esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis  
### Experiment Data  
This paper mainly evaluates and analyzes the model on two biomedical datasets: Drug-Drug Interaction (DDI) [22] and Clinical Interpretations of Variants In Cancer (CIVIC) [23]. The DDI dataset consists of two parts: the DrugBank database and the MedLine abstracts, which contain a total of 1025 text datas and define 5 types of drug-entity interaction relations: advise, effect, int, mechanism, negative. These 5 types of drug relation types are presented in the form of sentences, with at least two drug entities in each sentence sequence. Among these 5 types of relation types, the negative relation type accounted for a disproportionate share of 85.5%, and the data were severely unbalanced. Negative indicated that there was no interaction relation between the two drugs in the sentence. Previous researchers have typically filtered out these negative samples where no interaction relation exists, such as the study by [PERSON] et al. We therefore followed that practice by discarding that class and considering only data for which a relation existed for the experiment. Finally, we obtained 3000 training data (1.1 MB), 500 validation data (190 KB) and 500 test data (200 KB). Following the data preprocessing method proposed above, the positions of two drug entities were marked in each sentence and the true relation type were indicated. CIVIC refers to the interaction between drugs and variants, and the corpus comes from PubMed Central, which contains a large amount of biomedical literature. We obtained 3000 training data (1.5 MB), 500 validation data (270 KB) and 500 test data (250 KB) from it. The dataset defines four relation types: sensitivity, resistance, response, resistance or non-response, and the distribution of relation types is relatively balanced. For the CIVIC dataset, we follow the data processing method of previous researchers and combine it with the method of this paper. We use the entity tagger from Literome to tag the two entities mentioned in the sentences, namely drug and variant, and label the entity location and true relation labels.  
In addition, this paper uses the evaluation indexes P(Precision), R(Recall) and F1 score to analyze and evaluate the experimental results. Their formulas are expressed as:  
\[P=\frac{TP}{TP+FP} \tag{14}\]  
\[R=\frac{TP}{TP+FN} \tag{15}\]  
\[F1=\frac{2*P*R}{P+R} \tag{16}\]
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Microstructure Characterization Methods
#### 2.4.3 Raman Spectroscopy  
Raman spectroscopy is an analytical method based on the Raman scattering effect, which analyzes the scattering spectra that are different from the frequency of the incident light in order to obtain information about molecular vibration and rotation, and is applied to the study of molecular structure [33].  
Raman spectra with 533 nm laser wavelength were collected to analyze the structural state of carbon and examine the existence of graphene in composites at room temperature using a inVia confocal Raman microscope (Renishaw, England) over a range of 100-3500 cm\({}^{-1}\).
## 3 Notations and Assumptions
### Notations  
\begin{tabular}{r l} \(P\) & Manufacturer's production rate \\ \(A_{R}\) & Retailer's ordering cost of finished products per order \\ \(\hat{A}_{R}\) & Amount of fixed carbon emissions per order for the retailer \\ \(A_{M}\) & Manufacturer's ordering cost of raw material per order \\ \(\hat{A}_{M}\) & Amount of fixed carbon emissions per order for the manufacturer \\ \(r\) & Amount of raw materials required to produce one unit of finished product \\ \(S\) & Manufacturer's setup cost per production cycle \\ \(\hat{S}\) & Amount of fixed carbon emissions per setup for the manufacturer \\ \(c_{1}\) & Manufacturer's unit raw material cost \\ \(\hat{c}_{1}\) & Amount of associated carbon emissions per unit of raw material procurement for the manufacturer \\ \(c_{2}\) & Manufacturer's unit production cost \\ \(\hat{c}_{2}\) & Amount of associated carbon emissions per unit of production for the manufacturer \\ \(v\) & Retailer's unit purchase price at the manufacturer \\ \(\hat{v}\) & Amount of associated carbon emissions per unit of purchase for the retailer \\ \(h_{b}\) & Retailer's holding cost of finished goods per unit per unit time \\ \(\hat{i}_{b}\) & Amount of carbon emissions per unit of finished goods held per unit time for the retailer \\ \(h_{m}\) & Manufacturer's holding cost of raw material per unit per unit time \\ \(\hat{i}_{m}\) & Amount of carbon emissions per unit of raw material held per unit time for the manufacturer \\ \(h_{v}\) & Manufacturer's holding cost of finished goods per unit per unit time \\ \(\hat{i}_{v}\) & Amount of carbon emissions per unit of finished goods held per unit time for the manufacturer \\ \(C_{T}\) & Retailer's fixed shipping cost per shipment \\ \(\hat{C}_{T}\) & Amount of fixed carbon emissions per shipment for the retailer \\ \(C_{t}\) & Retailer's variable shipping cost per unit \\ \(\hat{C}_{t}\) & Amount of associated carbon emissions per unit shipped for the retailer \\ \(C\) & Carbon tax per unit of carbon emission \\ \(\theta_{1}\) & Carbon deterioration rate of the raw material \\ \(\theta_{2}\) & Deterioration rate of the finished goods \\ \(p\) & Retailer's unit selling price \\ \(D(p)\) & Demand rate, which is dependent on the unit selling price \(p\) \\ \(\omega\) & Technology investment for carbon emission reduction \\ \(m(\omega)\) & Proportion of reduced carbon emissions as a function of \(\omega\) \\ \(Q\) & Retailer's order quantity \\ \(n\) & Number of shipments from the manufacturer to the retailer during a production cycle \\ \(q\) & Quantity shipped from the manufacturer to the retailer per shipment \\ \(T_{b}\) & Length of the retailer's replenishment cycle \\ \(T_{v}\) & Length of the manufacturer's production cycle \\ \(T_{s}\) & Length of the manufacturer's production period per production cycle \\ \(T_{p}\) & Length of period for the manufacturer to manufacture and deliver the first batch of finished \\ \(n\) & products to the retailer \\ \(\ast\) & The superscript represents the optimal equilibrium value. \\ \end{tabular}
## 3 Results and Discussion
### Removal of Basic Blue Properties
#### 3.2.1 Effect of Initial Concentration  
The initial dye concentration provides an important driving force to overcome all mass transfer resistances of dye between the aqueous and solid phases [56; 57].  
The initial concentrations were varied in the range of 25 mg/L to 1000 mg/L and 0.1 g of WB was used at natural pH in this study. The removal percentage showed a decreasing trend as the initial dye concentration of the dye is increased. At lower concentrations, all basic blue 41 ions present in the medium could interact with the binding sites, resulting in higher dye removal (%). At higher concentrations, the dye removal (%) shows a decreasing behavior because of the saturation of the adsorption sites. The highest percentage of 100% was obtained for C\({}_{\text{i}}\) values between 25 mg/L to 150 mg/L. It reached a value of 48% at higher C\({}_{\text{i}}\) values of 1000 mg/L. However, the removal capacity of the WB materials for BB-41 dye increased gradually as the initial concentration (C\({}_{\text{i}}\)) of BB-41 dye increased from 25 mg/L to 1000 mg/L (Figure 5).  
The Jed-WB had different removal properties than the Med-WB material (Figure 5) and it exhibited a lower removal capacity and percentage value. The maximum removal percentage, of 85%, was achieved only at the C\({}_{\text{i}}\) of 25 mg/L. This difference could be related to the specific surface area. However, this fact did not explain the reason behind such behavior. Indeed, the Jed-WB exhibited a higher specific surface area, compared to Med-WB, but it has a lower removal capacity. Similar studies indicated that the removal capacity was not related directly to the specific surface area values [58; 33], however, it was related to the available removal sites on the surface of these materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **S\({}_{\text{BET}}\) (m\({}^{2}\)/g)** & **P.V (cc/g)** & **A.P.D. (nm)** \\ \hline Jed-WB & 1.00 & 0.001 & 7.81 \\ Med-WB & 2.76 & 0.005 & 7.95 \\ B-Med * & 3.77 & 0.003 & 4.60 \\ B-Jed * & 4.42 & 0.014 & 12.80 \\ \hline \hline \end{tabular}  
* stands for sample treated with the NaOH solution. S\({}_{\text{BET}}\): specific surface area, P.V.: average pore volume, A.P.D.: average pore diameter.  
\end{table}
Table 2: Microtextural properties of different waste bricks before and after treatment with the NaOH solution.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Methods
#### 2.3.4 Imbalanced Sample Processing  
Two critical challenges emerged from the dataset; namely, a regional distribution imbalance across geographic areas and a severe class imbalance with a positive to negative sample ratio of 1:10. These issues could compromise the model's reliability and spatial generalizability, therefore requiring appropriate solutions.  
