esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The RDF functions for other atoms belonging to acrylic acid molecule and Na\({}^{+}\) do not have such distinct peaks, and the RDF function for these molecules is greater than zero for distances above 2.34 A. The lack of distinct peaks is due to the random arrangement of atoms around O\({}_{\text{Si}}\) above 2.34 A. In the case of acrylate anions, the closest to O\({}_{\text{Si}}\) is Na\({}^{+}\) (Figure 5b). The first peak of the RDF function for the O\({}_{\text{Si}}\)-Na\({}^{+}\) atoms pair is for a distance of 2.35 A. The atoms that make up the acrylic anions are located above 4.5 A. This shows that there is no direct interaction between acrylic anion molecules and O\({}_{\text{Si}}\). The difference between the distances of the H\({}_{\text{COOH}}\)-surface (1.2(3) A) and O\({}_{\text{Si}}\)-H\({}_{\text{COOH}}\) (mean 1.75 A) exists because H\({}_{\text{COOH}}\) is not located directly above O\({}_{\text{Si}}\) but is slightly shifted toward the center of the [Si\({}_{6}\)O\({}_{\text{Si}}\)] \({}^{-12}\) ring. Figure 5c shows exemplary acrylic acid molecules in the neighborhood of O\({}_{\text{Si}}\). The simulations suggest that the -COOH group plays the most important role in the deposition of organic acid molecules on the montmorillonite surface. A greater attraction of the polymer to the numerous carboxyl groups in the molecule stabilizing the system and making it resistant to thermal movement of water can be expected. Figure 3: (**a**) Carbon quantity contour maps on the xy-plane parallel to the montmorillonite layer and (**b**) the quantity of Na\({}^{+}\), O\({}_{\text{COOH}}\) (O atoms in COOH group), H\({}_{\text{AA}}\) (H connected to C in acrylic acid), H\({}_{\text{COOH}}\) (H atoms in COOH group), and C (C in acrylic acid) as a function of distance from the montmorillonite layer surface for 36.35, 52.90, and 63.00 ps after adding acrylic acid. Figure 4: (**a**) Carbon quantity contour maps on the xy-plane parallel to the montmorillonite layer and (**b**) the quantity of Na\({}^{+}\), O\({}_{\text{COOH}}\), H\({}_{\text{NaNa}}\) (H in sodium acrylate), and C as a function of distance from the montmorillonite layer surface for 36.35, 52.90, and 63.00 ps after adding sodium acrylate. Figure 6 presents the montmorillonite/organic compound systems in which acrylic acid molecules and dispersed acrylate ions are visible after a simulation time of 63.00 ps. The accumulation of acid molecules in larger groups at the mineral surface is evident (Figure 6a), while acrylate ions are randomly distributed in the simulated space and are reluctant to interact with the surface of the mineral (Figure 6b). This may be related to the electrostatic repulsion between the ions and the negatively charged clay surface. Figure 5: DF functions of OSi–HCOOH, OSi–HAA, OSi–OCOOH, OSi–COOH, OSi–Na+ and OSi–Na+, OSi–HNa, OSi–OCOO–, and OSi–CCOO– atom pairs averaged over the time range from 62 to 63 ps after adding (**a**) acrylic acid, (**b**) sodium acrylate, and (**c**) exemplary acrylic acid molecules in the neighborhood of OSi.
