esa-sceva/satcom-chunk-collection huggingface dataset:
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 3 Problem Statement
### Causes of Duplicate Packets  
Primarily, the duplicate packets are generated due to the following facts:  
* Firstly, the holding time of packet p at node i \(HT_{i}^{p}\) is computed by a node i and the timer is started upon successful reception of packet p (refer to Figure 1). Node i does not forward the packet when \(HT_{i}^{p}\) is on, however, data packets from neighboring nodes can be received by it, which may be duplicates of p or other data packets. Before the expiry of \(HT_{i}^{p}\), if node i receives additional copies of p (a single or multiple copies), it abandons the transmission of p. However, for the case that no copies of packet p are received before \(HT_{i}^{p}\) expiry, packet p is forwarded by i. Hence, simply by duplicating broadcast overhead is minimized, which is essential when bandwidth and energy are scarcely available resources as in UASN scenario. However, if in case, the holding time difference between any two nodes A and B (\(HT_{A}^{p}-HT_{B}^{p}\)) is smaller than the propagation delay of a packet p from node A to B, the duplicate packets will be generated.
* The second reason for generating the duplicate packet is the hidden terminal problem. In a hidden terminal problem, the source node broadcasts and the potential forwarding nodes receive the packet. The problem occurs when the highest priority node broadcasts the packet while some of the potential forwarding nodes of the source node are not in the range and thus do not receive the duplicate packet, which causes these packets to be generated.
* Thirdly, relaying packets over multiple hops might result in a failed delivery of the packet to its destination because of high error rate of the acoustic channel, path losses and channel impairments. Duplicate packet generation and transmission become imperative because of the above-mentioned scenarios.
## 3 Field Studies and Measurements
### Creating a Three-Dimensional Model  
The 3D rockfall analyses applied within the scope of this study were examined and evaluated under three main headings: (i) creating a three-dimensional terrain model, (ii) modeling the worst-case condition (fall analysis for the block with a mass of 10 tons), and (iii) evaluating the performance of the proposed rehabilitation design.  
Cortak Village was established on the foot of a moderately natural dip slope. Dacidic rock masses presenting a nearly vertical topography on the upper floors of the slope have acquired a highly fractured structure depending on discontinuities and the degree of weathering. Blocks broken off from these source masses due to erosion and freeze-thaw processes move down the slope and cause rockfall problems. As a result of field observations and measurements, it has been seen that the dimensions of these blocks can reach 2.0 m in places. Within the scope of this study, it was thought that the most effective solution in order to minimize the risk of rockfall is a gabion wall. For this reason, 3D rockfall analyses were carried out in order to prevent the risk of rockfalls in the examined areas. Also, jump heights and energy amounts were calculated. Based on the results obtained, gabion wall positions and dimensions were determined. The 3D numeric surface model of the study area was produced as a result of digitizing the maps produced by drone flights in the study area (Figure 8).  
A DJI Matrice 300 drone was used in photogrammetric studies. While using the DJI Matrice 300, measurements were made with centimeter precision with the D RTK 2 module as a ground station. By performing a two-way and three-turn terrain oblique flight with the Zennume L1 Lidar Camera, the tree cover was removed, and the surface model was created. Since the slope of the land is high, the resolution was prevented from decreasing at the lower elevations of the land by terrain flying (following the land surface) from an altitude of 100 m. The DJI Matrice 300 Drone was connected to a geofixed point using the DRTK2 Mobile station to use the RTK feature at the centimeter level. The data was processed as UTM WGS4 six degrees and a high resolution orthophoto was created. The triangulated irregular network (TIN) obtained from the digitized map and underlying the 3D rockfall analyses is given in Figure 9 and the digital elevation model (DEM) is given in Figure 10.  
Figure 8: 3D solid model of the study site created using RocPro3D software (version 6.2).  
Figure 9: Triangulated irregular network (TIN) mesh created in RocPro3D software (version 6.2).
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 3 Methods
### RAS-Faster RCNN for ROI Extraction  
#### 3.1.1 RA-FasterRCNN  
Generally, the area of the prostate organs is relatively small in the original image, especially for the apex and base parts of the prostate. Therefore, to eliminate the interference with the adjacent tissues and various artifacts as much as possible, while ensuring the integrity of the organ area, this paper adopted the improved Faster R-CNN [34; 35; 36; 37] based on the residual network and the spatial attention mechanism (RA-FasterRCNN) to develop initial localization of the organ. Figure 4 shows the main flow of the network, which mainly includes two parts: region extraction network (RPN) and object detection network (Fast R-CNN) [38].  
