esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 2 Small Molecules
### Immunosuppressants
#### 2.2.3 Niosomes and Nanoparticles  
Researchers have developed a series of nanocarriers to enhance drug solubility and promote penetration, such as liposomes, transferosomes, ethosomes, etc. [PERSON] et al. first proposed ceramide niosomes loaded with methotrexate and niacinamide (MTX/NIC). Niacinamide is a hydrophilic substance that can form hydrogen bonds with MTX and significantly solubilizes MTX. In vivo penetration experiments have shown that ceramides can significantly promote skin penetration and retention. The total skin retention of MTX from MTX/NIC ceramide niosomes was measured at 0.47 \(\pm\) 0.04 \(\upmu\)g/cm\({}^{2}\), which is 3.36 times greater than that achieved from the MTX suspension. Proinflammatorycytokine levels were markedly reduced in both the LPS-stimulated HaCaT cell model and the imiquimod-induced psoriasis mouse model [63]. Gold nanoparticles (AuNPs) are an excellent option for localized drug delivery systems owing to their editable properties. [PERSON] et al. prepared functional gold nanoparticles to deliver MTX. The encapsulation efficiency of MTX on the nanoparticle is 70-80%, and the release rate reached 80% within 1 h and 95% within 24 h. Preliminary toxicity tests on keratinocytes showed that AuNPs were non-toxic. The distribution of the conjugate on bare mouse skin was monitored in vivo using UV-Vis spectroscopy following a 24 h administration period. The results demonstrated enhanced MTX penetration into both epidermal and dermal layers when delivered through the gold nanoparticle treatment, as opposed to the application of free MTX. Pentoxifiline has also been reported to have the activity of downregulating psoriasis-related inflammatory markers and can reduce the dose-dependent side effects of cyclosporine [64]. [PERSON] et al. designed a niosome that co-delivered cyclospor and pentoxifiline, with a \(-\)37.5 mV Zeta potential. Based on the permeation study data, niosomes raised the permeation rate of MTX from 23.4 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{cm}^{2}\)/h to 37.24 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{cm}^{2}\)/h, compared with dispersion. The restricted movement of negatively charged vesicles in the bloodstream is likely due to decreased electrostatic repulsion within the intercellular spaces [65].
## Experimental Results
### Overall Performance of SaSLM
#### 6.2.1 Performance with RealSet  
Note that we do not compare our method with BGCN for this experiment. The reason is that BGCN was originally designed for set-based recommendation, which requires constructing the user-bundle graph. However, the graph cannot be constructed for RealSet, since every set is randomly constructed, and therefore each user will interact with a unique set.  
Results are recorded in Table 4. We first look at the comparison on RealSet-explicit. As shown in the table, although SaSLM could not outperform the baselines for set-rating prediction, it always showed significant improvement over the compared methods for item-rating prediction. Since the ultimate goal of SPL is to reveal users' preferences for individual items, SaSLM shows its effectiveness for this task. Generally, SPL methods with MF had good performance, except that the second-lowest RMSE for set-rating prediction was achieved by BCF. While this can be counter-intuitive, since the methods with the more expressive NCF had poorer performance, it is justifiable for SPL. The reason is that the expressiveness of NCF can be shown only if enough strong supervision can be provided. However, RealSet contains relatively sparse data to train neural methods and SPL contains no direct supervision to reveal item-level preference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **RealSet-Explicit** & **RealSet-Implicit** \\ \hline ARM & 0.5861 & 0.4042 \\ VOARM & 0.5250 & 0.3860 \\ ESARM & 0.5176 & 0.3742 \\ SaSLM & **0.5151** & **0.3712** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting set ratings using different methods.  
