esa-sceva/satcom-chunk-collection huggingface dataset:
# Eco-Friendly Preservation of Pharaonic Wooden Artifacts using Natural Green Products
## 3 Results
### Experimental Aged Wooden Blocks Biodeterioration  
The twenty-six isolated fungal species (Supplementary Materials, Table S2) were inoculated onto three experimental wooden blocks (_Ficus sycomorus, Tamarix gennessarensis_, and _Acacia nilotica_). Among them, fourteen species were completely ramified over the experimental wooden blocks (such as: _Alternaria alternata, Aspergillus flavus_, _Aspergillus funigatus_, _Aspergillus niger_, _Aspergillus terreus_, _Cidasporium cladosporoides_, _Emericella nidulans_, _Fusarium oxysporum_, _Penicillium chrysogenum_, _Penicillium citrinum_, _Penicillium platburn_, _Penicillium multicolor_, _Rhizopus oryzae_, and _Trichoderma viidae_). On the other hand, four _Aspergillus_ species (_A. clavatus_, _A. ochraceus_, _A. parasiticus_, and _A. versicolor_) and _Penicillium oxalicum_ showed moderate growth. No growth was observed for _Chaetomium globosum_, _Curvalaria clavata_, _Epicoccum nigrun_, _Eurotium amstelodami_, _Mucor fuscus_, _Paeicolomyces varioti_, and _Ullocadium botrytis_. The fourteen most deterioration-causing fungal species were then selected for measuring the effect of various concentrations of the selected EOs and plant extracts.
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## Appendix A SWIFT Thrust Vector Model and Spacecraft Dynamics for 2D Mission Scenarios
Introducing the pitch angle \(\alpha_{n}\in[-\alpha_{\max},\ \alpha_{\max}]\) defined as the angle between the Sun-spacecraft (radial) line and the direction of the steerable part of the vector \(\mathbf{a}\)[1,9], where \(\alpha_{\max}\in(90,\ 180)\) deg is an angle that accounts for the geometric constraints due to the conical shape of the SWIFT tethers grid--see the red cone in the conceptual sketch of Figure 5--the unit vector \(\hat{\mathbf{t}}\) is written as  
\[\hat{\mathbf{t}}=\cos\alpha_{n}\hat{\mathbf{p}}+\sin\alpha_{n}\hat{\mathbf{\theta}} \tag{10}\]  
so that Equation (10) becomes  
\[\mathbf{a}=a_{D_{\oplus}}\ \Big{(}\frac{r_{\oplus}}{r}\Big{)}^{2}\left[(1+K\,\cos \alpha_{n})\hat{\mathbf{p}}+K\,\sin\alpha_{n}\hat{\mathbf{\theta}}\right] \tag{11}\]  
Accordingly, the heliocentric dynamics of the SWIFT-based spacecraft can be analyzed, as discussed in Ref. [9], by using the classical set of EoMs in the polar reference frame of unit vectors \(\{\hat{\mathbf{p}},\ \hat{\mathbf{\theta}}\}\)  
\[\hat{r}=u\quad,\quad\hat{\theta}=\frac{v}{r}\quad,\quad\dot{u}=-\frac{\mu_{ \odot}}{r^{2}}+\frac{v^{2}}{r}+a_{D_{\oplus}}\ \Big{(}\frac{r_{\oplus}}{r}\Big{)}^{2}(1+K\,\cos \alpha_{n})\quad,\quad\dot{v}=-\frac{u\,v}{r}+a_{D_{\oplus}}\ \Big{(}\frac{r_{\oplus}}{r}\Big{)}^{2}\,K\,\sin \alpha_{n} \tag{12}\]  
where \(\theta\) is the polar angle measured from the Sun-spacecraft line at the initial time instant, \(\mu_{\odot}\) is the Sun's gravitational parameter, \(u\) is the radial component of the spacecraft inertial velocity vector, and \(v\) is the transverse component of the spacecraft inertial velocity vector. The polar reference frame is sketched in Figure 10.  
In particular for a 2D scenario, the pitch angle \(\alpha_{n}\) is the single scalar control parameter in Equation (12), and its local value has been evaluated in Ref. [9] by using an optimization procedure based on the calculus of variations.
## 2 Related Works
### Quantization Compensation Module  
Recent studies have focused on developing compensation modules to predict and correct errors that occur during the quantization process. These compensation modules are trained in conjunction with the encoder and decoder, with an emphasis on minimizing errors arising from quantization. Specifically, quantization inevitably results in the loss of detailed signal information, leading to discrepancies between the original and reconstructed signals. Consequently, a method capable of compensating for these losses is essential.  
