esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 2 Materials and Methods
### Model Evaluation Metrics  
To evaluate and compare the RNN models, following evaluation metrics are applied. The coefficient of determination, \(R^{2}\), is a proportion of explained variance of the model and the total variance of the data. The value of \(R^{2}\) is usually between 0 and 1. An \(R^{2}\) of 0 means a bad fit and 1 indicates a perfect fit, meaning that the model can explain 100% variance of the data. Mathematically, \(R^{2}\) can be expressed as:  
\[R^{2}(y_{i},\hat{y})=\frac{ESS}{TSS}=1-\frac{RSS}{TSS}, \tag{13}\]  
\[RSS=\sum{(y_{i}-\hat{y})}^{2}, \tag{14}\]  
\[ESS=\sum{(\hat{y}-\overline{y})}^{2}, \tag{15}\]  
\[TSS=\sum{(y_{i}-\overline{y})}^{2}, \tag{16}\]  
where \(\hat{y}\) is the predicted value, and \(y_{i}\) is the true value, \(\overline{y}\) is the average of the true value \(y_{i}\). Total sum of squares (TSS) is the summation of residual sum of squares (RSS) and estimation sum of squares (ESS). Although \(R^{2}\) is a very common evaluation metric for regression tasks, it has some drawbacks, namely, the \(R^{2}\) value always increases when the number of variables increases. Hence, adjusted \(R^{2}\) has been introduced, which imposes a penalty for adding additional explanatory variables and only increases when a significant variable is added. The equation of adjusted \(R^{2}\) is shown below:  
\[R^{2}{}_{adj}(y_{i},\hat{y})=1-\frac{(1-R^{2})\times(n-1)}{n-k-1}, \tag{17}\]  
where \(n\) is the number of observations, and \(k\) is the number of features. Apart from \(R^{2}\) and adjusted \(R^{2}\), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are also used in this work for model evaluation and comparison. MSE measures the average of the squares of errors. RMSE is the square root of MSE. MAE is a measure of the average of the absolute errors. MAPE measures the accuracy as an average percentage of the absolute errors divided by true values. The corresponding mathematical expressions could be found below:  
\[MSE=\frac{1}{n}\sum\limits_{i=0}^{n-1}{(y_{i}-\hat{y_{i}})}^{2}, \tag{18}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=0}^{n-1}{(y_{i}-\hat{y_{i}})}^{2}}{n}}, \tag{19}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=0}^{n-1}{|y_{i}-\hat{y_{i}}|}, \tag{20}\]  
\[MAPE=\frac{1}{n}\sum\limits_{i=0}^{n-1}{\Big{|}\frac{y_{i}-\hat{y_{i}}}{y_{i} }\Big{|}}, \tag{21}\]  
where \(n\) is number of observations, \(\hat{y_{i}}\) is the predicted value, and \(y_{i}\) is the true value.
## 3 Results and Discussion
### Results of Photocatalytic Degradation under Different Conditions
#### 3.4.4 Effect of MB Dye Wastewater Concentration on the Treatment Effect  
Figure 7 shows the treatment effect of 200 mL MB dye wastewater at initial concentrations of 10, 20, 30, 40, 50, 60, 70, and 80 mg/L, after 2.5 h of 200 mg composite photocatalyst treatment. The degradation effect of MB dye wastewater peaked at the concentration of 10 mg/L. If the concentration of synthetic wastewater is too high, the transparency of wastewater will be reduced, the light will difficult to enter the wastewater and react with the photocatalyst, and the dye molecules will accumulate on the surface of the photocatalyst, which exceeds the degradation ability of the active site on the surface of the catalyst. Therefore, the degradation rate of wastewater will be reduced. It is concluded that such amount of composite photocatalyst is suitable to treat lower concentrations of synthetic wastewater.