We then analyze the results on the RealSet-implicit dataset. The results are similar to those of RealSet-explicit. SaSLM outperformed other methods significantly for item-rating prediction. The difference is that the lowest and second-lowest RMSE for set-rating prediction were achieved by VOARM and MFSet, respectively. In set-rating prediction, SaSLM significantly outperformed ARM, VOARM and ESARM when using NCF, though being significantly outperformed when using MF. While similar to RealSet-explicit, for which methods with MF generally perform better than those with NCF, SaSLM-NCF achieved a lower RMSE in set-rating prediction than SaSLM-MF. In fact, by sampling to select subsets from sets of items, SaSLM can take advantage of a method similar to dropout to avoid overfitting.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 5 Experiments and Results
### Training Phase Experiments  
Effect of Pair Similarity Assumptions  
The Baseline experiment generates pairs under the assumption that a patch and its identity are a similar pair, whereas a patch and its 90-degree-rotated identity are a different pair, in terms of the text line pattern they contain. The network could potentially use a trivial frame alignment to classify the similarity of a pair as they are extracted from the same spatial location. Experiments with spatial jittering and gapping show that removing the clues at the frame is not necessary for binary document images, likely because all possible low-level features may come across the frame of randomly cropped patches, and eventually the network has to learn all the low level features.  
We experiment with different similarity assumptions for training the network. The second column in Table 4 presents the comparison among using similarity assumption identical, neighbouring, horizontal rotation, and horizontal flipping. The results show that assuming identical and neighbouring patches are similar, is advantageous in terms of performance. A potential reason for the poor performance of using horizontal rotation and horizontal flipping might be due to the inessential low-level features that are not used during the pseudo-RGB image generation phase.  
To further demonstrate the advantage in terms of training time, we reported the accuracy logs in addition to qualitative results. Figure 9 shows that augmenting similarity assumption with horizontal rotation and horizontal flipping increases the training time. This is because the network additionally has to learn particular features that are present in rotated and flipped inputs but not in the input document. In addition to quantitative results, the qualitative results show that the similarity assumption of identical and neighbouring patches provide sharper blob lines. It is worth noting, that the validation accuracy reaches higher accuracy than the training accuracy. This can be explained by considering the size of the each subset. As the training set is much larger than the validation set and is built using random patches, it will contain a higher level of noise. Thus, the model can reach higher accuracy on the validation set much faster.
## 3 Results and Discussion  
### Address Semantic Model Pretraining  
We examine the semantic understanding effectiveness of the ASM by examining the prediction accuracy of address characters for permutation language objective. In the experimental design, we refer to the influencing factors of pretraining in a study by [PERSON] et al. and use the number of address-transformer modules and whether the numbers in the address records are replaced with uniform identifiers as the independent variables for the analysis of the pretraining hyperparameters [36]. The purpose of the experiments in this section is to validate the effectiveness of the ASM without testing or predicting it, so only the training set and the verification set are required for pretraining the model. Due to the large address corpus, we set the proportions of training set and verification set to be approximately 99% (1,537,532) and 1% (15,000), respectively.  
The optimal values for the relevant hyperparameters were determined by drawing on previous studies of pretrained language models and previous experiments. It is shown  
Considering that most of the numbers in the address records have only geospatial property and no semantic information (e.g., \"No. 116 Tianmushan Road\" and \"No. 226 Tianmushan Road\", there is no difference in their contexts other than numbers, so the model cannot make accurate predictions at all. Therefore, we replaced all Arabic numerals in the address corpus with a uniform identifier: \"CODE\". Since [PERSON] et al. [36] used BERT [32] to construct the ALM for pretraining the address corpus, we used their method for comparison. Figure 4 shows a comparison of the training loss gradient curves of the replaced and unreplaced \"CODE\" corpus under the ASM. The training loss curve of the model with the \"CODE\" replacement is always below the original address corpus, demonstrating that the numbers confound the predictive target of the model and reduce the predictive power of the model. As can be seen from Table 5, the prediction accuracy of the ASM increased by approximately 7 percentage points after the replacement with \"CODE\". In addition, when compared with the ALM, our model's prediction accuracies all improved, indicating that the ASM performs better in understanding address semantics.