The dataset was initially divided through stratified sampling [49], with 80% of samples from each macro-region allocated for training and validation, while the remaining 20% were reserved as a test set for subsequent analysis. For the training and validation portion, leave-one-region-out cross-validation (LORO-CV) was employed to ensure spatial  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Search Space** \\ \hline \multirow{5}{*}{XGBoost} & n\_estimators & [50, 100, 200, 300] \\  & max\_depth & [5, 10, 20, 40] \\  & learning\_rate & [0.01, 0.1, 0.2, 0.3] \\  & subsample & [0.8, 0.9, 1.0] \\  & colsample\_bytree & [0.8, 0.9, 1.0] \\ \hline \multirow{5}{*}{RF} & n\_estimators & [50, 100, 200, 300] \\  & max\_depth & [5, 10, 20, 40] \\  & min\_samples\_split & [2, 5, 10] \\  & min\_samples\_leaf & [1, 2, 4] \\  & max\_features & [’sqrt’, ’log2’, ’None] \\ \hline \multirow{5}{*}{LightGBM} & n\_estimators & [50, 100, 200, 300] \\  & max\_depth & [5, 10, 20, 40] \\  & learning\_rate & [0.01, 0.1, 0.2, 0.3] \\  & subsample & [0.8, 0.9, 1.0] \\  & colsample\_bytree & [0.8, 0.9, 1.0] \\ \hline \multirow{5}{*}{LR} & C & [0.01, 0.1, 1, 10, 100] \\  & penalty & [’l1’, ’12’, ‘elasticnet’] \\ \cline{1-1}  & solver & [’liblinear’, ’lbfgs’, ’saga’] \\ \cline{1-1}  & max\_iter & [100, 200, 500, 1000] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Target hyperparameters and search spaces.  
generalizability, which is a region-based extension of k-fold cross-validation [50]. During LORO-CV, one macro-region served as the validation set while all remaining regions were used for training, with this process repeated until each region had been validated once. This approach guarantees model evaluation on geographically unseen areas, providing a robust assessment of spatial transferability.  
Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), applied exclusively to training data within each cross-validation fold [51]. This technique generates synthetic minority class samples by interpolating within the feature space, effectively balancing class distribution while mitigating overfitting risks and enhancing model sensitivity to the minority class. Validation sets remained unmodified to preserve their original class distributions and prevent data leakage, thereby ensuring objective evaluation and model generalizability. Table 4 presents the regional composition and sample distributions across macro-regions.
# Link Pruning for Community Detection in Social Networks
## 5 Conclusions  
In this paper, we have proposed an efficient community detection algorithm based on link pruning. We also analyzed the three types of link attributes, which varied depending on internal or external links, and theoretically proved that the proposed algorithm is effective using the stochastic block models. We have conducted extensive experiments using various synthetic and real-world social networks. Our proposed algorithm shows that the pruning of more links with low values of link attributes increased graph sparsification and the strength of the community structure. Overall, we believe that our work is a step toward developing an efficient community detection algorithm for social network analysis.  
Conceptualization, J.K., S.J. and S.L.; methodology, J.K., S.J. and S.L.; software, J.K. and S.J.; validation, J.K., S.J. and S.L.; formal analysis, J.K., S.J. and S.L.; investigations, J.K., S.J. and S.L.; resources, J.K. and S.J.; data curation, J.K. and S.J.; writing--original draft preparation, J.K., S.J. and S.L.; writing--review and editing, S.L.; visualization, J.K., S.J. and S.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government(MOE).  
Not applicable.  
Not datasets were generated during the current study.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pruning Rate (\(\alpha\))** & **Louvain** & **LPA** & **Infomap** & **Fastgreedy** & **Walktrap** \\ \hline
0 & **0.926** & 0.786 & 0.825 & 0.867 & 0.849 \\ \hline
0.1 & **0.911** & 0.760 & 0.803 & 0.889 & 0.811 \\ \hline
0.2 & **0.892** & 0.741 & 0.782 & 0.886 & 0.793 \\ \hline
0.3 & **0.873** & 0.725 & 0.767 & 0.868 & 0.783 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modularity values of clustering algorithms with various link pruning rates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pruning Rate (\(\alpha\))** & **Louvain** & **LPA** & **Infomap** & **Fastgreedy** & **Walktrap** \\ \hline
0 & **0.0012** & 0.0033 & 0.0029 & 0.0015 & 0.0028 \\ \hline
0.1 & **0.0011** & 0.0033 & 0.0029 & **0.0011** & 0.0029 \\ \hline
0.2 & **0.0012** & 0.0033 & 0.0031 & **0.0012** & 0.0031 \\ \hline
0.3 & **0.0028** & 0.0033 & 0.0031 & **0.0028** & **0.0028** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Conductance values of clustering algorithms with various link pruning rates.
