esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Probability of Detection (PoD) with Inter-Granular Cracks
As depicted in Figure 34, the PoD exhibits the expected behavior, where an increase in crack length corresponds to an increase in PoD. The PoD curves obtained from the BE simulations aligned with this expectation, thus demonstrating the potential of simulations for PoD characterization (particularly for crack lengths exceeding 3 [mm]). However, within the defect length range of 0-3 [mm], the PoD values were notably low. This discrepancy may be attributed to the limitations of the assumed parameters in the BEM simulations, which failed to accurately capture the intricacies associated with the smaller cracks in reality. Additionally, factors such as the noise sources that diminished the measured reflection amplitude, as well as the actual conditions of the wave generation and reception, needed  
Figure 24:1 PoD curve: 100 grains for aluminum. Figure 24:2 Regression: 100 grains. to be incorporated into the simulations for a more comprehensive representation. Ongoing research aims to refine the representation and establish bounds for parameter uncertainties, which will contribute to enhanced predictions of PoD based on the BEM approach. However, for defect lengths ranging from 0 to 3 [mm], a low PoD value was observed, possibly due to uncertainties in the assumed parameters not capturing the reality for smaller cracks. Incorporating finer details such as noise sources, which reduce the amplitude of measured reflection, and realistic wave generation and reception conditions into simulations is an ongoing effort to improve the accuracy of PoD predictions based on the BEM.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 6 Conclusions  
To overcome the problems of low efficiency, high cost, and the low recognition rate of existing radio recognition through deep learning, a CNN-LSTM-DNN model is proposed. The pruning strategy of the CLDNN model is first used, which greatly reduces the complexity of the model and reduces the risk of overfitting, by removing unnecessary neuron nodes from the original model. Then the double-layer LSTM is employed by considering the time characteristics of the radio signals, to further improve the accuracy of modulation mode recognition  
Throughout the experiments to adjust the parameters of each layer to a better value, the model in the training time slightly increases the accuracy of the results. In comparison with previous research [6], our model training results can be about 10% more accurate than the original model. As the neurons, connections between layers, and weights are reduced, there is a reduction in storage requirement and heat dissipation in deployed hardware, which can be used in embedded devices with limited hardware resources.  
In our future works, we will select the compression methods and the pruning strategy according to the architecture of specifically targeted hardware to reduce the inference time and memory constraints.  
Data curation, Z.X. and M.J.; funding acquisition, Q.Y. and G.Y.; investigation, Z.X.; methodology, X.H. and Z.X.; supervision, Q.Y.; validation, Z.X. and Q.Y.; writing--original draft, X.H.; writing--review and editing, G.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian Province under Grant 2021J01865, the Open Project Fund of the Fujian Shipping Research Institute (No.HHXY2020014), and the Jimei University National Fund cultivation program (No. F0107).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Comparison of the classification accuracy with the existing models.
## 2 Technologies in DTx
### Recommendation System  
One of the most representative advantages of digital therapeutics compared to traditional treatment methods is that it can provide the most optimized treatment for individual users based on the users' data or record histories. This user customization is highly related to a recommendation system (RS) that provides a personalized treatment design based on user data. It is possible to suggest more dynamic treatments by collecting users' daily data from DTx products and not just relying on the treatment provided by hospitals collectively.  
Typically, an RS provides customized systems to users using algorithms that are content-based, collaborative, or hybrid filtering [52]. In particular, hybrid filtering has been applied to extract hidden data features, program usage, and user information to calculate  
Figure 4: Human-centered system flowchart proposed to increase the effectiveness of DTx.  
the similarity between users and systems. For example, in the RS field, there are studies on content filtering systems that recommend subsequent media through simple user usage records [53; 54]. Many studies suggest movies or media by analyzing user review texts, and image features using deep-neural-network-based algorithms [55; 56; 57].  
The digital therapeutics products may apply a hybrid-filtering recommendation system among the structures as shown in Figure 5. For example, [PERSON] designed the clinical care system, which used an fb-KNN algorithm-based RS for data collected when diagnosing and treating diseases [58]. Furthermore, [PERSON] presented a diabetes prediction method, which adopted a machine-learning-based smart healthcare RS for electronic health records [59]. As such, DTx can collect information about users from devices and mobile applications and apply recommendation systems in more detail than pharmaceutical approaches or offline treatment programs. In the future, the performance of DTx products will be determined based on how detailed the provided user-specific treatment is.  
Moreover, it should be continuously developed to use an improved hybrid-filtering recommendation system based on multiple data features extracted through machine learning algorithms.  
