esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cancer is a major global public health problem because of its high disease burden. New reports show that lung cancer has been rising in men and women as leading cancer over the past decade [1]. Besides those tumors that directly affect the lungs, the pleura can also be affected by the development of fibrous tumors. A solitary fibrous tumor (SFT) are soft tissue sarcomas that usually form in the pleura, is not malignant, and causes symptoms depending on its size [2].  
SFT causes Doege-Potter syndrome (DPS), a paraneoplastic disorder with symptomatic, severe, and persistent hypoglycemia, being a rare cause (2-4% of cases) of which some cases have been described [3; 4; 5]. To distinguish SFT from other intrathoracic tumors, computed tomography (CT) is advantageous since it presents as a homogeneous round and well-defined mass between 10 and 20 cm in diameter [6]. Surgical management of the tumor is the treatment of DPS. Chemotherapy, radiofrequency ablation, cryoablation, or chemoembolization are tools used in SFT/DPS. Additionally, management of hypoglycemia is mandatory and glucocorticoids improve insulin-related outcomes in the course of the disease. The prognosis is favorable but depends on the size of the tumor and the paraneoplastic complications, so there have been reports of cases with successful follow-up [7; 8]. Given a tumor that causes a paraneoplastic syndrome, with slow growth and that adopts a large size oppressing the affected lung, it is crucial to approximate the actual size of the tumor with the use of software from the images obtained in radiology [7].  
Manual segmentation of MRI slices has long been the standard for volume measurement; however, this process tends to be time-consuming and inaccurate [9]. Recently, different semi-automatic and automatic segmentation methods have simplified this task and tend to have greater accuracy in calculating these volumes [10]. We aimed to use three different open-source software used for semi-automated glioblastoma segmentation; these are 3D Slicer, ITK-Snap, and Inveslius, to compare the tumor volumes obtained later.
# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 4 Experiment and Results  
We tested the performance of the multi-layer fusion 3D object detection network for proposal generation and object detection tasks on three classes of the KITTI object detection benchmark. According to the 7481 training frames provided by the KITTI dataset, we divided the training set and testing set into a ratio of about 1:1. For the evaluation, we followed KITTI's easy, moderate and hard difficulty levels. We evaluated and compared the four versions we implemented as follows: the first version using early fusion, the second version using late fusion, the third version using deep fusion and the fourth version using our D-fusion.  
The training and testing of the network was run on an NVIDIA GeForce GTX 1080 Ti GPU (NVDIA, Santa Clara, CA, USA) with 11 GB of memory. This network includes feature extraction networks based on AFM and SENet, as well as fusion networks based on D-fusion. Figure 5 shows the final output result. The comparison of small object detection results is shown in Figure 6. We only take the car class for demonstration because the small object problem and occlusion are more common and prominent in the car class. As can be seen in Figure 6, both small-size objects and occluded small-size objects can be effectively detected.  
The training results of detection accuracy are shown in Figure 7. As the number of iterations increases, the accuracy of object detection continues to improve. We use three indicators to evaluate the performance of the network, namely AP\({}_{\text{2D}}\), AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\). Figure 7 shows the performance of the network on the car class. The performance of the network for pedestrian and cyclist classes is shown in Figure 7. As the number of training epochs increases, the accuracy also continues to improve. For cars, the accuracy of a 2D prediction box is close to 90%, while the accuracy of a 3D prediction box is about 85%. The accuracy of 2D and 3D prediction boxes on the pedestrian class is close to 60%. The accuracy of the cyclist class is close to 65%. As shown in Figure 7, the detection accuracy of cars is much higher than that of pedestrians and cyclists. This is because the sample number of cars is relatively large and abundant.
