esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of High-Efficiency Manganese Sand and Determination of Removal Rate  
Studies have shown that Mn\({}^{2+}\) is very stable under acidic conditions and difficult to remove through oxidation. Nevertheless, modified manganese sand can reduce the lower limit of pH in the manganese removal. In order to obtain high-efficiency manganese sand, we adopted the impregnation method to modify manganese sand [23]. Untreated manganese sand (0.9-1.7 mm) was cleaned with deionized water to remove the impurities on the surface and then placed in a drying oven at a constant temperature of 100 \({}^{\circ}\)C. The dried manganese sand was put into a 500 mL beaker and mixed with 5% potassium permanganate solution, and the mixture of manganese sand and the modifier was then heated at 50 \({}^{\circ}\)C for 16 h in a constant-temperature water bath. Then, it was dried again for 12 h in an oven at 100 \({}^{\circ}\)C to obtain the modified manganese sand.  
To confirm the effect of adsorption, we studied the removal rate of the adsorption process and configured several water samples with different iron and manganese ion concentrations. Briefly, 2 mg/L iron together with the manganese ion water sample and the manganese sand adsorbent were added to a 250 mL conical flask. The pH of the solution was adjusted with 0.1 mol/L NaOH and HCl. After sealing, it was placed in a constant temperature shaker at 25 \({}^{\circ}\)C with a rotating speed of 120 r/min. For the sampling after adsorption, a 0.45 \(\upmu\)m filter membrane was used. The absorbance was measured at 510 nm via o-phenanthroline spectrophotometry (detection limit: 0.03 mg/L; the relative standard deviation of the laboratory was 0.44%). The absorbance was measured at 525 nm through potassium periodate spectrophotometry (detection limit: 0.05 mg/L; the relative standard deviation was 3.94%). The iron and manganese ion removal rate is calculated according to Formula 1.  
\[\mathrm{R}=\left(\frac{\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}}}{\mathrm{C}_{0}} \right)\times 100\% \tag{1}\]where C0 is the initial concentration of adsorbate, mg/L. C**e** is the final concentration of adsorbate, mg/L.
## 3 Documents by Type
### Bibliographic Coupling (Documents)  
The findings of this analysis provide valuable insights into emerging trends within the field and help outline potential directions for future research. A bibliographic coupling map was generated using VOSviewer based on shared references among the research documents included in the study. It analyzes the thematic structure of the selected research documents and how they are grouped based on what they cite. By setting a minimum citation threshold of three, 39 out of 75 research items met the criteria and were included in the visualization shown in Figure 7. These documents were grouped into eight distinct clusters, reflecting the diversity and interconnectedness of the research through citation relationships. Key metrics in bibliographic coupling include co-citation strength and total link strength, which indicate the influence and connectivity of individual documents.  
Table 2 lists the documents with the highest total link strength based on bibliographic coupling. For instance, the study by [8] (Cluster 4--Yellow) has the highest total link strength (26), indicating a strong thematic alignment with numerous other studies in the dataset. This study explored the impact of Industry 4.0 technologies on OHS, but it has a modest citation count of 35. This highlights the role of [8] as a core integrative study, particularly in linking Industry 4.0 technologies to OHS frameworks. In contrast, the research study [36], also in Cluster-4 (Yellow), is the most highly cited document in the review (307), but its total link strength is only 12. This suggests that while it is broadly influential, it shares fewer references with the other documents, possibly due to its broader framing or foundational nature. This study examined whether Industry 4.0 technologies adequately address OHS challenges, highlighting the critical need for integrated OHS strategies to mitigate emerging risks.  
Figure 6: Keywords co-occurrence analysis (minimum occurrence of keyword = 5).  
Similarly, research item [40] in Cluster-1 (Red) has a total link strength of 17 and focuses on the effectiveness of immersive VR training in improving safety behaviors in confined space environments. Meanwhile, study [27] in Cluster-2 (Green) demonstrates a total link strength of 11 and investigates the use of computer vision, specifically for human and PPE detection, and Mixed Reality (MR)-based training. Both these studies represent emerging themes in Safety 4.0, with strong linkages to the evolving body of research. These findings show that studies with high link strength often occupy thematic centers within the ecosystem of DT and IM applied to OHS, offering practical alignment and relevance even if they have fewer citations. This supports the argument that thematic connectivity, not just citation count, should guide the identification of key works in emerging interdisciplinary fields like smart manufacturing safety.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 3 Methodology
### Proposed Semi-Supervised Learning Models
#### 3.3.4 Combining These Concepts  
In semi-supervised learning with decision trees and entropy-based uncertainty sampling, the workflow typically includes the following:  
1. **Initial Training**: Start with a small labeled dataset and train a decision tree.
