esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Prediction and Optimization  
The behavioral embedding representation of the user and the project is obtained through the local structure and global structure respectively, and fused so that the user's individual information and the global structure information can be included at the same time, which is expressed by the Equations (19) and (20):  
\[e_{u}=MLP_{U}\big{(}e_{u_{i},g}\big{)} \tag{19}\]  
\[e_{v}=MLP_{V}\Big{(}e_{v}^{(0)}\big{\|}e_{v_{g}}\Big{)} \tag{20}\]  
The correlation score is calculated between the target site \(v\) and the target customer \(u\) interaction sites under the specified \(k\) behaviors as Equations (21) and (22):  
\[y1(k)_{u,v}=e_{U}^{T}\ \cdot\ \text{diag}\big{(}e_{\gamma_{k}}\big{)}\ \cdot\ e_{v} \tag{21}\]  
\[y2(k)_{u,v}=\sum_{j\in N_{\gamma_{k}}(u)}\frac{h_{\gamma_{k}}^{T}\cdot h_{j \gamma_{k}}}{|N_{\gamma_{k}}(u)|} \tag{22}\]  
where \(diag(.)\) denotes a diagonal matrix with diagonal elements equal to \(e_{\gamma_{k}}\); and \(N_{\gamma_{k}}(u)\) denotes the set of sites for user \(u\) under \(k\) behavior.  
The predictive scoring Equation between the target site \(v\) and the target customer \(u\) interaction site is as follows:  
\[y(k)_{u,v}=\lambda_{1}\ \cdot\ y1(k)_{u,v}+(1-\lambda_{1})\ \cdot\ y2(k)_{u,v} \tag{23}\]  
The optimization model, the recommendation task loss function, is defined with Equation (24):  
\[L_{0}=\sum_{(u,v_{i},v_{j})\in(0)}\log\sigma\big{(}y(k)_{u,v_{i}}-y(k)_{u,v_{ j}}\big{)} \tag{24}\]  
where \(\big{\{}(u,v_{i},v_{j})|(u,v_{i})\in O^{+},(u,v_{i})\in O^{-}\big{\}}\) denote paired training samples, and \(O^{+}\) and \(O^{-}\) denote observed and unobserved interaction behaviors that can be observed and unobserved, respectively, for user \(u\) and site \(v\). \(\sigma\) is the _sigmoid_ activation function, andthe combined cross-behavioral, cross-structural comparison loss, and the overall loss \(L\) Equation is expressed as follows:  
\[L=L_{0}+\lambda\left(L_{B}^{G}+L_{B}^{S}+L_{V}\right)+\mu\|\theta\|_{2}^{2} \tag{25}\]  
where \(\theta\) denotes the chance of all trainable parameters, \(\mu\) denotes the parameter controlling \(L2\) regularization, and \(\lambda\) denotes the parameter controlling the proportion of contrast loss. The Nomenclature presents a full explanation of mathematical symbols in the proposed model.
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## 4 Methodology  
### Sample and Collection  
This research is based on empirical data, which was collected in August 2016 in Chongqing municipality, China. The survey methodology was chosen in order to strengthen generalizability, facilitate replicability, and maximize statistical value [47]. Chongqing was chosen as the surveyed city because: (1) it is one of the four direct-controlled municipalities in China with a large population of approximately 33 million in 2015 [48]. Since direct-controlled municipality in China has the highest administrative power equivalent to province [49], local e-government projects can be more easily observed in direct-controlled municipality than normal city because of the better ICT infrastructure and autonomous ability; (2) As the youngest, and only nationally-centered city located in southwest (which is less developed in comparison with the east coast), Chongqing has absorbed a good deal of its workforce from surrounding cities including farmers, workers and other immigrants. Compared with other three direct-controlled municipalities (Beijing, Shanghai and Tianjin), Chongqing is the place where sees the most possibility of containing people in different socio-economic brackets. The survey in Chongqing may provide a good way to test the digital divide on e-services adoption in different sides: resident vs. non-resident; less educated vs. highly educated, etc. In general, sample collection in Chongqing furnishes diverse cases of citizens that enhancing the representativeness [50].  
