esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 5 Conclusions  
The location-aware deep interaction forest for web service QoS prediction approach (LDIF) has the greatest performance regardless of the data sparsity. Firstly, we use word vector embedding technology to enrich the feature information of users and services, and consider the geographical similarity as the potential feature representation between them. In this paper, a scanning interaction structure (SIS) is proposed based on a multigranularity scanning mechanism. We extract local feature combinations of different sizes from the interaction between user features and service features and use interaction calculation to obtain feature interaction information. The dimension enhancement of the feature  
Figure 8: Comparison in different highest orders. (**a**,**b**) represent the prediction performance of different highest orders on MAE and RMSE metrics in TP, respectively.  
Figure 9: Comparison in different depths of layers. (**a**,**b**) represent the prediction performance of layer depths on MAE and RMSE metrics in TP, respectively.  
vector is realized through feature fusion, which effectively alleviates the impact of sparse data. Furthermore, we demonstrate the effectiveness of the scanning interaction structure (SIS). The layer-by-layer cascade composed by SIS effectively achieves the purpose of fusing low- to high-order feature interaction information. Experiments demonstrated that the performance of this approach has a certain improvement compared with traditional approaches and deep learning approaches. In this paper, we fuse the feature interaction information generated by each layer in a concatenation manner. Considering the connection between layers may further improve the performance. Therefore, in the next step, we will focus on mining the feature interaction information between each layer in the forest through group convolution to further improve the performance and efficiency of the approach.  
Conceptualization, S.Z., J.D. and J.Y.; methodology, S.Z.; software, J.Y.; validation, S.Z., J.D. and J.Y.; formal analysis, S.Z. and J.Y.; investigation, J.Y.; resources, S.Z.; data curation, J.Y.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z. and J.D.; visualization, J.Y.; supervision, J.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China (No. 62262034, No. 62262035).  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://wsdream.github.io](https://wsdream.github.io)]([https://wsdream.github.io](https://wsdream.github.io)).  
The authors declare no conflicts of interest.
# Neither Denied nor Exposed: Fixing WebRTC Privacy Leaks
## 8 Related Work
WebRTC's IP leak issue is also addressed by some browser add-ons for content-filtering, including ad-blocking. Two of them, namely Privacy Badger [34] and uBlock Origin [35], both multi-platform and open-source, seem to stand out. However, while these extensions prevent WebRTC from leaking the peer machine's private IP address by blocking it, they do not solve the problem of public IP leakage happening through STUN requests. There is also a volume of works which utilize the WebRTC leak in order to fulfil other purposes. In [36; 37; 38], WebRTC is used to collect private IP addresses for user tracking and device fingerprinting. The authors in [39] utilize WebRTC for device fingerprinting in order to trace Web attackers even when they are hidden behind VPN or anonymous networks. WebRTC is also used in [40] for device fingerprinting in order to provide augmented user authentication. In all the previous cases, no countermeasures against the WebRTC leak have been implemented. It is worth noting here that, as explained in Section 2, the signaling protocol between web servers is not part of WebRTC. This means that, even if a competent solution that protects users' privacy is employed on WebRTC, personal user information can leak from the signaling protocol [41]. On the application level, this can be prevented with solutions like [42; 43] when SIP is utilized as the signaling protocol. On the network level, Tor-based solutions ([44; 45]) can prevent leaking IP addresses to unauthorized entities. Overall, while the given vulnerability of WebRTC is addressed by a number of works in the literature, most of them either use this vulnerability to fingerprint clients inside a network or propose countermeasures that partially protect user privacy, such as the use of a VPN or a combination of browser extensions. In addition, some works propose the complete deactivation of WebRTC on the browser, which would obligate the user to re-enable it every time they need it. To the best of our knowledge, the work at hand is the first to propose two different practical and effective schemes which can detect the use of WebRTC by a webpage in time, i.e., before any WebRTC requests are made. The proposed solutions can inform the user on-the-fly about whether their privacy is at risk by WebRTC when they try to visit a webpage. Thus, if the user is really in need of WebRTC capabilities, then the webpage is allowed to load; otherwise, the user can choose to continue by disabling the corresponding JavaScript code.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models
### Temporal Inversion
#### 4.3.1 A Method without Any Geometric a Priori from the Static Inversion: The Ellipse Method  
The so-called Ellipse method uses a planar elliptical source shape with eight parameters to define its geometry: location of the center (x,y,z), orientation (\(\phi\), \(\theta\), azimuth) and size (2 axes lengths) and one parameter for the overpressure \(P\). We used this method to check whether models based solely on GNSS data are reliable, given that the permanent GNSS network on Piton de la Fournaise is one of the densest in the world for a volcano and that often, continuous GNSS data are inverted omitting the information that could be brought by available InSAR data ([PERSON] et al., 2016; [PERSON] et al., 2016).