## 3 D2 WaVe: Design Rationale and System Architecture
### Video Frames Authentication Scheme
#### 3.3.1 Watermark Generation  
For a street view, a surveillance camera \(S\) produces video streams for a traffic management system. At the same time, a CAV camera \(C\) also continuously produces drive-related video streams that record road situations. Here, we use \(F_{i}\) to represent a frame in a video recording \(V=\{F_{1},\dots,F_{n}\}\) where \(n\) is the total number of frames. Given a frame \(F_{i}\), the FIAE-GAN-based encoder can generate a watermark \(W_{in}\) and output a watermarked frame \(F_{i}^{{}^{\prime}}=DW_{embed}(F_{i},W_{in})\). Through the FIAE-GAN watermark hiding process, a set of watermarked frames \(F^{{}^{\prime}}=\{F_{1}^{{}^{\prime}},\dots,F_{m}^{{}^{\prime}}\}\) can be published along with raw video data \(V\). For efficiency consideration, a data owner embeds \(W_{in}\) in some frames for authentication purposes. Thus, the total number of watermarked frames \(m\leq n\).
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 2 Results
### Antioxidant Activity of Hops and Cannabis  
The observed TEAC and FRAP values determined in hops and cannabis are presented in Figure 3. The antioxidant activity of extracts derived from fresh, untreated hops was 3.6 TEAC (M) dry matter\({}^{-1}\) and 2.32 FRAP (M) dry matter\({}^{-1}\). Extracts from fresh, untreated cannabis samples had 2.29 TEAC (M) dry matter\({}^{-1}\) and 0.25 FRAP (M) dry matter\({}^{-1}\) antioxidant values. The lower antioxidant activity observed in cannabis relative to hops can be attributed to the presence of \(\alpha\)-acids and \(\beta\)-acids in hops [25]. Analysis of variance tests showed that pre-freezing, freeze-drying and MAHD significantly affected (\(p<0.05\)) the antioxidant activity of hops and cannabis when evaluated with the DPPH and FRAP assays.  
Figure 1: Reaction underlying the (**A**) DPPH (2,2-diphenyl-1-picrylhydrazyl) and (**B**) FRAP (ferric 2,4,6-tri(2-pyridyl)-s-triazine) antioxidant assays.  
Figure 2: Calibration curves used for (**A**) DPPH (2,2-diphenyl-1-picrylhydrazine) reduction and (**B**) FRAP (reduction of Fe\({}^{3+}\) to Fe\({}^{2+}\)) assays in the presence of different Trolox concentrations.  
Pre-freezing the hops and cannabis samples before drying increased the antioxidant values by 13% (DPPH assay) and 29.9% (FRAP assay) for hops, and by 7.7% (DPPH assay) and 19.4% (FRAP assay) for cannabis (Figure 3). DPPH assays used for this study show that freeze-drying and MAHD significantly (\(p<0.05\)) reduced the antioxidant activity in hops by 79% and 80.2%, respectively, compared to pre-frozen, undried samples. A similar observation was made for hops using the FRAP assay, as antioxidant activity was reduced by 70.1% and 70.4% under freeze-drying and microwave-assisted hot air drying, respectively, when compared to pre-frozen, undried hops. Both freeze-drying and MAHD significantly (\(p<0.05\)) reduced the antioxidant activity of cannabis by 60.5% using the DPPH assay. However, there was no significant (\(p<0.05\)) difference between the antioxidant activity values for pre-frozen, freeze-dried and microwave-assisted hot air dried cannabis samples using the FRAP method.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.3 Computation Model  
In this subsection, we investigate the transmission time and energy consumption associated with processing each task at the available servers. Moreover, the available capabilities in CPU cycles for each server \(j\) are denoted by \(f_{j}\), where \(j\in\{0...J+1\}\).  
