esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Research
### Impact of Dark Spots on OLED and the Use of Artificial Intelligence  
In a research paper titled \"Effect of Organic Layer Combination on Dark Spot Formation in Organic Light Emitting Devices\" authored by [PERSON] et al., the evolution of dark spots in OLEDs was examined through optical image analysis technology [13]. The study unearthed that both single and multilayered OLEDs can develop dark spots, negatively influencing the performance of OLEDs' organic constituents. During the operational degradation of OLEDs, non-emissive \"dark spots\" can emerge, with potential causes being manufacturing defects and damage to surface protection [14]. The insights garnered from this paper lead to the understanding that the organic layers integral to OLEDs contribute to the formation of dark spots.  
Therefore, the paper's primary focus lies in investigating the morphology of dark spots and enhancing the lifespan and efficiency of OLED displays by understanding the implications of dark spots on OLEDs.  
Currently, automated optical inspection (AOI) is commonly used in production to detect pre-defined Mura, providing accurate and fast results compared with human expertise [15]. However, AOI has a limitation as it cannot detect new Mura [16; 17]. Another approach for defect detection is through a vision system, often employed for position-based defect detection of panels. This method requires high precision inspection equipment, and errors can lead to significant costs [18]. In the paper titled \"A Study on Defect Detection of Flexible OLED Using Deep Learning\" by [PERSON] et al., the researchers used a deep learning algorithm to identify defects in flexible OLEDs [19]. After learning about defects in OLEDs, a deep learning algorithm was applied. The test results showed high performance in detecting not only previously learned defects but also multiple defects [20]. In this study, machine learning was used to detect Mura based on the panel, in contrast to the current research that focused on the cell unit level and used different artificial intelligence algorithms for detecting dark spots and predicting lifespan.  
Based on the case study above, it is confirmed that dark spots can harm OLED cells and ultimately cause Mura expansion. Therefore, dark spots play an important role in predicting the lifespan of OLEDs. Several studies have explored the use of artificial intelligence in inspection processes. However, this study is unique in its focus on cell-level analysis rather than panel-based inspection.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### Decentralized State Estimation
#### 3.4.1 Hybrid Decentralized SE Using PMUs  
Phasor measurement units (PMUs) have significant advanced SE. Hybrid DSE blends the measurements of supervisory control and data acquisition (SCADA) systems and PMUs via single- and multi-stage approaches. In the single-stage approach, a local estimator integrates the SCADA and PMU data into a single hybrid measurement function. A weighted least-squares (WLS) optimization problem is then formulated that functions and is solved in a distributed manner. The sampling rates of SCADA and PMU measurements differ and thus, the single-stage approach often requires substantial modification of existing SCADA-based SE systems [69]. Recently, the multi-stage approach was considered a good alternative. The SCADA and PMU data are subjected to separate measurement functions  
Figure 15: Experimental case study results for the (**left**) conventional and (**right**) proposed FR strategies: (**a**) frequency deviations \(\Delta f\), (**b**) load demands \(P_{L}\), dispatchable generation \(P_{G}\), and total generation \(P_{G}+P_{PV}\), (**c**) variations in generator output power \(\Delta P_{G}\) and in battery input power \(\Delta P_{B}\), and (**d**) dynamic ramping rate of generation \(\left.\right|d\Delta P_{G}/dt\left.\right|.\)in a hybrid DSE. This can proceed in either a sequential or parallel manner. Most recent studies (e.g., [70; 71; 72]) have focused on the sequential approach; PMU estimators first obtain and deliver SE results to SCADA estimators, or vice versa. For example, in [70; 71], the PMU estimators first obtained the SE models and then provided them to the SCADA estimators, along with information on the boundary states and measurement couplings. The SCADA estimators then used linear SE models to reduce the sizes and complexities of non-linear SE problems. However, the sequential approach is less time efficient than the parallel approach [72], in which SCADA- and PMU-based systems acquire state estimates separately and calculate their weighted sum to determine the final SE results.
