esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spatial Prediction and Validation  
Expansion from point to surface was performed using the ordinary Kriging interpolation method in ArcGIS10.0, and the spatial distribution of SOM was obtained in different numbers of point.  
To measure the accuracy of the SOM spatial-interpolation results at different numbers of point, validation of the spatial-interpolation results was needed. The common verification methods include cross-validation and independent validation. Because cross-validation is simple and fast, some scholars chose this method to validate the result, namely excluding a sample point and then using remaining points to predict the value on this position [21; 22; 23]. However, cross-validation could not accurately describe the prediction error of spatial interpolation in many cases, so the independent validation method was selected in this study. After extracting 800 samples from the entire 900 sample-point sets, the remaining 100 samples were taken as the validation dataset for verifying the spatial-interpolation results of the 12 sample-point sets, respectively. The mean error (ME), root-mean-square error (RMSE) and average standard error (ASE) were selected to measure the accuracy of spatial interpolation. The closer the absolute value of the ME is to 0, the smaller RMSE; and the closer the RMSE to the ASE, the higher the accuracy of spatial prediction. The calculation formulas for each verification index are as follows:  
\[\text{ME}=\frac{\sum_{i=1}^{n}[z(x_{i})-z^{*}(x_{i})]}{n} \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}[z(x_{i})-z^{*}(x_{i})]^{2}}{n}} \tag{2}\]\[\text{ASE}=\sqrt{\frac{\sum_{i=1}^{n}\sigma(x_{i})}{n}} \tag{3}\]  
where \(z(x_{i})\) = SOM observations, \(z^{*}(x_{i})\) = SOM predictions, \(\sigma(x_{i})\) = prediction standard error at the point \(x_{i}\) and \(n\) = the number of sampling points in the validation dataset (in this paper, \(n\) = 100).
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 4 Proposed System Overview
### Trust and Threat Model  
Advertisers are assumed to fully trust the Broker to distribute their adverts, as well as to provide them with accurate billing information. We need to point out that this assumption is not entirely realistic as the Broker has a great interest to lie to the Advertisers in order to overcharge them. As the main focus of this research is the privacy of the user, this constitutes an entirely different problem that only concerns the Advertisers and the Brokers. This problem is also present in the current OBA model and can be addressed in the future in a manner that does not affect the rest of the system.  
The Ad-Dealers are considered by the Broker as trustworthy enough not to compromise the integrity of the system by exposing secret cryptographic keys or tampering with the infrastructure. No trust is required on behalf of the Ad-Dealers towards the Broker as he/she has no means or interest to interfere with the Ad-Dealers' business.  
The Ad-Dealers, as well as the Broker are considered to be honest enough to provide relevant adverts that are not fake or malicious, but at the same time they are curious and very determined to obtain access to private data. The user can therefore trust the provided material but is not willing toexpose any information that can link his/her true identity (name, address, banking information) to any specific advertising preferences.  
Despite being part of the same social circle, users do not fully trust each other. A user can easily be compromised and act maliciously by revealing private information or by propagating content that is fake and harmful. Compromised users are also considered as a threat by the Broker and Ad-Dealers, as they can cause downtime or undermine the quality of service by attacking the system.
## 2 Experimental Work
### Experimental Equipment  
The test system mainly consisted of a rock indentation test loading system and an acoustic emission monitoring system. The former, as shown in Figure 2b, modified based on the RYL-600 rock shear rheometer test machine, consisted of a vertical loading system, horizontal loading system, control box, and computer-controlled system. It was capable of providing the maximum normal load of 600 kN and the maximum shear load of 200 kN. The loading rates in both directions were between 0.001 and 50 mm/min. In the loading test, the penetration force and penetration depth were measured and transmitted by the built-in sensor of the vertical loading system, and the relevant test data, such as force and displacement, could be recorded during the loading process, with the test curve drawn in real time. The horizontal loading system was used to apply confining stress to the specimens to simulate the lateral constraint on the lateral displacements of the specimens. Figure 2c shows the acoustic emission monitoring (AE) system, that is a Micro-II 6-channel rock mechanics test system manufactured by Physical Acoustics Corporation (PAC), consisting of a system host (6 high-performance channel AE cards PCI-2 and 4 external parameter acquisition channels), 4 AE probes, 4 pre-filter amplifiers, and the corresponding signal transmission lines. In accordance with previous studies [29; 31], the AE data acquisition threshold was set to 40 dB, and the sampling frequency was 1 MSPS. The rock loading system and AE monitoring system for the rock indentation test are shown in Figure 2.  
