esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Implications for Inclusive Digital Transformation and Net-Living  
The concept of Net-Living envisions a future in which smart systems and digital infrastructures are embedded seamlessly into everyday life to support well-being, inclusion, and autonomy across public, private, and professional domains. Users in digitally underserved regions actively assess digital interactions based on their perceived utility and ease of use. Well-designed omnichannel systems can foster trust, reduce friction, and enhance user autonomy. In such contexts, access to coherent digital services can be a key enabler of socio-digital inclusion beyond mere convenience. It allows individuals with varying degrees of digital fluency to participate in structured systems without being excluded by complexity or technological opacity.  
Furthermore, the results suggest that the \"intelligent\" component of smart service systems must be relational as much as technical. In other words, success in Net-Living is not achieved merely through automation or digitization but through the system's ability to recognize and respond to users' socio-emotional and informational needs [46]. Thisincludes adapting interfaces to local expectations, maintaining redundancy (e.g., offering both digital and face-to-face channels), and progressively integrating new technologies at a pace that users can assimilate and adopt confidently. This approach supports the ethos of inclusive innovation, which prioritizes the following:  
* Accessibility: ensuring that systems can be used by people regardless of income, education, or digital literacy.
* Responsiveness: enabling feedback loops that adjust service behavior in real time.
* Simplicity: reducing cognitive and procedural burdens that discourage use or that lead to abandonment.  
Additionally, these findings have policy implications. Public institutions and regulatory bodies aiming to foster digital ecosystems should recognize omnichannel systems as infrastructures of trust and equity, especially in sectors such as energy, health, and public administration. Investments in interoperable platforms, mobile-friendly applications, and data transparency mechanisms can yield not only economic efficiency but also social dividends in the form of expanded participation and enhanced subjective well-being [12]. In sum, this study positions omnichannel strategies not merely as commercial tool but as architectures of digital dignity--structures that mediate the user's encounter with technology in a way that either empowers or marginalizes. By focusing on the user experience as an ethical and design priority, organizations and governments alike can align digital transformation with the principles of Net-Living, ensuring that technological advancement serves as a vector for human development, not a new source of inequality.
###### Abstract  
Deep learning-based denoising methods have proved efficient for medical imaging. Obtaining a three-dimensional representation of a scanned object is essential, such as in the computed tomography (CT) system. A sufficient radiation dose needs to be irradiated to a scanned object to obtain a high-quality image. However, the radiation dose is insufficient in many cases due to hardware limitations or health care issues. A deep learning-based denoising method can be a solution to obtaining good images, even when the radiation dose is insufficient. However, most existing deep learning-based denoising methods require numerous paired low-dose CT (LDCT) images and normal-dose CT (NDCT) images. It is almost impossible to obtain numerous well-paired LDCT and NDCT images. Self-supervised denoising methods were proposed to train a denoising neural network on only noisy images. These methods can be applied to the projection domain in LDCT. However, applying denoising in the projection image domain is a challenging task, because the projection images for LDCT have extremely weak signals. To solve this problem, we propose a noise reduction method based on the dropped projection strategy. The proposed method works by first reconstructing the 3D image with the degraded versions of the projection images generated by Bernoulli sampling. Subsequently, the denoising neural network is trained to restore the signal dropped out by Bernoulli sampling in the projection image domain. As such, the method we propose solves the over-smoothing problem in previous methods and is able to be trained with a small amount of data. We verified the performance of our proposed method on the SPARE challenge dataset and the in-house lithium polymer dataset. The experiments on two datasets show that the proposed method outperforms the conventional denoising methods by at least 4.47 dB of PSNR value.  