This algorithm first performs pixel intensity normalization and size normalization on each 2D axial scan in the 3D MR image. Then, each of the 2D slices is inputted into the RA-FasterRCNN to obtain a collection of prostate region proposals with high predicted probability and accurate locations. Compared with the original Faster RCNN, we used ResNet50 with spatial attention modules, which is described in detail in Figure 5, instead of the original VGG16 as the feature extraction part of the network.  
Besides, the function of the spatial attention module is shown in Equation (1), in which \(F\in R^{H\times W\times C}\) represents the input feature map, \(F_{GAP},F_{GMP}\in R^{H\times W\times 1}\) denote the global average pooling and global  
Figure 4: The flowchart of RA-FasterRCNN.  
Figure 5: The architecture of the feature extraction module of RA-FasterRCNN.  
maximum pooling of \(F\) along the channel axis, respectively, \(Conv^{7\times 7}\) represents a convolution operation with a \(7\times 7\) convolution kernel, \(S\) is the Sigmoid function, [ ] means the splicing operation of two feature maps.  
\[Attention\_map(F)=S(Conv^{7\times 7}[F_{GAP},F_{GMP}]), \tag{1}\]  
Considering the two tasks of bounding box classification and regression prediction, the training loss function is defined in Equation (2). Among them, \(L_{cls}\) and \(L_{reg}\) represent the binary cross-entropy loss and smoothL1 regression loss, respectively [39], \(\lambda\) is a weight coefficient between classification loss and regression loss.  
\[L=L_{cls}+\lambda L_{reg}, \tag{2}\]  
It is worth noting that the trainable parameters in Resnet-50 in the network are pretrained on the ImageNet dataset [40]. The transfer learning from natural images to medical images is often used to improve the small-scale defects of prostate image datasets [41; 42].
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 4 Result
### Quantitative Result
#### 4.1.2 Quantitative Result of Object Detection  
The Object Detection model was designed to display bounding boxes to highlight the affected area of the plant, and each label had different colors of boxes. The actual classes for prediction are seven rather than nine, since Powdery Mildew was detected in red peppers and zucchinis, while leaf blight was shown in tomatoes and beans. Meanwhile, among five pretrained models of YOLOv5, which are YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, our team chose YOLOv5m.  
The confusion matrix on Figure 11 shows the performance of our Object Detection model.  
Figure 12 displays the precision-recall curve of our Object Detection model.  
According to Figures 12 and 13, Anthracnose, Alternaria Leaf Spot, and Leaf Mold were detected quite well, while detecting Powdery Mildew, Leaf Blight, and Frogeye Leaf Spot shows low level of precision and F1-confidence. Figure 12 suggests mAP50 of each class, which has a similar output compared to the confusion matrix. The average mAP50 was 0.382, indicating that the performance of the model is low. Anthracnose, Alternaria Leaf Spot, and Leaf Mold had a mAP50 score above 0.5, while other classes showed the mAP50 below 0.3. Meanwhile, Figure 13 suggests the highest average F1-score is 0.44 at 0.204 confidence, which shows the significant difference among the classes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BLEU\_1** & **BLEU\_2** & **BLEU\_3** & **BLEU\_4** & **BLEU\_AVG** \\ \hline
78.64 & 69.77 & 61.08 & 50.58 & 64.96 \\ \hline \hline \end{tabular}  
All scores were calculated by discarding the third decimal place.  
\end{table}
Table 2: BLEU scores (%).  
Figure 11: Confusion Matrix of Object Detection Model.  
Figure 12: Precision-recall curve of the Object Detection model.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 3 Nonlinear Analysis Algorithms  
### Newton-Raphson Method  
The iterative Newton-Raphson method is commonly used for solving nonlinear structural systems. In this procedure, an approximate response is guessed for a structure described with nonlinear equations, and then an unknown corrector value is added to refine the initial response. Implementing approximation series (e.g., Taylor series), the nonlinear equations could be changed to a linear format equation, and by solving this linear system the corrector factor is achieved and the solution would be improved accordingly. This process is repeated until an acceptable approximation is obtained [25; 26].