# Support of Advanced Technologies in Supply Chain Processes and Sustainability Impact
## 5 Analysis and Discussion of Results
### Advanced Technologies Used by the Case Studies  
In this sub-section an analysis of the advanced technologies used by the researched companies is provided. Regarding the technologies focused on in this study, Cybersecurity and the IT-based production management are the most commonly used by the researched companies. This result is expected as IT security solutions are essential for all kinds of businesses, particularly when we think of the importance of the internet and digital systems for the daily operations of companies. Following closely are Data analytics/Big Data and Energy saving/efficiency technology. Data analytics/Big Data has an important presence among the companies, which could be justified because companies more than ever must be able to handle huge amounts of data in a fast and reliable way in order to be more rigorous in the decision making process [125].  
Then, with five companies using them, come Intelligent control systems, Smart products/parts/materials, Cloud computing, Three-dimensional (3D) printing/additive manufacturing and Advanced manufacturing, Figure 3.  
In Figure 4, for each case study, the total number of advanced technologies and the total number of SCOR processes supported by advanced technologies are presented. In general, there are no significant differences amongst the case studies in the number of technologies adopted, except for case 4 (Biomass energy). The result for case 4 is not expected since according to [PERSON] [126] there are many advanced technologies identified in the ICT-BIOCHAIN project, such as ICT, Internet of Things (IoT), and Industry 4.0 technologies, that are utilized in biomass supply chains and constitute the current state-of-the-art.  
The differences become higher when analyzing the total number of SCOR processes that are supported by advanced technologies.
## 1 Introduction
Considering that an efficient exploitation of multi-channel diversity may bring large beneficial to system throughout, we formulate the problem as a CA game. By this way, the failed multicast recipients and the successful multicast recipients are regarded as bidders and goods, respectively, while the successful multicast recipients are goods waiting to be selected as relays. Each bidder has a valuation for the relays, and multiple bidders can form a package that share the same relay. During the auction, the bidders submit bids and the auctioneer, _i.e.,_ the eNB decides the allocation of the relays. Furthermore, we propose an auction algorithm which runs iteratively until reaching an equilibrium state. We also discuss the properties of our algorithm and the simulation results show a good performance on the sum data rate and system efficiency. The rest of the paper is organized as follows. In Section 2, a system model of intra-cluster D2D communication is introduced. Then we formulate the primary problem as a CA game in Section 3. Next, in Section 4, the CA algorithm for D2D relay selection is proposed, and the main properties of the proposed algorithm are discussed. In Section 5, we present the simulation results. Finally, we conclude this paper in Section 6.
## 5 Object Depth Modelling
### Estimating the Full Depth Map of Images  
A sharp or blurred image generally contains edges. A \"Canny\" edge detector is used to obtain the magnitude of the edges. Equation (13) is implemented on the image in which (a) a variable Gaussian model \(\hat{\mathsf{g}}(\mathrm{x})\) is generated with a stepwise variation in the standard deviation of \(\sigma_{\mathrm{v}}\) from 0.5 to 0.75 with 0.05 in each step, (b) the image is filtered with each generated Gaussian model from (a). The amplitude ratio between the generated blurred images and the image, according to Equation (14) for a sharp image, or the unintentionally blurred image, according to Equation (16), is calculated, which results in obtaining different T\({}_{\mathrm{dv}}\) and P\({}_{\mathrm{dv}}\), respectively. By finding the maximum of T\({}_{\mathrm{dv}}\) and P\({}_{\mathrm{dv}}\) values, the maximum depth ranges of T and P are obtained, respectively. A sparse depth map of the edges is computed by quantizing the amplitude ratio values in the found depth range. A joint bilateral filtering is applied on the sparse depth map to refine inaccurate estimates [25]. The obtained sparse depth map of edges is then propagated to the entire image to have a full depth map using the matting Laplacian method [26].