When applying this recommendation system, personal health information protection technologies should be considered together. Providing personalized therapeutic content based on each user's data variables and attributes means checking their own data. However, this can make users feel that their information has been violated. Therefore, a recommendation algorithm based on federated learning described in Section 2.2.2 should be considered. Rather than collecting and analyzing each user's data, a structure that develops into a separate recommendation algorithm with a personalized federated learning structure by forming an initial global algorithm should be designed.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 2 Recent Research in Stope Design Methods  
The mining industry needs to adopt more sustainable methods to reduce the impact of mining activity on the surface. Stoping-based methods are a potential solution to reduce unwanted surface effects. However, these methods still have risks related to the local instability of the rock mass, making site-specific rock mass characterization, evaluation of the in situ stress and induced stresses, and stope design essential.  
Mine design schemes based on experience and intuition have been used since the 1970s and are still used today for detailed planning. Empirical methods, such as the rock mass rating (RMR and MRMR) system, Q-system, geological strength index (GSI), Laubscher block caving rules, hard rock pillar design chart, and tributary area method, are commonly used [2]. A comprehensive overview of these methods can be found in [3]. A recent software development that enables the creation of regional and mine-specific case studies is presented in [4].  
With advancements in computing power, numerical modelling methods may take local rock-type domains and rock mass features such as fractures or brittle failure zones into account more accurately when conducting numerical model-based stope design [5]. Both continuum and discrete numerical methods are used to simulate jointed rock masses in the stope scale, mainly to optimize stope boundaries, blasting design, the production schedule, and development activities [6]. Software for stope design and mine planning is discussed in [7]. Recent research to improve stope design is summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Research Area**} & \multicolumn{1}{c}{**Advantages and Disadvantages**} \\ \hline Grade uncertainty and associated risk with stope design optimization [10; 11] & + Cutoff grade-related optimization \\  & + Conclusive approach \\  & \(-\) Iterative evaluation necessary \\ \hline Iterative cutoff grade optimization [12; 13] & + Conclusive approach \\  & \(-\) High computing power need \\ \hline Integration of stope design into mine & + Most conclusive approach \\  & \(-\) Challenges in the inclusion towards iteration \\ \hline Empirical software to create deposit-specific case & + Based on real experience \\  & + Simple \\  & \(-\) Most likely not a perfectly ideal solution \\ \hline Integration of real-time instrumentation and risk & + Necessary for implementing adequate iteration processes \\  & \(-\) Requires additional equipment \\  & \(-\) More complex \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent research on stope design. Compiled and modified from [7; 8; 9].
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.1 Average Packet Delay
In the non-cross-UPF handover scenario, the average packet delay performance of BDMM is similar to that of ProPull and LPBMMS.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 5 Experiment Validations  
### Construction of Experiment Platform  
In order to collect an infrared thermal-imaging video that is more consistent with actual working conditions, a self-designed experimental system for a shearer cutting coal was set up in Figure 9, which mainly contains a shearer, simulated coal wall, infrared thermal imager, and data acquisition software. When the shearer was cutting the coal wall, the dust cloud was very large and it was difficult for the personnel to get close. In this experiment, the model of the infrared thermal imager is MAG32, which has high image clarity and temperature sensitivity. It is widely used in industrial detection, research and development, on-line detection, human body temperature measurement, and other fields. MAG32 can realize automatic focusing, and can automatically correct emissivity, background temperature, input transmittance, and atmospheric transmittance, so that the measured temperature data is more accurate, and can display the punctuation temperature in real time. In the platform, the infrared thermal imager was fixed on a long rod to be close to the coal wall and collect the infrared thermal imaging signal. The shearer model is MG500/1130-AWD, the total installed power is 1130 KW, and the power of each cutting motor is 500 KW. The drum diameter is 1.2 m and the traction speed is set to 2.5 m/min during the experiment. Normally, the shearer is equipped with some water sprays for cooling cutting bits and dust suppression, and the spray pressure is 1 MPa. The length of the coal wall  
Figure 8: Online recognition system of a shearer cutting state using infrared thermal images.  
is 70 m and the height is 3 m. It was composed of four parts according to the Protodikonov's hardness coefficient \(f\), i.e.,\(f\) = 1\(\sim\)2,\(f\) = 2\(\sim\)3\(f\) = 3\(\sim\)4 and \(f\) = 4\(\sim\)5. Thus, the shearer mainly contained four cutting patterns, represented by F1, F2, F3 and F4, respectively.  
In this paper, we simulated four typical harnesses of coal seams and the shearer was cutting only one type of material at a time with constant traction speed and drum height. The center height of the front drum was aligned with the center of the coal seam and the trailing drum was tangent to the bottom floor. An infrared thermal imager was used to observe the temperature changes when shearers cut each type of coal seam. In the obtained infrared thermal-imaging video, the iron-red was selected as the output color to better reflect the appreciation. The pixel size of the video was 384 \(\times\) 288 and the rate was one frame per second. Some infrared thermal imaging-video screenshots of the shearer cutting a coal wall are shown in Figure 10.