The quantization-compensation module proposed in LightCodec[14] employs a shallow convolutional neural network to predict and reduce errors that occur during the quantization process. This module is positioned immediately after the quantization layer to correct errors arising from the quantized latent vectors. Specifically, the quantization-compensation module consists of two shallow convolutional layers and takes the reconstructed latent vector as input to calculate the expected quantization error. The predicted error is then added to the reconstructed latent vector before being transmitted to the decoder. Through this process, the decoder can minimize the losses caused by quantization, thereby restoring signals that are closer to the original. Furthermore, to enable the LightCodec compensation module to accurately predict and correct quantization errors, a  
\begin{table}
\begin{tabular}{c c} \hline
**Encoder (Dim)** & **Decoder (Dim)** \\ \hline Noisy input (3125) & Latent vector (10) \\ Linear (3125–1000) & Linear (10–100) \\ ReLU & ReLU \\ Linear (1000–500) & Linear (100–500) \\ ReLU & ReLU \\ Linear (500–100) & Linear (500–1000) \\ ReLU & ReLU \\ Linear (100–10) & Linear (1000–3125) \\ ReLU & Tanh \\ Latent vector (10) & Output (3125) \\ \hline \end{tabular}
\end{table}
Table 1: Model structure of the conventional DIFAR sonobuoy autoencoder [6].  
loss function is defined. In general, MSE loss is utilized to train the system in a way that minimizes the difference between the encoder's output and the quantization results processed by the compensation module. This MSE loss allows the quantization-compensation module to more precisely predict and correct quantization errors, ultimately contributing to enhanced reconstruction quality for the decoder.
## 5 Results and Analyses
### Prediction Models
#### 5.3.4 Third Scenario: Datasets without FS and with Sampling  
Table 14 shows the AUC values for prediction models across seven datasets in the third scenario. This scenario provides the prediction accuracy using ensemble sampling techniques mentioned in Section 4.1.4. From Table 14, it is evident that the AUC values were extremely high. This good performance was achieved because the datasets were modified with sampling techniques proposed in Section 4.1.4 without applying FS. However, some features were excluded after applying data analysis. The most interesting finding from this scenario was that applying ensemble sampling techniques on the datasets that exclude improper features (i.e., features that have zero values and correlated with each other) in Section 4.1.3 is enough to reach a high prediction accuracy. Another notable result is that sampling techniques in third scenarios in Table 14 had a great impact on SVM. The result of SVM in the first and second scenarios was 0.5, which indicates no discrimination according to the published criteria [52]. Furthermore, RF achieved the best prediction accuracy in all datasets except NB in mcMMO, and RF in mct attained the close to optimal results of 0.99. KNN was the second-best prediction model and outperformed other individual models.
## 5 Conclusions  
The quantitative evaluation model established in this study utilizes technologies including land use dynamic index, transition matrix, landscape pattern analysis, and NDVI time series to accurately measure changes in ecological quality and conservation effectiveness across PAs with varying ecosystems. By comparing reserves protecting different ecosystems, this research validates the methodological approach of using multiple quantitative techniques to comprehensively evaluate indicators of conservation effectiveness. The novel ecological and technological methodologies introduced establish a scientific system to assess conservation effectiveness. We found that significant disparities were observed in land use changes between the two PAs. ZINNR experienced a substantial impact from wetland reduction, whereas MdfNNR was predominantly affected by forest reduction. The landscape patterns inside and outside PAs exhibited notable differences, characterized by higher internal landscape quality and reduced fragmentation. Both reserves exhibited smaller internal fluctuations in landscape pattern indexes compared to external fluctuations, indicating relative ecological stability. However, ZINNR demonstrated higher landscape quality and stability compared to MdfNNR. Additionally, the percentage area of NDVI in ZINNR exhibited a more pronounced increasing trend versus MdfNNR. Our evaluation method holds substantial theoretical and practical value, enriching the foundation and techniques for evaluating nature reserve efficacy and offering innovation for ecology and environmental management fields.  
[PERSON] wrote the manuscript. [PERSON] conceived the idea and framework of the study. [PERSON] reviewed and provided critical feedback. [PERSON] and [PERSON] analyzed data. All authors contributed to the manuscript. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Natural Science Foundation of Heilongjiang Province of China (Fund number: LH2020C032, funder: Heilongjiang Provincial Department of Science and Technology).  