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 2 Tensor Preliminaries
### Tensor Basics  
Simply speaking, a tensor is a multi-dimensional array. The order of a tensor is defined as the number of dimensions of the tensor. Vectors and matrices can be viewed as special cases of tensors with one and two orders, respectively. Let an \(N\)-th order tensor be denoted as \(\mathcal{Y}\in\mathbb{C}^{I_{1}\times I_{2}\times I_{3}\cdots I_{N}}\) with \((i_{1},i_{2},\cdots,i_{N})\)-th entry \(y_{i_{1}\cdots i_{N}}\). Fibers are the higher-order analogue of matrix rows and columns. The _mode-n_ fibers of \(\mathcal{Y}\) are defined as \(I_{n}\)--dimensional vectors obtained by fixing all but one index \(i_{n}\). Slices are two-dimensional sections of a tensor, defined by fixing all but two indices. The _mode-n_ unfolding (i.e., matricization) is an operation that transforms a tensor into a matrix. The _mode-n_ unfolding of a tensor \(\mathcal{Y}\), denoted as \(\mathbf{Y}_{(n)}\), arranges the _mode-n_ fibers as the columns of the resulting unfolding matrix.
## 3 Change Detection Approaches
In  
Figure 3: Frequency of midwinter daily average backscatter increases of more than 1.5 dB as detected with Ku-band QuikSCAT for the months November to February of winter 2000/1–2008/9 (no masking applied for lakes). order to determine how often such events actually occur the backscatter difference \(\Delta\sigma_{t}^{0}\) on day \(t\) can be extracted considering, e.g., a three day window before and after the event [40]. A significant change due to thaw and refreeze can be assumed for a threshold of 1.5 dB, which equals approximately three times the typical estimated standard deviation of noise \(s_{\sigma}\). Figure 3 shows the number of events captured over un-glaciated terrain in high latitudes (above 60 \({}^{\circ}\) North) during the months from November to February for the entire ten year record.
## 3 Experimental Section
### Statistical Methods  
The statistical processing of data was performed using the SPSS 23.0 Statistics software [40]. Three different samples from each cultivar were analyzed (n = 3). In order to test the differences between the cherries' cultivars, with respect to physicochemical and sensory characteristics, One Way Analysis of Variance (ANOVA) was conducted, for each one of the eight dependent variables, used in the analysis (TA, MI, pH, TSS, TPC, Flavour, Texture, Colour). The independent variable is the cherry cultivar, with 4 levels (treatments) (Canada giant, Ferrovia, Lappins and Skeena). This variable is a fixed one, since the four levels have been specifically chosen by the analyst [41]. The same analysis (One Way ANOVA) was conducted, in order to detect differences among cherry cultivars with respect to volatile compounds. The dependent variables were the volatile compounds detected in at least one cultivar.  
The results of ANOVAs showed, that almost all the dependent variables are significant for the determination of differences among the cherry cultivars (_p_-values \(<\) 0.05). Only flavour was non-significant (_p_-value \(>\) 0.05) for this purpose.  
In order to specify the differences between the means, we applied multiple comparison methods. The Duncan's test was used, since all the treatments have the same number of experimental observations.  
The adjusted model has the form:  
\[Y_{ij}=\mu+\tau_{i}+\varepsilon_{ij}\text{, }i=1,2,3,4j=1,2,3\]  
where \(\mu\) is a parameter common to all treatments, and \(\tau_{i}\) is a parameter unique to the \(i\)-th treatment, which is actually the effect of the \(i\)-th treatment (cherry cultivar). \(Y_{ij}\) is the \(ij\)-th observation _i.e._, the \(j\) observation of the \(i\)-th treatment (volatile compound or physicochemical attribute). \(\varepsilon_{ij}\) is the random error of the \(ij\)-th observation.  
