esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link-Budget Design
## 6 Conclusions  
We have updated the global SST by reformulating it according to a larger database of rain rate time series collected in several sites in different climatic regions (Table 2). For each site, the average annual probability distribution of rain attenuation obtained with the global SST, \(P_{SST,glo}(A)\), has been compared with that given by the full SST, \(P_{SST}(A)\), which we have considered as experimental data. The test has been conducted for frequencies ranging from 10 to 100 GHz, for elevation angles \(\theta\) ranging from 20\({}^{\circ}\) to 60\({}^{\circ}\) and for annual probabilities 10% to 0.01%.  
To present the results oriented specifically to the link budget design, we have studied the probability ranges 10% to 0.1% and 0.1% to 0.01% separately because of the extremely large values of rain attenuation at high frequencies, which would require unavailable system power margins, if site diversity or other fade countermeasures are not used.  
The global SST tends to underestimate the attenuation by approximately 10% for elevation angle \(\theta\leq 30^{\circ}\) and about 20% for \(30^{\circ}<\theta<60^{\circ}\) in the probability range 10% to 0.1%, and approximately 15% in the probability range 0.1% to 0.01%.  
For any probability, the error is zero for \(\theta=90^{\circ}\) because at the zenith the global SST coincides with the full SST.  
[PERSON] and [PERSON] have developed the software, modelled the results and written the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are available from the authors, on request.  
We wish to thank our colleagues: [PERSON], researcher at NASA some years ago, for providing the rain rate data of the North America sites; [PERSON], Institute of Atmospheric Physics in Prague, for providing the rain rate data of Prague; [PERSON], National Technical University of Athens, for providing the rain rate data of Athens; [PERSON], Universidad Politecnica de Madrid, for providing the rain rate data of Madrid. [PERSON], Politecnico di Milano, for providing the wind speed values at 700 mbar.  
The authors declare no conflict of interest.  
Figure 6: (a) Average (continuous lines) and standard deviation (dashed lines) of the error \(\varepsilon\) versus \(A_{SST}\); (b) \(\varepsilon_{r}\). Probability range 0.1% to 0.01%.  
Appendix A Scatter plots of the error \(\varepsilon=A_{SST,glo}-A_{SST}\) (dB) for all other sites.  
Figure A4: Scatter plots of the error \(\varepsilon=A_{SST,glob}-A_{SST}\) versus \(A_{SST}:\)(a) \(\theta>30^{\circ}\); (b) \(\theta\leq 30^{\circ}\). Prague.  
Figure A7: Scatter plots of the error \(\varepsilon=A_{SST,glo}-A_{SST}\) versus \(A_{SST}:\textbf{(a)}\ \theta>30^{\circ}\); (**b)**\(\ \theta\leq 30^{\circ}\). White Sands.
## 4 Experiments and Results
### Experiment Results
#### 4.2.3 Comparisons with Existing Models  
Due to the differences in data preprocessing, model training methods, and learning targets, it is not easy to directly compare among existing works, we try to select some models commonly used in financial forecasting and adjust these models to the best state to make relatively fair comparisons. The experimental results are shown in Table 2.  
Figure 8: Visualization of the trend prediction by different models on test example 3.  
Figure 7: Visualization of the trend prediction by different models on test example 2.  
From Table 2, we can find that the proposed hybrid neural network performs better than other models, whether the forecast horizon is one week or one month. On the one hand, SVM is a commonly used machine learning model in financial forecasting, while CNN and LSTM are commonly used deep learning models. Compared with the Simplistic Model, these models can extract profitable information, but they only learn features from a single scale and ignore some useful information. On the other hand, the Multiple Pipeline Model and MFNN are models based on multi-scale feature learning for financial time series forecasting. They use different branches or different networks to extract different scale features. However, it will increase the complexity of the model. The model we proposed only utilizes a CNN to extract features of different scales simplifying the model and predicting more accurately. Therefore, we can conclude that the proposed hybrid neural network is superior to the commonly used models in the existing works.