## 3 Results and Discussion
### Preparative Chromatography
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Chromotographic Support**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-5}  & **Histidine–Agarose** & & & **Arginine–Macorporous** & **Histidine–Monolith** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Proteins** \\ (ug/mL) \\ \end{tabular} } & \multirow{2}{*}{Undetectable} & \multirow{2}{*}{Undetectable} & \multirow{2}{*}{Undetectable} & \multirow{2}{*}{Undetectable} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **RNA** \\ \end{tabular} } & \multirow{2}{*}{Undetectable} & \multirow{2}{*}{Undetectable} & \multirow{2}{*}{Undetectable} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Endotoxins** \\ (EU/ug pDNA) \\ \end{tabular} } & \multirow{2}{*}{0.010} & \multirow{2}{*}{0.014} & \multirow{2}{*}{0.652} & \multirow{2}{*}{\textless{}0.1 EU/ug pDNA} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Genomic DNA** \\ (ug/ug pDNA) \\ \end{tabular} } & \multirow{2}{*}{0.0008} & \multirow{2}{*}{0.0063} & \multirow{2}{*}{0.0009} & \multirow{2}{*}{\textless{}0.01 ug/ug pDNA} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Purity** \\ \end{tabular} } & \multirow{2}{*}{97\%} & \multirow{2}{*}{92\%} & \multirow{2}{*}{100\%} & \multirow{2}{*}{\textgreater{}90\% of sc pDNA} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Recovery** \\ \end{tabular} } & \multirow{2}{*}{42\%} & \multirow{2}{*}{43\%} & \multirow{2}{*}{71.6\%} & \multirow{2}{*}{Not applicable} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Purity analysis of the purified sample of supercoiled (sc) p53 encoding pDNA. isoform purity obtained using the distinct chromatographic matrices was assessed quantitatively by the CIMac analytical column and the results showed that the histidine-monolith allowed the recovery of the sc pDNA with a higher purity degree (100%), followed by histidine-agarose (97%) and arginine-macroporous (92%) (Table 1). The recovery yield was also higher in samples purified through the histidine-monolith (71.6%), followed by the other two supports (43%) (Table 1). Overall, although a higher recovery yield and purity degree were achieved with histidine-monolith, it was not possible to eliminate endotoxins to levels that meet the requirements of regulatory agencies. Moreover, the arginine-macroporous support allowed the use of milder conditions and the recovery yield and purity levels were also acceptable, which make this support an interesting alternative for pDNA purification.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 3 Proposed CAFT Method
### Structure of Correlation Volume  
The correlation volume consists of four layers, \(C_{1}\), \(C_{2}\), \(C_{3}\) and \(C_{4}\), by pooling into kernel sizes 1, 2, 4, and 8 of the last two dimensions. These four kernels inherited the values used in RAFT. Therefore, each volume \(C_{k}\) has a dimension of \(H\times W\times H/2^{k}\times W/2^{k}\). This set of volumes provides information about the displacements of large and small, respectively. However, by maintaining the first two dimensions (the dimension of \(I_{1}\)), high-resolution information can be maintained to recover the motion of small and fast-moving objects.  
Next, we define \(L_{C}\), an inspection operator that generates feature maps through indexing in a correlation volume structure. When the estimated values \(F_{1}\) and \(F_{2}\) of the current optical flow are given, each pixel \(x=(u,v)\) is mapped to the estimated  
Figure 10: Update block works in optical flow.  
corresponding point \(x^{\prime}=(u+f^{1}(u),v+f^{2}(v))\) of \(I_{1}\) to \(I_{2}\). The surrounding local grid of \(x^{\prime}\) is defined by using the \(L_{1}\) distance as shown in Equation (7).  
\[N(x^{\prime})_{r}=\left\{x^{\prime}+dx\Big{|}dx\in\mathbb{Z}^{2},\ \|dx\|_{1}\leq r\right\} \tag{5}\]  
A grid is a set of integer offsets within the unit of radius \(r\) at \(x^{\prime}\). Indexing is performed on the correlation volume using the local neighborhood \(N(x^{\prime})\). Since \(N(x^{\prime})_{r}\) is a real-name grid, bilinear sampling is used. Here, inspections are performed on all correlation volumes, and \(C^{k}\), the correlation volume at level \(k\), is indexed using a grid \(N(x^{\prime}/2^{k})_{r}\). A constant radius over several levels means a larger context at a lower level. For the lowest level, radius 4 is used, and \(k=4\) corresponds to the range of 256 pixels of the original resolution. The values at each level are then associated with a single feature map.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 3 The Proposed Method  
### Data Preparation  
We first illustrated the raw data used to detect the non-active state of a machine. Our dataset contained real-world information obtained from an automobile part manufacturing located in South Korea. This factory applied the continuous flow process in which one-line batches were generated using various machines to produce automobile parts. All the machines operated for 24 h a day. Here, the machines were equipped with programmable logic controller (PLC) sensors that produced the data related to the machine state. Table 1 presents a detailed description of the raw dataset.  