Before analysis, the homogeneity of variances was tested by the [PERSON]'s test, where all the ANOVAs seem to satisfy this requirement (\(p\)-values \(>0.05\)).  
To determine correlation coefficients, Pearson correlation coefficient was used. This coefficient is suitable for quantitative variables and varies between \(-1\) and \(1\). Values at the ends of the interval indicate perfect linear correlation. More specifically, the value \(-1\) indicates perfect linear negative correlation, whereas value \(1\) indicates perfect linear positive correlation. Values above \(0.7\) in absolute values, indicate strong relationship, values in the interval \(0.4\)-\(0.7\) in absolute values, show medium linear correlation and \(<\)\(0.4\) show weak linear relationship.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 5 Discussion
### Theoretically Derived Direct (Physical) Model and Hybrid Inverse, Validated by Simulation  
Based on the results of literature review, a physical model of radiation thermometry was defined and scalar and hybrid inverse models were derived.  
To correctly account for the interactions between the spectral distributions of the influence parameters and the involved signals, the hybrid inverse model should be used where the scalar influence parameters are determined as effective scalar parameters according to Equation (28).  
Considering the results of the model error simulation in Figure 14, the hybrid inverse model, where the instrumental parameters are set to the effective values, was validated as a correct approach for radiation thermometry of real objects, resulting in negligible conversion error. This approach requires an individual calculation of the effective scalar equivalent for each spectral parameter and an iterative calculation of the object temperature.
## 2 Methods
### 2.2. Svm  
SVM is a very effective machine learning technique widely used in classification, regression, anomaly detection, and other learning tasks [24; 25]. Given the training samples and labels \(x_{i}\in\mathbb{R}^{n},y_{i}\in\{-1,+1\},i=1,\ldots,m\), SVM can solve the following optimization problem:  
\[\begin{split}\min_{\mathbf{w},b,\xi_{i}}&\frac{1}{2}\| \mathbf{w}\|^{2}+C\sum_{i=1}^{m}\xi_{i}\\ \text{s.t.}& y_{i}\left(\mathbf{w}^{\mathrm{T}}x_{i}+b \right)\geqslant 1-\xi_{i},\\ \xi_{i}\geqslant 0,i=1,2,\ldots,m,\end{split} \tag{3}\]  
where \(w\) represents the weight and \(y\) is the sample label, \(\xi_{i}\) is the relaxation variable, C is the penalty coefficient, m is the number of samples, and b is the bias. SVM finds a linear partitioned hyperplane with maximum margin in a high-dimensional space. By solving this optimization problem for \(\mathbf{w}\), \(b\), and \(\xi_{i}\), the optimal hyperplane which can be used to classify samples is obtained. This optimization problem can be solved with the help of the Lagrange multiplier method or quadratic programming. However, this optimization problem is usually complicated and needs to be solved with the help of the dual problem. The dual form is given by Equation (4).  
\[\begin{split}\max_{\mathbf{\alpha}}\sum_{i=1}^{m}\alpha_{i}-\frac{1}{2} \sum_{i=1}^{m}\sum_{j=1}^{m}\alpha_{i}\alpha_{j}y_{i}y_{j}{x_{i}}^{T}\mathbf{x}_{j} \\ s.t.\sum_{i=1}^{m}\alpha_{i}y_{i}=0,\\ 0\leq\alpha_{i}\leq C\quad i=1,2,\ldots,m,\end{split} \tag{4}\]  
where \(\alpha_{i}\) represents the Lagrange multiplier. After solving the dual problem, the solution of the original problem can be obtained:  
\[\mathbf{w}=\sum_{i=1}^{m}\alpha_{i}y_{i}x_{i}, \tag{5}\]  
\[b=y_{j}-\sum_{i=1}^{m}\alpha_{i}y_{i}x_{i}^{T}x_{j} \tag{6}\]  
SVM can be extended to a nonlinear classifier by introducing kernel functions. There are many commonly used kernel functions such as linear, polynomial, sigmoid, and radial basis function (RBF) [26]. Among them, the RBF function is the most widely used kernel function [27] because it has a solid ability to distinguish the non-linearly separable data. The formula of the RBF function is shown in Equation (7).  