## 3 Results and Discussion
### Characteristics of Dehydrated Water  
The COD of dehydrated water at different temperatures was investigated and shown in Figure 6. The COD of the supernatant obtained by centrifugation of raw sludge was 4553.5 mg L\({}^{-1}\) at room temperature. When the temperature varied from 45 to 95 \({}^{\circ}\)C, the values of COD were within the range of 4500-4600 mg L\({}^{-1}\), and there was no significant change compared with the sample without thermal solidification treatment. The experimental results indicated that the significant changes in morphology and characteristics of sludge during the thermal treatment process were not caused by hydrolysis of organic matter or solubilization of solid substrates, which was very different from hydrothermal treatment of activated sludge. For the activated sludge, it is known that the lysis of cell walls during hydrothermal treatment releases solutions due to the disruption of sludge microorganism cells [19], which results in the hydrolysis of organic matter and reduction in viscosity. The water bonded into flocs was set free and transformed into bulk water due to the improvement of the characteristics of sludge particles. Hydrolysis of organic matter and solubilization of solid substrates during hydrothermal treatment will inevitably lead to increased pollutants in the solutions, thus giving the residue solutions a high COD [20]. In contrast, thermal solidification treatment in this paper is a thermophysical process due to the low temperature and almost unchanged COD value of dehydrated water.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wet weight of sludge cake (g) & 9.318 \\ Dry weight of sludge cake (g) & 5.911 \\ The moisture content (\%) & 36.6 \\ The amount of free water (g/g dry solid) & 0.393 \\ The free water proportion (\%) & 68.2 \\ The bound water content (\(g\)/g dry solid) & 0.183 \\ The bound water proportion (\%) & 31.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of sludge cake after thermal solidification (volume of raw sludge was 100 mL).  
Figure 6: Effect of temperature on COD of dehydrated water (the setting time was 60 min).
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 6 Simulation Verification
### Discussion
From the user's perspective, the LB-OLSR protocol offers multiple benefits, optimizing route selection and reducing redundant multiple forwarding nodes to ensure that users can receive the required data in a timely manner. The LB-OLSR protocol intelligently adjusts data transmission strategies based on network load conditions, avoiding network congestion and thereby improving the overall response speed. By reducing redundant control messages and optimizing data transmission paths, the LB-OLSR protocol can lower energy consumption within the network, which is particularly important for battery-powered LoRa nodes, extending the lifespan of the devices. These advantages make the LB-OLSR protocol more attractive for practical applications, meeting users' demands for efficient, stable, and secure IoT communication.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Link Cost Metric  
In common MANET routing protocols, drones periodically broadcast HELLO messages to estimate link costs and detect link failures. In contrast, drones in GCS-routing conduct link cost estimation and link failure detection without HELLO messages. This section explains link cost estimation and detection of link failure in GCS-routing.
## 8 RAN Decomposition Scheme
### RAN Functional Split
#### 8.2.2 Low Layer Split
This helps relatively in reducing the DU footprint for better installation on the street lamp poles or utility poles [8]. Though the MFH bandwidth is approximately reduced to the wireless data rate, a realization of centralized MIMO processing is relatively demanding since computationally intensive PHY layer functions are executed in the DU [46]. Consequently, the offered DU is relatively complex [8]. The required bandwidth for the DL and UL in this option are given, respectively, as [425; 430; 432]\[R_{DL}^{MAC-PHY}= \left(R_{p}^{DL}+S_{o}^{\mathcal{S}\mathcal{R}C}\right)BW_{s}^{SY}L_ {s}^{MIMO_{DL}}M_{s}^{DL}, \tag{16a}\] \[R_{UL}^{MAC-PHY}= \left(R_{p}^{UL}+S_{o}^{\mathcal{S}}\right)BW_{s}^{SY}L_{s}^{MIMO_ {UL}}M_{s}^{UL}, \tag{16b}\]  
where \(BW_{s}^{SY}\) is for the system bandwidth scaling, \(S_{o}^{\mathcal{S}\mathcal{R}C}\) denotes the signal overhead owing to scheduling/control signaling to DL-PHY and \(S_{o}^{\mathcal{S}}\) represents the signal overhead owing to the UL-PHY's response to the schedule, \(R_{p}^{DL}\) and \(R_{p}^{UL}\) denote the reference LTE peak data rate for the DL and UL, respectively, \(M_{s}^{DL}\) and \(M_{s}^{UL}\) denote the modulation order (QAM) scaling for the DL and UL, respectively. Parameters \(M_{s}^{(\cdot)}\), and \(BW_{s}^{SY}\), can be defined, respectively, as [425; 430; 431]  
\[M_{s}^{(\cdot)}= M_{s}^{base(\cdot)}/M_{s}^{LTE(\cdot)}, \tag{17a}\] \[BW_{s}^{SY}= B^{base}/B^{LTE}, \tag{17b}\]  
where \(B^{base}\) and \(M_{s}^{base(\cdot)}\) are the baseline parameters, and \(B^{LTE}\) and \(M_{s}^{LTE(\cdot)}\) represent the LTE reference parameters.