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 2 Design of Antenna
### Step 4 Antenna  
The proposed antenna is designed on an FR4 substrate (\(\epsilon_{\mathrm{r}}\) = 4.4) with a thickness of 0.8 mm and a loss tangent of 0.02. In the final stage, a ring shape with a cross-shaped structure is added at the center of the patch, which is used for better results [11]. The proposed antenna has a partial ground configuration. The antenna has a peak gain of 8.05 dB at 12 GHz and a 7.13 dB gain at 7.6 GHz. The proposed antenna covers C-, X-, and Ku-bands which are used for satellite and high-speed communication. The proposed antenna is dual-band and operates at two different frequencies: 7.6 GHz, and 12 GHz. The pentagonal-shaped patch is used for higher gain while the small four circles are used for better bandwidth. The ring structure in the patch is used to obtain the desired frequency band. The design and dimension of the proposed antennas are shown in Figure 1d and Table 1, respectively.
## 3 Evaluation Results  
To evaluate the performance of our proposed method, we obtained correlations between the quality scores of our proposed method and the subjective scores (visual preferences of HVS). Ten observers participated in the subjective evaluation. For the purpose of comparison with other methods, correlations between the scores of the conventional methods (NIQE and CQE) and the subjective scores were also obtained. Spearman's rank ordered a correlation coefficient (SROCC) and Pearson's linear correlation coefficient (PLCC), which were used to compare the performances. We used the Spearman and Pearson coefficients methods for performance evaluation because these methods have been widely used for correlation metrics between image quality assessment and subjective scores [21; 22; 23; 24; 25].  
The results of the correlations with the subjective scores are shown in Table 1. In all the cases, our proposed method achieved higher correlations with the subjective scores than those of the NIQE and CQE. These results demonstrate that our proposed method is more suitable for evaluating the colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images than the other two conventional methods. This is because the conventional methods were mainly developed for evaluating images distorted by compression or noise corruption (NIQE) and did not consider HVS properties (CQE).
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Design Methodology
#### 2.2.1 System Architecture  
This study aims to create a system for encoding and transferring EEG signals recorded from patients suffering from epilepsy while retaining the signal quality and conserving energy.  
As a result, meticulous design is necessary before implementation to avoid omissions, understand its functioning, and completely describe the device's constituent subsystems. First and foremost, the system's expected input must be determined. As shown in the diagram (Figure 1), this input consists of four channels of EEG signals; the data correspond to 90 s recordings that represent 30 s before and 60 s after stimulation, respectively [9]. The data is then sent through an Analog-to-Digital converter (ADC), which is encoded and sent to the microcontroller unit (MCU).  
In the first sub unit of the MCU, the data is encoded using the Delta technique, which calculates the differences between the samples. The encoded data is subsequently stored in system memory, where it is retrieved and transmitted whenever possible or requested. The communication is wireless and uses the BLE protocol, which divides the information into data packets stored and analysed on a host computer. The Control Unit supervises all of these modules, which synchronises them; when a connection signal from the BLE module alerts the Control Unit that a connection has been established, the system functions (Figure 1).
# Design and Experiment of a Passive Vibration Isolator for Small Unmanned Aerial Vehicles
## 4 Vibration Shaker Experiment: Analysis of Damping Characteristic
### Analysis of Damping Characteristic of Vibration Isolators
The results of the random vibration tests reveal that the vibration isolator with a stiffness of 30 exhibits damping ratios of 2.2% and 1.9%, which are higher than those of the vibration isolators with stiffness of 50 and 70. In this study, the comparative analysis of vibration characteristics showed that while a stiffness level of 30 had a high damping ratio in Section 1, two resonance points were identified within the operational frequency band. In the case of a stiffness level of 70, with only one resonance point and relatively high amounts of damping and performance, it was selected as the material for the vibration isolator.