The questionnaire was pre-tested with 10 local persons and revised based on their feedback. Considering that people may need flexibility of answering the questionnaire, both offline and online questionnaires were distributed. Offline questionnaires were distributed to citizens in railway stations and main streets with crowded populations; online questionnaires were released on a survey website and were distributed via a QR code printed on the offline questionnaire for those who could choose to fill in the items through social networking application afterwards. It is worth noting that the survey was reviewed and obtained approval from local public officers to publish online due to the context involved with questionnaires regarding governmental issues. After receiving responses from both channels, there were 605 valid responses included in the total 739 returned questionnaires.
## 3 Results and Discussion
### Influence of Biochar Hydroinfiltrators on Microbial Communities
On the other hand, the increase in _Bacillus_ and _Splingomonas_ in soils with hydroinfiltrators could also be associated with the use of resources released from the death of microorganisms more sensitive to the initial toxicity of fresh biochar, as proposed in other studies [7; 71]. Additionally, biofilm formation is also promoted under these conditions, facilitating microbial cooperation via quorum sensing and improving environmental resistance [95; 100; 101]. Under environmental stress conditions, the use of hydroinfiltrators with biochar showed positive effects on microbial diversity, presumably due to improved water retention and nutrient availability, as previously pointed out by some authors [5; 76; 102]. However, a decrease in the abundance of _Ramlibacter_ (Figure 6), common in arid environments [66], was also observed. This finding would suggest that increased moisture conditions may be unfavourable for certain xerotolerant populations. Nevertheless, other genera, such as _Splingomonas_, are able to thrive due to their multiple physiological functions [53; 66]. An in vitro assay with halotolerant bacteria highlighted the efficacy of _Bacillus cabrialesii_ compared to _Pseudomonas neuropathica_ (Figure 7), likely due to the high ecological adaptability of _Bacillus_ in saline environments and its ability to maintain key metabolic functions under adverse conditions [21; 35]. Studies with strains of _Bacillus_, _Pseudomonas_, _Azospirillum_ and _Halomonas_ have also reported improvements in salt tolerance in crops such as wheat, rice and tomato, including enhanced root development, water retention, chlorophyll content and higher biomass production [1; 11]. This finding reinforces the use of biochar as a vector for microbial inoculation in saline environments, as previously proposed by several authors [22; 27; 103]. Under the current climate change scenario, where an increase in soils with aridity and salinity problems is expected, this approach could be a recommended strategy for adaptation. Finally, the increase in genera such as _Microvira_, _Skernanella_, _Bacillus_, _Splingomonas_, and _Blastococcus_ (Figure 6) indicates that the use of hydroinfiltrators promotes microbial communities functionally involved in N, P, and carbon biogeochemical cycles [29; 32; 35; 70; 104; 105; 106; 107]. This pattern aligns with previous studies indicating that biochar can improve ammonium retention, reduce denitrification, and increase P solubilisation [108; 109; 110; 111; 112; 113].
# Establishment of a Standard Method for Boundary Slip Measurement on Smooth Surfaces Based on AFM
## 3 Standard Process of Measuring Boundary Slip
### Experimental Process
#### 3.1.2 Probe Preparation  
The preparation of the colloid probe was to make different radii borosilicate glass spheres (radius: 26-34 \(\mu\)m) stick to the end of AFM probe cantilever. It was necessary to ensure that probe surface was smooth and free from impurities when measuring. First, several borosilicate glass spheres were soaked in acetone solution and ultrasonically cleaned for 10 min, and then the acetone soaking solution was filtered out. A methanol solution was used to soak and filter repeatedly to remove residual acetone. Finally, the mixture of methanol solution and glass spheres was obtained. The process of probe preparation is shown in Figure 2, showing the six steps to prepare the colloidal probe.  