###### Abstract  
Background/Objectives: The primary purpose of the study is to investigate the bond strength of apical fillings following retreatment in teeth with failed apical resection. Methods: After the preparation and obturation of the 120 human upper central and canine teeth, apical 3 mm was resected and separated into two main groups to prepare retrograde cavities using tungsten carbide burs or ultrasonic retro-tips. Each main group was separated into three subgroups according to retrograde filling material (Glass ionomer cement, MTA and Biodentine), and each subgroup was divided according to placement technique: manual condensation and indirect ultrasonic vibration. After the retrograde filling, retreatment procedures were performed, and 2 mm sections were removed from the apical filling and analyzed for push-out test. A Kolmogorov-Smirnov test was used to check the normal distribution of the data while [PERSON]'s test was used to check the homogenity of group variances. The data were analyzed using three-way ANOVA. Results: The analysis of variance demonstrated a significant difference between cavities prepared with tungsten carbide and ultrasonic retro tips in push-out bond strength. Conclusions: There was no effect on the bond strength of the retrograde filling material and the placement technique of the material.  
apical resection; Biodentine; dentistry; endodontics; MTA; push-out; ultrasonic vibration
## 1 Introduction
To obtain better insight into the processing, we applied LIPA experiments onto fused quartz glass with economic 304-type stainless steel, and an exhaustive morphological study of ablation performance under different influences and laser pulse numbers was conducted with a view to understanding the mechanism. The present paper is organized as follows: firstly, the manufacturing system and the properties of the materials are presented in detail. Secondly, the morphological characteristics of the LIPA channels in different cases are compared, and the ablation mechanism is investigated by discussing and analyzing the experimental results. Finally, through optimization of the parameters, high-quality microchannels were obtained for biomedical applications.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 4 Proposed System
### Uploading and Sharing Processes
#### 4.3.2. Data Sharing Process  
Mobile users can access information from the cloud through an information sharing mechanism that incorporates access control. The steps of the procedure are as follows.  
1. [label=Step 1]
2. Request Generation (Performed by the Mobile User)  
A user must create an online transaction (Tx) as a request message to view data from the cloud. Members can view our blockchain by establishing a blockchain profile with a secret key for signing the transaction and a public key for the user's identity. To receive data, users must also provide the region's address via metadata for the transactions. Subsequently, the transaction (Tx) is forwarded to the cloud based on EHR management for authorization and verification (Steps 8, 9, and 10 in Figure 6).  
1. [label=Step 2]
2. Data Retrieval  
The EHR manager receives the client transactions for auditing and validation. The EHR management immediately sends a message to the smart contract to validate the transaction. The smart contract verifies and sends a message to the EHR management based on the policies table, including user authentication tokens. However, if a user's public key is found in the policies table, the requester's access authorization is approved and the desired information can be accessed. Otherwise, the smart contract imposes penalties on the submitter and refuses the recipient's future requests (Step 10 in Figure 6).  
1. [label=Step 3]
2. Sharing Transactions to Blockchains (Performed by Miner)  
Transactions are organized as bundles and sent to a transactional pool for processing by a group of miners, similar to how data are uploaded. All authenticated transactions are documented and added to the blockchain, available to all smartphone users. The blockchain application incorporated with the end user's device allows users to update the contents of their operations (Step 12 in Figure 6).