## 2 Theoretical Background
### Cost-Sensitive Classification  
The theory behind cost-sensitive classification is to either (a) re-weight training inputs in line with a pre-defined class cost, or (b) predicting a class with lowest misclassification cost [1,40]. This scheme transforms the classifier into cost-sensitive meta-classifier. Improving the estimates probability of the base classifier is done usually by the use of bagged classifier. In essence, the predictor splits the outputs into minority or majority class according to an adjusted probability threshold. A general cost matrix \(C\) is illustrated in Equation (1). Where \(\lambda\) and \(\mu\) denote the cost of each class misclassification.  
\[C=\left[\begin{array}{cc}0&\lambda\\ \mu&0\end{array}\right] \tag{1}\]  
Matrix \(C\) shown in Equation (1) is a general illustration of cost re-weighting, in which \(\lambda\) and \(\mu\) are pre-defined cost factors that are determined by domain experts. Therefore, the classification algorithm uses matrix \(C\) to predict the minority and majority classes according to a specific probability threshold over the output of the classifier.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Forecast Evaluation Metrics  
This section discusses the three evaluation metrics for the individual and combined point forecasts. The mathematical formulations for these evaluation metrics are presented. Three main accuracy metrics for evaluation of the point forecasts made from the prediction models and their combination (ANN, SGB, GAM and QRA) are the mean absolute error (MAE), mean absolute percentage error (MAPE) and the root mean squared error (RMSE). They are as formulated in Equations (15)-(17), where \(m\) is the number of observations in the test dataset, \(y_{t}\) is the estimated values of the response variables and \(\epsilon_{t}\) is the residual of the \(i\)th observation given as \(\epsilon_{t}=y_{t}-\hat{y}_{t}\)  
\[\text{MAE}=\frac{1}{m}\sum_{i=1}^{m}|\epsilon_{t}| \tag{15}\]  
\[\text{MAPE}=\frac{100}{m}\sum_{i=1}^{m}|\frac{\epsilon_{t}}{\hat{y}_{t}}| \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}|\epsilon_{t}^{2}|} \tag{17}\]
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 6 Conclusions  
In this paper, a hybrid DL model is proposed for PPI extraction tasks. The model innovatively integrates bidirectional LSTM with CNN in a two-embedding channel (word embedding and SDP embedding) architecture.  
CNN with word embeddings has been shown to be effective in PPI extraction tasks, as it has the ability to extract salient n-gram features from the input sentence to create an informative latent semantic representation of the sentence. However, a main issue with CNNs is their inability to model long-distance contextual information for PPI extraction in a long sentence. To solve this problem, we employ bidirectional LSTM to extract the semantic information in both the preceding and succeeding contexts, because the architecture of LSTM is able to model long-distance contextual information by memorizing over previous computations and utilizing this information in current processing. Furthermore, CNN is applied to encode the important information contained in the bidirectional LSTM networks, and to capture the local features and structure information effectively.  
Under the same experimental environment, the results show that the proposed model is superior to the non-hybrid DL models, including CNN-based and LSTM-based models. In addition, the proposed bidirectional LSTM+CNN model outperformed another hybrid model (CNN+bidirectional LSTM model) greatly for PPI extraction. The visualization and comparison of the hidden features learned by different DL models further confirmed the effectiveness of the proposed model.  
For future work, we will apply the proposed hybrid DL model to other NLP tasks in order to explore its applicability, and will consider more complex network architectures (e.g., a deep hybrid model with more layers or a deep reinforcement learning model) to improve the performance.  
[PERSON] and [PERSON] conceived and designed the model; [PERSON] performed the experiment and analyzed the results; [PERSON] wrote the preliminary version of this manuscript; [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the National Natural Foundation of China under grant no. 61472117.  
The authors declare no conflict of interest.