The mixture of sphere and methanol was dropped on a silicon wafer. After the alcohol completely was completely volatized as the cleaner, the left side of the spheres faced up, as shown in Step 1. Electrostatic guns were used to \"Jet\" cations onto the silicon wafer, which contained spheres and \"Jet\" anions on another clean wafer. Then, the clean wafer was placed close to the wafer containing spheres, face to face. After the two wafers were separated, some spheres were stuck to the surface of the clean silicon wafer as the cleaning side face down, as shown in Step 2. Through a microscope, the tip of the probe was made to touch epoxy glue (Araldite, Bostik, Coubert) placed on the microslide gently, as shown in Step 3. When the probe touched the epoxy glue, it was lifted away from the glue to minimize the glue amount on the probe, as too much glue may contaminate the working side of the sphere. The clean wafer containing spheres was placed under the microscope, and the sphere was stuck in the center of the probe tip so that the sphere's cleaning side faced down as the working side of the colloid probe, as shown in Step 4.  
The working surface of the sphere was clean because the probe was prepared in a clean environment. Because of the gravity force and electrostatic adsorption, the dust and particles were moved to the non-working surface of the sphere or silicon wafer. As a result, the probe preparation method improved the experimental success rate and data validation.  
Figure 2: The preparation principle of colloidal probe.
## 4 Experimental Results
### State Messages Pass-Through Delay and Queue Sizes  
This additional group of tests and analysis has been designed in order to analyze the behavior of the sidecar object related to the publishing of state messages coming from the smart object at different rates. The test configuration takes into account: (i) 1 full message processing chain (from smart object to the cloud); (ii) 300 messages sent and (iii) a message payload size of 4 KB. Messages have been sent at fixed rates of 0.7, 0.8, 0.9, 0.95, 1.0, 1.1, 1.2 and 1.5 messages per second. Obtained results are shown in Figure 10a,b.  
As described in the implementation chapter, the Google Cloud Platform applies a limit of one state message per second for each device. If this limit is exceeded the connection is interrupted giving a proper \"error message\", and it must be reestablished. During our tests no disconnection has been registered: this proofs that the sidecar object has never exceeded the limit imposed by the platform. Graphs show that until messages arrive from the smart object with an acceptable rate, pass-through delays remain low and stable and queue sizes close to zero. With message rates equal or greater than  
Figure 8: (**a**) Telemetry messages pass-through delay average; (**b**) telemetry messages ack delay average; (**c**) telemetry messages ack delay average; (**d**) telemetry messages ack delay; (**e**) telemetry messages pass-through delays; (**f**) telemetry messages ack delays.  
one per second, pass-through delays and queue sizes rise linearly: this is the expected behavior as the sidecar object keeps in memory the received State messages publishing them at the aforementioned rate of one per second, in order to avoid the disconnection from the Google Cloud Platform. It is worth mentioning that the slight increase of pass-through time and queue size at the limit rate (1 message per second) is caused by the publishing overhead, as this is a synchronous operation.