## 5 The Establishment of Integrated Beef and Dairy Systems
The use of phenotypic breeding values for better beef production from dairy cattle progeny is also not unprecedented. For example, in Sweden, Denmark, and Finland, Nordic Cattle Genetic Evaluation ([[http://www.nordicebv.info/about-nav](http://www.nordicebv.info/about-nav)]([http://www.nordicebv.info/about-nav](http://www.nordicebv.info/about-nav)), accessed on 2 February 2024; [53]) has developed joint Nordic breeding values that aid farmers in their choice of the right beef sires to use on their dairy cows to enable better opportunity for a profitable production of beef \(\times\) dairy crossbred animals. In this context, it was described how Sweden benefitted from the integration of dairy and beef production into environmentally and economically sustainable systems, and how this has led to novel breeding and management practices to enable the production of better-quality meat [54]. In New Zealand, it was suggested that farmers consider using BREEDPLAN ([[https://breedplan.une.edu.au/](https://breedplan.une.edu.au/)]([https://breedplan.une.edu.au/](https://breedplan.une.edu.au/)), accessed on 2 February 2024) eBVs when selecting sires to mate to dairy cows, so as to ensure the resulting calves are born safely and on time, and that they will grow well to produce carcasses of suitable meat and fat composition [55], albeit similar evaluations are also available from the dairy industry's organisation for genetics and animal evaluation, NZAEL, and could be better aligned with valuable dairy traits in appropriate selection indices. [PERSON] et al. describe the value of the meat from the progeny of dairy cows produced using beef semen [56]. They illustrated that prime cuts from beef \(\times\) dairy cattle could be presented alongside those from conventional beef cattle without consumer discrimination based on colour or steak shape, as might be experienced for products derived purely from dairy cattle. Beef from beef \(\times\) dairy cattle also had similar parameters for eating quality (flavour, tenderness, and juiciness), exhibiting similar, or improved performance for these parameters, relative to beef from conventional beef cattle. Perhaps unsurprisingly, it has been illustrated that using low birthweight beef bulls, such as Angus bulls over Jersey cattle does not adversely affect the dairy production traits of the dam, confirming the viability of the strategy for integrating beef genetics, without compromising dairy performance [57]. Another study also illustrated the impact of dairy breed genetics on carcass characteristics, with this study revealing that Jersey cattle genetics from dairy cows may negatively influence progeny carcass weight and meat quality (the occurrence of yellow fat), underscoring the need for improved strategies to produce more and better beef [58]. Until recently though, the meat derived from the progeny of dairy cows, especially Jersey cattle, has typically been deemed to be less suitable for premium beef markets and was often relegated to producing cheaper products.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 5 Crack Width Analysis
### Calculation Models for Crack Width
#### 5.2.2 EN 1992-1  
In EN 1992-1, the crack width and maximum crack spacing are calculated as follows:  
\[w_{\rm k}=S_{\rm r,max}\left(\varepsilon_{\rm sm}-\varepsilon_{\rm cm}\right) \tag{10}\]  
\[S_{\rm r,max}=k_{\rm 3}c^{\prime}+\frac{\phi k_{\rm 1}k_{\rm 2}k_{\rm 4}}{ \rho_{\rm p,eff}} \tag{11}\]  
\[\varepsilon_{\rm sm}-\varepsilon_{\rm cm}=\frac{\sigma_{\rm s}-k_{\rm t}\frac {f_{\rm t,eff}\left(1+\alpha_{\rm e}\rho_{\rm p,eff}\right)}{\rho_{\rm p,eff}} }{E_{\rm s}}\geq 0.6\frac{\sigma_{\rm s}}{E_{\rm s}} \tag{12}\]  
where \(S_{\rm r,max}\) is the maximum crack spacing, \(\varepsilon_{\rm sm}\) is the average strain of the reinforcement under the relevant load combination, \(\varepsilon_{\rm cm}\) is the average strain of concrete between cracks, \(k_{\rm 1}\) is the coefficient of the bonded reinforcement, \(k_{\rm 2}\) is the strain distribution coefficient, \(k_{\rm 3}\) and \(k_{\rm 4}\) are two empirical coefficients (the recommended values are 3.4 and 0.425, respectively), \(c^{\prime}\) is the cover to the longitudinal reinforcement, \(\rho_{\rm p,eff}\) is the effective reinforcement ratio for longitudinal reinforcement, \(\phi\) is the diameter of the reinforcement, and \(\alpha_{\rm e}\) is the ratio of the elastic modulus of the reinforcement to the secant modulus of elasticity of concrete; \(k_{\rm t}\) is a factor dependent on the duration of the load, taking a value of 0.4 for the long-term load and a value of 0.6 for the short-term load.