From the raw data provided in Table 1, we can see that a machine was managed as one of the following five states: Run, wait, stop, offline, and manual. The run state indicated that the current machine was operating appropriately. The wait state showed that the current machine was waiting for the previous machine to finish its job. These 2 states were defined as active. The stop state indicated that the current machine was stopped, and the manual state indicated that the current machine was operating manually. Finally, the offline state indicated that the machine was not connected to any network such that data gathering from the machine was not achieved. These 5 states of a machine were referred to as a machine state. Among these 5 states, we can consider run and wait as active states. On the other hand, stop, offline, and manual were considered as non-active states. Figure 1 shows the machine operating processes for a period of the day in the automobile manufacturer displayed through the Web monitoring system. In the figure, green and yellow colors indicated the active state of the machine, and red indicated non-active states of the machine.  
From Table 1, we can also observe that we have another type of data related to alarms that were triggered when a machine entered into a non-active state. Specifically, the alarm signal notified the machine operators of an abnormal situation. Besides, considering that a machine was operated to produce certain products, we could monitor the machine state by the number of produced good and NG products.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Description** & **Data Type** \\ \hline Machine name & Indicate machine name & String \\ Date and time & Indicate date and time of all events in the machine & Time \\ Machine status & Indicate machine states: Run, Wait, Stop, Manual, Offline & Int \\ Machine state duration & Duration of machine state from one state to another & Int \\ Alarm status & Alarm status: 1 = alarm triggered; 0 = alarm not triggered & Binary \\ Alarm duration & Duration of alarm & Int \\ Continuous good product & Cumulative number of good products produced & Int \\ Continuous NG product & Cumulative number of poor-quality products produced & Int \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of raw data gathered from PLC.  
Figure 1: Examples of machine operating processes.
## 3 Proposed Solution to Predict the AoA
### System Model and Mathematical Formulation  
#### 3.3.1 System Geometry and Architecture  
The proposed beamforming framework is implemented within an O-RAN Category B deployment, adhering to the Split 7-2x functional disaggregation model defined by the O-RAN Alliance. In this architecture, the O-RU is physically co-located with the antenna array and is responsible for performing real-time lower PHY processing, includinglocalised beamforming operations. Specifically, the O-RU is equipped with a uniform linear array (ULA) consisting of N antenna elements spaced at half the carrier wavelength, which provides high angular resolution for effective AoA discrimination, even under dense multipath conditions.  
In contrast to conventional centralised RAN systems that rely on periodic CSI feedback and gNB-side beam index selection, our design follows a decentralised philosophy. Each O-RU maintains a local per-user angle history buffer and signal observation statistics indexed by unique User Equipment Identifiers (UEIDs). This memory enables the O-RU to track the AoA trajectory of each user u \(\in\) {1, 2, \(\ldots\), U} using a clustered, weighted sliding window approach. The O-RU employs jump detection logic within this history, segmenting the window and applying adaptive Linear Regression only to the most relevant post-jump segment for each user. This advanced buffer logic allows for the O-RU to perform slot-level, cluster-based AoA prediction, enabling highly responsive and robust beamforming, even during rapid mobility events or abrupt trajectory changes, without waiting for explicit feedback or full CSI reports.  
Meanwhile, the O-DU acts as a higher-layer coordination node, executing curvature-adaptive SVR on a broader AoA history for each user and dynamically fusing these predictions with O-RU results. It manages the confidence metrics, orchestrates fallback logic (including MUSIC re-scans), and ensures overall system robustness via long-term trajectory tracking and model calibration. This decoupled design minimises latency, improves responsiveness, and aligns with the distributed processing goals of the Split 7-2x model, where near-field prediction and beam alignment decisions are made autonomously at the edge (O-RU). At the same time, centralised learning and validation occur asynchronously at the O-DU.