## 2 Materials and Methods
### Mathematical Preliminaries
#### 2.2.3 Analysis of Second-Order Moments  
Similar analysis may be derived for second-order moments \(E[z_{1}^{2}(t,1)]\) and \(E[z_{1}^{2}(t,2)]\). If we denote  
\[\phi_{1}(t)=E[z_{1}^{2}(t,1)],\ \ \ \phi_{2}(t)=E[z_{1}^{2}(t,2)], \tag{31}\]  
then Equations (13) and (14) can be represented in the form  
\[\frac{d}{dt}\phi(t)=A\phi(t)+\psi(t),\ \ \ \phi(0)=\phi_{0} \tag{32}\]  
where  
\[\phi(t)=\left[\begin{array}{c}\phi_{1}(t)\\ \phi_{2}(t)\end{array}\right],\ \ \ A=\left[\begin{array}{cc}-\rho_{1}&\rho_{1}\\ \rho_{2}&-\rho_{2}\end{array}\right],\ \ \psi(t)=\left[\begin{array}{c}\delta(t,1)\\ \delta(t,2)\end{array}\right],\ \ \phi_{0}=\left[\begin{array}{c}\phi_{10}\\ \phi_{20}\end{array}\right], \tag{33}\]  
\[\delta(t,1)=2\alpha_{x}^{1}E[z_{1}(t,1)]-2\beta_{x_{1}}^{1}q_{1}^{1}E[z_{1}(t,1)z_{2}(t,1)]+(\gamma_{x_{1}}^{1}q_{1}^{1})^{2}, \tag{34}\]  
\[\delta(t,2)=2\alpha_{x}^{2}E[z_{1}(t,2)]-2\beta_{x_{1}}^{2}q_{1}^{2}E[z_{1}(t,2)z_{2}(t,2)]+(\gamma_{x_{1}}^{2}q_{1}^{2})^{2}. \tag{35}\]  
Using Equalities (29) and (30) we find  
\[\delta(t,1)=a_{1}t+b_{1},\ \ \ \ \ \delta(t,2)=a_{2}t+b_{2} \tag{36}\]where  
\[a_{1}=2\alpha_{x}^{1}\Big{[}\alpha_{x}^{1}+\rho_{1}(\alpha_{x_{0}}^{2}-\alpha_{x_{ 0}}^{1})\Big{]},\quad a_{2}=2\alpha_{x}^{2}\Big{[}\alpha_{x}^{2}+\rho_{2}( \alpha_{x_{0}}^{1}-\alpha_{x_{0}}^{2})\Big{]}, \tag{37}\]  
\[b_{1}=2\alpha_{x_{0}}^{1}\alpha_{x}^{1}-2\beta_{x_{1}}^{1}q_{1}^{1}E[z_{1}(t,1 )z_{2}(t,1)]+(\gamma_{x_{1}}^{1}q_{1}^{1})^{2}, \tag{38}\]  
\[b_{2}=2\alpha_{x_{0}}^{2}\alpha_{x}^{2}-2\beta_{x_{1}}^{2}q_{1}^{2}E[z_{1}(t,2 )z_{2}(t,2)]+(\gamma_{x_{1}}^{2}q_{1}^{2})^{2}. \tag{39}\]  
The general solution of Equation (23) has the form  
\[\phi(t)=e^{At}\phi_{0}+\int_{0}^{t}e^{A(t-\tau)}\psi(\tau)d\tau. \tag{40}\]  
If we assume \(\phi_{10}=c_{0_{x}}^{1}\) and \(\phi_{20}=c_{0_{x}}^{2}\) are constant parameters, and we use approximation (28), then after algebraic manipulation, we obtain approximate formulas for second-order moments in the form of quadratic linear time-dependent functions  
\[E[z_{1}^{2}(t,1)]=c_{0_{x}}^{1}+\Big{(}b_{1}+\rho_{1}(c_{0_{x}}^{2}-c_{0_{x}}^ {1})\Big{)}t+\frac{\rho_{1}a_{2}+\rho_{2}a_{1}}{2\bar{\rho}}\ t^{2}, \tag{41}\]  
\[E[z_{1}^{2}(t,2)]=c_{0_{x}}^{2}+\Big{(}b_{2}+\rho_{2}(c_{0_{x}}^{1}-c_{0_{x}}^ {2})\Big{)}t+\frac{\rho_{1}a_{2}+\rho_{2}a_{1}}{2\bar{\rho}}\ t^{2}. \tag{42}\]  
We note that in the case of nonstationary solutions of the first and second moment of the process \(z_{x_{1}}(t,l)\) without Markov switchings (\(\rho_{1}=\rho_{2}=0\)), we obtain equalities  
\[E[z_{x_{1}}(t,l)]=\alpha_{x}^{l}t+\alpha_{0_{x}}^{l}, \tag{43}\]  
\[E[z_{x_{1}}^{2}(t,l)]=(\alpha_{x}^{l})^{2}t^{2}+2\alpha_{x}^{l}\alpha_{0_{x}}^ {l}t+c_{0_{x}}^{l}, \tag{44}\]  
where \(\alpha_{0_{x}}^{l}\) and \(c_{0_{x}}^{l}\) are constants of integration and \(l=1,2\).