## 4 Statistical Properties
### Moments  
We derived a computational representation of the general order moments corresponding to the proposed NGLog-X of distributions. If x is an RV from the NGLog-X family, then about the origin, the \(r^{\text{th}}\) moments are given by  
\[E(x^{r})=\int_{-\infty}^{\infty}x^{r}g(x;\delta,\Theta)dx \tag{19}\]  
Putting the density of the proposed family in Equation (19), we obtain  
\[\mu^{\prime}=\int_{-\infty}^{\infty}x^{r}\frac{f(x;\Theta)e^{\delta}\Big{\{}1+ \delta(e-\log F(x;\Theta))^{-1}\Big{\}}}{\left(e-\log F(x;\Theta)\right)^{ \delta}}dx\]  
\[\mu^{\prime}=\int_{-\infty}^{\infty}x^{r}f(x;\Theta)\bigg{(}1-\frac{\log F(x; \Theta)}{e}\bigg{)}^{-\delta}\Bigg{\{}1+\delta e^{-1}\bigg{(}1-\frac{\log F(x ;\Theta)}{e}\bigg{)}^{-1}\Bigg{\}}dx \tag{20}\]  
Using the expansion \((1-x)^{-n}=\sum\limits_{i=0}^{\infty}\binom{i+n-1}{n-1}\ x^{i}\), where \(x=\frac{\log F(x;\Theta)}{e}\), \(n=\delta\), and \(n=1\) in Equation (20), respectively, then we obtain  
\[\mu^{\prime}=\sum\limits_{i=0}^{\infty}\binom{i+\delta-1}{\delta-1}e^{-i} \Psi_{i}+\delta\sum\limits_{i=0}^{\infty}\sum\limits_{j=0}^{\infty}\binom{i+ \delta-1}{\delta-1}e^{-(i+j+1)}\Psi_{ij,1} \tag{21}\]  
where  
\[\Psi_{i}=\int_{-\infty}^{\infty}x^{r}f(x;\Theta)(\log F(x;\Theta))^{i}dx\]  
and  
\[\Psi_{i,j,1}=\int_{-\infty}^{\infty}x^{r}f(x;\Theta)(\log F(x;\Theta))^{i+j}dx\]  
Similarly, using the PDF \(g(x;\delta,\Theta)\), a general explicit expression for the MGF (moments generating function) M\({}_{X}(t)\) of the NGLog-X is derived as  
\[\text{M}_{X}(t)=\int_{-\infty}^{\infty}e^{tx}g(x;\delta,\Theta)dx=\sum\limits_ {r=0}^{\infty}\frac{t^{r}}{r!}\mu^{\prime} \tag{22}\]  
By simplifying, we obtain the MGF  
\[\text{M}_{X}(t)=\sum\limits_{i,r=0}^{\infty}\frac{t^{r}e^{-i}}{r!}\Psi_{i}+ \delta\sum\limits_{i,j,r=0}^{\infty}\frac{t^{r}e^{-(i+j+1)}}{r!}\Psi_{i,j,1}\]
## 3 Materials and Methodology
### The Experimental Setup and Procedure  
The setup for the experiment includes a combination of hardware and software for carrying out non-contact roundness tests through a vision system. Proper lighting is essential for acquiring good-quality images of the test objects. The workpiece is rotated through the use of a servo motor, while the encoder response on the fixed axis measures the angle of rotation. Simultaneously, a COMS camera captures visual data of the targeted region. The experimental setup for the Smart System-based STEP-NC file-21 for the Machine Vision Inspection (3 SMVI) prototype, which incorporates cloud-based MQTT connectivity, is depicted in Figure 3. This system was implemented on an INTELITEK PROLIGHT three-axis milling machine, designed in accordance with Example 1, Part 21, of the ISO 14649 standard.  
Figure 3: The 3 SMVI prototype based on MQTT.  
Figure 2: The CAD Design of STEP-NC file 14649 Part 21.  
Case Study  
Based on the ISO 14649 standard's Example 1, Part 21, a system design was formulated specifically for the INTELITEK PROLIGHT 3-axis milling machine. The CAD model was utilized to identify the surface region of the side view layout, with a specific focus on one hole and one pocket that necessitate inspection. The selected circle designated for inspection represents the most critical section of the model area, ensuring precise measurement of the outer surface of the edge.  
This case study provides comprehensive information regarding the machining parameters employed for a Delrin prismatic mill workpiece. Notably, the workpiece has a roundness hole with a diameter of 30 mm. The specified parameters consist of a spindle speed of 2000 rpm, a feed rate of 0.1 mm/rev, a depth of cut of 20 mm, and a cutting speed of 250 m/s. To further elaborate, this case study encompasses an expanded version of Example 1 ISO 14649 Part 21 file, incorporating four holes and one pocket, as illustrated in Figure 2. Additionally, Table 1 presents an overview of the characteristic parameters, hardware, and software components involved in the study.
