipe above it. To reduce the influence of the background and to speed up the forward inference and backpropagation of the model, the image of the sow back was cropped and grayscaled to retain more information about the sows in the middle limit fence and to reduce the information about the sows in the side limit fence as much as possible. The resolution of the image was reduced from 1280 \(\times\) 720 to 1080 \(\times\) 450. Part of the image is shown in Figure 1. The cropped image was combined with the backfat thickness of the sov to construct the dataset.  
The body condition scores of 48 sows are shown in Table 1. The actual measured backfat thicknesses of the sows were all within the backfat thickness intervals corresponding to body condition scores 2, 3, and 4. The dataset was randomly divided into a training set, a validation set, and a test set with a ratio of 8:1:1, with the number of samples being 37, 6, and 5. To validate the stability of the model's generalization performance across different datasets, the division of the five datasets was repeated with the five test sets. The samples were not repeated, and the division samples of each dataset are shown in Table 2.
# Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults
###### Abstract  
The measurement of personal exposure to radiofrequency electromagnetic fields (RF-EMFs) is important for epidemiological studies. RF-EMF exposure can be measured using personal exposimeters that register RF-EMFs over a wide range of frequency bands. This study aimed to measure and describe personal RF-EMF exposure levels from a wide range of frequency bands. Measurements were recorded from 63 participants over an average of 27.4 (\(\pm\)4.5) hours. RF-EMF exposure levels were computed for each frequency band, as well as from downlink (RF from mobile phone base station), uplink (RF from mobile phone handsets), broadcast, and Wi-Fi. Participants had a mean (\(\pm\)SD) age of 36.9 \(\pm\) 12.5 years; 66.7% were women; and almost all (98.2%) from urban areas. A Wi-Fi router at home was reported by 61 participants (96.8%), with 38 (61.2%) having a Wi-Fi enabled smart TV. Overall, 26 (41.3%) participants had noticed the existence of a mobile phone base station in their neighborhood. On average, participants estimated the distance between the base station and their usual residence to be about 500 m. The median personal RF-EMF exposure was 208 mV/m. Downlink contributed 40.4% of the total RF-EMF exposure, followed by broadcast (22.4%), uplink (17.3%), and Wi-Fi (15.9%). RF-EMF exposure levels on weekdays were higher than weekends (\(p<0.05\)). Downlink and broadcast are the main contributors to total RF-EMF personal exposure. Personal RF-EMF exposure levels vary according to day of the week and time of day.  
radiofrequency electromagnetic fields; personal exposure measurement; mobile phone base stations; downlink; uplink +
Footnote †: journal: International Journal of Environmental Research and Public Health
# Development of Hybrid Airlift-Jet Pump with Its Performance Analysis
## 2 New Concept of Hybrid Airlift-Jet Pump
### Operating Principle of Hybrid Pump  
A schematic diagram of the hybrid airlift-jet pump is shown in Figure 3. As described above, the driving force of the jet pump is the pressure difference induced by the nozzle (ejector), while that of the airlift pump is the buoyancy induced by the lower density of the mixture. The basic idea of the hybrid airlift-jet pump, as schematically depicted in Figure 3, is to increase its efficiency by arranging these two driving forces to act complementarily. The upper and lower passages guide the air flow from an air compressor to drive the jet and airlift parts, respectively. The additional advantage of the hybrid pump is its versatile applicability to various operating environments, while retaining its high efficiency by controlling the flow rate entering each passage. For example, when the suction head is high, all the air from the compressor is initially supplied to the airlift part to overcome such a high head height. As the water column approaches the jet part, some of the air is diverted to the jet part to drive the water to the exit of the outlet pipe. After this transient state, the operating condition of the hybrid pump is set to be in the best efficiency or performance condition. A primary goal of the present study is to investigate the performance and efficiency of the hybrid pump in various operating conditions.  
Figure 3: Schematic design of a hybrid pump.  
Figure 2: Schematics of an airlift pump.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 2 Network Model and Problem Formulation  
In this work, we focus on tree network topologies that are formed in the case of active probing from a single source. In essence, a designated root node (i.e., the source) actively sends probes to the leaves (i.e., the destination nodes), where traffic is monitored. To facilitate the collection of end-to-end measurements in our experiments using the tools described in Section 4.1, we design the examined networks with the root having only one child. The internal nodes can have exactly two children, in which case the resulting tree is binary, or two or more children, in which case the resulting tree is general. In greater detail, assuming that during the measurement period the underlying routing algorithm determines a unique path from a source to each destination that is reachable from it, the physical routing topology from a source node to a set of destination nodes is a tree called _physical routing tree_. The _logical routing tree_ consists of the source node (root), the destination nodes (leaves), and the branching points (i.e., nodes with two or more outgoing links) of the physical routing tree, along with the logical links between them. Consequently, a logical link comprises a sequence of one or more consecutive physical links and the degree of every internal node of the logical routing tree is at least three. The root (i.e., source node) has only one child (therefore a degree of one), and the leaves (i.e., destination nodes) none (therefore a degree of one). Figure 1 presents an indicative topology example of both the physical and the corresponding logical routing trees.  