2. **Uncertainty Estimation**: Use the trained decision tree to predict class probabilities for the unlabeled instances and calculate their entropy.  
3. **Instance Selection**: Select the unlabeled instances with the highest entropy for labeling.
4. **Labeling**: Query an oracle (e.g., a human annotator) to obtain the true labels for these selected instances.
5. **Model Update**: Retrain the decision tree with the expanded labeled dataset, now including the newly labeled instances.
6. **Iteration**: Repeat the process of uncertainty estimation, instance selection, labeling, and model updating until a stopping criterion is met (e.g., a certain number of iterations, achieving a performance threshold, or exhausting the labeling budget).  
The workflow is summarized in the following Algorithm 1.  
```
0: Labeled dataset \(\mathcal{L}\), Unlabeled dataset \(\mathcal{U}\), Oracle for labeling;
0: Trained Decision Tree Model \(\mathcal{M}\);
1: Initialize: Train initial decision tree \(\mathcal{M}\) on labeled set \(\mathcal{L}\);
2:while stopping criterion not met do
3:for each instance \(x\in\mathcal{U}\)do
4: Predict class probabilities for \(x\) using \(\mathcal{M}\);
5: Calculate uncertainty (entropy) \(H(x)\) based on the predicted probabilities;
6:endfor
7: Select instances with the highest entropy from \(\mathcal{U}\);
8: Request labels for the selected high-entropy instances from the oracle;
9: Add newly labeled instances to \(\mathcal{L}\) and remove them from \(\mathcal{U}\);
10: Retrain the decision tree \(\mathcal{M}\) using the updated labeled set \(\mathcal{L}\);
11:endwhile
```  
**Algorithm 1** Semi-Supervised Learning Workflow Using Decision Tree  
**(2) Logistic Regression with Self-Training.** This model uses a self-training classifier with logistic regression as the base estimator. By combining logistic regression with self-training for semi-supervised learning, it integrates concepts from both logistic regression and semi-supervised learning. This approach was chosen for its binary classification suitability as well as to explore performance improvement through self-training [31; 32]. A comprehensive overview is provided below.
## 3 Evaluation of Surface Quality, Material Utilization, and Efficiency
### Evaluation of Surface Quality
#### 3.1.4 Developing Surface Roughness Model  
From Figure 3d, it is clearly seen that the bead width and the cutting depth satisfy the following relation:  
\[C_{\mathrm{D}}\ =\ (B_{\mathrm{W}}-T_{\mathrm{W}})/2 \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Regression Model 1**} & \multicolumn{3}{c}{**Regression Model 2**} \\ \hline
**Source** & \(F\)**-Value** & \(p\)**-Value** & **Source** & \(F\)**-Value** & \(p\)**-Value** \\ \hline \(A\)-\(W_{\mathrm{FR}}\) & 234.16 & \(<\)0.0001 & \(A\)-\(S_{\mathrm{S}}\) & 66.73 & \(<\)0.0001 \\ \(B\)-\(T_{\mathrm{S}}\) & 52.82 & \(<\)0.0001 & \(B\)-\(T_{\mathrm{FR}}\) & 5.11 & 0.0473 \\ \(C\)-\(W_{\mathrm{V}}\) & 0.058 & 0.8147 & \(C\)-\(C_{\mathrm{D}}\) & 8.20 & 0.0169 \\ \(AB\) & 1.42 & 0.2607 & \(AB\) & 0.23 & 0.6390 \\ \(AC\) & 2.79 & 0.1260 & \(AC\) & 0.16 & 0.7020 \\ \(BC\) & 2.05 & 0.1830 & \(BC\) & 0.080 & 0.7827 \\ \(A^{2}\) & 2.30 & 0.1604 & \(A^{2}\) & 16.72 & 0.0022 \\ \(B^{2}\) & 7.01 & 0.0244 & \(B^{2}\) & 5.13 & 0.0469 \\ \(C^{2}\) & 0.15 & 0.7086 & \(C^{2}\) & 1.04 & 0.3318 \\
**Model** & 33.54 & \(<\)0.0001 & **Model** & 11.21 & 0.0004 \\
**Lack of Fit** & 1.52 & 0.3275 & **Lack of Fit** & 4.33 & 0.0669 \\ \(R^{2}\) & 0.9679 & \(R^{2}\) & & 0.9099 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA results of the two regression models.  