# Computing Idle Times in Fuzzy Flexible Job Shop Scheduling
## 2 The Fuzzy Flexible Job Shop Problem
### Comparing Fuzzy Schedules
#### 2.3.2 Measures Based on Possible Crisp Realisations  
According to the semantics of fuzzy schedules proposed in [49], a fuzzy schedule, \(\phi\), is an a priori or predictive solution providing a possibility distribution (in the form of TFNs) for the starting times of all operations and, in consequence, for the values of the resulting performance function, \(f\). When operations are actually executed according to the resource assignment and the ordering induced via the schedule, uncertainty is no longer present: for each operation, \(o\), its executed processing time, \(p_{\phi}(o)\), is a crisp value in support of the fuzzy processing time \(\widehat{p}_{\phi}(o)\); i.e., it is a possible crisp realisation of the fuzzy processing time. The vector of executed processing times, \(\rho\), where \(\rho(o)=p_{\phi}(o)\), is a vector of possible realisations of the fuzzy processing times, also called a possible scenario. In consequence, the performance \(f_{\phi}^{\rho}\) of the executed schedule is a crisp value in support of the fuzzy performance \(\widehat{f}_{\phi}\). Thus, the executed schedule can be seen as the a posteriori realisation of the fuzzy schedule \(\phi\).  
Under these semantics, the quality of the a priori fuzzy schedule can be measured relative to its a posteriori performance in a possible scenario or, more generally, across a set of different possible scenarios. To this end, let \(\mathbf{S}\) be a set of possible scenarios. We can define the following measures for \(\phi\):  
* Relative distance to the expected value (RDEV) in a possible scenario: \[RDEV(\phi,\rho)=\frac{f_{\phi}^{\rho}-\mathbb{E}[\widehat{f}_{\phi}]}{\mathbb{ E}[\widehat{f}_{\phi}]}\] (14) If this value is negative, that is, the cost in the executed schedule is smaller than expected, it means that the a priori schedule objective function is pessimistic. On the contrary, if \(RDEV\) is positive, the a priori schedule could be seen as optimistic. Overall, the smaller the absolute value of \(RDEV\), the closer the predicted performance of the solution to the real performance once the schedule is executed.
* Used uncertainty (UU) in a set of possible scenarios: \[UU(\phi,\mathbf{S})=\frac{\max_{\rho\in\mathbf{S}}f_{\phi}^{\rho}-\min_{\rho \in\mathbf{S}}f_{\phi}^{\rho}}{f_{\phi}^{3}-f_{\phi}^{4}}\] (15) This is a value in \([0,1]\) measuring the proportion of the support of the predicted fuzzy objective function \(\widehat{f}_{\phi}\) that is actually covered across the range of possible scenarios in \(\mathbf{S}\). When \(\mathbf{S}\) is representative enough of all the possible scenarios, a value of \(UU\) close to 1 means that the range of values considered as possible by the TFN \(\widehat{f}_{\phi}\) is actually obtainable in real situations. On the other hand, small values of \(UU\) would mean that the fuzzy schedule incorporates \"artificial\" uncertainty by considering possible values of \(f\) that are in fact never achieved, thus being less informative.
## 2 Tabular Method for Reducing UGR Calculations, for Every Point on the Room Midline  
### Introduction to the Method  
UGR calculation for points inside an indoor space in lighting design is essential. Since, as stated in AS/NZS 1680, not only criteria such as minimum needed work-plane illuminance for a particular task should be met, but also UGR criterion observation for users' visual comfort is mandatory [28]. By referring to Equation (1), it can be deduced that UGR calculation for a large number of luminaires in a room requires a high number of calculations. The reason is the number of summands in front of the sigma operator, which is equivalent to the number of luminaires. An approach that drastically reduces the calculation number can proportionally reduce the time and energy consumption for UGR calculation.  
According to CIE 117-1995 [8], UGR tables are provided by luminaires' manufacturers. This table is designed as a very useful and efficient tool for fast estimation and calculation of glare only at the midpoints of the room's walls. A sample of a UGR table is illustrated in Table S1 in the Supplementary File. By finding the desired X and Y dimensions, the corresponding luminaire direction, and the surface's reflection factor, the UGR can be obtained.  
Where intended number exists in the table, it can be used directly, and in case of not existence, simple interpolation (extrapolation) using numbers in the UGR table can be used as a tool to estimate UGR. (All interpolations (extrapolations) used in this research are well-known linear (1D) [29] (for 2 known points) or bilinear (2D) [30] (for 4 known points) interpolations (extrapolations).)  