## 3 Materials and Methods
### Creating Dataset  
After collection of sparse matrices from multiple domains and their conversion from the matrix market format to BCSR format, SpMV computation is performed on each matrix with multiple block sizes and their execution time is recorded. SpMV operation is performed 2k times to avoid any anomalies (execution time variations of low dimensional matrices) and used the average execution time of 2k iterations. With 2k iterations the execution time remained very much consistent. The dataset was labeled with block size associated with the minimum execution time recorded against each matrix. The average execution time was computed by taking the average time associated with multiple block sizes. The maximum execution time is the worst-case scenario.  
Our dataset comprises 43 application domains, as listed in Table 2. The column name shows the largest matrix from each domain along with its dimensions (rows and columns). The number of non-zero entries in the largest matrix for each domain is also listed. These matrices are collected from multiple domains to diversify the dataset and thus avoid the situation of being biased towards specific domains or targeting specific structure of matrices.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**No.** & **Name** & **Non-Zero** & **Rows** & **Columns** & **Domains** \\ \hline
1 & 1138\_bus & 4054 & 1138 & 1138 & Power Network Problems \\
2 & abb313 & 1557 & 313 & 176 & Least Squares Problem \\
3 & arc130 & 1037 & 130 & 130 & Materials Problem \\
4 & bcsstk13 & 83,883 & 2003 & 2003 & Computational Fluid Dynamics Problem \\
5 & bcsstk12 & 34,241 & 1473 & 1473 & Duplicate Structural Problem \\
6 & bcsstk31 & 1,181,416 & 35,588 & 35,588 & Structural Problem \\
7 & bcsstk32 & 2,014,701 & 44,609 & 44,609 & Structural Problem \\
8 & jagmesh4 & 9504 & 1440 & 1440 & 2D/3D Problem \\
9 & saylr4 & 22,316 & 3564 & 3564 & Computational fluid Dynamics Problem \\ \hline \hline \end{tabular}
\end{table}
Table 2: Application domains.
## 2 The Topology of HFL Three-Phase Four-Leg MC  
The HFL three-phase four-leg MC adopts the three-phase four-wire power supply topology which shown in Figure 1. The main circuit adopts DC/AC1/AC2 two stage power conversion mode, defining the pre-stage and rear-stage of the HFL \(3\times 4\) MC circuit with a high frequency transformer in the middle of the topology. The pre-stage DC/HFAC high-frequency square wave inverter is composed of a single phase full bridge inverter circuit and a high-frequency transformer, output a high frequency alternating current square wave AC1 to achieve a first stage power conversion; a three-phase four-leg matrix converter and a filter inductor \(L\) and a filter capacitor \(C\) are used to constitute a rear-stage HFAC/LFAC matrix conversion circuit, which converts the high-frequency alternating square wave AC1 into a power frequency sinusoidal wave AC2 to realize the second stage power conversion [20]. The switches used in the rear-stage MC topology are bidirectional switching tubes composed of two IGBTs. \(u_{\mathrm{a}}\), \(u_{\mathrm{b}}\), \(u_{\mathrm{c}}\), and \(u_{\mathrm{n}}\) are the bridge leg terminal voltages, \(i_{\mathrm{a}}\), \(i_{\mathrm{b}}\), \(i_{\mathrm{c}}\), and \(i_{\mathrm{n}}\) are the filter inductor currents, \(u_{\mathrm{AO}}\), \(u_{\mathrm{BO}}\), and \(u_{\mathrm{CO}}\) are the three-phase output voltages of the matrix converter.  
Figure 1: Topology of high-frequency link (HFL) three-phase four-leg matrix converter (MC).