The rock breakage caused by the TBM cutter is actually a process in which rocks are broken by the rolling cutter under a penetration force, tangential force, and rolling force. Considering the limitations of the experimental equipment, the rock breakage platform, which was mod  
Figure 1: The saturation process.  
Figure 2: Rock loading system and acoustic emission (AE) monitoring system for the rock indentation test. TBM: tunnel boring machine.  
rock shear rheometer test machine, only considers the cutter's penetration force, which is, in fact, the main component that leads to rock breakage, and the core part of the cutter that penetrates and breaks the rocks is only the part of the TBM that is in contact with the rocks. Therefore, the modified rock breakage test platform does not have a complete disc cutter, but only a part of the cutter, as shown in Figure 3a. The geometric parameters of the disc cutter are shown in Figure 3b. The cutter has a width of 13 mm, a cutting edge of 3.5 mm, and a phase angle of 20\({}^{\circ}\). The cutter is made of high-hardness steel and has been heat treated to ensure a small deformation of the cutter during the test so as to reduce the impact on the test result.  
Figure 3: Sketch of the TBM indentation cutter: (**a**) a photograph of the cutter; (**b**) geometric parameters of the cutter (unit: mm).
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Experimental Design
#### 3.4.2 Model Performance Comparison  
Previous studies have shown that the LSTM and GRU models are effective in predicting time series. To evaluate the performance of the LSPP model, we compare it to GRUs and LSTM. To remain unbiased, we performed a grid search to identify the optimal hyperparameters for each model. We designed three different prediction distances, short, medium, and long, as shown in Table 4.
## 4 Results and Discussion
### Direct Comparison of Spectra and Semi-Quantitative Results
Spectra are normalized to the Rayleigh diffusion peak due to the acquisition conditions; Pantelleria spectrum has been cut in the vertical axis. Bivariate graphs (not reported in this paper) obtained from our data allowed us to discriminate one or two sources from the others depending on the chosen elements or element ratios, but we were not able to discriminate all the classes at once. As a few examples, the plot of the relative concentration of Rb vs. Nb allowed us to discriminate Palmarola and Pametelleria materials from the three other sources. Pantelleria was again recognized by plotting the ratios of Rb/Nb vs. Sr/Nb, but Monte Arci and Porto Melos were grouped together, such as Palmarola and Lipari materials. The best results applying bivariate analysis to discriminate the five considered provenance were obtained plotting Sr/Fe vs. Rb/Fe, but again, Porto Melos and Monte Arci samples are not distinguishable yet.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 7 End-To-End Delay Analysis
### Sojourn Times in a CH  
For each CH under study, let us denote by \(\overline{b}_{i}\) the mean number of data packets at the beginning of the time slot \(i\) (\(i=0,1,...,N_{sCF}-1\)) of a Combi-Frame. Then, \(\overline{b}_{i}\) can be expressed as,  
\[\overline{b}_{i}=\left\{\begin{array}{ll}\pi_{T}^{\prime}(1),\quad\text{for $i=0$} \quad\text{See Equation (\ref{eq:T2})}\\ \pi_{T}^{\prime}(1)-D^{\prime}(1),\quad\text{for $i=1$}\\ \overline{b}_{1}+\sum_{j=1}^{i-1}A_{j}^{\prime}(1),\quad\text{for $i=2,\ldots,N_{sCF}$} \end{array}\right. \tag{18}\]  
where the \(A_{j}(z)\) are given in Equation (14). See Figure 5 where several nodes located at \(A_{0}\cup S_{0}\cup A_{1}\cup\) are involved. Now we identify \(Z_{i}=N_{msC}\), respectively \(Z_{i}=N_{msT}\), if \(i\in intra\) time slot, respectively \(i\in inter\) time slot. Obviously \(Z_{0}=N_{msT}\) since time slot \(0\) is the transmission slot, mode \(\mathcal{T}\). In addition, we approximate the mean number of data packets in the system as,  
Figure 5: The role of \(A_{i}(z)\) in several nodes located at \(S_{0}\) when \(r_{\text{max}}=4\).  