## 3 Application in the Reconstruction of the HWRR Decommissioning Model
### Scheduling  
The 3D model reconstruction of the HWRR main building area, secondary water pump house, ventilation center, and low-level radioactive wastewater storage tank area was conducted using laser scanning. The project took a total of 70 working days, with 28 working days being dedicated to point cloud data acquisition and 49 working days dedicated to point cloud data processing and 3D model reconstruction. Some tasks, such as point cloud data acquisition, processing, and 3D model reconstruction, had overlapping timelines. The specific project schedule is outlined in Table 5.
## 3 Results
### Exploratory Analysis  
The application of statistical tools such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) enabled the evaluation of similarities and differences in the volatilome profiles of the soil samples across the studied land use transects (Figure 3). The PCA plot (Figure 3a) highlights the compositional differences among the three land use types: forest (F), grassland (G), and agriculture (A). The first principal component (PC1) explained 41.7% of the variance, whereas the second principal component (PC2) accounted for 31.1%, with both together capturing 72.8% of the total variability in the dataset. The volatilome profiles of soil samples are distinct across land uses. Agricultural soils exhibit high internal similarity and unique compositional characteristics, whereas forest soils display moderate variability within their group. In contrast, grassland soils demonstrate greater internal variability than the other two land use soils do. The inclusion of 95% confi  
Figure 2: GC–MS analysis provided staked TICs of the soil volatilome profile within the three land uses.  
dence ellipses reveals slight overlaps, suggesting that some compositional characteristics are shared among the groups while their overall distinctiveness is maintained.  
The HCA dendrogram (Figure 3b) further corroborates these findings, showing the apparent clustering of samples by land use. Samples from the same land use category tend to cluster together, underscoring the significant influence of land use on their volatilome profiles. Forest soils form tight clusters, indicating high internal similarity within this category. Grassland soil also clusters together but exhibits slightly more significant variability. The agricultural samples formed distinct clusters that were clearly separate from those in the other two categories. The branch heights in the dendrogram reflect the level of dissimilarity between clusters, with shorter branches indicating more remarkable similarity. The PCA and HCA analyses provide robust evidence that land use significantly shapes the  
Figure 3: Soil volatilome profile discrimination across a land use transect. (**a**) PCA score plot; (**b**) HCA dendrogram. F means forest, G means grassland, and A means agricultural land.  
volatilome profiles, but distinct compositional characteristics were also observed for each land use type.
## 2 Methods and Data
### New Algorithmic Ateps
#### 2.2.2 LSK Module
A sigmoid activation function is used on each spatial attention map to generate a corresponding selection mask. Each mask is used to selectively enhance or suppress the feature response at a specific spatial location. The multi-scale features are weighted by the spatial selection mask, and then fused again by the convolutional layer to generate the final attention features. This step allows the model to further strengthen the most important feature information based on the input features. This weighting process can be expressed as:  
\[\mathrm{F_{weighted}=F_{s}\bigotimes F_{att}} \tag{7}\]  
where \(\mathrm{F_{weighted}}\) represents weight features, \(\mathrm{F_{s}}\) represents spatial features, and \(\mathrm{F_{att}}\) represents attention features. Finally, the output of the LSK module is the element-wise product of the input features and the attention features, which ensures that the final output features contain both the original input information and the multi-scale selective attention information. The final output can be expressed as follows:  
\[\mathrm{F_{output}=X\bigotimes F_{weighted}} \tag{8}\]  
Figure 3: Architecture diagram of LSK. The part framed by the dotted line represents the large convolution kernel. where \(\rm{F_{output}}\) represents the output feature, \(\rm{X}\) represents the input feature, and \(\rm{F_{weighted}}\) represents the weight feature. In this way, the LSK module achieves effective fusion and selective enhancement of multi-scale features, thus improving the flexibility and accuracy of the model when dealing with objects of different scales.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Control Plane Interfaces
#### 5.2.4. The VPN Gateway  
The VPN gateway includes network protocol interfaces as follows:  
* An IPVPN BGP peering interface peering towards one or more Service Provider neighbours.
* A VPN tunnel or a direct interface to all other Service Providers.
* A BGP peering interface towards the Tier 1 route reflector that announces the VPN links.
* A Tiers 2 and 3 BGP peering over the Transport Link.
* A configuration interface such as RESTconf or CLI to set up VPN links.
* A KMS server interface to accept VPN connections authorized by the KMS server.  