Let \(T(s,D)=(V,E)\) denote the logical routing tree from source \(s\) to a set of destination nodes \(D\), which consists of node set \(V\) and edge set \(E\). Every node \(k\in V\setminus\{s\}\) has a parent \(f(k)\in V\) such that link \(e_{k}\equiv(f(k),k)\in E\). Additionally, every node \(k\in V\setminus D\) has a set of children \(c(k)=\{j\in V:f(j)=k\}\). Each link \(e\in E\) is associated with a performance parameter \(\theta_{e}\) (e.g., loss rate, jitter). Finally, the unique path (i.e., sequence of links) that connects nodes \(i\) and \(j\) on the logical routing tree is denoted by \(p(i,j)\). The network inference problem that we consider in this paper regards using aggregate end-to-end measurements obtained at the terminal nodes (i.e., \(k\in\{s\}\cup D\)) for: (i) inferring the routing tree topology and (ii) estimating the fine-grained network characteristics, i.e., the individual link parameters \(\theta_{e}\) (loss rates or jitter).
## 3 Motivation and Objectives  
The performance of CA as a diversity technique was evaluated in [22] by means of simulations carried out with the ns-3 simulator [25, 26]. The simulation results presented in [22] demonstrated that CA can be exploited as a diversity technique to enhance the overall system performance and increase the network capacity without increasing the total amount of spectrum. Moreover, it was also observed in [22] that there exists an optimum number of CCs that maximises the throughput for each propagation scenario (depending on the radio propagation distance among other parameters). As a result, the system performance cannot be improved indefinitely by making the number of employed CCs arbitrarily large. On the one hand, a higher number of CCs enables the proposed approach to exploit frequency diversity with a finer granularity, thus increasing the data rate. On the other hand, a higher number of CCs also reduces the total amount of bandwidth available for data transmission because a higher amount of spectrum needs to be reserved for the guard bands between adjacent spectrum sub-blocks (see, for example, table 5.4.2A-1 in [27]) and to accommodate a higher amount of signalling traffic given that each CC runs its own dedicated instance of the MAC- and PHY-layer protocols (see, for example, section 7.6.1 in [28]). For CA to be beneficial as a diversity technique, the gain in spectrum efficiency obtained from frequency diversity needs to overcome the bandwidth penalty incurred by the required guard bands and signalling overhead, thus determining an optimum number of CCs for each propagation scenario.  
Simulation studies can be valuable tools for exploring the performance of a particular technique. In fact, the simulation study presented in [22] was instrumental in understanding and demonstrating the effectiveness of CA when used as a diversity technique. However, an analytical study is also necessary to provide a comprehensive understanding of the technique being investigated. In this context, this work fills such an existing gap. The main objective is to develop a mathematical model and a corresponding set of closed-form expressions that can characterise the operation of CA as a diversity technique and support, from a theoretical point of view, the conclusions obtained in the simulation study presented in [22].
# Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity
## 4 Results and Discussions
### Water Environmental Capacity Calculation
#### 4.1.1 Yearly Scale
There are large water intakes in the 2, 10, and 12 water function areas, and large tributaries in the 6, 9, 11, and 14 water function areas. The water intake reduces the flow, resulting in the WEC also being small. On the contrary, the tributary increases the flow, leading to the WEC increasing. It shows that the larger water intake and tributaries in the water function area will affect the size of WEC. In addition, it can be seen from Figures 8 and 9 that among the 14 water functional areas in the Shaanxi section of the Wei River, the WEC of the Weinan agricultural water use area (ID:13) is the largest, and the WEC of the Baoji agricultural water use area (ID:2), Baoji landscape area (ID:3), and Xianyang sewage control area (ID:10) are the smallest. By analyzing the WEC of different design frequencies in Figures 8 and 9, it can be concluded that WEC is negatively correlated with the size of design frequency, that is, WEC decreases with the increase in design frequency. Figure 8: Calculation results of COD WEC under different design frequencies.
## 2 Materials and Methods
### Classification of Referable Diabetic Retinopathy
#### 2.1.2 Filtering Suspicious Data  
To compensate for the severe class-imbalance in the training set, we propose a method that filters data with positive labels. The filtration networks introduced examine the training set with positive labels only.  