## 3 Related Works on Models Compression
### Model Compression Metric  
Two metrics are generally used to evaluate the compression of a model: the compression ratio and the speed up. The compression ratio is the ratio of memory required to inference before and after compression. It can be evaluated by the compression factor (\(CF\)) given in Equation (2) or the gain (R) in memory footprint given in Equation (3).  
\[CF=\frac{FBC}{FAC} \tag{2}\]  
\[R=1-\frac{1}{CF} \tag{3}\]  
where \(FBC\) is the footprint before compression and \(FAC\) is the footprint after compression.  
The speed up measures the model acceleration before and after compression in terms of inference time. It is usually expressed in terms of Floating-Point Operations (FLOPs). The FLOPs evaluate the number of multiplication and addition used to complete a process, which is a common practice to evaluate the computational time. Improvements in FLOPs result in decreasing inference time of the networks because of removing unnecessary operations. However, time consumed by inference depends on the implementation of convolution operator, parallelization algorithm, hardware, scheduling, memory transfer rate, etc.
## 1 Introduction
The main contributions of this paper are: (1) we propose a strategy to minimize energy consumption during the localization process that assures a given localization accuracy and is based on local information at each node; (2) we include two effective modifications of the above theoretical strategy in order to consider the effects of the signal propagation in real environments. The structure of the paper is as follows. Section 2 reviews some related work regarding the consumption-accuracy trade-off in localization systems. Section 3 defines the application scenario and the communication models that have been selected for our analysis. Section 4 analyzes the energy consumption of the localization procedure and proposes a transmission strategy, based on local information at each node, which minimizes the total power consumption of the network and at the same time achieves a given accuracy in the distance estimations. Some modifications to this theoretical strategy are also proposed to achieve the desired performance under real propagation conditions. The performance of the proposed strategy is evaluated in Section 5 and compared with other strategies through a set of simulations based on experimental measurements. Finally, Section 6 extracts some conclusions and proposes some future work.
# Regional Adaptability Analysis of Solar Roof Utilization Technologies in China
## 2 Methodology
### Parameters of the Buildings  
In order to calculate the thermal performance of the typical buildings, the typical U values of the building envelop should be used. Table 5 shows heat loss coefficients of the building envelope in different climate zones, according to the relevant guidelines, standards, or codes in China [36; 37; 38; 39; 40]. The typical U values represent the thermal insulation properties of the buildings constructed in China from 2003.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Building Type** & **No.** & \begin{tabular}{c} **Length–Width** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Ground** \\ **Floor/Rooftop** \\ **Area (m\({}^{2}\))** \\ \end{tabular} & **Stories** & **Remarks** \\ \hline \multirow{4}{*}{Residential building} & 1 & 1:1 & 200 & 2 & Single-family villa \\  & 2 & 3:1 & 600 & 6 &
\begin{tabular}{c} Multi-story, \\ one-elevator-two-apartments, 100 m\({}^{2}\), \\ 3 units \\ High-rise, \\ one-elevator-three-apartments, \\ 100 m\({}^{2}\), 4 units \\ High-rise, \\ one-elevator-three-apartments, \\ one-elevator-three-apartments, \\ one-elevator-three-apartments, \\ one-elevator-three-apartments, \\ one-elevator 2, 4 units \\ Multi-story \\ High-rise \\ One-elevator-three-apartments, \\ one-elevator 2, 4 units \\ Multi-story \\ High-rise \\ One-elevator-three-apartments \\ One-elevator 2, 4 units \\ Multi-story \\ High-rise \\ One-elevator 2, 5000  
The internal heat loads of the buildings are also considered in the investigations, inclusive heat loads due to lighting, occupant, equipment, and air exchange. The parameters of heat loads are listed in Table 6 for different building types, according to the national guideline.  
Relative indoor air humidity is controlled between 30% and 60%, and the COP of the heat pump system is 2.5 for cooling and 3.5 for heating. The hourly occupancy rate and on/off schedule of lighting, equipment and air conditioners are strictly set according to the operation time of the buildings. The operation time and designed indoor temperatures for an office building and a commercial building are listed in Table 7.  
With the aforementioned parameters as inputs, the thermal performance of all the investigated buildings can be calculated using EnergyPlus, and the energy consumption will be analyzed.