## 2 Model  
### System Model  
The undirected graph defined by the node \(v\in\mathcal{V}\) and a set of edges \((r,t)\in\mathcal{E}\) is used to represent the node location problem. Each node represents an intelligent unmanned platform. For the following discussion, we divide the nodes into anchor nodes \(\mathcal{V}_{a}\) that know their positions and nodes that need to be located \(\mathcal{V}_{l}\). Each edge represents the communication link between sensors. If there is \((r,t)\in\mathcal{E}\), it means the communication relationship established between node \(T\) and node \(R\), which can transfer the information of nodes and their mutual constraints Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(x_{t}\) & State of node \(T\) \\ \(\psi_{t}(x_{t})\) & Potential function of node \(T\) \\ \(\psi_{tu}(x_{t},x_{u})\) & Paired potential function between node \(T\) and node \(U\) \\ \(m_{ut}(x_{t})\) & Message from node \(U\) to node \(T\) \\ \(m_{ut}^{\prime}(x_{t})\) & The message passed by node \(U\) to node \(T\) in the \(I\)-th iteration \\ \(B_{t}(x_{t})\) & Belief function of node \(T\) \\ \(B_{t}^{\prime}(x_{t})\) & Belief function of node \(T\) in the ith iteration \\ \(\mathcal{G}_{t}\) & Neighbor node of node \(T\) \\ \(\mathcal{G}_{t}^{0}\) & The neighbor node of node \(t\) that is not the anchor node \\ \(N(\mu,\Sigma)\) & Multi-dimensional gaussian distribution \\ \(U(a,b)\) & Uniform distribution \\ \(z\) & Normalization factor \\ \(\alpha_{k}\) & The weight of the \(K\)-th Gaussian distribution in the Gaussian mixture model \\ \(\theta_{k}\) & Parameters of the \(k\)th Gaussian distribution in the mixed Gaussian model, including \(\mu_{k}\) and \(\Sigma_{k}\) \\ \(\gamma\) & Characteristic function, which indicates the source of the sample. The dimension is \(K\)-dimension, when sample \(J\) comes from the \(K\)-th Gaussian distribution, \(\gamma_{jk}=1\), and the rest \(\gamma_{jn_{jk}\
eq k}=0\) \\ \(G(\alpha,\mu,\Sigma)\) & Gaussian mixture model \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic Symbols Description.  
LBP, which can be applied to a graph with loops, adds iteration based on BP. The message from node \(u\) to node \(t\) in the i-th iteration is:  
\[m_{ut}^{i}(x_{t})=\int_{x_{u}}\psi_{tu}(x_{t},x_{u})\frac{B_{u}^{i-1}(x_{u})}{m_{ tu}^{i-1}(x_{u})}dx_{u} \tag{8}\]  
The belief update of node \(t\) in the i-th iteration is:  
\[B_{t}^{i}(x_{t})=z\psi_{t}(x_{t})\prod_{u\in\mathcal{G}_{t}}m_{ut}^{i}(x_{t}) \tag{9}\]
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
###### Abstract  
This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark's in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making.  
HPC; parallel algorithms; water demand forecasting; Apache Spark; Message Passing Interface (MPI); machine learning; time-series analysis; real-time data processing; urban water management; computational efficiency; deep learning models; data imputation; scalability; anomaly detection; distributed computing frameworks 2025 18, 182
## 3 Methods  
We use two frequency-domain EM (FDEM) forward-modelling approaches to study the feasibility of CSEM monitoring the TU Delft campus reservoir: (1) A one-dimensional (1D) reflectivity formulation for a layered model [39], in order to find a preferential survey configuration and source frequency at a low computational cost. This approach is implemented by [40]: (2) a finite-integration technique to determine the electric field response in a three-dimensional (3D) medium, as coded by [41]. We use the latter approach to investigate the source-receiver offsets, the sensitivity of CSEM data to volumetric and incremental changes, and the impacts of undesired effects.  