The GlobeLand30 is available at [[http://globallandcover.com/](http://globallandcover.com/)]([http://globallandcover.com/](http://globallandcover.com/)) (accessed on 10 April 2023). The remote sensing image data are available at [[https://code.earthengine.google.com/](https://code.earthengine.google.com/)]([https://code.earthengine.google.com/](https://code.earthengine.google.com/)) (accessed on 29 June 2023).  
We appreciate the Institute of Remote Sensing and Digital Earth for providing the GlobeLand30 dataset, as well as the Google Earth Engine platform for offering remote sensing datasets and cloud computing services. We also thank the Natural Science Foundation of Heilongjiang Province of China (LH2020C032).  
The authors declare no conflict of interest.
# Step 12: Compile the grid search model: compile_model(grid_search_model)
Step 13: Perform grid search to find optimal model parameters with training data: best_model = perform_grid_search(X_train, y_train, grid_search_model, param_grid, cv = 3)
Step 14: Use the best model to make predictions on new data: new_predictions = make_predictions(best_model, new_data)  
Route Formation  
The formation of transportation routes in humanitarian logistics utilizes deep learning neural network algorithms, specifically artificial neural networks (ANN). Figure 3 illustrates the ANN structure employed in the creation of these routes.  
The input layer of the ANN is derived from the grouping of demand points J based on the assigned DC \(i\) and vehicle \(k\). The hidden layer in the ANN represents the transformation of information from the location of the demand point \(j\). The output layer then combines this location distribution by incorporating the location of the demand point \(j\) from the hidden layer \(L_{ij}\). As a result of this combination, the \(Y_{i}\) route is formed.  
Upon combining the routes, a route selection process is conducted to ensure that each demand point is visited only once. This selection process helps optimize the transportation routes. The vehicles follow the selected routes, starting from DC \(i\) and returning to the same DC \(i\), which is assigned to a specific cluster \(k\). By limiting the visits to each demand point and establishing a circular route from and back to the distribution center, the transportation process becomes more efficient and organized within the designated cluster.  
Variables:  
\(y_{i}\): Route \(i\)  
\(L_{ij}\): Demand point location  
Formulation  
Once the grouping is generated, the formation of N transportation routes is formulated using a deep learning model known as an artificial neural network (ANN).  
\[y_{i}=\bigcup_{i=1}^{i=n}L_{ij} \tag{5}\]  
where,  
\[y_{i}=\left\{\begin{aligned} 1,& L_{ij}\
eq L_{ij}\\ 0,& L_{ij}=L_{ij}\end{aligned}\right. \tag{6}\]  
Equation (5) is utilized to form the transportation route based on the distribution of demand point locations \((L_{n})\). This equation captures the process of determining the optimal route considering the specific distribution center and cluster assignment.  
Furthermore, Equation (6) incorporates the route selection mechanism, which ensures that each demand point is visited only once. By imposing this constraint, the transportation route is designed to efficiently cover all the required demand points without revisiting any of them.  
The vehicles follow the established transportation route, commencing from the assigned DC \(i\) distribution center IK and returning to the same DC \(i\) within the designated cluster \(k\). This approach helps streamline the logistics process and ensure effective distribution of goods.
## 3 Experiments and Results
### Classification Performance on Individual Level  
To evaluate the effectiveness of our proposed model, we conducted binary classification experiments using the EEG data from ten different subjects. The hyperparameter settings for each subject were kept consistent. The experimental results are presented in Figure 3, which shows that our proposed model achieves good classification performance across all ten subjects. Notably, Sbj4 achieves the highest accuracy of 99.77%, while Sbj3 obtains the lowest accuracy of 99.15%.  