## 3 Results  
### Reliability  
Reliability results are presented in Table 1. A small to moderate learning effect was observed for the scores computed during the _Beat Master_--_Never Stop_ game, while this was large to very large for the scores computed during _Beat Master_--_Turbo_. The relative and absolute reliability of _Beat Master_--_Never Stop_ was poor to moderate, while it was good to very good for the _Beat Master_--_Turbo_ version. Regarding the _Tracker Master_ game, we observed a tendency toward a moderate systematic effect for Sensibility but not for the other scores. Relative and absolute reliability were considered as low to moderate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **Test 1 (Mean \(\pm\) SD)** & **Test 2 (Mean \(\pm\) SD)** & **Hedges’ g (g)** & **ICC** & **SEM (\%)** & **MD (\%)** \\ \hline \multicolumn{6}{c}{_Beat Master_—_Never Stop_} \\ \hline Achievement (\%) & 55.0 \(\pm\) 19.8 & 67.7 \(\pm\) 19.6 * & 0.62 & 0.68 & 26.1 & 44.3 \\ \hline F1 score (\%) & 91.9 \(\pm\) 4.8 & 94.6 \(\pm\) 4.7 & 0.55 & 0.61 & 4.3 & 11.1 \\ \hline Accuracy (\%) & 90.2 \(\pm\) 6.2 & 93.6 \(\pm\) 6.0 & 0.54 & 0.51 & 6.5 & 16.6 \\ \hline Sensibility (\%) & 93.8 \(\pm\) 4.9 & 95.7 \(\pm\) 3.8 & 0.41 & 0.54 & 3.2 & 8.3 \\ \hline Efficiency (\%) & 87.8 \(\pm\) 7.3 & 91.9 \(\pm\) 7.1 & 0.55 & 0.58 & 6.7 & 16.6 \\ \hline \multicolumn{6}{c}{_Beat Master_—_Turbo_} \\ \hline Achievement (\%) & 57.9 \(\pm\) 16.0 & 74.1 \(\pm\) 14.5 ** & 1.01 & 0.87 & 18.2 & 33.3 \\ \hline F1 score (\%) & 92.7 \(\pm\) 3.9 & 96.1 \(\pm\) 2.9 ** & 0.94 & 0.85 & 3.2 & 8.3 \\ \hline Accuracy (\%) & 92.1 \(\pm\) 3.9 & 95.2 \(\pm\) 4.3 ** & 0.67 & 0.78 & 3.2 & 8.3 \\ \hline Sensibility (\%) & 93.6 \(\pm\) 5.2 & 97.2 \(\pm\) 1.7 * & 0.87 & 0.77 & 4.2 & 11.1 \\ \hline Efficiency (\%) & 88.8 \(\pm\) 5.8 & 93.9 \(\pm\) 4.5 ** & 0.92 & 0.85 & 4.4 & 11.1 \\ \hline \multicolumn{6}{c}{_Tracker Master_} \\ \hline Achievement (\%) & 82.8 \(\pm\) 5.2 & 85.2 \(\pm\) 4.9 & 0.45 & 0.38 & 6.0 & 13.9 \\ \hline F1 score (\%) & 82.4 \(\pm\) 4.0 & 83.9 \(\pm\) 2.1 & 0.45 & 0.41 & 3.6 & 8.3 \\ \hline Accuracy (\%) & 92.0 \(\pm\) 3.5 & 92.1 \(\pm\) 1.8 & 0.04 & 0.22 & 3.3 & 8.3 \\ \hline Sensibility (\%) & 74.7 \(\pm\) 4.7 & 77.2 \(\pm\) 3.3 & 0.58 & 0.60 & 5.3 & 11.1 \\ \hline Efficiency (\%) & 88.7 \(\pm\) 2.3 & 89.5 \(\pm\) 1.3 & 0.37 & 0.24 & 2.2 & 5.5 \\ \hline \end{tabular}  
ICC = intraclass coefficient correlation; SEM = standard error of measurement; MD = minimum difference to be considered real; * significative difference from Test 1 with \(p\) < 0.05; ** significative difference from Test 1 with \(p\) < 0.01.  
\end{table}
Table 1: Reliability of the scores provided by the virtual reality device during the games.
# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 4 Deep Learning-Based Ensemble Method for VA Measurement
### ML Models and Classification Performances
#### 4.2.2 ML Model for Step-2-1: SVM (RBF Kernel)
Using the SVM class in the scikit-learn and the resized dataset above, the SVM-RBF-Kernel model is trained by varying kernel functions, \(c\) that determines how much error the model tolerates, and \(\gamma\) that determines how flexible the hyperplane is set. In this study, we select the SVM model using the RBF kernel and train the model by setting \(\gamma\) to 0.1 and increasing \(c\) from 1 to 100 by 20. The confusion matrix from the testing of SVM-RBF-Kernel model is shown in Figure 10.  
In the confusion matrix from testing of an SVM-RBF-Kernel, the classification accuracy is about 58% for Class 1 and about 38% for Class 2, so the overall classification accuracy is about 96%. There are more Class 1 misclassified images than Class 2 misclassified images. The reason might be that there are similar abnormalities such as picture blurring or macular pigmentation and depigmentation in Class 2 as in Class 1.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 2 Related Works  
The optimization of energy consumption and the maximization of a network's lifetime are related to different factors such as data routing, sink assignment, and optimal multiple sink locations (i.e. finding the optimal number and the best placement of the sinks).