To assess the CSEM detectability of the resistivity changes, we define confidence intervals for the difference between time-lapse electric fields. We treat differences below 1% as undetectable, while differences between 1% and 10% offer low confidence, those between 10% and 30% moderate confidence, and differences greater than 30% high confidence. We define these intervals based on the influence of the undesired effects in Section 6. The difference \(\Delta E\) between the electric field of the base state \(E_{1}\) and the monitoring state \(E_{2}\) is given by  
\[\Delta E=\frac{\|E_{2}-E_{1}\|}{\|E_{1}\|}, \tag{2}\]  
where \(\|\ldots\|\) refers to the absolute value. We mask the time-lapse electric fields and difference when the amplitude of \(E_{1}\) or \(E_{2}\) is below the noise floor. The noise floor of electric field data is frequency-dependent [42]. In [43], it was shown that the noise floor decreases with increasing frequency until it reaches about \(4\times 10^{-12}\) V/m for frequencies equal to or greater than 1 Hz. For simplicity, we set a noise floor of \(10^{-12}\) V/m for all frequencies.  
Figure 2: Doublet well design for TU Delft campus geothermal project. The well separation at the surface is 10 m. The red and blue arrows refer to the direction of hot and cold water, respectively.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 4 Models Including Blockchain Technology
### BD Model: Dual Channel with Blockchain Technology
: _When \(\frac{c_{r}+c_{b}}{b\beta s}\leq e\leq\frac{1+c_{r}+c_{b}-\beta s}{b}\), the dual channel exists:_  
\((a)\,{p_{d}^{B}}^{*}>{p_{s}^{B}}^{*}\)_, \(CS_{d}^{B}}^{*}>CS_{r}^{B}\); (b) if \(C+B<\frac{(c_{r}+c_{b}-bc+1-\beta s)^{2}}{4 bc(1-bc)}\), \({p_{d}^{B}}^{*}>{p_{r}^{B}}^{*}\), \(CS_{d}^{B}}^{*}>CS_{r}^{B}\)._  
Akin to Proposition 1, Proposition 2 provides insight into the conditions for choosing the dual-channel strategy with the adoption of BCT. Proposition 2(a) implies that compared with the pure SataaS strategy, the SO will always obtain a higher profit using the dual-channel strategy. Plus, consumers also benefit more with this strategy. Proposition 1(b) shows that, compared with the pure sell strategy, whether to choose the dual-channel strategy hinges on the sum fixed cost of SataaS and BCT. That is, if \(C+B\) is small, then the dual-channel strategy is better for the SO. Otherwise, the pure sell channel will generate a higher profit. For customers, the consumer surplus of the dual-channel strategy is always higher than that of the pure sell channel. In conclusion, Proposition 2 provides a similar suggestion that if the dual-channel exits, it is always the best strategy for consumers. This is because dual channels provide two service options, allowing more consumer needs to be met. For the SO, the condition for the choice of the dual-channel strategy is that \(C+B\) is relatively small. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Condition** & \(\mathbf{0}\leq e\leq\frac{c_{r}+c_{b}}{b\beta s}\) & \(\frac{\xi+c_{b}}{b\beta s}\leq e\leq\frac{1+c_{r}+c_{b}-\beta s}{b}\leq e\leq 1\) \\ \hline
**Strategy** & **SataaS** & **Dual** & **Sell** \\ \hline \(p_{r}\) & - & \(\frac{be+c_{r}+c_{b}}{2b}\) & \(\frac{be+c_{r}+c_{b}}{2b}\) \\ \(p_{s}\) & \(\frac{1-\beta s}{2}\) & \(\frac{1-\beta s}{2}\) & - \\ \(D_{r}^{N}\) & - & \(\frac{b\beta s-c_{r}-c_{b}}{2 br(1-be)}\)
## 3 Results and Discussion
### Bioactivity  
Glasses exposed to SBF solution for 21 days were observed via SEM. In Figure 6 it is possible to see some globules on disks that are soaked in SBF. This shape is typical of hydroxyapatite formed on a glass surface. Those particles were visible over the entire surface of all materials. The EDS analysis (Figure 6) of the observed crystals shows their chemical composition. The atomic content ratio between Ca and P is 1.6 and is in agreement with the chemical formula of hydroxyapatite [29]. The mechanism of the hydroxyapatite formation is explained in Figure 7: the Si-OH groups present on the surface of the material combine with the positively charged Ca\({}^{2}\,^{+}\) ions present in the SBF fluid, forming calcium silicate. The accumulation of calcium ions allows the increase in the positive charge on the surface which, in turn, combines with the negative charge of the phosphate ions to form amorphous calcium phosphate, which spontaneously transforms into hydroxyapatite [Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)] [30].  