\[\overline{N_{p}}=\frac{\sum_{i=0}^{N_{\text{c}CF}-1}Z_{i}\overline{b}_{i}}{N_{ msCF}} \tag{19}\]  
Therefore, the mean dwell time (waiting + service) of an arbitrary data packet in a given CH, expressed in Combi-Frames, can be estimated as (Little formula),  
\[\overline{W}(\text{st, CF})=\frac{\overline{N_{p}}}{D^{\prime}(1)}=\frac{ \overline{N_{p}}}{F^{\prime}(1)} \tag{20}\]  
where the last equality in Equation (20) is obtained using Equation (16). Alternatively, expressing Equation (20) in mini-slots we have:  
\[\overline{W}(\text{st, ms})=\overline{W}(\text{st, CF})N_{msCF} \tag{21}\]
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 4 Model Verification
RMSE can effectively show the degree of deviation between the predicted value of the model and the true value. The smaller the RMSE value, the closer the predicted value will be to the true value. Table 1 shows the accuracy index:  
In Figure 10, the M-CM-GA-ELM prediction model has good generalization performance. Although some points have slight fluctuations, the overall prediction accuracy is high. Table 1 shows that the prediction results are M-CM-GA-ELM > CM-GA-ELM > GA-ELM > CM-ELM > ELM. Compared with M-CM-GA-ELM, CM-GA-ELM, GA-ELM and CM-ELM, the GA algorithm has greater advantages in improving accuracy. The M-CM-GA-ELM and CM-GA-ELM precision comparison shows that the precision of the combined model is significantly enhanced.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Experimental Settings
#### 4.1.4 Comparative Methods  
We compare our proposed model with the following methods:  
\(\bullet\) User-based CF (UCF)--UCF is the classical neighborhood-based collaborative filtering method. UCF obtains the new predictor scores by averaging (weighted) ratings over similar users. In other words, a user might choose according to ratings given to that item by the other users who have a similar preference with that of the target user. Similarity computation is critical to UCF. In this paper, we adopt the Pearson correlation coefficient because of its better performance. The Pearson correlation coefficient between users \(u\) and \(v\) is denoted as  
\[\begin{split} pearson(u,v)=\frac{\sum_{i\in I}\left(r_{ui}-\bar{r }_{u}\right)(r_{vi}-\bar{r}_{v})}{\sqrt{\sum_{i\in I}\left(r_{ui}-\bar{r}_{u} \right)^{2}}\sqrt{\sum_{i\in I}\left(r_{vi}-\bar{r}_{v}\right)^{2}}}\end{split} \tag{19}\]  
where, \(I\) is the set of items.  
\(\bullet\) Baseline-MF--MF evaluates the observed user-item rating matrix by a linear combination of user and item latent features. It is one of the most successful realizations of latent features models. MF can be enforced by using the embedding neural network [39]. In our work, we apply an embedding layer to obtain the nonlinear latent features of baseline-MF.  
As mentioned in Section 2, the inputs of the embedding layer are the one-hot vectors of users and items, and the outputs are the low-ranked vector representations. In this way, the users and items are embedded into the latent space with the help of the embedding layer. Finally, the MF used the dot of users and items to predict users' preferences on each item.  