Through replacing \(C_{\text{D}}\) in Equation (4) with Equation (5) and combining Equation (2), the final surface roughness model is obtained:  
\[R_{\text{a}}\ =\ 1.98-0.065T_{\text{W}}+0.08W_{\text{FR}}-0.0377T_{\text{S}}+0.0137T _{\text{S}}^{2}-0.23S_{\text{S}}+0.11S_{\text{S}}^{2}+0.063T_{\text{FR}}+0.061T_{ \text{FR}}^{2} \tag{6}\]  
which clearly exhibits the dependence of the surface roughness on both the deposition and the milling parameters. In addition, the target width also affects the surface roughness.
## 4 Results and Discussion
### Ideal Spectral Efficiency in Carrier Phase Recovery  
For the binary symmetric channel (binary input and output alphabets, symmetric transition probability), the coding rate \(R_{C}\) in the \(n\)-PSK optical fiber communication systems (assuming an ideal hard-decision forward error correction coding) can be expressed as follows [53],  
\[R_{C}=1+BER\cdot\log_{2}\left(BER\right)+\left(1-BER\right)\cdot\log_{2}\left( 1-BER\right). \tag{16}\]  
Correspondingly, the ideal spectral efficiency (assuming an ideal hard-decision forward error correction coding) in the carrier phase recovery in the \(n\)-PSK coherent optical fiber communication systems can be calculated as:  
\[SE=R_{C}\cdot N_{p}\cdot\log_{2}\left(n\right), \tag{17}\]  
where \(SE\) is the ideal spectral efficiency, \(n\) is the modulation format level of the communication system, \(N_{p}\) is the number of polarization states. The BER limits in Equation (16) can be obtained from the BER floors in the three carrier phase recovery approaches according to Equations (9), (12) and (15), respectively.
## 1 Introduction
Although an enhanced modulation bandwidth of 18.7 GHz has been demonstrated in our previous work [29], the tuning range of the laser section for the mutually injection-locked state was only 4 mA due to poor single-mode stability resulting from the uniform grating and laser facets without AR coatings. As such, further enhancement of the modulation bandwidth is limited, and the practical performance of IMC lasers under large-signal modulation has not been reported. Therefore, it is important to ensure that IMC lasers work under a stable MIL state, as this enables the modulated laser section to work at a higher bias and helps increase the CPR frequency and mitigate its roll-off effect. Moreover, the cavity parameters of IMC lasers need to be well-designed to achieve an appropriate PPR frequency for modulation response enhancement [27]. Additionally, the PPR peak should not be too far from the CPR peak, or there will be a significant gap between the two resonance peaks, which is detrimental to broadband communication [8]. In this paper, we demonstrate modulation bandwidth enhancement and large-signal modulation performance of IMC lasers for the O-band with identical MQW layers under MIL. The IMC laser is composed of two DFB laser sections with an SOA section in between. Phase-shifted grating and an antireflection (AR) coating are adopted in the IMC laser to achieve stable single-mode operation and increase the locking range. Furthermore, the PPR frequency's dependence on the SOA length has been investigated by numerical simulation, and it is observed that the IMC laser can effectively take advantage of the PPR effect by optimizing the SOA length. In addition, by adjusting the currents of three sections, flat small-signal response with a 3-dB bandwidth of 38.7 GHz was realized in our device with an optimum SOA length. Moreover, at a modulation rate of 40 Gb/s, clearly opened eyes for the case of the back-to-back (BTB) and 25-km single-mode fiber (SMF) transmissions were realized.
## 3 System Model
### Network Model  
The network model consists of multiple UEs and distributed MEC servers. In an ad hoc network [28; 29] environment, each UE can generate computationally intensive tasks, while the MEC servers and available computing nodes nearby provide the necessary computational resources. \(M\) represents the maximum workload of the MEC server and \(C\) represents the maximum computational resources provided by the MEC server. Among the network entities, let \(U=\{1,2,3,\ldots,i,\ldots,N\}\) as the set of all UEs in the network. All UEs can be represented as \(UE_{i}=\{m_{i},f_{i},t_{i},r_{i},s_{i}\}\), where \(m_{i}\) denotes the size of the user task. \(f_{i}\) is the operating frequency of the CPU for processing computational tasks and \(t_{i}\) represents the maximum tolerable time for the current UE transmission or computation. The variable \(r_{i}\) represents the current computing state. When \(UE_{i}\) executes a computation task, \(r_{i}=1\)  
Figure 2: MEC offloading model.  
otherwise, \(r_{i}=0\). The variables \(s_{i}\in\{0,1,2,3\}\) denote the system offloading decision set, representing different offloading strategies. The variables \(S=\{s_{i},i\in U\}\) represent the system offloading strategies, which represent different offloading decisions: \(s_{i}=0\) for local computation, \(s_{i}=1\) for offloading the task via D2D, \(s_{i}=2\) for offloading the task via the current MEC, and \(s_{i}=3\) for migrating the task to another MEC server, which is called migration calculation in this article.