# Typhoon Track Prediction Based on Deep Learning
## 2 Materials and Methodology  
This part mainly covers data acquisition, missing data filling, multi-feature selection, and construction of a C-LSTM-based typhoon path prediction model.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 2 OPM--Object-Process Methodology  
Object-Process Methodology (OPM) is a conceptual modeling language [1; 2] recognized as ISO 19450 [3]. Based on a minimal universal ontology of stateful objects and processes that transform them, OPM supports modelling in a plethora of domains, including systems engineering, business processes, and biological processes. Each OPM model contains one or more hierarchically organized Object-Process Diagrams (OPDs). Each OPD contains OPM things--processes and objects--and links that connect them, each with its own type and semantics. Each OPM thing can be refined (in-zoomed or unfolded), creating a new, descendant OPD that describes the nested processes and related lower-level objects that are involved in these subprocesses.  
The elements of an OPM model are OPM things and links, which connect the OPM things. A link can be procedural or structural. A procedural link connects two OPM things with opposite persistence, i.e., an object (rectangle) and a process (ellipse; see Figure 1). These links provide information about the system's dynamic, time-dependent aspects, including generation and consumption of objects or a change in an object state. Structural links provide time-invariant information, such as aggregation-participation, between two or more OPM things with the same persistence, i.e., between objects or between processes (see Figure 2). OPM is bimodal--it is expressed simultaneously in both graphic diagrams, the OPD set, and in text, a subset of a natural language called Object-Process Language (OPL). As shown in Figures 1 and 2, the OPL sentences are visible below their corresponding OPDs. The textual OPL representation is generated on the fly in response to the modeler's graphic interaction with OPCloud [4; 5], the OPM modeling software. We implemented the graph-based querying and insight gaining extension within OPCloud.  
The multi-layered structure of an OPM model provides both a high-level view and an option for refinement, \"deep-dive\", or drill-down, to specify additional details at increasing levels of detail as the modeler deems necessary to describe the system fully. The system that the model represents can be easily understood, since each OPD is not overly complicated, and the relations between any two OPDs in the model are direct refinement-abstraction relations, where one of the things in the descendant OPD is a refined (in-zoomed or unfolded) version of an OPM thing in the ancestor OPD. The entire OPM model is thus presented as a hierarchy of OPDs--the OPD tree. The entire model includes the union of the model facts expressed in all the OPDs of the model.  
To enhance model understanding, the query capability is fully integrated into the two OPM modalities, so the query results are both graphically and textually, with each modality cognitively complementing the other.
## 2 Methodologies
### Modeling: Partial History-Dependent Markov Decision Process
In this case, the agent updates \(s_{c}^{(t+1)}\) to be the last fresh state \(s_{c}^{(t)}\) using \(\delta(s_{c}^{(t+1)}-s_{c}^{(t)})\). Then, the agent adds the newly generated action \(a_{t}\) into the partial history action buffer to be \((a_{Id(s_{c}^{(t)})},...,a_{t-1},a_{t})\) using \(\prod_{i=1}^{t-Id(s_{c}^{(t)})}\delta(a_{t-i}^{(t+1)}-a_{t-i}^{(t)})\delta(a_ {t}^{(t+1)}-a_{t})\). * Fresh state is received by the agent at time \(t\). In this case, the agent updates \(s_{c}^{(t+1)}\) to be the fresh state so that \(s_{c}^{(t+1)}\
eq s_{c}^{(t)},Id(s_{c})^{(t+1)}>Id(s_{c}^{(t)})\); therefore, there will be multiple transition processes from \(s_{c}^{(t)}\) to \(s_{c}^{(t+1)}\) using \(p(s^{(i)}|s^{(i-1)},a_{Id(s^{(i-1)})}^{(t)})\) where \(s^{(0)}=s_{c}^{(t)}\) and \(s_{c}^{(t+1)}=s^{(Id(s_{c}^{(t+1)})-Id(s_{c}^{(t)}))}\). In addition, the partial history action buffer is cut off and also added with the newly generated action \(a_{t}\) to be \((a_{Id(s_{c}^{(t+1)-1})},...,a_{t-1},a_{t})\) using \(\prod_{i=1}^{t+1-Id(s_{c}^{(t+1)})}\delta(a_{t-i}^{(t+1)}-a_{t-i}^{(t)})\delta( a_{t}^{(t+1)}-a_{t})\). Reward function. The PH-MDP shares the same reward function as the standard MDP and the reward metrics are randomly delayed along with state collection; therefore, there is a partial history reward buffer to help to sample trajectories. An action will be executed and a state will be collected at each time step (e.g., \(a_{t}\), \(s_{t}\)) so that there will be a reward (e.g., \(r_{t}\)) showing how good the last executed action (e.g., \(a_{t-1}\)) on the last collected state (e.g., \(s_{t-1}\)), that is \(r_{t}=r(s_{t-1},a_{t-1})\).