We further evaluated the four-category classification performance using the same experimental settings. The results are shown in Figure 4. It can be seen that the proposed model achieves an average accuracy of 90.90% across all subjects, with the highest accuracy of 95.93% and the lowest accuracy of 82.12% in the four-category classification task.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Local CNN** & **Global CNN** & \multicolumn{2}{c}{**BiRAU**} & \multirow{2}{*}{**Sbj1**} & \multirow{2}{*}{**Sbj2**} & \multirow{2}{*}{**Sbj3**} & \multirow{2}{*}{**Sbj4**} & \multirow{2}{*}{**Sbj5**} & **Mean** \\ (Filters) & & **(Filters)** & & & & & & & \\ \hline
16 & 256 & 2-32 & 85.39\% & 73.05\% & 68.35\% & 89.22\% & 80.24\% & 79.25\% \\
**32** & **256** & **2-32** & **93.93\%** & **86.01\%** & **82.12\%** & 95.93\% & **93.28\%** & **90.25\%** \\
64 & 256 & 2-32 & 93.51\% & 67.90\% & 69.50\% & 87.17\% & 86.57\% & 80.93\% \\
32 & 64 & 2-32 & 88.25\% & 71.05\% & 69.18\% & 87.28\% & 75.55\% & 78.26\% \\
32 & 128 & 2-32 & 86.87\% & 81.78\% & 76.18\% & 81.98\% & 78.07\% & 80.98\% \\
32 & 512 & 2-32 & 90.35\% & 67.56\% & 65.85\% & 89.83\% & 85.45\% & 79.81\% \\
32 & 256 & 1-32 & 88.76\% & 61.46\% & 67.78\% & 87.30\% & 77.76\% & 76.61\% \\
32 & 256 & 3-32 & 86.89\% & 64.88\% & 73.36\% & **96.67\%** & 75.77\% & 80.45\% \\
32 & 256 & 2-64 & 92.56\% & 67.46\% & 69.35\% & 76.82\% & 82.60\% & 77.76\% \\ \hline \end{tabular}
\end{table}
Table 2: Parameter tuning experiments.  
Our results demonstrate that the proposed model can effectively differentiate brain states across diverse subjects, even with consistent hyperparameter settings. This suggests that our model has great potential for real-world applications that involve a variety of subjects.
## 2 Materials and Methods  
### Preparation of Composite PCMs  
Hainan Province in China is located in a hot summer and warm winter region, with the summer climate predominantly hot and humid. Based on a thermal comfort survey of Hainan residents and the research presented in the literature, it has been found that long-term residents of Hainan have adapted to the local climate. With good ventilation, they can feel comfortable even if the outdoor temperature exceeds 36 \({}^{\circ}\)C, as long as the indoor temperature remains below 28 \({}^{\circ}\)C [21,22]. Therefore, the suitable phase change temperature for the Hainan region is around 28 \({}^{\circ}\)C.  
Paraffin and butyl stearate, commonly used organic solid-liquid PCMs, have become prominent in recent research due to their high phase change latent heat, chemical stability, non-corrosiveness, and cost-effectiveness. To meet the specific needs of buildings in Hainan, composite PCMs with suitable phase change temperatures can be prepared by adjusting the mass ratio of paraffin and butyl stearate [23,24]. The paraffin selected for this study had a solid density of 760 kg/m\({}^{3}\), a specific heat capacity of 2140 J/(kg-K), a thermal conductivity of 0.21 W/(m\(\cdot\)K), and a melting point of 30 \({}^{\circ}\)C to 3 \({}^{\circ}\)C. The butyl stearate selected for the study had a liquid density of 865 kg/m\({}^{3}\), a specific heat capacity of 2300 J/(kg\(\cdot\)K), a thermal conductivity of 0.15 W/(m\(\cdot\)K), and a solidification point of 19 \({}^{\circ}\)C to 21 \({}^{\circ}\)C.  
Paraffin wax was produced by Shengbang Plastic Business Department of Dongguan City, China, and n-butyl stearate was produced by Xinfuhai Company of Zhangjiagang City, China.  
Different mass ratios of butyl stearate/paraffin composite PCMs were prepared using a melt blending method. The temperature of a constant-temperature heating magnetic stirrer was set to 80 \({}^{\circ}\)C. The constant-temperature heating magnetic stirrer was manufactured by Yuexin Instrument Company of Changzhou City, China. Once the water bath reached 80 \({}^{\circ}\)C, a beaker containing paraffin was placed into the water bath. After the paraffin had completely melted, different mass ratios of butyl stearate were added. The stirrer was then turned on to mix the materials thoroughly. After mixing for approximately 1 h, the mixture was allowed to cool naturally, resulting in composite PCMs with different mass ratios. Table 1 shows the mass of n-butyl stearate (m\({}_{1}\)), the mass of paraffin (m\({}_{2}\)), and the mass ratio of the two (m\({}_{1}\):m\({}_{2}\)) in different experimental groups.
# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 3 The Augmented Digital Control Room
### Augmenting the Digital Control Room
#### 3.3.2 Interface Design for Menu Navigation  
Interface design in a digital control room is a key issue you have to solve in order to create a digital control as described in Section 3.2. In MR, interfaces can be created in various ways. Firstly, there are traditional design elements one can use in desktop applications arranged on a plane in the augmented space. Microsoft provides design templates and most of them follow this design paradigm. Secondly, MR capable devices such as the HoloLens provide in contrast to monitors a three-dimensional space, which enables more immersive and interactive design possibilities. However, interaction is not a goal itself, but should support and motivate the user.  