Furthermore, XRD measurement was used to identify the presence of hydroxyapatite on the hybrid surface. The intense peaks of crystalline hydroxyapatite are visible in Figure 8 (\(\blacklozenge\)), confirming that the material surface was covered by a hydroxyapatite layer after 21 days of soaking in SBF. In Figure S3, XRD measurements show the amorphous structure of the sample before being soaked in SBF. Moreover, this result suggests that the hydroxyapatite layer is too thick to allow the detection of XRD signals from the hybrids' surface [24; 31; 32; 33; 34].  
Figure 6: SEM micrograph of globular hydroxyapatite crystals after the bioactivity test, and EDS elementary analyses of crystals.  
Figure 7: Mechanism of hydroxyapatite formation.
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
## 3 Methodology and Materials  
### Workflow  
The overall workflow of the proposed approach is shown in Figure 1 where the main phases are identified with numbers. The phases are described in the following bullet list:  
1. The network flows for the labeled legitimate traffic are collected in a sliding window (the windows are not overlapping) of size \(W_{S}\) using all the 78 network features present in CICIDS2017 data set (see Section 3.2). Different sizes \(W_{S}\) of the sliding window are used in this study: \(W_{S}=(100,200,300,400,500)\) network flows.  
2. The sliding window data (of size \(W_{S}*78\)) is converted to gray images by rescaling the values of the network flows features. The rescaling is implemented by converting the original values of the network flows in the sliding window to the range 0-256 (for each network flow feature) to obtain 256 levels of gray. A linear conversion is used. Examples of the resulting gray images for the Legitimate traffic and the Port Scan traffic are shown in Figure 2, where the \(y\)-axis represents the id of the network flow feature, while the \(x\)-axis represents the flow id. The sliding window is applied in sequential order regardless of the IP origin as it was created in the public data set [5] used in this paper.
3. The GLCM is applied to the gray images with different values of the GLCM hyperparameters. See Section 3.3 for the definition of GLCM and hyperparameters. One of the important hyperparameters is the quantization factor \(Q_{F}\). In other words, different GLCMs are created for each of the distances and directions considered in Section 3.3 (even for different values of \(G_{D}\)) and for the value of the quantization factor \(Q_{F}\). The resulting size of the GLCM is \(Q_{F}*Q_{F}\).
4. The GLCM features (e.g., contrast) are calculated. In addition, the 2 DDE is also calculated on the images. The definition of the 2 DDE is presented in Section 3.4.
5. The ML algorithm is applied to the features calculated in the previous step. The description of the algorithm used in this study and the related hyperparameters are described in the Section 3.5.  
Finally, the hyperparameters of the GLCM and of the ML algorithm are tuned using the Error Rate (ER) as evaluation metric. The definition of ER and the other evaluation metrics are provided in Section 3.6.  
Figure 1: Overall workflow.  
Figure 2: An example of the grayscale images with \(W_{S}=200\) for legitimate and Port Scan network flows.
## 2 Experiments Details
### Optimization of the Slot Waveguide
#### 2.3.1 Optimization of Slot Waveguides Combined with Maximum Confinement Factors
Since the light at 980 nm already confined most of the light to the Si\({}_{3}\)N\({}_{4}\) strip waveguide, it continued to increase its height and had little effect on \(\Gamma_{\mathrm{S}}\). Meanwhile, too high an H\({}_{\mathrm{r}}\) led to an increase in transmission loss and introduced extra difficulty to the coupling between the optical fiber and the end face of the waveguide.