Given the uncorrupted validation dataset \(\mathcal{D}_{val}\) and a classification network \(f:x\mapsto\hat{p}\in\Delta(\mathcal{Y})\), which takes values on the probability simplex over classes \(\mathcal{Y}=\{0,1\}\), a noise filtration network \(b(\hat{p})=w^{T}\hat{p}\) was trained to minimize the logistic regression loss over validation data \((x,y)\). This 'optimization' is not to be regarded as a traditional 'training' procedure in that the filtration network does not contribute to the test accuracy and only serves to detect mislabeled cases in the training set. Because the filtration network was trained on data with clean labels, a well-trained filtration network would identify clean positive data by predicting high values on positive images with clean labels and predicting low values for suspiciously negative images.  
The filtration network \(b(\hat{p})\) is trained at each validation step (one training epoch) and is used to mask the loss values whenever noise is detected. Specifically, the gradient descent step for the classification network is computed as  
\[\theta\leftarrow\theta-\eta\
abla\mathcal{L}(\theta;x,y)\mathbf{1}\{b(\hat{p} )\geq\tau\} \tag{1}\]  
with learning rate \(\eta\) on training samples \((x,y)\) and a threshold \(\tau\). The loss function \(\mathcal{L}\) can be any loss function that aims to classify objects (e.g., cross entropy). Our approach of data filtration by examining the classifier's confidence reduces negative effects caused by incorrect annotations, outperforming curriculum learning-based strategies [28]. Note how the time required to train the filtration network \(b(\hat{p})\) is negligible compared to that required for the classifiers \(f\), as both the input \(\hat{p}\) and parameter dimensions are substantially smaller for \(b(\hat{p})\).  
Figure 1: Fundus images of healthy (**left**); referable diabetic retinopathy (**middle**); and pathologic myopia (**right**). The referable fundus shows hemorrhage and white cotton-like patches (better view in the electronic version). Fundus with PM shows large patches of choriorefinal atrophy spread over the retinal fundus in many cases (**top-left**) or retina detachment may occur in severe cases (**bottom-right**).
## 2 Materials and Methods
### Methods
#### 2.5.2 L*, a* and b* Color Parameters  
Color parameters were determined on the surface of the beef slice (30 min after package opening) in five repetitions on each sample from each treatment and the average was used as the final result. The MINOLTA(r) CR-400 colorimeter (Minolta, Tokyo, Japan) was used, with the following settings: light source D65, observer 10\({}^{\circ}\), and a measuring head hole of 8 mm. A white standard (Y: 95.2 x: 0.3159, y: 0.3326) was used to calibrate the device before the measurement. The L* parameter refers to lightness, where 0 indicates black and 100 indicates white. The a* parameter indicates the level of greenness (\(-\)a*) or redness (\(+\)a*), while the b* parameter indicates the level of blueness (\(-\)b*) or yellowness (\(+\)b*). Based on the average values of color parameters L*, a*, and b*, absolute color differences (\(\Delta\)E) between samples for each treatment on day 1 of storage and following storage times were calculated as follows:  
\[\Delta\mathrm{E}=\sqrt{\Delta\mathrm{L}^{*2}+\Delta\mathrm{a}^{*2}+\Delta \mathrm{b}^{*2}}\]  
where \(\Delta\mathrm{L}^{*}\) is the difference in average L* values between samples, \(\Delta\mathrm{a}^{*}\) is the difference in average a* values between samples, and \(\Delta\mathrm{b}^{*}\) is the difference in average b* values between samples.  
The results were interpreted according to the criteria presented by [PERSON] and [PERSON] (2007), where 0 \(<\)\(\Delta\)E \(<\) 1 indicates that the color difference is not noticeable, 1 \(<\)\(\Delta\)E \(<\) 2 indicates that the color difference is noticeable only to an experienced observer, 2 \(<\)\(\Delta\)E \(<\) 3.5 indicates that the color difference can be noticed by an inexperienced observer, 3.5 \(<\)\(\Delta\)E \(<\) 5 indicates a clear difference between colors, and \(\Delta\)E \(>\) 5 indicates that the observer perceives two distinct colors.
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 2 Early Exploration of Aerial Electrostatic Spray Technology in China  
In the 1970s, China began to study the new technology of electrostatic spraying for agricultural application [11]. In 1977, Shenyang Spray Factory used hand-held sprayers to carry out field spraying experiments on the seedlings and young trees of cloves and found that the application of electrostatic spraying treatment could increase the amount of pesticides applied per unit area of plants while reducing the labor intensity [12]. After the application of electrostatic spray technology in hand-held and knapsack sprayers, it has also achieved success in the application of ground agricultural machinery equipment in greenhouses, orchards, and other operating scenarios [13; 14]. A large number of studies on the effects of electrostatic spray equipment have been carried out on droplet size, droplet deposition distribution in each part of crop canopy, nozzle structure, and working parameters on droplet quality, etc. [15; 16; 17; 18; 19].  