\(\bullet\) Singular Value Decomposition (SVD)--SVD is a matrix factorization technique commonly used in a recommender system, producing low-rank approximations [27]. In the recommendation scenario, SVD is used to decompose the rating matrix \(Q\) with the rank \(r\) as  
\[f_{SVD}(Q)=U\times S\times V^{T} \tag{20}\]  
where, \(U\in R^{|U|\times|U|}\) and \(V\in R^{|I|\times|I|}\) are the orthogonal matrices, singular matrix \(S\in R^{|U|\times|I|}\) is a diagonal matrix whose diagonal elements are non-negative real values. Especially, the rank \(r^{\prime}\) of the matrix \(S\) is less than \(r\). In this way, the rating matrix is transformed into a lower-ranked matrix. In this paper, we conduct an experiment for SVD on the e-government services dataset with the rank \(r^{\prime}=64\).  
\(\bullet\) Time-Decayed BPR--On the basis of the Bayesian personalized ranking (BPR) framework, time-aware information is introduced [40]. Time-decayed BPR integrated the time-aware information and time-invariant information to model users' preferences. In this paper, the method was denoted as TBPR.
## 1 Introduction
For example, the digital image correlation (DIC) technique has been widely applied because it enables measuring the deformation of the material surface [22; 23; 24; 25; 26]. [PERSON] et al. [8] used the DIC method to assess strain around the eye during blinking and further explored the relationship between wrinkle formation and strain distribution. Moreover, a phase-contrast optical microscope was used by [PERSON] et al. [27] to reconstruct an epithelial tissue folding model and examine the in situ biomechanical wrinkling. To directly obtain the internal wrinkling state of human skin, a novel in vitro technique that merges tissue engineering with 3D Swept Source-Optical Coherence Tomography imaging technology was introduced by [PERSON] et al. [28]. Existing studies show that in vitro experiments are often utilized in research on skin wrinkles. The formation of wrinkles is intrinsically linked to facial expressions, which are driven by underlying muscle contractions. Repeated facial movements and muscle contractions create dynamic stress on in vivo skin. However, limited research exists on the direct measurement in vivo for skin wrinkling. In addition, current FE models for wrinkling often overlook the role of the muscle and hypodermis layers, although skin wrinkling is the result of muscle contraction and layer-layer strain transmission to the skin surface. Facial expressions can serve as a loading method to study the deformation characterization of in vivo skin, which is related to wrinkles [6]. This study aims to examine the potential relationship between deformation and skin wrinkling by introducing facial expressions. Moreover, how muscle-driven mechanical stress transmission through facial tissue layers governs wrinkle formation is still not clear. The skin movements during six basic facial expressions are generated according to FACS and recorded using two CCD cameras. The displacement and strain distributions on the skin's surface during these expressions are obtained using the 3D-DIC method. The principal strains and their directions are introduced to qualitatively explain the wrinkle formation. To further quantitatively investigate the relationship between the mechanical parameters and wrinkle formation, a four-layer FE model considering muscle and hypodermis layers is proposed. The rest of this paper is organized as follows. The experimental and FE methods are described in detail in Section 2. The results of the mechanical deformation of the skin and wrinkle formation mechanisms are presented in Section 3. In Section 4, the influence of the loading method and the mechanical properties of skin and muscle on facial wrinkles is discussed. The important conclusions are summarized in Section 5.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 4 Experimental Analysis  
In order to promote the practical industrial application, the proposed WMSCCN algorithm is tested under different working conditions and noise environments. The results of two comparative experiments well verify the generalization and noise immunity of the WMSCCN algorithm. The model was developed in Python 3.5 through the deep learning library keras 2.2.4, and experiments were performed using the CentOS 7.6 operating system and hardware with Intel (R) Xeon (R) Gold 5120 CPU and NVIDIA GeForce GTX 1080 Ti GPU.