[PERSON] et al. [25] have developed two applications, for example, in which they use traditional design concepts for AR. The first one they describe is for mobile phones and uses touch control. The other one is more relevant in the context of this work as it is an application for Microsoft HoloLens. They developed their application using recurring user interviews. Therefore, user feedback was already accounted for in the final evaluation. According to them, it was methodically very important to include user feedback very early in the development cycle. On the design side of things, they found, it is important that the user always knows about which functions and objectives are active. Generally, they concluded, traditional design philosophies can be used as a starting point for making design decisions, but also added there were no design guidelines yet and emphasized further research. [PERSON] [26] proposes design guidelines for user interfaces in AR. He recommends that volumetric elements should be used and underlines that spacing is important for visual clarity.  
In this work, we use examples to compare traditional design elements and a more immersive counterpart for navigating menu elements. Our research question on this topic is therefore:  
_RQ 2: Can three-dimensional elements be used to make the operation of MR applications more intuitive, or is the application of \"classical\" menus superior?_
## 5 Experiment Setup and Results
### Experimental Results

Table 5: Performance improvements of ROUGE metric on 3-word summaries for CNN/DailyMail Dataset (comparison of baseline and improved models). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**ROUGE Performance Values (CNN/DailyMail—3-Word Abstracts)**} \\ \cline{3-13}  & & \multicolumn{3}{c}{**XLNet**} & \multicolumn{3}{c}{**Roberta**} & \multicolumn{3}{c}{**Distilbent**} & \multicolumn{1}{c}{**Electra**} & \multicolumn{1}{c}{**Albert**} & \multicolumn{1}{c}{**Pegasus**} \\  & & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** & **Baseline** & **Prop.** \\ \hline \multirow{6}{*}{**Conference Values**} & R & 0.03106 & **0.10828A** & 0.10053 & 0.07424 & 0.12366 & 0.09124 & 0.15122 & 0.14147 & 0.05610 & **0.10345A** & 0.10697 & 0.09945 \\  & & & (+0.0772) & & & & & & & (+0.0473) & & \\  & P & 0.02826 & 0.03471 & 0.03416 & 0.02551 & 0.04174 & 0.02900 & 0.15210 & 0.14193 & 0.02986 & 0.02772 & 0.03548 & 0.02697 \\  & & & (+0.0064) & & & & & & & & \\  & F1 & 0.02284 & **0.04899A** & 0.04408 & 0.03507 & 0.05722 & 0.04094 & 0.15114 & 0.14103 & 0.03285 & **0.04162A** & 0.04659 & 0.04090 \\  & & & (+0.0261) & & & & & & & (+0.0087) & & \\ \hline \multirow{6}{*}{**Conference Values**} & R & 0.03960 & **0.12013A** & 0.11230 & 0.08574 & 0.13499 & 0.10416 & 0.15358 & 0.14382 & 0.06701 & **0.11635A** & 0.11915 & 0.11093 \\  & & & (+0.0805) & & & & & & & (+0.0493) & & \\  & P & 0.03572 & 0.03878 & 0.04104 & 0.03059 & 0.04719 & 0.03388 & 0.15449 & 0.14430 & 0.03632 & 0.03239 & 0.04262 & 0.03094 \\  & & & (+0.0030) & & & & & & & & (+0.0038) & & \\ \hline \multicolumn{13}{c}{**A Improvement from baseline**} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Time reductions ranged from 54% to 69% for 200-word summaries, indicating that longer texts enhance the framework's efficiency. For 400-word summaries, the gains were between 48% and 62%. These results show that the proposed approach remains effective even as the text length increases. For short summaries, time reductions varied between 25% and 63%. Since processing times are already low for shorter texts, the relative impact of the framework is more limited. Overall, the proposed method offers substantial time savings for both large and lightweight LLMs. A \(t\)-test was conducted to evaluate the impact of the MIS framework across different summary lengths (400-word, 200-word, and 3-word) and ROUGE metrics. As shown in Table 7, the \(p\)-values for all summary lengths and each ROUGE metric (Rouge-1, Rouge-2, Rouge-L, Rouge-W, Rouge-S, and Rouge-SU) are below 0.05. This finding demonstrates that the improvements in summarization performance are statistically significant and not due to chance. The t-statistics and \(p\)-values for each summary length and ROUGE metric further support the robustness of the MIS framework and the effectiveness of the method. Table 7 confirms that the proposed approach leads to statistically significant improvements in summary quality.