Figure 17: BGP announcements Tier 1.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology
### Underwater Channel Model  
The behavior of the underwater channel is influenced by many factors such as signal attenuation, noise and the speed of sound in the underwater medium which are described as follows:  
**Transmission Loss:** Transmission loss is the drop in the intensity of the transmitted signal as it travels the medium from the sender to the receiver. Transmission loss has two major constituent factors; absorption loss and spreading loss. Equation (1) [25] represents transmission loss in an underwater acoustic channel over distance \(l\) and frequency \(f\).  
\[TL=k.10\log_{10}(l)+l.10\log_{10}\alpha(\mathrm{f}) \tag{1}\]  
where \(k\) is the spreading factor and the \(\alpha(\mathrm{f})\) stands for absorption coefficient. Commonly used values of \(k\) are 1.5 for practical spreading, 2 for spherical spreading and 1 for cylindrical spreading where \(\alpha(\mathrm{f})\) can be computed using Equation (4).  
**Attenuation:** The power of a transmitted signal in the underwater acoustic channel reduces as it travels from source to destination. Major factors contributing to the attenuation of a transmitted signal in the underwater acoustic channel are distance, frequency spreading of the acoustic waves and absorption. Attenuation over a distance \(l\) using frequency \(f\) in an underwater acoustic channel can be estimated using Equation (2) [26],  
\[\mathrm{A}(\mathrm{l},\mathrm{f})=\ A_{\mathrm{o}}\mathrm{l}^{\mathrm{c}} \alpha(\mathrm{f})^{\mathrm{c}} \tag{2}\]  
where \(\mathrm{l}\), \(\alpha\), \(\mathrm{f}\), \(\mathrm{c}\) and \(\mathrm{A_{\mathrm{o}}}\) represent distance, absorption coefficient, frequency, spreading factor and normalization constant, respectively. The attenuation can be denoted as in decibel (dB) using Equation (3) [26].  
\[\frac{10\mathrm{log}\mathrm{A}(\mathrm{l},\mathrm{f})}{\mathrm{A_{o}}}=10 \mathrm{log}\mathrm{d}*\mathrm{k}+10\mathrm{log}\,\alpha(\mathrm{f})*\mathrm{d} \tag{3}\]  
The absorption coefficient \(\alpha\) is represented by Thorps model (Equation (4)) [26].  
\[10\log\alpha(\mathrm{f})=0.11\frac{\mathrm{f}^{2}}{1+\mathrm{f}^{2}}+44\ \frac{\mathrm{f}^{2}}{4100+\mathrm{f}^{2}}+2.75\times 10^{-4} \mathrm{f}^{2}+0.003 \tag{4}\]  
Equation (4) is used for frequencies above a few hundred Hertz. However, Equation (5) [26] is used for smaller frequencies.  
\[10\mathrm{log}\,\alpha(\mathrm{f})=0.002+0.11\ \frac{\mathrm{f}^{2}}{1+\mathrm{f}^{2}} +0.011\mathrm{f}^{2} \tag{5}\]  
**Speed of Sound:** The speed of sound underwater may vary between 1450 m/s to 1550 m/s based on temperature, depth and salinity of water [26]. In [27], the authors modeled an empirical equation for the speed of sound in water given as Equation (6) below:  
\[\begin{array}{c}\mathrm{c}\ =1448.96+4.591\mathrm{T}-5.304\times 10^{-2} \mathrm{T}^{2}+2.374\times 1  
It is important to note that scenario 1 occurs before the neighborhood information acquisition phase, whereas scenario two occurs during the data forwarding after the neighborhood dissemination phase.
## 2 Materials and Methods
### Feature Selection  
Pearson correlation coefficient (PCC) measures the linear correlation between each input feature with the solar radiation. It represents the ratio between the covariance of two features and the product of their standard deviations [(43; 44)].  
\[\rho_{\text{x,y}}=\frac{\text{cov (X,Y)}}{\sigma_{\text{x}}\sigma_{y}} \tag{3}\]  
where \(\,\text{cov}\) is the covariance, \(\sigma_{\text{x}}\) the standard deviation of one input feature, and \(\sigma_{\text{y}}\) the standard deviation of the solar radiation feature (output). This technique allows determining the correlation of each meteorological measurement with solar radiation [(14)], correlations that can be different with the season. The prediction accuracy can be statistically evaluated using PCC as a metric; a larger PCC intuitively reflects a higher linear correlation between the predicted and true values [(45; 33)].  
Figure 4 shows the PCC values ranking for all meteorological parameters with respect to solar radiation. The threshold value equal to 0.5 (red line in Figure 4) between the selected and discarded parameters was determined during the learning phase of the model. It is found that the significant parameters, with PCC values greater than or equal to 0.5, selected for the solar radiation prediction in the time interval from 1 January 2017 to 21 August 2021 are: Direct Short-wave Radiation, Diffuse Short-wave Radiation, Temperature, Vapor Pressure Deficit at 2 m, Relative Humidity at 2 m, Growing Degree Days at 2 m elevation, Sunshine Duration and Soil Temperature (0-10 cm) (Figure 4).