\[\mathbf{K}\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\exp\left(-\frac{\big{\|}\mathbf{x}_ {i}-\mathbf{x}_{j}\big{\|}^{2}}{2\delta^{2}}\right),\delta>0, \tag{7}\]  
where \(\delta\) is the hyperparameter.
# Borehole Condition and Limit Pressure Differential Analysis in Carbonate Reservoirs
## 5 Field Application Examples
To maintain the volume ratio of the plastic zone to the wellbore volume below 20%, the test pressure differential should be below 23.3 MPa. To ensure that the maximum failure angle on the wellbore wall does not exceed 90\({}^{\circ}\), the test pressure differential should be below 22.4 MPa. Based on comprehensive judgment, the maximum test pressure differential for the reservoir interval in Well S5-6 is 22.4 MPa. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Vertical depth/m & 7478–7839.6 \\ Measured depth/m & 7478–7884 \\ Maximum deviation angle/(°) & 44.5 \\ Borehole azimuth/(°) & 40–45 \\ Pore pressure/MPa & 81.5–91.7 \\ Overburden pressure/MPa & 173.7–183.5 \\ Maximum horizontal principal stress/MPa & 175.5–192.3 \\ Minimum horizontal principal stress/MPa & 122.5–151.3 \\ Effective stress coefficient & 0.6 \\ Plastic yield strength/MPa & 53.9–72.8 \\ Peak strength/MPa & 56.3–82.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic parameter Table of reservoir section of Well S5-6. Figure 11: The variation in the failure zone around the wellbore, the volume ratio of the plastic zone to the wellbore, and the failure angle of the wellbore with the test pressure differential in the reservoir section of Well S5-6. In adherence to the reservoir development plan's stipulations, Well S5-6 was designed with a maximum test pressure differential of circa 25 MPa, marginally exceeding the computed threshold of 22.4 MPa. To guarantee subterranean safety throughout the testing phase, perforated liners were strategically deployed within the open hole section to bolster the wellbore's integrity and avert extensive collapses. Implementation of these perforated liners as a protective measure effectively ensured that the actual testing proceedings unfolded without any safety incidents.
## 5 Discussion  
The results are summarized in Figure 3. When there is not an exchange of materials (corresponding to null AiP) the value of ISI is 0, while when all the recovered material is exchanged and recycled or reused by the companies (corresponding to AiP = 100%), the value of ISI increases (dashed line), serving as a piece of feedback information concerning the level of circularity achieved by the symbiotic network. The solid line points out the ISI reference level, corresponding to the current situation, that can be improved. The ISI indicator doubles its value when most of the available plastic fibers are used, showing an improved circularity performance of the symbiotic network.  
The ISI indicator, as demonstrated by [PERSON] et al. [18], can identify distinct levels of symbiosis. The ISI calculation methodology is easy and does not require a large amount of input data, which is usually difficult to obtain. Other indicators, such as those based on the eco-efficiency concept or those used within environmental impact assessment methods (LCA) and material flows (MFA), require specific data and long processing times, as well as expertise often not available in the companies.  
Even though the ISI indicator is not comparable with other indicators, its application could serve in attracting companies to apply symbiotic raw material exchanges and also in persuading decision-makers of development through IS and CE initiatives. In our study, the use of the ISI indicator allowed us to make both the anchor company (company 1) and the partners belonging to the network aware of the potential benefits of implementing growing levels of symbiosis, and circularity.  
Further research should address the limitations of this study. Firstly, additional evaluation of the level of circularity under different scenarios are needed, considering the inclusion of other companies or stakeholders and the changes in the amount of by-product. Moreover, comprehension and comparability of the indicator could be increased by adding an environmental indicator, such as the ecoefficiency of the entire network.