## 4 Conclusions  
In the present work, we aimed to develop a general model approach to predict a cyclist's head thermal comfort using nonintrusive and easily measured variables, which can be measured using impeded sensors in a bicycle helmet. During the first experimental stage, 15 participants were exposed to different levels of mechanical activity, ambient temperatures, helmet thermal resistance and wind velocities in order to develop a general model to predict a cyclist's head thermal comfort. The results showed that ambient temperature, average air temperature under the helmet, cyclist heart rate, cyclist mechanical work and helmet thermal resistance significantly influenced the cyclist's head thermal comfort. A reduced-order general linear regression model with three input variables, namely, temperature difference between ambient temperature and average under-helmet temperature, cyclist's heart rate and the interaction between ambient temperature and helmet thermal resistance, was the most suitable to predict the cyclist's head thermal comfort, showing a maximum MAPE of 8.4%. The developed general model structure was based on easily measured variables that can be measured continuously using impeded sensors in the bicycle helmet but is still of reduced order and low computational cost, which is suitable for streaming and adaptive modelling. Based on the selected model variables, a smart helmet prototype (SmartHelmet) was developed using impeded sensing technology as a proof of concept. The developed general model was validated using the developed SmartHelmet prototype. During the validation experimental phase, seven well-trained male cyclists were exposed to a hot (34 \({}^{\circ}\)C and 56% relative humidity) environment and completed a 30 km cycling TT inside an environmental chamber. The validation results showed that the developed general model was able to predict the thermal comfort of the seven participants and had a maximum MAPE of 10%. By retuning the model parameters, the maximum MAPE decreased to 7.8%. Finally, we introduced a calculation framework of an adaptive personalised model based on the developed general model to predict a cyclist's head thermal comfort based on streaming data from the SmartHelmet prototype.  
Conceptualisation, A.Y., J.-M.A. and G.D.B.; methodology, A.Y., J.C. and K.M.; software, A.Y., P.G. and K.M.; validation, A.Y., J.C. and K.M.; formal analysis, A.Y., J.C. and K.M.; investigation, A.Y., J.C. and K.M.; resources, P.G. and G.D.B and A.F.; data curation, A.Y., J.C. and K.M.; writing--original draft preparation, A.Y., J.C.; writing--review and editing, A.Y., J.-M.A., G.D.B, A.F. and T.M.; visualisation, A.Y. and J.C.; supervision, J.-M.A. and A.Y.; project administration, T.M. and J.-M.A.; funding acquisition, T.M. and J.-M.A.  
**Funding:** Marie Sklodowska-Curie grant agreement no. 645770.  
**Acknowledgments:** We acknowledge the support from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 645770.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Unmanned aerial vehicle (UAV)-assisted relay mobile edge computing (MEC) network is a prominent concept, where network deployment is flexible and network coverage is wide. In scenarios such as emergency communications and low-cost coverage, optimization of offloading methods and resource utilization are important ways to improve system effectiveness due to limited terminal and UAV energy and hardware equipment. A multilevel edge computing network resource optimization model on the basis of UAV fusion that provides relay forwarding and offload services is established by considering the initial energy state of the UAV, the green energy charging function, and the reliability of computing offload. With normalized system utility function maximization as the goal, a Markov decision process algorithm meets the needs of the practical application scene and provides a flexible and effective unloading mode. This algorithm is adopted to solve the optimal offloading mode and the optimal resource utilization scheme. Simulations verify the effectiveness and reliability of the proposed multilevel offloading model. The proposed model can optimize system resource allocation and effectively improve the utility function and user experience of computing offloading systems.  
**Keywords:** Unmanned aerial vehicle (UAV); Mobile Edge Computing (MEC) Network; green energy resources; MDP algorithm
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm
### Algorithm A: Beam-Footprint Partitioning Algorithm
#### 3.1.3 Iterative Optimization  
Due to the possibility that the algorithms in step 1 and step 2 may generate beams with insufficient user coverage, an iterative refinement mechanism is introduced to improve overall beam utilization. Specifically, we define a beam as inefficient if it serves fewer than \(F\) users. After the initial beam-user assignment is completed, all beams are examined sequentially. The users assigned to inefficient beams are released and merged with the pool of unscheduled users. A new round of clustering and beam-user assignment is then performed on the updated user set. The procedure is repeated until either the user capacity requirement is met for all beams, or a maximum number of iterations is reached.