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.10 Interoperability with Multiple Cloud Providers (Multicloud)  
Resilient systems must allow for connectivity and interaction between the services deployed by different cloud providers while complying with quality of service standards. There is a trend to build services leveraging microservices-based architectures in a multi-cloud environment where microservices solutions have to be portable and interoperable, facilitating integration across independent cloud providers and preventing vendor-lock issues generated for proprietary services [57].
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
###### Abstract
As a related research topic, there was a case of selectively extruding a metal-containing filament with an ME-type 3D printer where the metal layer was then activated with a laser [14]. A highly conductive copper pattern was formed on the activated copper metal layer through an electroless deposition process. As a result, it was possible to form a 3D embedded circuit with a resistance of 0.15 \(\Omega\cdot\)cm\({}^{-1}\). Similarly, in PP-type 3D printing, an activated seed layer was manufactured through a UV laser treatment after printing a precise shape using a metal composite resin containing nickel powder [15]. In this case, it was possible to make a pattern with a superior surface quality and precision compared with the previous ME type. However, in both cases, the entire specimen had to be immersed in a chemical deposition solution for a long time to impart conductivity after the formation of the metal seed pattern. Due to this, disadvantages such as an increase in the number of process steps and unavoidable micro-contamination on the entire surface of the structure during the deposition process occurred. In order to remove such contaminations, it was inevitably necessary to go through a complicated washing procedure after the process. Therefore, in this study, a high-precision shape was made based on PP-type 3D printing and a conductive pattern was selectively formed using a metal composite resin. In particular, as the PP-type 3D printer used UV laser technology, it was possible to directly irradiate a high-power laser without an additional device compared with the other processes. Therefore, if the metal content of the composite resin was maximized and selective local sintering was performed with a high-power laser, it was possible to form a conductive pattern on various shapes with a simple process without an additional wet process such as chemical deposition/cleaning. To this end, in this study, we proceeded with the fabrication of conductive patterns directly on the 3D-printed surface without additional processes through experiments on various variables, ranging from the composition of the metal materials and composite resins suitable for selective local sintering to the process conditions.
# Cross-Entropy Method in Application to the SIRC Model
## 2 Optimization of Control for SIRC Model
### Optimization of the Epistemological Model by the CE Method
Update: for all \(i=0,\ldots,n\) let \[\hat{\mu_{ki}}^{(u)}=\frac{1}{p}\sum_{m\in I}C_{mi}; \qquad(\hat{\sigma_{ki}}^{(u)})^{2}=\frac{1}{p}\sum_{m\in I}(C_{mi}-\mu_{ki}^ {(u)}),\] \[\hat{\mu_{ki}}^{(v)}=\frac{1}{p}\sum_{l\in I}D_{li}; \qquad(\hat{\sigma_{ki}}^{(v)})^{2}=\frac{1}{p}\sum_{l\in I}(D_{li}- \mu_{ki}^{(v)}).\]
6. Smooth: For a fixed smoothing parameter \(0<\alpha\leqslant 1\) let \[\hat{\mu_{k}}^{(u)}=\alpha\mu_{k}^{(u)}+(1-\alpha)\mu_{k-1}^{(u)}; \qquad\hat{\sigma_{k}}^{(u)}=\alpha\sigma_{k}^{(u)}+(1-\alpha){\sigma_{k-1}^{ \star}}^{(u)}\] \[\hat{\mu_{k}}^{(v)}=\alpha\mu_{k}^{(v)}+(1-\alpha)\mu_{k-1}^{(v)}; \qquad\hat{\sigma_{k}}^{(v)}=\alpha\sigma_{k}^{(v)}+(1-\alpha){\sigma_{k-1}^{ \star}}^{(v)}\]
7. Repeat 2-6 until \(max_{i}\sigma_{ki}^{(u)}<\epsilon\) and \(max_{i}\sigma_{ki}^{(v)}<\epsilon\) with \(\epsilon=10^{-5}\). Let \(L\) be the final iteration number. Return \(\mu_{L}\) as an estimate of the optimal control parameter \(c^{\star}\)
```  
**Algorithm 1**(Modification of the [PERSON]'s algorithm (v. [6]) to two optimal functions case):  
The value of the criterion \(J(\cdot,\cdot)\) is calculated in steps 3 and 4. The selection of the function \(\varphi\) allows us to fit the cost function to the modeled case. Here, the criterion given by (6) is adopted.