caputted tomography (CT); denoising; noise reduction; self-supervised
# A Generalized Framework for the Estimation of Edge Infection Probabilities
## 2 Definitions
### General Inverse Infection Model
```
1:Inputs: \(G,\mathcal{I},T,O,a\)
2:Choose initial edge infection probability assignment \(W^{\prime}\)
3:repeat
4: Compute \(O^{\prime}=Inf(G,W^{\prime},\mathcal{I},T)\)
5: Compute \(d(O,O^{\prime})\)
6:if\(d(O,O^{\prime})\leq a\)then
7:return\(W^{\prime}\)
8:else
9: Choose new \(W^{\prime}\)
10:endif
```  
**Algorithm 1 Generalized Inverse Infection Model**  
It should be noted that depending on the application, the weight assignment function \(W\) may also be time-dependent; i.e., the weights themselves may change in time. As this scenario was already investigated in [24; 25], we will treat all edge weights and \(W\) as static in this paper. Function Estimation  
Finding an edge weight assignment that minimizes the difference between the reference and computed infections can be computationally expensive, as we will see in Section 4, as this requires finding the edge infection probability for all edges of the graph. Another approach to direct edge weight estimation is to compute the edge weights as a function of existing attributes, as seen in [4; 23; 24; 25]. These works propose that additional information can be assigned to the edges or vertices of the graph. Examples include the number of passengers or travel distance in an air traffic network [25] or the number or frequency of transactions [4]. The edge infection probabilities themselves can be defined as functions of these attributes, in which case, the optimization task changes to finding the proper function formulation explaining the reference infection process. This approach is often an easier estimation task compared with finding all of the edge weights. As the function estimation approach has already been investigated in [24; 25], we refer the reader to these works for further information.
## 4 Verification of the Established Rock Constitutive Model
### Comparison of the Numerical and Model Test Results
For a comparative analysis, the numerical results were expressed as smoothed curves of numerical transmission coefficients under different biaxial loads. Figure 10 shows that the measured and numerical transmission coefficients of the jointed rock mass contained in each numerical model under different static loads were relatively close to each other, and when the static load increased from 0, the measured and numerical transmission coefficients both showed a trend of increasing first and then decreasing. Based on the numerical simulation results in Figure 10, the stress wave transmission coefficient of the jointed rock mass containing different angles and numbers reached its maximum value when the static load was about 2.2 MPa, which was about 28.1% of the biaxial compressive strength. It can also be seen in Figure 10 that the measured and numerical transmission coefficients of the jointed rock masses within different numerical models were relatively close in the ascending part of the curve, while a certain deviation occurred in the descending part. The reason was that in the model test when the static load increased to 3 MPa, which was about 38.3% of the biaxial compressive strength, the closed microcracks within the intact rock started to expand, and new microcracks were initiated, resulting in a decrease in the transmission coefficient [40]. However, in the numerical calculation, the expansion of the microcracks within the intact rock was not considered, which led to the larger numerical results. The last section obtained a high agreement between the numerical and experimental results, which verified the accuracy of the established rock constitutive model considering the initial damage and the feasibility of the adopted numerical simulation method. Due to the limited angle and number of joints set in the model test, a numerical calculation of the stress wave propagation in rock masses with various angles and numbers of joints under different biaxial static loads was carried out based on the same numerical simulation method in Section 4 for a more thorough study of the effect of the angle and number of joints on the stress wave propagation. The physical and mechanical parameters of the rocks and joints in the numerical calculations are shown in Table 2.
# Fact-Checking Reasoning System for Fake Review Detection Using Answer Set Programming
## 4 On the Characteristics of Fake Reviewers and Reviews  
Behind every fake review, there is a user that normally has a well-defined malicious behavior. As described in Reference [14], certain patterns, such as posting only positive or negative reviews, or posting reviews in a specific time interval, for example, every day or every week at the same period of time, can be detected as a spamming behavior. Another interesting pattern for a fake review can be the content itself. For example, there are many redundant reviews with only minor changes or spam reviews with a bad quality texting and few counts of words [27].  
Another well-described pattern of malicious behavior, described in both References [27; 28], is the control of different accounts by a single spammer, who uses them for spamming and domination. These accounts are also known as bot accounts or sockuppets and can be recognized by following their actions in connection with other similar users. Most likely, the grouping that will appear is going to be bot users. Another aspect for fake review recognition could be a single individual user coupling in a group, which describes the behavior of multiple users acting similarly relatively at the same time, which is often done by bot accounts [27]. Furthermore, accounts with some special \"sequential\" user naming are often associated with bot users. Another way to detect fake reviews can be achieved by checking the dislike counts. Many e-commerce websites offer some functionality that allows people to vote if a review was helpful or not. Sometimes the community is doing the job for us by disliking the suspicious reviews. This makes the dislike count be represented as a feature that needs to be taken into consideration.  