## 2 Problem Formulation
### Dynamic Cooperative Path Planning Constraints of UAVs
#### 2.1.1 UAV Performance Constraints  
The cooperative path, \(\sigma_{i}\), of any \(\mathrm{UAV}_{i}\) in \(\mathrm{UAV}_{N}\) is composed of a group of path nodes \(P_{i}:\{(p_{1},\ldots,p_{K})|p_{1}=s_{i},p_{K}=g_{i},p_{k}=(x_{k},y_{k},z_{k},t _{k})\}\). \(\sigma_{i}\) needs to meet the performance constraints of the UAVs, including the maximum flight distance \(L_{\text{max}}\), the maximum steering angle \(\phi_{\text{max}}\), the maximum climbing/diving angle \(\gamma_{\text{max}}\), and the shortest path segment distance \(l_{\text{min}}\). UAV performance constraints are shown in Definition 1.  
**Definition 1** (UAV performance constraints): _For any \(\mathrm{UAV}_{i}\) in-planning space \(\mathcal{X}\), the maximum flight distance constraint is:_  
\[L_{\mathrm{UAV}_{i}}=\sum_{i=0}^{k}\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left( y_{i+1}-y_{i}\right)^{2}+\left(z_{i+1}-z_{i}\right)^{2}}\leq L_{\text{max}} \tag{1}\]  
_and the maximum steering angle constraint is:_  
\[\left|\phi_{\mathrm{UAV}_{i}}\right|=\left|\frac{\lambda_{i}^{T}\lambda_{i+1} }{\left\|\lambda_{i}\right\|_{2}\cdot\left\|\lambda_{i+1}\right\|_{2}}\right| \leq\phi_{\text{max}} \tag{2}\]  
_where \(\lambda_{i}=\left[x_{i}-x_{i-1},y_{i}-y_{i-1}\right]^{T}\).__The maximum climbing/diving angle constraint is:_  
\[\left|\gamma_{\text{UAV}_{i}}\right|=\left|\arcsin(\frac{z_{i}-z_{i-1}}{\left\| \lambda_{i}\right\|_{2}})\right|\leq\gamma_{\text{max}} \tag{3}\]  
_and the shortest path segment distance constraint is:_  
\[l_{\text{UAV}_{i}}=\left\|p_{i+1}-p_{i}\right\|_{2}\geq l_{\text{min}} \tag{4}\]
## 4 Conclusions  
In this contribution, a two-layer DPR has been developed to classify an unknown driving cycle into one of the predefined templates or RDPs. The DPR has been implemented with an EV model and SQP-based optimized EMS. The predicted energy consumption has been analyzed along with the implementation of EMS to achieve an optimal speed profile for minimizing energy consumption. The simulation results indicate that the DPR is able to generalize well over different routes and traffic conditions. However, training with a larger database with data from multiple regions under varied traffic conditions can improve the testing accuracy further. A consideration of other factors, such as weather, driving style, unknown incidents during the drive, and charging patterns of the EV, may also provide more insight into the applicability of the DPR. As concluded from the present work, DPR-based EMS can provide lower energy consumption. The optimal speed profile can either be maintained by virtue of eco-driving or provided as advice to the human driver. As part of future work, complete control loop may be implemented to realize eco-driving. More advanced artificial intelligence (AI) tools may be implemented to improve the accuracy of classification. A hardware application can also be considered with on-board (edge-AI) and off-board (cloud AI) systems.  
Conceptualization, B.M.; methodology, B.M.; validation, B.M. and S.K.; formal analysis, M.I.; resources, B.M. and S.K.; writing--original draft preparation, B.M. and S.K.; writing--review and editing, M.I.; supervision, M.I.; project administration, M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be made available upon reasonable request from the authors.  
The authors declare that they do not have any conflicts of interest, and nor do they have any financial or non-financial interests that are directly or indirectly related to the work submitted for publication.  
\begin{table}
\begin{tabular}{c c c} \hline
**Representative Pattern** & **Optimum Speed (m/s)** & **Optimum Energy (kWh)** \\ \hline Highway High Traffic & 9.07 & 3.1356 \\ \hline Highway Low Traffic & 23.31 & 4.94 \\ \hline Suburban High Traffic & 5.171 & 0.565 \\ \hline Suburban Low Traffic & 9.61 & 0.552 \\ \hline Urban High Traffic & 6.996 & 2.94 \\ \hline Urban Low Traffic & 11.25 & 1.351 \\ \hline \end{tabular}
\end{table}
Table 8: Optimal speed and energy consumption.