# Carbon Nanoparticles and Their Biomedical Applications
## 4 Adsorption (Hemosorbents)/Hemofiltration/Hemodialysis  
Carbon materials have high adsorption capacities and are widely used in clinical practice. CNPs can be used to adsorb ions, drugs, toxins, proinflammatory proteins, metabolites, and lipids from blood circulation and, thereby, reduce toxicity.  
The study by [PERSON] et al. showed that graphene nanoplatelets can adsorb proinflammatory cytokines (IL-6, IL-8, and TNF-\(\alpha\)) that can drive sepsis and cause multiorgan failure. Their incorporation into a flexible polytetrafluoroethylene (PTFE) film could improve the result of hemoperfusion [43]. [PERSON] et al. confirmed the absorption of proinflammatory cytokines by graphene-based nanomaterials. They used granulated graphene nanoplatelets and granted graphene nanoplatelets with PTFE. Both types of CPNs have high removal efficiencies, in some cases even up to 100% [44]. Nanodiamonds also have high absorption capacities for pro-inflammatory cytokines. [PERSON] et al. showed that nanodiamonds can absorb inflammatory cytokines (IL-1\(\beta\), IL-4, IL-6, IFN-\(\gamma\), and TNF-\(\alpha\)) and, thus, alleviate inflammation and decrease the risk of multiorgan failure and death due to sepsis [45].  
[PERSON] et al. fabricated lysine-immobilized chitin/carbon nanotube microspheres and attempted to verify their capacity to remove bilirubin from human blood. The level in plasma reflects the dysfunction of the hepatobiliary system or hemolysis. Bilirubin is toxic to the central nervous system and causes bilirubin encephalopathy. The prepared CNPs exhibited a high bilirubin adsorption capacity and increased bilirubin clearance [46].  
[PERSON] et al. exploited the extreme efficiency of CNPs in the absorption of chemicals from the blood. They constructed a hemodialysis membrane from polytheresulfone and carboxyl functionalized multi-walled carbon nanotubes (MWCNT). This membrane had excellent blood compatibility; membrane fouling was reduced by 30%, while urea and creatinine removal significantly increased [47]. CNPs can also absorb cholesterol, which is a risk factor for several diseases (cardiovascular and liver disorders, and cancer). The most effective are carbon nanotubes that bind cholesterol to both external and internal surfaces [48].
# Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
## 5 Ablation Studies
### Semantic Bias Caused by Prefix Data Augmentation  
To further explore the semantic bias introduced by prefix data augmentation and verify the impact of sentence length on the encoding results, we conducted experiments on the SICK-R test set [36]. Based on whether the length of the shorter sentence in a sentence pair was less than eight, we divided the SICK-R test set into two groups and evaluated them separately, comparing the Spearman correlation coefficients. As shown in Table 11, both PrdSimCSE and SimCSE performed much worse on sentences with lengths \(>8\) than on those with lengths \(\leq 8\). However, upon further analysis of the original SICK test set results, PrdSimCSE exhibited larger fluctuations, especially in sentences with lengths \(\leq 8\), where the fluctuation in PrdSimCSE was notably wider than that of SimCSE. This indirectly confirmed our speculation that models trained with Prd tend to produce semantic deviations when encoding sentences.  