The work in Reference [9] discusses the anonymity factor that may need further consideration, as well. For example, on Amazon, users can post a review under a nickname or as an anonymous user. Anonymous users post many fake reviews because the user may want to avoid getting tracked. In our proposed system, the requirement to post a new review is that the user initially registers with the help of the registration step; the anonymous review feature is not feasible.  
Another significant factor in the recognition process is the review feature similarities of a new or updated review with all other reviews in the system. It is possible to compute these similarities by using dynamic algorithmic approaches as discussed in Reference [2]. Given the deviation values results of the analyzed reviews, the three reviews with the highest similarity scores are considered, and the number of high and low deviating reviews is used in the ASP knowledge base (see Section 5.5).
## 5 CFD Validation
### Gas Temperatures  
To cover the gas layer temperature for all cases, the top back and front right corner thermocouples measurements are presented and compared with the results from the simulations to understand to what extent FDS replicated the gas layer development. Figures 18 and 19 show that the current model well replicated the growth phase in all the cases, and that was expected as this was mainly driven by the accelerant (heptane) and the fire at that stage still being over-ventilated. Additionally, FDS managed to capture the time to flashover and the peak gas layer temperature for all of the cases except the cases with the biggest horizontal openings (C and S_0.16 m\({}^{2}\)). The FDS underestimated the gas layer temperature at the back of the compartment by \(-22\%\) and overestimated the gas layer at the front by around \(+60\%\). FDS, however, managed to capture the front gas layer temperature for the S_0.16 case accurately, while it underestimated the back gas layer temperature by around \(-30\%\). The decay was also captured well by the FDS in all cases.  
The combustion efficiency impact was clear on the gas layer temperature: the bigger the horizontal opening, the higher the turbulence, which leads to more eddies. To further discuss the effect of the turbulence modelling on the combustion efficiency and eventually the combustion products (gas concentrations and temperatures) it is important to explain how FDS models turbulent combustion.  
Figure 16: Gas concentration at the back of the compartment for the closed and S_0.04_Open cases.  
Figure 17: Gas concentration at the back of the compartment for the S_0.09 and S_0.16 cases.  
It is usually mathematically challenging to model chemical reactions in any turbulent flows, due to the fact that the length and time scales associated with the reactions are orders of magnitude below what can be spatially and temporally resolved by the physical model. FDS uses a simple mixing environment method to close the mean chemical source term in complex reactions. Each cell in this case will be considered a turbulent batch reactor, and turbulence will dominate the rate of mixing. Therefore, FDS is based on considering the three physical processes of diffusion, SGS advection, and buoyant acceleration to take the fastest of the three (locally) as the controlling flow time scale. As mentioned earlier, the LES filter width (cell size) is considered as one of the main parameters in this process, which affects the SGS model and eventually the mixing modelling. The mixing process determines the combustion rate and/or efficiency, based on the infinitely fast chemistry that occurs within the cell. The authors therefore expect that using a cell size smaller than the 5 cm in use will lead to even more accurate results for the combustion efficiency and the gas layer temperature for cases with larger horizontal openings and/or higher turbulent flows.  
Figure 18: Gas layer temperature for corner cases.
## 3 Results
### Code Bias Analysis  
Compared with the residual analysis, the direct analysis of code biases can better reflect their time-varying characteristics. Figures 4 and 5 show the SCB time series extracted from the Undifferenced-SCB and Undifferenced-SRCB models, respectively. For the convenience of analysis, only six satellites are displayed, and the time series of each satellite is shifted to different degrees. As shown in the two figures, the time-varying code biases of the same satellite extracted using different models are consistent, which indicates that the excessive constraints imposed on the Undifferenced-SRCB model do not significantly damage the time-varying characteristics of the SCB.  