## 2 Methodology  
### Mammographic Images Collection  
We collected the digital mammographic images from the MIAS image database (v1.21, 2015), including the original 322 images (161 pairs, including right and left images) at a spatial resolution of 50 \(\upmu\)m\({}^{2}\) pixel edge with a linear response in the optical density range of 0.0\(-\)3.2 at 8 bits/pixel in a portable gray map format [22; 23]. Overall image filenames consisted of three-digit serial numbers, \(l\) or \(r\) for left and right breast, respectively, and \(s\), \(m\), \(l\), and \(x\) denoted the image sizes. The most common image size was 4320 pixels \(\times\) 2600 pixels, which was selected for the proposed study in breast tumor screening. The clinical information was confirmed by expert radiologists for biomarkers, such as image size, image category, background tissue (fatty, fatty glandular, and dense glandular), class of abnormality (calcification, masses, asymmetry, architectural distortion, and Nor), severity of abnormality (B and M classes), and location of center of abnormality [22; 23]. A total of 59 subjects (35 normal subjects and 24 abnormal subjects) with 118 mammographic images (59 pairs, including right and left images) were selected for experimental verification. According to the abnormality location, the ROI of each image could be extracted with a 100 \(\times\) 100 bounding box, around the center, and a total of 500 feature patterns (200 Nors, 150 Bs, and 150 Ms) could be extracted from the 118 images, as seen in the feature templates in Figure 1, which are available for further training and validating the classifier at learning and recalling stages.
## 2 Proposed Method  
### Overview  
Our proposed method consists of two phases--the offline phase and the online phase--as shown in Figure 2.  
Figure 2: Flow of the proposed method. The arrows indicate the flow direction of the process.The blue and yellow blocks belong to the offline phase, the red and pink blocks belong to the online phase.  
In the offline phase, the Canny algorithm was used to extract the edge of the one-pixel width in a specific training image and obtain the gradient orientations to build a shape template. Within the selected Region Of Interest (ROI), a second template was built from a feature vector of the texture-less gray-scale surfaces.  
In the online phase, Sobel gradient detection is performed on the input image to acquire the gradient orientation image, on which the shape-based matching is first performed to achieve candidates. Next, texture-less gray-scale surface matching is performed on these candidates; the comprehensive similarity is then estimated under the hybrid representations. After the NMS process, the detection outputs are obtained.
# Flexural Buckling Failure of Rock Slopes: A Review of Influencing Factors and Analysis Methods
## 2 Analysis of Previous Studies on Flexural Buckling of Dip Slopes
### Brief Historical Retrospect of the Buckling Failure Phenomenon in the Literature  
In terms of buckling failure, [PERSON] [17] introduced this phenomenon that occurred in British open pit coal mines and discussed the underlying mechanism and the corresponding recognition criterion. Later, [PERSON] [18] summarized three modes of buckling failure for stratified rock slopes (flexural buckling of plane slopes, three hinge buckling of plane slopes, and three hinge buckling of curved slopes) and proposed corresponding quantitative evaluation methods for buckling stability. The aforementioned study laid the foundation for the subsequent development of analytical approaches for buckling stability assessment in the twentieth century [19; 20; 21; 22; 23]. After that, with the progress and innovation of monitoring and modelling technologies [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34], the academic and engineering communities have conducted in-depth studies on buckling failure through in situ investigations and modelling (analytical, numerical, and physical), mainly focusing on the deformation and failure mechanisms, as well as the stability analyses of buckling landslides.
## 4 Experimental Results and Analysis  
### Experimental Environment and Parameter Settings  
The parameters of the model training equipment used in the experiment: operating system, Windows 11; CPU, 11 th Gen Intel(R) Core(TM) i7-11800H 2.30 GHz; GPU, NVIDIA GeForce RTX 3080; deep learning framework, Torch 1.9.0 + CUDA 11.3.  
During the training process, a random Stochastic Gradient Descent (SGD) optimizer is used for parameter updates to ensure the scientific reliability of the experimental conclusions. The number of iterations is set to 300, and the batch size is 16. The initial learning rate is established at 0.01 with a weight decay coefficient of 0.0005 to prevent the network from overfitting during training. A momentum factor of 0.937 is employed to prevent the model from becoming trapped in local optima or bypassing the global optimum.