## 4 Method and Discussion
### Interpolation  
An interpolation process was used here to retrieve a representative grid from the CYGNSS observation points. As shown in Figure 7A, the data derived from CYGNSS have irregular structures based on the satellite along-tracks. We used the natural neighbor interpolation method for gridding. The method was developed by [PERSON] [46] and is a multivariate interpolation according to Voronoi tessellation [47]. The principal formula is [48]:  
\[G(x,y)=\sum\
olimits_{i=1}^{N}w_{i}\,f(x_{i},y_{i}) \tag{5}\]  
where \(G\) is the estimated value at \((x,y)\), \(w_{i}=Q_{k}/R_{k}\) is the weights, and \(f(x_{i},y_{i})\) is the known data at \((x_{i},y_{i})\), \(R_{k}\) is the area of the initial Voronoi diagram element for point \(P_{k}\ =\ (x_{i},y_{i})\). \(Q_{k}\) is the intersection  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PRN** & **Bias (dB)** & **PRN** & **Bias (dB)** & **PRN** & **Bias (dB)** & **PRN** & **Bias (dB)** \\ \hline
1 & 1.017 & 9 & 1.498 & 17 & 0.256 & 25 & 0.880 \\
2 & 0.004 & 10 & \(-\)0.783 & 18 & \(-\)0.206 & 26 & 0.163 \\
3 & 1.636 & 11 & \(-\)0.230 & 19 & \(-\)0.206 & 27 & 0.409 \\
4 & - & 12 & \(-\)1.021 & 20 & 0.345 & 28 & \(-\)0.712 \\
5 & \(-\)0.610 & 13 & 0.007 & 21 & \(-\)0.909 & 29 & \(-\)1.032 \\
6 & 0.24 & 14 & \(-\)0.730 & 22 & \(-\)0.838 & 30 & 0.877 \\
7 & \(-\)0.709 & 15 & \(-\)0.376 & 23 & \(-\)0.858 & 31 & \(-\)0.562 \\
8 & 0.605 & 16 & \(-\)0.481 & 24 & 1.140 & 32 & \(-\)0.819 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Empirical biases in \(SNR_{c\ dB}\) according to pseudorandom noise (PRN).  
area of \(R_{k}\) and newly constructed element for the point \((x,y)\). Therefore, the method algorithm is the algorithm to insert an additional point into the existing Voronoi diagram. Figure 8 illustrates the visual view of the natural neighbor interpolation method.  
For CYGNSS data interpolation over our region of interest, we generated a grid with the resolution of 0.1\({}^{\circ}\) along the geodetic longitude and latitude and applied the mentioned interpolation method. Figure 9 shows the data before and after gridding. As can be seen from the figure, the gridded data is more sensible compared to the satellite tracks representation. Since \(SNR\) was not equal in magnitude to the \(SNR_{c\ dB}\), the observations and corrections made in Equation (4) resulted in magnitudes greater than 140 dB (Figure 7A). To see the anomalies of the corrected \(SNR_{c\ dB}\) in a visually reasonable range [11], 140 dB was subtracted from the original \(SNR_{c\ dB}\) values.  
Figure 8: The natural neighbor interpolation method. The area of the colored circles are the interpolating weights. The shaded area is a new Voronoi element for the point to be interpolated [48].  
Figure 7: Statistical information of the data preparation step for the CYGNSS observations over a period of three days (13 January to 15 January 2020). (**A**) Corrected signal-to-noise ratio (SNR) before and after the preprocessing step. (**B**,**C**) The distribution of data according to incidence angle and antenna gain before and after data preparation.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 5 Data Analysis
### Analysis of Battery Systems  
It is necessary to select the right battery for the proposed system, as this should satisfy the household demand throughout the day with the given requirements, avoiding over and undersizing. The maximum discharge power is higher than the household demand at any step of time, the battery capacity is used almost completely, and the system allows the use of the stored energy in peak hours while in off-peak hours it is loaded according to the necessities. Consequently, it can be accepted that the selected battery is the right one.  
Figure 2: Energy demand during the year for each system: (**a**) Current AC system. (**b**) AC system with battery. (**c**) Combined AC/DC system with battery.  
The charging curve was evaluated in different days of the year. Figure 3 shows the curves corresponding to a winter day, 16 th December and a summer day, 17 th June, both of them Wednesdays.  
As can be seen in Figure 3a,c, for the summer day in both systems, from 1 p.m. to 11 p.m., the battery is permanently discharged. Off-peak hours commence at 11 p.m., when the battery starts to charge up to the maximum.  