Through theoretical and experimental investigations in this paper, it is proved that in addition to the points in the room for which the UGR table is designed, this table can be used for all points on the room's midlines to obtain the UGR (Figure 2).  
A method for using numbers in the UGR table for obtaining UGR on the room's midlines is proposed. In the proposed method, by simply using numbers in the UGR table issued by luminaires' manufacturer, we attempt to obtain UGR for every point on the room's midlines. The proposed method is fast because, as mentioned previously, for a room with dimension \(\text{XH}\times\text{YH}\), for the very first points of the observer's movement path, the number of summands in front of the sigma operator in Equation (1) is \(n=\text{X}\times\text{Y}\) (number of luminaires in front of the observer). However, in the proposed method, only one 2D interpolation or extrapolation between related adjacent numbers of the table is required.
## 3 Results and Discussion
### Effects of EGR Rate
#### 3.2.3 Emission Characteristics  
Figure 14 shows the effect of EGR rate on the NOx emissions of the two fuels at high and low pressures. EGR introduced exhaust gas into the cylinder, the oxygen content in the mixed gas was reduced, and the total heat capacity of the working gas increased, which helped to reduce the maximum temperature in the cylinder. EGR suppressed the high-temperature and oxygen-enrichment conditions for NOx forming, so the NOx emitted by the two fuels decreased with the increasing EGR rate [40; 41]. Although the two fuels released similar volumes of NOx under the same conditions, it was also found that the NOx displacement of P50D50 was slightly higher than that of D100. This could be caused by the small amount of oxygen in palm oil.  
Figure 15 plots the trend of smoke emissions with the EGR rate. Under different injection pressures, the smoke opacity displayed by D100 and P50D50 was positively correlated with the EGR rate. With the introduction of EGR, the oxygen entering the cylinder was reduced. A lower combustion temperature and the low oxygen concentration had an adverse effect on the oxidation of smoke, which accelerated the rate of formation of carbon nuclei from unsaturated soot precursors; therefore, more smoke was generated [34; 35].  
Figure 14: NOx emissions under different EGR rates at 300 and 450 bar.  
Figure 13: EGT under different EGR rates at 300 and 450 bar.  
It can be noted that, under the same conditions, the smoke displacement produced by D100 was more sensitive to the EGR rate. At 300 and 450 bar injection pressures, 20% EGR increased the smoke displacement of D100 by 69.2% and 56.3%, respectively, while for P50D50, the increase was of 25% and 33.3%. The oxygen atoms contained in palm oil increased the oxygen concentration of the fuel, which promoted the oxidation of smoke under the same injection conditions. As a result, the concentration of smoke produced by P50D50 showed lower variation. This could be due to the oxygen atoms in palm oil, which could better cope with changes in oxygen concentration in the intake air, showing lower fluctuations.
# Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
## 1 Introduction
DBNs possess excellent nonlinear mapping capabilities, allowing them to learn complex mapping relationships [28; 29; 30] efficiently. They demonstrate good model representation ability even in small sample spaces, presenting a significant advantage in fault diagnosis. However, the performance of DBNs is greatly influenced by parameters such as the number of neurons in the hidden layers, the network learning rate, and the number of backward fine-tuning. The proper configuration of these parameters is crucial for achieving high recognition accuracy. To address these challenges, we introduce a promising intelligent optimization algorithm, the WOA [31; 32; 33; 34]. WOA offers advantages such as avoiding local optima, high adaptability, and fast and accurate parameter optimization. It provides an effective means to optimize DBN parameters and improve fault diagnosis accuracy. Based on the analysis above, this paper proposes a universal circuit breaker fault diagnosis method that combines VMD and an improved DBN. This method initially decomposes the vibration signal into multiple modal components using VMD. These modal components' time and frequency domain features information are extracted to construct the feature sample space. The parameters of the DBN are optimized using the WOA. Subsequently, the optimized DBN is utilized for fault diagnosis of universal circuit breakers, resulting in a multi-classification fault diagnosis model suitable for small sample spaces.