## 5 Conclusions  
In this paper, we develop and validate a logarithmic shadow index (_LSI_)-based shadow detection approach mainly employing the properties of typical invariant color components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) in terms of both higher hue and lower intensity components, as well as the dramatical decrease of near-infrared component against the visible band components (i.e., red, green and blue components). Additionally, a better visual sense and higher overall accuracies (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) are acquired by the proposed _LSI_ shadow detection approach against other comparative algorithms (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) in the comparative experiments, which reveals that the excellent performance and robustness of the proposed _LSI_ shadow detection approach for high-resolution satellite images. Therefore, the proposed _LSI_ shadow detection approach is a promising one, further settling typical shadow detection problems of high small shadow omission and typical nonshadow misclassification for high-resolution satellite images. In the future, we will further research the shadow detection techniques considering the interference of water, snow and desert on the base of our current study.  
Conceptualization, H.H., X.X., T.L. and L.H.; Data curation, H.H., C.H. ([PERSON]) and L.H.; Formal analysis, H.H., C.H. (Chengshan Han) and X.X.; Investigation, H.H.; Methodology, H.H. and X.X.; Project administration, C.H. (Chengshan Han) and X.X.; Resources, C.H. (Chengshan Han) and X.X.; Software, H.H., T.L., C.H. ([PERSON]) and L.H.; Supervision, C.H. (Chengshan Han) and X.X.; Validation, H.H. and C.H. ([PERSON]); Visualization, H.H. and L.H.; Writing--original draft, H.H.; Writing--review & editing, H.H., C.H. (Chengshan Han), T.L. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific and Technological Developing Scheme of Ji Lin Province (20190302082 GX).  
The authors would like to thank the kind support of Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. The authors also express their gratitude to Digital Global Inc. for providing the WorldView-2 and WorldView-3 image samples.  
The authors declare no conflict of interest.
# _Beta vulgaris_ subsp. _maritima_: A Valuable Food with High Added Health Benefits
## 3 Results and Discussion
### Identification and Quantification of Phenolic Compounds by HPLC  
The phenolic composition of _Beta vulgaris_ subsp. _maritima_ extracts was assessed by HPLC analysis. Most of the external standards used in this study were phenolic acids, flavonoids, and terpenes that commonly occur in vegetables. The phenolic compositions of the aqueous and the methanolic extracts were found to be different. The salicylic acid was the most abundant molecule in the aqueous extract and not detected in the methanolic extract. Nine compounds were identified in the aqueous extract and four compounds in methanolic extract (Table 2). The most abundant compound in the aqueous extract was salicylic acid (758.52 mg/100 g), followed by hesperidin (189.07 mg/100 g). In the methanolic extract, the most abundant compound was hesperidin (68.82 mg/100 g). However, some compounds that were present in the aqueous extract were not detected in the methanolic extract such as caffeic acid, salicylic acid, syringe acid, p-hydroxybenzoic acid, and rutin. Furthermore, the gallic acid and limonene were not detected in both extracts from this plant.  
There are only a few studies that have been conducted to analyze the phytochemical composition of the _Beta vulgaris_ genus and even just one exploration has focused on _Beta vulgaris_ subsp. _maritima_. To the best of our knowledge, this is the second study that reported the chemical composition of _B. maritima_. Therefore, HPLC analysis of some _Beta vulgaris_ species demonstrated the presence of phenolic acids such as vanillic acid [27], catechin-hydrate, epicatechin, and rutin [28] and 4-hydroxybenzoic acid, caffeic acid, and chlorogenic acid in concentrations ranging from 0.12 to 0.047 mg/g of dry extract [29].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Extract Yield (\%)** & **Polyphenols (mg GAE/g dw)** & **Flavonoids (mg QE/g dw)** \\ \hline Aqueous extract & 21.71 & 12.377 \(\pm\) 3.324  & 26.023 \(\pm\) 3.565  \\ \hline Methanolic extract & 18.82 & 37.117 \(\pm\) 6.336  & 38.785 \(\pm\) 1.148  \\ \hline \end{tabular}
\end{table}
Table 1: Bioactive compounds (mean \(\pm\) SD) obtained for the studied _Beta vulgaris_ subsp. _maritima_.  
Considering the fact that there is one datum concerning the phytochemicalimensional composition of _B. maritima_ essential oil, the present investigation could be of great interest and the newly identified compounds can be added to the known compounds identified in this species.