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 2 Related works  
Most of the work in wireless sensor networks with mobile elements and ad-hoc networks aim at providing wireless communication during natural disaster phenomena [7; 10; 11; 12]. Moreover, the ubiquitous nature of smart devices such as smartphones is largely exploited by many works, whose main focus consists of extending the wireless connectivity coverage in areas with missing or damaged infrastructure [5; 13].  
Smart devices constitute one key element in survivor-rescuer systems [14; 11]--they send out location data of the survivors to rescue teams, for instance. Accordingly, the works in [1; 3] show how such entities communicate with each other via aerial base stations, i.e., UAVs that fly over a disaster area with on-board femto-cells. However, such works provide no considerations on the energy-efficiency of the proposed solutions.  
Other works with flying ad-hoc networks present performance tradeoffs as a function of parameters such as the UAV height and placement to maximize the coverage in a multi-UAV system [2; 15; 16]. By contrast, we focus on a single-UAV system which requires no synchronization, placement map over a given area, or task scheduling among UAVs. Moreover, our approach leverages the heterogeneity and ubiquitous nature of smart devices (representing survivors) to build a cooperative scheme underlaying the UAV. In fact, such a scheme results in a larger number of alive nodes over time, hence ensuring a wider coverage area from which nodes can disseminate their data and ask for help; at the same time, it reduces the flying time (number of stops) of an UAV over the area of interest, hence its energy consumption.  
Recently, interesting works have appeared addressing the drone trajectory optimization regarding different parameters (altitude, energy consumption and harvesting, throughput, etc) [32; 33; 34; 35]. These approaches do not couple their approach with a dynamic ground device organization but we believe they constitute a complementary approach to ours that deserves to be further investigated.  
While most of the current work leverages only few of the available network technologies to build their solutions upon [7], the work in [17] exploits all such interfaces for alert diffusion during disasters. However, it only mitigates the energy expenditure of the nodes by scheduling shorter wake-up periods for nodes with low available energy levels. By contrast, this work devises a cooperative and multi-tier communication scheme that achieves energy fairness among the nodes by designating only few nodes to switch on their interfaces and relay the data of the other nodes in the network. Moreover, such nodes vary over time, hence fairly distributing the energy expenditure across all the nodes in the network.
## 3 Application of PSHRisk-Tool
### Section 1: Probabilistic Seismic Hazard Analysis (PSHA)  
As explained in the methodologies in previous sections, the PSHA curve for a definite GMPE (attenuation) model depends on a definite seismic parameter. The user needs to input the seismicity constants like a, b and slip rates and as well as the magnitude of interest range. For each attenuation, the model has an individual popup interface to input the required soil condition of the site such as soil type, share wave velocity of the top 30 m of subsurface profile (\(V_{s30}\)), fault mechanisms, etc. This tool is dedicated to showing the single source PSHA as well as multiple source PSHA. By selecting a PGA range through the input panel and using the selected attenuation equation, this window will show the segmental view of the source, distance and magnitude distribution for the individual source (point, line, area, and 3D fault).  
In the case of line and area source, the source-to-site distance is coded in such a way so that these can show fixed equal ten segments between the shortest circle and the longest circle from the site. As a result, the ten bars in the plot express the probability density function for distance \(f_{R_{i}}(r)\) and magnitude \(f_{M_{i}}(m)\).  
Then, the probability of exceedance is determined using strong GM models, where PSHRisk-tool accumulated 21 default attenuation models (GMPE). To allow the selection of GMPE to draw the hazard  
Figure 4: PSHRisk-tool flowchart showing the whole process along with output GUI.  
curve, the PSHRisk-tool will show the sub-popup-interface for each attenuation model. The single source PSHA as well as multiple sources PSHA has been adopted here to show the mean annual rate of exceedance always in terms of PGA with any user-defined range and increment. In this part of the tool, the lambda (\(\lambda\)) calculated for a single source can be saved in an EXCEL spreadsheet and the multiple sources lambda (\(\lambda\)) (total or heavy average) can be calculated by recalling the EXCEL spreadsheet stored.