# Augmented Reality and Virtual Reality in Exergaming
## 4 Discussion
### Related Studies Analysis
Although positive outcomes were generally reported and there was a shared consensus about the motivational benefits associated with the use of exergames, a great variety in the study settings was observed and according to the PEDro classification, methodological issues were observed, which hindered the generalizability of the results. Therefore, the study highlighted the need for more experimental studies to be conducted to provide stronger scientific evidence and external validity regarding the impact of VR exergames on older adults. [PERSON] and [PERSON] [115] put emphasis on improving older adults' cognition and depression through the use of VR exergaming. Through their systematic literature review and meta-analysis, they highlighted that VR exergames have moderate positive effects on older adults' function and memory and greatly reduce their depressive outcomes. Additionally, they pointed out that higher intervention duration resulted in more favorable outcomes. Therefore, the outcomes of this study suggest that besides an increase in participants' activity engagement and interactions, VR exergames can also positively influence older adults' memory, depression, and cognition. Furthermore, [PERSON] et al. [116] compared different VR devices in the context of exergaming, exploration, navigation, and rehabilitation. Specifically, they put emphasis on their technical capabilities and their feasibility to be used for health-related applications. Their results revealed that head-mounted devices can effectively be used within VR environments for exergaming, rehabilitation, navigation, and exploration purposes. However, the devices used can influence users' experience and outcomes. In their study, [PERSON] et al. [117] presented their VR cycling exergame. Specifically, they highlighted the potential of using VR in conjunction with sensors and haptic devices to enhance users' physical activity experiences. Finally, they commented how immersive exergaming experiences that fully immerse and engage users can be created through this approach. Similarly, [PERSON] et al. [118] presented and assessed their immersive VR exergame, which focused on encouraging users to be involved in rigorous, full-body exercise. The exergame was positively viewed by the participants who varied in age, background, and skills. Their results highlighted that VR exergames can motivate both children and adults to actively exercise within immersive environments.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 4 Results
### Impact of Outliers on the p-Value  
Discrepant values are often called outliers. They are normal in industrial sensor readings and they have an impact on the data analysis process, as they affect data averages, standard deviation, and many other statistical characteristics of a series.  
The relationship between the discrepant data and the \(p\)-value was also sought. We are particularly interested in outliers because, in the absence of labelled data about the failures' true timestamps, outliers are possible indicators of failures. They may be sources of true or false positives.  
In this study, a discrepant value was defined as one 200% higher than the mean recorded for each analysed variable. In our data, 5 days of discrepant values were identified. Figure 7 shows the discrepant values, which are marked with bounding boxes. They correspond to the following dates:  
* 6 March 2018 (VCP 3);
* 16 May 2018 (VCP 2);
* 8 June 2018 (VCP 1);
* 17 July 2018 (VCP 1);
* 25 September 2018 (VCP 1).  
Performing a more specific analysis of the \(p\)-value values on the different dates identified, it is possible to observe some variations in the \(p\)-value during or around the week that includes the date of the discrepant values. As an example, Figure 8 shows the weekly and daily \(p\)-values for VCP 3, where a spike in vibration was observed on 6 March. As the figure shows, there is an increase in the \(p\)-value in the first week of March, followed by a steady decrease until the end of the month. The daily chart shows the highest \(p\)-values were registered on days 12, 16, and 30--in other words, after the discrepant value was registered. These values are compared in Figure 6, which shows the variation of the \(p\)-value and the mean of the variable VCP 3 corresponding to the identified day and which highlights some peaks of the \(p\)-value over four months, including March.  
Figure 6: Daily variation of p-value and variable VCP3, with overlap.  
The sources of these spikes may be numerous, and during the present study it was not possible to explain them. Hence, the conclusion from this observation is that outliers have  
Figure 8: \(p\)-value variation in March 2018.  
Figure 7: Discrepant values marked with bounding boxes.  
an influence on the \(p\)-value, but whether they are true or false positives, regarding fault diagnosis, is left for future work, where more data may be available.