In English transfer tasks, we observed that PrdSimCSE significantly underperformed SimCSE on the TREC dataset [43], a discrepancy related to this specific error. PrdSimCSE produced a larger error for sentences shorter than eight words. The TREC test set contains 500 sentences, with an average length of 10 words per sentence. Each sentence is associated with one coarse class label and one fine class label, with the label being a single word. We attempted to remove sentences from TREC that were shorter than eight and six words, respectively, and then re-evaluated PrdSimCSE; the results are shown in Table 12. After removing the shorter sentences, PrdSimCSE's performance improved by 6.23%, indirectly  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Type of NegPrd** & **STS-B** \\ \hline SimCSE \(\heartsuit\) & 82.5 \\ one-reversal & 80.4 \\ prefix1 & 81.3 \\ prefix2 & 81.1 \\ prefix3 & 82.9 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of different types of NegPrd on STS-B. \(\heartsuit\): results from [13].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Type of PosPrd** & **STS-B** \\ \hline SimCSE \(\heartsuit\) & 82.5 \\ one-[CLS] & 81 \\ one-[MASK] & 81.8 \\ one-um & 81.5 \\ level-[CLS] & 82.8 \\ level-[MASK] & 80.6 \\ level-um & 83.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of different types of PosPrd on STS-B. \(\heartsuit\): results from [13].  
confirming our hypothesis. Due to PrdSimCSE's use of repetitive, meaningless prefixes during training, the encoder reduces its attention to the tokens at the beginning of sentences, thus weakening its understanding of short texts compared with SimCSE.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### 5G Cryptomining Attack Detection Scenario
#### 9.1.4 Description of the Cryptomining Attack Exercise
The difference between these three models is the hyperparameters used to train them, which will greatly influence their performance and the confidence of their predictions. In Phase E1, the blue team will be tasked with starting the STA containers in the VM1 with the provided command and restarting it with a different model each time. In Phase E2, by sending traffic from the VM2 using the methods described in the previous sections, the STA container will be able to analyze it. In the Kibana display, as part of Phase E3, the blue team will be able to see the classification of the traffic in real-time and analyze the confidence of the predictions, as well as the IPs and ports that are being classified as cryptomining attacks. The Kibana dashboard also displays the average confidence of the predictions, which the blue team will have to analyze along with the number of attacks detected to determine the best model of the three. Once the blue team has decided which is the most effective model, they will be provided with a command to compare the hash of their selected model against the truly best one in order to determine if their answer is correct. The blue team will also be tasked with providing the IPs of the flows that the selected model has classified as cryptomining attacks. Finally, the blue team is asked to reflect on the differences and advantages between this method and Snort. In particular, the blue team is expected to conclude that Snort will have trouble detecting cryptomining attacks when traffic is encrypted, a situation in which the use of rules for attack detection is completely ineffective. However, since the ML models integrated into the STA component are trained to detect these cryptomining attacks without having to rely on the payload as they are based on flow statistics, the use of ML models can overcome Snort's limitations in this regard. Furthermore, the trainee has to conclude that ML performance is not guaranteed and, therefore, appropriately trained ML models should be used to obtain the best performance.
## 3 RACH Success Probability and Power Consumption Model for LEO Satellite-Based NB-IoT Networks
### RACH Success Probability Analysis
For this reason, we combine these two conditions to establish an optimization problem and solve it in the next section.