Figure 3: Time series of RMS of the phase residuals for different models on DOY 031.  
Though SCBs are contaminated by code noises due to the low accuracy of code observables, it can be found that SCBs appear in a peak-to-peak range of about 3 ns, as shown in Figure 4. If those time-varying SCBs are driven into estimated satellite clocks, it will significantly damage the stability of clocks and reduce the accuracy of PPP at the user end.  
Additionally, the RCB time series extracted from the Undifferenced-RCB and Undifferenced-SRCB models are also displayed in Figures 6 and 7. For the convenience of analysis, only six receivers are displayed, and the time series of each receiver is shifted to different degrees. Compared with Figure 6, the white noise characteristic of the RCB in Figure 7 is destroyed, and the constraint of Equation (15) should be responsible for this. Different from SCBs, the variation of the RCB with time is more severe. Therefore, if those biases are not driven into code residuals such as Equation (2), it will bring adverse effects on the estimated parameters [25].  
Figure 4: Time series of SCBs extracted by Undifferenced-SCB model on DOY 031.  
Figure 5: Time series of SCBs extracted by Undifferenced-SRCB model on DOY 031.
## 4 Conclusions  
The operational monitoring service of a power grid enterprise is pioneering work in the control practice of large enterprise groups. This paper evaluated the operational efficiency of power grid enterprises, and obtained the following conclusions:  
1. The index concerned with power grid enterprises may reach hundreds of items. The monitoring index system and identification method used in this study played a guiding role in simplifying the index.
2. The key indicator identification method in this paper is feasible and applicable to power grid enterprises, and plays a supporting role in improving operations monitoring and the decision making technology of power grid enterprises.
3. This paper compared the effectiveness of SFM, traditional DEA, and D-DEA for operational efficiency evaluations. Results showed that D-DEA had a stronger rationality when compared with the static efficiency evaluation where the average change ratio was 4% of the dynamic efficiency evaluation results.  
The method proposed in this paper not only is applicable to the efficiency evaluation of regional power grids, but can also be applied to efficiency evaluations of district heating and cooling networks. And, there is still plenty of improved space for model design methods in further study, and is the subject of future research.  
**Acknowledgments: The paper is supported by the Fundamental Research Funds for the Central Universities 2016X82, the National Science Foundation of China (Grant No.: 71273090, 71573084), the Beijing Municipal Social Science Foundation (16)DYJB044), and the Science and Technology Project of State Grid Corporation (Research on Application Technology and Model of Big Data in Key Fields of the Company).**Author Contributions: [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools and collected data; [PERSON] and [PERSON] wrote the paper and modified the paper.** Conflicts of Interest: The authors declare no conflict of interest.**  
**Nomenclature**  
DMU Decision Making Unit  
D-DEA Dynamic Data Envelopment Analysis  
ISM Interpretive Structural Model  
SFM Stochastic Frontier Model  
\(i\) input index  
\(r\) output index  
\(j\) Decision Making Unit  
\(t\) time  
\(k\) division  
\(s\) input factors category  
\(x\) the value of input index  
\(y\) the value of output index  
\(S\) slack variable  
\(z\) the associated value  
\(\theta\) efficiency value  
\(C\) cost  
\(p\) price  
\(l\) labor force  
\(v\) random error  
\(u\) non-technical efficiency  
\(\alpha\) elasticity coefficient of input index  
\(\beta\) elasticity coefficient of labor force  
\(A\) constant
# Test Center Location Problem: A Bi-Objective Model and Algorithms
## 3 Test Center Location Problem
### Integer Linear Programming Model for the Test Center Problem  
In the following sections, we provide a bi-objective Integer Linear Programming (ILP) model. The model is based on the formulation presented by [PERSON] [13], which we extend for the weighted demand points and the two contrasting objectives. To this end, we define the following binary variables:  