In 2005, China introduced the electrical parts and nozzles of the aerial electrostatic spray system from the SES company and carried out simulation tests and flight tests [20], marking the beginning of China's research in the field of aerial electrostatic spraying. Two years later, universities and scientific research institutions in China began to comprehensively study the aerial electrostatic spray technology based on manned aircraft platforms in aspects of hardware structure improvement, mechanism of influencing factors, and theoretical simulation.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 3 A Unified Framework  
In practice, energy trading occurs across various levels involving corresponding stakeholders within different energy markets. Within an energy distribution network, generators sell energy to electricity retailers and large consumers in a well-structured wholesale market in a peer-to-peer way. At the end-user level, prosumers and consumers directly trade energy in a local market within a microgrid. For instance, residents of smart communities may sell excess electricity generated by solar panels to their neighbors. A microgrid connects to the main grid and participates in the wholesale market as a single entity. The microgrid first self-suffices its local residents then sells its surplus aggregated energy in the wholesale market or purchases energy from it.
## 2 Material and Methods  
### Samples and Image Preprocessing  
The multimodal brain images used in this study came from the ADNI's (Alzheimer's Disease Neuroimaging Initiative) open database. Thousands of individuals were enlisted for the ADNI study from around North America to collect clinical data and brain scans. In this study, we used the baseline sMRI scans from pMCI and sMCI patients to train and evaluate our model. Table 1 displays demographic and clinical data from the ADNI used in this study. The official website should be considered for more information regarding the data acquisition protocol ([[https://adni.loni.usc.edu/about/](https://adni.loni.usc.edu/about/)]([https://adni.loni.usc.edu/about/](https://adni.loni.usc.edu/about/)), accessed on 15 October 2022). In this paper, sMCI refers to individuals diagnosed with MCI who did not develop AD within a period of 36 months and pMCI refers to individuals diagnosed with MCI who did develop AD within a period of 36 months. All brain sMRI scans were preprocessed based on the Voxel-based morphometry (VBM) technique using the CAT12 and the Statistical Parametric Mapping toolbox version 12 ([[http://www.fil.ion.ucl.ac.uk/spm/software/spm12/](http://www.fil.ion.ucl.ac.uk/spm/software/spm12/)]([http://www.fil.ion.ucl.ac.uk/spm/software/spm12/](http://www.fil.ion.ucl.ac.uk/spm/software/spm12/)), accessed on 15 October 2022) with default settings. In general, MRI pre-processing is comprised of correcting the bias-field distortions; removal of non-brain tissue; segmentation of brain tissue into the components of GM, white matter, and cerebrospinal fluid; and DARTEL-normalization to MNI-space. In this paper, only GM of a brain MRI was used. The technical details of VBM-based sMRI pre-processing were described previously [27].
# Several Cases for the Validation of Turbulence Models Implementation
## 2 Physical Model
### Turbulence Models
#### 2.2.2 Spalart-Allmaras Turbulence Model  
The Spalart-Allmaras turbulence model [5] is a one-equation turbulence model that solves the transport equation of an eddy viscosity-like variable, \(\bar{v}\),  
\[\frac{D\bar{v}}{Dt}=P-D+\frac{1}{\sigma}\left[\
abla\cdot((\
u_{L}+\bar{v}) \
abla\bar{v})+c_{b2}(\
abla\bar{v})^{2}\right] \tag{7}\]  
where \(P\) and \(D\) are the production and destruction source terms,  
\[P =c_{b1}(1-f_{2})\bar{S}\bar{v} \tag{8}\] \[D =\left(c_{w1}f_{w}-\frac{c_{b1}}{\kappa_{SA}^{2}}f_{i2}\right) \left[\frac{\bar{v}}{d}\right]^{2}. \tag{9}\]  
Constant coefficients include \(\sigma\), \(c_{b1}\), \(c_{b2}\), \(c_{w1}\), and \(\kappa_{SA}\). The value \(d\) is the distance to the wall. Function \(f_{w}\) is known as the wall function, as it is dependent on distance to wall, \(d\). Viscous function \(f_{v2}\) depends heavily on the ratio between the molecular kinematic viscosity, \(\
u_{L}\), and the eddy-viscosity-like variable \(\bar{v}\). The modified vorticity term, \(\bar{S}\), is a function of the magnitude of vorticity, wall distance, and viscosities [5]. The Spalart-Allmaras turbulence model is a popular turbulence model for turbomachinery flows as it was designed for wall-bounded flows. As the Spalart-Allmaras turbulence model only solves for one equation, it is also inherently less computationally expensive than Shear Stress Transport.  
The original turbulence model [5] has an additional trip term, \(f_{t1}\), used to model transition to turbulence. [PERSON] et al. [7] found that the two trip terms--\(f_{t1}\) and \(f_{t2}\)--are unnecessary to simulate fully turbulent flows. The implementation used in this work does not use \(f_{t1}\)[8]. In addition, the implementation is based on the compressible version that includes an update of the modified vorticity term [7].