As can be seen in Figure 3b, for the winter day in the AC system, from 12 p.m. to 8 p.m., the battery is permanently discharged. However, peak hours in winter go from 12 p.m. to 10 p.m. As can be seen, the battery is charged at full power regardless of the period because the charge at 8 p.m. is below a 20%. The battery is discharged again at 9 p.m., because it is still during peak hours. When off-peak hours begin, at 10 p.m., the battery starts to charge again up to its maximum.  
As can be seen in Figure 3d, for the winter day in the combined AC/DC system, from 12 p.m. to 10 p.m., the battery is permanently discharged. Off-peak hours commence at 10 p.m., so the battery starts to charge until full capacity is reached.  
In all cases, due to the demand along the hours, the maximum level of battery charge depends on the demand at each moment, establishing an almost constant maximum charge in off-peak hours.
## 1 Introduction
Performance, fatigue performance, and optimum distribution height are important factors, and the study showed that the height of the concrete layer of different materials significantly affects the bending performance of concrete beams, but there is no study on the effect of different concrete layer thickness ratios on the bending performance of concrete beams with the same material. Currently, research on the performance of concrete stacked beams is still in its developmental stage, with studies primarily focusing on the impact of various concrete material factors. Research on concrete layering mainly concentrates on the optimal thickness distribution under the composite effect of different materials and the deformation and stress characteristics of the overall lining, considering interlayer effects. However, there is limited research on the impact of different thickness ratios on the bending performance of shotcrete. Single-layer lining is a support structure that effectively transfers shear force between layers, so it is important to study how layering impacts the strength of the structure. The thickness ratio of the different layers of the same concrete material is one of the key factors in the construction and affects the effectiveness of the support of the entire lining structure. On this basis, this study aims to investigate the flexural performance of single-layer lining under different thickness ratios by means of experimental research, combined with 3D-DIC technology, using high-performance fiber shotcrete, to reveal the influence of the law of the thickness ratio on the damage mode of the lining. The calculation of the bending stiffness of single-layer lining concrete beams is corrected by combining the theory of stacked beams, and the optimized design of the thickness ratio is proposed to provide a theoretical basis for the safe application of single-layer lining in tunnel engineering.
## 1 Introduction
### Frequency Domain
#### 2.1.2 Triple-Soliton Bound States  
We express a triple-soliton bound state with complex amplitude of solitons in time domain as \(f(t)\), \(f(t-\Delta T)\text{exp}(j\theta_{1})\) and \(f(t-2\Delta T)\text{exp}(j\theta_{2})\). Then, its spectral amplitude reads  
\[\begin{split}& F(\
u)+F(\
u)\exp(-j2\pi\
u\Delta T)\exp(j\theta_ {1})+F(\
u)\exp[-j2\pi\
u\cdot(2\Delta T)]\exp(j\theta_{2})\\ &=F(\
u)\exp[-j(2\pi\
u\Delta T-\frac{\theta_{2}}{2})]\cdot\{2 \cos(2\pi\
u\Delta T-\frac{\theta_{2}}{2})+\exp[j(\theta_{1}-\frac{\theta_{2} }{2})]\}.\end{split} \tag{4}\]  
If \(\theta_{1}=\pi\) and \(\theta_{2}=0\), its spectral intensity is then proportional to  
\[\Big{|}F(\
u)\Big{|}2\cdot[2\cos(2\pi\
u\Delta T)-1]^{2}=\Big{|}F(\
u)\Big{|}2 \cdot[2\cos(4\pi\
u\Delta T)-4\cos(2\pi\
u\Delta T)+3]. \tag{5}\]  
For a guide to eyes, we plot an exemplary optical spectrum of a triple-soliton bound state shown in Figure 2. It exhibits a symmetrical spectrum with respect to the centre wavelength and meanwhile two high peaks are located on both sides of the low central peak.  
Figure 1: Spectra of two-soliton bound states with \(\theta_{1}\)= (**a**) \(\pi\); (**b**) 0; (**c**) \(-\pi/2\); and (**d**) \(\pi/2\) according to Equation (2). Blue dotted curve is for single soliton (unbound) and black solid curve is for bound solitons. For all figures, 0.8-ps chirp-free sech-shaped pulses with centre wavelength of 1560 nm and pulse separation of 6 ps are used. (Reproduced with permission from [28]. © The Optical Society of America (Washington, DC, USA), 2013).  