## 2 Materials and Methods
#### TRNSYS Model  
The hybrid model is built and simulated using TRNSYS software. TRNSYS is beneficial as it enables time based solutions crucial to CST plants. The PTCs used are Eurotrough ET-150 collectors with Therminol VP-1 as the Heat Transfer Fluid (HTF). The PTC field is designed based on the HTF being heated from 293 \({}^{\circ}\)C to 393 \({}^{\circ}\)C with an HTF mass flow rate that is sufficient to generate adequate steam at the design point [20]. The steam turbine in the topping cycle power block is designed using data from the literature with an inlet temperature and pressure of 370 \({}^{\circ}\)C and 1 MPa, respectively [20]. Moreover, the exit quality of the turbine is desired to be at least 99.9% [12]. The total power output design is 16 MW\({}_{\text{e}}\), and the isentropic efficiency is chosen as 70%, as per the literature [21]. Hence, using Equations (1) and (2), the required steam mass flow rate for the total topping cycle is 25.56 kg/s. The steam mass flow rate for the CST part (10 MW\({}_{\text{e}}\)/16 MW\({}_{\text{e}}\) total) is 15.98 kg/s. On the other hand, the steam mass flow rate for biomass designed for 6 MW\({}_{\text{e}}\) + 4 MW\({}_{\text{e}}\) oversized capacity is again 15.98 kg/s.  
\[\text{Isentropic efficiency}=\frac{w_{act}}{w_{s}}=\frac{h_{2}-h_{1}}{h_{2 s}-h_{1}} \tag{1}\]  
\[\text{Quality}=x_{2}=\frac{h_{2}-h_{f}}{h_{fg}} \tag{2}\]  
The PTC field is sized based on the design steam mass flow rate of 15.98 kg/s. An H-type configuration is used for the field with 28 loops, each loop having four collectors, two in each row. To generate the steam at the turbine inlet conditions, this PTC field has an HTF mass flow rate at the design point of 202.75 kg/s. The actual HTF flow rate is adjusted based on the direct solar resources such that the HTF exits the PTC field at 393 \({}^{\circ}\)C by using a control signal with a coefficient based on the PTC area, the energy required to increase the enthalpy of the HTF, and the direct irradiance, using Equations (3) and (4), where the coefficient \(\phi\) is found as 0.00025552.  
\[\phi=\frac{N_{coll,loop}\cdot N_{loops}\cdot L\cdot W_{a}}{\dot{m}_{loop} \cdot(h_{HTF@393^{\circ}\text{C}}-h_{HTF@293^{\circ}\text{C}})} \tag{3}\]  
\[\text{Control Signal: }\gamma=\phi\cdot I_{beam,useful} \tag{4}\]  
The detailed specifications of the hybrid plant are presented in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Source** \\ \hline
**PTC Field-ET-150 Collectors** & & \\ Focal length & 1.71 m & [22] \\ Absorber radius & 35 mm & [22] \\ Aperture width & 5.76 m & [22] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the hybrid plant.
## 6 Simulation Environment and Results
### Simulations Results
#### 6.2.4 False Service Discovery Ratio  
In this section, we compare the false service discovery ratio in OLSRv1 and in OLSRv2 according to the node speed and the number of nodes offering the same service in the network. We do the comparison in simulation scenarios with 50 nodes. The parameters for sending periodic HELLO and TC messages are the ones established by OLSRv1 and OLSRv2 RFCs. SDM INTERVAL_TIME to announce services is set to 5 s.  
In Figure 17, we can see that the false service discovery ratio is reduced in OLSRv2, since in OLSRv2, we check in the routing table if the node that offers the service is still in the network.  
Like the results obtained for service discovery delay, when the network is more mobile, the false service discovery ratio increases. Furthermore, when the number of servers increases, the service discovery ratio decreases.  
Figure 16: Service discovery ratio _vs._ average speed (**left**) _vs._ service redundancy (**right**) in SD-OLSRv2 and SD-AODV.  
Figure 17: False discovery ratio _vs._ average speed (**left** ) _vs._ service redundancy (**right**).  
Regarding the false service discovery ratio in SD-AODV, with the scenario simulated in Table 1, the results obtained are similar to the ones in SD-OLSRv2, independent of the flooding SREQ scope. In SD-AODV, when a node needs a service for the first time, it requests it. If a service discover reply is obtained, there is no possibility of a false positive reply. However, when a node request a service repeatedly, it first verifies if it does not already have a route to the service provider. If it does, it does not request it. However, it may have the route active, when, actually, it is not. A false service discovery will occur.