\[x_{ij}=\begin{cases}1&\text{, if demand point $p_{i}$ is served by center $c_{j}$ (or if $p_{i}\in\Delta(c_{j})$)}\\ 0&\text{, otherwise}\end{cases} \tag{3}\]  
\[y_{j}=\begin{cases}1&\text{, if center $c_{j}$ is selected to be opened (or if $c_{j}\in C$)}\\ 0&\text{, otherwise}\end{cases} \tag{4}\]  
By having these \(m(n+1)\) binary variables, the ILP for the test center location problem can be formulated as below:  
\[\text{\emph{Minimize}}\ \ F_{1}=u-l\] \[\text{\emph{Minimize}}\ \ F_{2}=\frac{1}{\Sigma_{i=1}^{n}w_{i}} \Sigma_{i=1}^{n}w_{i}(\Sigma_{j=1}^{m}x_{ij}d_{ij})\] \[\text{\emph{Subject}}\ to:\] \[\sum_{j=1}^{m}y_{j}=k\] \[x_{ij}\leq y_{j},\ \ \forall i\in\{1,2,...,n\},\ \ \forall j\in\{1  
The formulation is similar to the \(k-median\) problem formulation, except the last constraint of the model, \(\sum_{j^{\prime}=1}^{m}d_{ij^{\prime}}x_{ij^{\prime}}+(M-d_{ij})y_{j}\leq M\), where \(M\) is a sufficiently large number (e.g., \(M=\max\limits_{1\leq i\leq m,\;1\leq j\leq m}d_{ij}\)). This constraint guarantees each demand point is allocated to its closest center. We call this bi-objective ILP problem the test center location problem, and denote it by TCLP.  
To address the TCLP, an ideal algorithm should aim to yield a set of Pareto optimal solutions that exhibit diversity across the objective space. While the number of Pareto optimal solutions in the TCLP is finite, it can potentially be exponential in the worst case. To facilitate effective decision-making, the focus is to find a limited number of Pareto optimal solutions that cover all Pareto regions. This concept is commonly referred to as providing a \"handful\" of diverse Pareto optimal solutions [28; 29]. Typically, this would encompass approximately 10 solutions, including not only extreme solutions for objectives \(F_{1}\) and \(F_{2}\) but also covering a broad spectrum of the objective space. Then, the decision-maker can choose one of the provided Pareto-optimal solutions based on high-level information or any preferences that have not been integrated into the model. It is notable, that there are studies that suggest picking one Pareto-optimal solution like _knee point_ or other Nash solutions [30; 31]. In the next section, we propose two approaches to find the Pareto optimal solutions of the TCLP.
## 3 Results and Discussion
### Independent Model Verification  
In the preceding section, it was demonstrated that the DT-based ML model can provide an accurate prediction of the foundation stiffness in layered soil and revealed the relationship between the input variables and the stiffness coefficients. To further demonstrate the general accuracy of the trained model, we utilized the DT-PSO model to predict the basic stiffness for an additional 96 independent validation cases.  
As shown in Figure 14a, for \(K_{\text{v}}\), \(K_{\text{h}}\), \(K_{\text{m}}\), and \(K_{\text{c}}\), the R values between the true and predicted values reached 0.97, 0.99, 0.99, and 0.95, respectively, indicating that the model performance is pretty good. The differences between the four true and predicted foundation stiffness coefficients are all within the acceptable error range (Figure 14b). More importantly, it should be noted that the DT model was run on a personal computer with the CPU and RAM of an Intel (R) Xeon (R) W-2223 CPU @ 3.60 GHz and 16 GB. For all the validation cases, the prediction using the DT model took less than 1 s. We are therefore confident in saying that the DT model is reliable and efficient in predicting the foundation stiffness in layered soil and can be used in foundation design.  
Figure 14: Independent verification of model feasibility: (**a**) Evaluation of four foundation stiffness coefficient models using R; (**b**) Consistency of foundation stiffness calculated by FE model and DT model.  
Figure 13: PDP interaction of \(T/2R\), \(G_{\text{sant}}/G_{\text{clay}}\), and \(Z_{\text{D}}/2R\) on the \(K_{\text{c}}\) dataset.