Figure 2: Spectrum of a triple-soliton bound state with \(\theta_{1}=\pi\) and \(\theta_{2}=0\) according to Equation (5). Blue dotted curve is for single soliton (unbound) and black solid curve is for bound solitons. Here 0.8-ps chirp-free sech-shaped pulses with centre wavelength of 1560 nm and pulse separation of 4 ps are employed. (Reproduced with permission from [28]. © The Optical Society of America (Washington, DC, USA), 2013).
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### The Need for Blockchain and IoT in the Coffee Supply Chain
#### 1.3.3. Smart Contracts for Rule Enforcement and Workflow Automation  
Smart contracts are self-executing programs that enforce predefined business rules on the Ethereum blockchain [14; 15]. Within a coffee supply chain, they can validate sensor inputs and enforce quality or compliance thresholds. For instance, a smart contract can prevent the transfer of ownership if a shipment's temperature exceeds an acceptable range or automatically release payment once verified delivery is confirmed. By replacing manual verification with autonomous logic, smart contracts improve fairness, accuracy, and efficiency across the system.
# Sensors and Communication Simulation for Unmanned Traffic Management
## 5 Proposed Simulation Models for Key Elements
### Communications Simulation
Therefore, we have modeled network parameters as dependent of a stationary network load status, determined not by the simulated UTM-related traffic but by the rest of the communication network users. To implement these disturbances, each communication node consists of a communication  
Figure 7: Simulated network architecture. Beyond visual line of sight (BVLOS) C2 traffic is shown in green, VLOS C2 link in blue, and UTM-bound traffic in red. queue, as shown in Figure 8, that groups all received traffic, simulates the disturbances, and routes it to the next node. Each disturbance has been modeled using stochastic models (packet loss rate, packet burst rate, mean burst length, mean transmission delay, etc. ), and an independent realization of each model is drawn to generate the corresponding disturbance for each packet. These parameters are included in both CNodeModel and NetworkModel and can be defined by the user. Once we are able to forward traffic, we can analyze the specific behavior of the mobile communication nodes. We must take into account that a packet must only be sent by one node. Therefore, it is necessary to simulate the process of assigning a mobile communications node to each agent connected to the mobile network. This assignment depends on two important factors that we will simulate: the radio link between agents and the presence of different types of communication technologies. Starting with the communication technologies, each model of a communication node has assigned a communication type (WiFi, 3G/4G/5G mobile communications, etc.). At the same time, the drones, sensors, and GCS agents' models detail their compatible communication technologies. As for the radio range between agents, we are going to implement a simplified coverage model. For each communication node, we define its maximum horizontal isotropic range and vertical range. If multiple nodes fulfil these requirements, the node closest to the agent is chosen. This process is performed in an orchestrated way among all agents using drone/GCS/sensor state update messages to know their position.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## Principle  
### Dimensionless Methods  
Dimensionless methods are commonly used data normalization methods. Due to the different dimensions of indexes, they are not comparable. Therefore, the indexes should be processed by the dimensionless method first and then analyzed after eliminating the dimensional influence. In this paper, we selected four dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method and normalized processing method) to process the original index data.  