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 4 Problem Formulation and Algorithm Development
### Alternating Direction Method of Multipliers (ADMM)
 \tag{25}\]  
By the steps developed in Equations (19)-(23), the estimates for \(\boldsymbol{\alpha}\) and \(\mathbf{g}\) can be respectively obtained by  
\[\boldsymbol{\alpha}=(\rho\mathbf{I}+2\mathcal{W}^{\mathrm{H}}\hat{\mathbf{H}} ^{\mathrm{H}}\hat{\mathbf{H}}\mathcal{W})^{-1}(\rho(\mathbf{g}^{l}-\mathbf{u}^ {l})+2\mathcal{W}^{\mathrm{H}}(\mathbf{y}-\hat{\mathbf{n}})), \tag{26}\]  
\[\mathbf{g}^{l+1}=\mathbf{T}_{\lambda/\rho}\big{(}\boldsymbol{\alpha}^{l+1}+ \mathbf{u}^{l}\big{)}. \tag{27}\]  
Now, we turn our attention into solving the **Step 2** in Equation (16), and its ADMM steps are  
\[ \begin{split}\mathbf{H}^{l+1}&=\mathrm{minimize}_{ \mathbf{H}}\big{(}\|\mathbf{y}-\mathbf{H}(\mathcal{W}\hat{\mathbf{n}}+\hat{ \mathbf{n}})\|_{2}^{2}+(\rho/2)\|\mathbf{H}-\mathbf{Z}^{l}+\mathbf{U}^{l}\|_{ \mathrm{F}}^{2}\big{)}\\ \mathbf{Z}^{l+1}&=\mathrm{minimize}_{\mathbf{Z}} \big{(}\lambda\|\mathbf{Z}\|_{1}+(\rho/2)\|\mathbf{H}^{l+1}-\mathbf{Z}+\mathbf{ U}^{l}\|_{\mathrm{F}}^{2}\big{)}\\ \mathbf{U}^{l+1}&=\mathbf{U}^{l}+\mathbf{H}^{l+1}- \mathbf{Z}^{l+1}.\end{split}  \tag{28}\]Similarly, the estimates of \(\mathbf{H}\) and \(\mathbf{Z}\) are respectively calculated by  
\[ \begin{split}\mathbf{H}=(\rho\mathbf{I}+2(\mathcal{W}\mathbf{\hat{a} }+\mathbf{\hat{n}})^{\mathrm{H}}(\mathcal{W}\mathbf{\hat{a}}+\mathbf{\hat{n}}) )^{-1}(\rho(\mathbf{Z}^{l}-\mathbf{U}^{l})\\ +2(\mathcal{W}\mathbf{\hat{a}}+\mathbf{\hat{n}})^{\mathrm{H}} \mathbf{y}),\end{split}  \tag{29}\]  
\[\mathbf{Z}=\mathbf{T}_{\lambda/\rho}\big{(}\mathbf{H}^{l+1}+\mathbf{U}^{l} \big{)}. \tag{30}\]  
In accordance with Equation (29), as one sees, the terms of \(\mathcal{W}\) and \(\mathbf{y}\) are known in this step and, therefore, the \((\rho\mathbf{I}+(\mathcal{W}\mathbf{\hat{a}}+\mathbf{\hat{n}})^{\mathrm{H}}( \mathcal{W}\mathbf{\hat{a}}+\mathbf{\hat{n}}))^{-1}\) and \((\mathcal{W}\mathbf{\hat{a}}+\mathbf{\hat{n}})^{\mathrm{H}}\mathbf{y}\) can be computed in advance to save the computational complexity. With the ADMM steps developed, the algorithm to solve Equation (14) is summarized in Algorithm 1. ```
Objective function: \(\lambda_{1}\|\boldsymbol{\kappa}_{WT,FT}\|_{1}+\lambda_{2}\|\mathbf{H}\|_{1}+ \lambda_{3}\|\mathbf{n}\|_{1}+\|\mathbf{\hat{y}}-\mathbf{H}(\mathcal{W}_{WT, FT}^{\mathrm{T}}\boldsymbol{\kappa}_{WT,FT}+\mathbf{n})\|_{2}^{2}\) Outputs: Estimates of \(\mathbf{n}\), \(\boldsymbol{\kappa}\) and \(\mathbf{H}\) Initialization: \(l=1\) Repeat \(l=l+1\) Step 1: Produce the estimate of \(\mathbf{n}\) by Equations (18)-(23) Step 2: Produce the estimate of \(\mathbf{a}\) by Equations (25)-(27) Step 2: Produce the estimate of \(\mathbf{H}\) by Equations (29) and (30) Step 3: Update the residual \(\mathbf{\hat{y}}=\mathbf{\hat{y}}-\mathbf{H}(\mathcal{W}_{WT,FT}^{\mathrm{T}} \boldsymbol{\kappa}_{WT,FT}+\mathbf{n})\) Until\(l>T\) {maximum iteration} or norm\((\mathbf{\hat{y}})<\delta\) {predefined threshold}. ```  
**Algorithm 1** Alternating Direction Method of Multipliers (ADMM) based approach.