Note that the computation capabilities at the cloud are more powerful than GBSs and UAVs and the capabilities of GBSs and UAVs are more powerful than those of mobile devices. In addition, in this article, we assume that the computation capabilities of GBSs and UAVs are equally shared between all the mobile devices that transmit their tasks to the same server. Moreover, the computation capabilities assigned for each device at server \(j\) can be expressed as follows:  
\[f_{i}=\sum_{j=0}^{J+1}\frac{fy_{i,j}}{\sum_{i=1}^{K}y_{i,j}} \tag{7}\]  
As a consequence, the time and energy spent on executing the task of each mobile \(i\) can be computed as:  
\[T_{i}^{Comp}=\frac{\beta_{i}}{f_{i}} \tag{8}\]  
\[E_{i}^{Comp}=\zeta_{i}T_{i}^{Comp} \tag{9}\]  
where \(\zeta\) is a constant coefficient that denotes the consumption of energy where the mobile device is being idle.  
Finally, the overhead for processing the task of each device user \(i\), in which load balancing, communication, and computation models are considered, can be expressed as:  
\[\Omega_{i}=w_{i}^{t}(T_{i}^{Comm}+T_{i}^{Comp})+w_{i}^{\epsilon}(E_{i}^{Comm} +E_{i}^{Comp}) \tag{10}\]where \(w_{i}^{t}\) and \(w_{i}^{e}\in[0,1]\) are scalar weights denoting the time and energy consumption, respectively, which depends on the nature of the application. For instance, if \((w_{i}^{t}=0)\) and \((w_{i}^{e}=1)\), the mobile user targets an application with an energy-sensitive, or maybe the battery of the device is in a low state. Whereas, if \((w_{i}^{t}=1)\) and \((w_{i}^{e}=0)\), the mobile user targets an application that is time-sensitive. Additionally, for different objectives, \(w_{i}^{t}\) and \(w_{i}^{e}\) are set at different values. Moreover, through the application settings, these weights can be adjusted at any time.
# Wavefront Shaping and Its Application to Enhance Photoacoustic Imaging
## 5 Discussion and Conclusions
However, the effective rotation range is only a few milliradians in the targeted scenario. Beyond that, the new position is far away from the initial focus, the phase compensation difference is no longer \"geometrical\" as expressed by the memory effect, and the phase pattern on the DMD must be updated accordingly. One potential solution is to measure the transmission matrix of the experimental sample in advance [53]. Through manipulating the transmission matrix, we are able to \"raster\" scan the optical focus and, hence, form 2D or 3D PA images, within a field of view that is much larger than the optical focal spot size [21; 22; 53]. Measurement of the transmission matrix and the scanning speed are only limited by the refreshing rate of the DMD; there is no need to perform a full process of iterations or transmission matrix measurements for each individual point. This can greatly reduce the data transfer and computation burden on the processor, until the studied sample has moved or changed that significantly alters the transmission matrix. Faster optimization may also allow for higher focal enhancement ratio R at the ROI before and after the optimization, which is imperative for many applications that desire intensive and tightly-focused light delivery within tissue. As discussed earlier, the enhancement ratio is proportional to N/M [80], i.e., the more is the input modes N on the wavefront modulator, the higher R can be. For example, [PERSON] et al. [48] used N = 192 \(\times\) 108 on an SLM and obtained a focal enhancement ratio of \(\sim\)6000, with an optimization process of several hours. Slow speed has prevented this implementation from further applications. More recently, [PERSON] et al. [56] used megapixels on a DMD and achieved an enhancement ratio of \(>\)100,000, with an optimization process slightly longer than one hour, even though a much higher N was employed. This will not be possible without the use of a much faster-responding modulator DMD, even though the full power of the DMD has not been released yet and the focusing speed can be further improved. In conclusion, using photoacoustic signal as a virtual internal guide star allows for acoustic- or optical-diffraction limited optical focusing with an intensive focal-to-background ratio within biological tissue or tissue-like media. With further engineered, PAWS can be a promising tool to bring the sensitivity, the resolution, and potentially the penetration depth of existing photoacoustic imaging implementations to a new level. The work has been supported by the Hong Kong Research Grant Council (no. 252044/16E) and the National Natural Science Foundation of China (no. 81671726 and no. 81627805)  
[PERSON] initiated the manuscript. All authors participated in the revision of the manuscript. [PERSON] provided overall supervision. The authors declare no conflict of interest.