1. Unified extreme value processing method The principle of the unified extreme value processing method is shown in Equation (1): \[{x_{ij}}^{*}=\frac{{x_{ij}}-\min(x_{j})}{\max(x_{j})-\min(x_{j})}(i=1,2,\ldots,m;j=1,2,\ldots,n)\] (1) where \(x_{ij}\) is the original data of the \(j\)-th index of the \(i\)-th sample; \({x_{ij}}^{*}\) is the data after processing, where the value is between 0 and 1, and the value distribution is consistent with that before processing; \(\max(x_{j})\) and \(\min(x_{j})\) are the maximum and minimum values of the original data of the \(j\)-th index, respectively. (2) Differentiated extreme value processing method The unified extreme value processing method adopts a unified formula for the forward index (that is, the larger the data, the more dangerous the rockburst, such as \(\sigma_{\varnothing}/\sigma_{c}\) and \(W_{\text{et}}\)) and reverse index (that is, the smaller the data, the more dangerous the rockburst, such as \(\sigma_{c}/\sigma_{t}\)). However, the indexes are processed differently by the differentiated extreme value processing method, as shown in Equation (2) for the forward index and Equation (3) for the reverse index: \[{x_{ij}}^{*}=\frac{{x_{ij}}-\min(x_{j})}{\max(x_{j})-\min(x_{j})}\] (2) \[{x_{ij}}^{*}=\frac{\max(x_{j})-x_{ij}}{\max(x_{j})-\min(x_{j})}\] (3)
3. Data averaging processing method The principle of the data-averaging processing method is shown in Equation (4): \[{x_{ij}}^{*}=\frac{{x_{ij}}}{\overline{x}}\] (4) where \(\overline{x}\) is the average value of the original samples data of the \(j\)-th index. This method maintains the overall consistency of the original data well while eliminating the effects of dimension and order of magnitude, which retains the information of the original variation degree of each index. (4) Normalized processing method The principle of the normalized processing method is shown in Equation (5): \[{x_{ij}}^{*}=\frac{{x_{ij}}}{\sum\limits_{i=1}^{m}{x_{ij}}}\] (5)  
This method converts the actual value of the index into its proportion in the total value of the index, so it is also called the proportion method.
## 3 Review of Energy Efficiency in WSNs
### Energy Efficiency in Heterogeneous Protocols  
[PERSON] et al. [47] presented a novel clustering approach called Energy Centers Searching using Particle Swarm Optimization (EC-PSO). For selecting suitable CHs, this approach searches for energy centers and avoids energy gaps. A geometric technique is used to choose the CHs. The nodes were then clustered using EC-PSO after the network's energy dispersed. An improved PSO approach is used to discover the energy center. The nodes in the area are then selected to serve as Central Hubs (CH). Data is collected by a mobile data collector. A protection mechanism will prevent low-energy nodes from operating as forwarders.  
Enhanced balanced energy efficiency network-integrated super-heterogeneous routing (E-BEENISH) protocol was introduced to evaluate the number of energy clusters used to communicate with each other [48]. At the same time, the broad range of energy levels in heterogeneous WSNs was also considered. E-BEENISH assigns a weighted probability to each node as the cluster leader based on the residual power and the distance between the node to the sink. Energy usage is also affected by node heterogeneity.  
A variety of routing strategies with dynamic nodes based, including an efficient centroid-based routing protocol (ECBRP), are used in WSNs to reduce energy consumption [49]. The majority of data in sensor nodes comes from a densely traveled area, which causes collisions. Multi-channel communication is one way for reducing sensor node conflict. different algorithms with their characteristics and features presented in Table 3. such as clustering, CH failure, and high energy usage, are also present.  
The energy consumption model is a probabilistic method that includes CPU prediction for wireless sensor nodes [55]. The power consumption was considered during several stages of the CPU's operation which included standby, power-up, idle, and active. The energy consumption can be determined accurately by calculating the quantity of CPU consumption and the expenditure of time. This research has focused on the routing methods as classified into three categories: hierarchical, flat, and location-based. Additionally, heterogeneous and multi-path-based routing protocol techniques have been discussed.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 3 System Model
### Local Execution  
When the users choose to execute the computing tasks locally, we set the size of the \(m\)th computing task generated by user \(n\) as \(l_{nm}\) and set the energy consumption required by the users to process each bit in the computing tasks as \(e_{\text{local}}\). Therefore, the energy consumption of user \(n\) locally executing task \(m\) is given by:  
\[E_{nm}^{\text{local}}=l_{nm}e_{\text{local}}. \tag{1}\]  
We denote the processing time of each data bit processed locally by user \(n\) as \(t_{\text{local}}\). Therefore, the delay for user \(n\) to process task \(m\) locally is:  
\[T_{nm}^{\text{local}}=l_{nm}t_{\text{local}}. \tag{2}\]  
Therefore, the total delay for user \(n\) to process its tasks locally is:  
\[T_{n}^{\text{local}}=\sum_{m=1}^{M}T_{nm}^{\text{local}}(1-x_{nm}),\forall n\in N. \tag{3}\]