esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 3 Wind Turbine Blades and Its Protection Methods  
### Wind Turbines and Blades  
There are two main types of wind turbines on the market nowadays: vertical axis and horizontal axis turbines. Due to the lower efficiency, vertical axis turbines were not considered in this paper. Modern turbines are dominantly composed of horizontal axis models, since with rotor blade pitching, the speed of rotation and hence the power output can be controlled, and the blade aerodynamics can be optimized for maximum efficiency. In most cases, the three-blade model is used as it has the highest efficiency in ratio of the number of blades and their overall weight.  
At blade design, the actual shapes are very similar within commercial turbines, although, slightly differs by each manufacturer for the best possible aerodynamics according to company preferences [9]. Common characteristics are the hollow design, to reduce weight and the turnable rotor blade tip to help overspeed limitation [10]. Modern blades generally made of Fiber-Reinforces Composites such as carbon fiber and glass fiber with a matrix material of polyester resins or epoxy resins.  
Carbon fiber generally has good braking and elasticity characteristics, with stiffness not far from steel, although it is the most expensive material component among the possible choices. Also, in regardsto lightning protection, it requires special considerations due to the fact of its material properties which are similar to a semiconductor, creating issues with lightning attachment and flashovers on the surface of the blade.  
Glass fiber, on the other hand, has lower ratings in almost all the characteristics mentioned before, but it is considerably cheaper, and it acts as an insulator. Manufacturers tend to use it with more expensive but high-quality epoxy resins to enhance the required physical properties of it [10; 11]. Although, the blade is nonconductive, it still attracts lightning due to the fact of its height; therefore, lightning protection is necessary.
# Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
## 5 Conclusions and Future Work  
We found that classification substantially outperforms clustering for PSS, even more so when the number of documents in a stream is given. Clustering with representations of the classification model gives better results than with pretrained representations. However, clustering with finetuned representations still faces two problems: the _similarity problem_, which we solved using \"switch clustering\", and the _entry problem_, which we were not able to solve. We conclude that pretrained representations are too simplistic for such a complicated task as PSS.  
In future work, we will consider changing certain parameters of the agglomerative clustering method to more suit the task of PSS. One such change could be the definition of the linkage function to only consider specific pages when merging clusters, such as the first N pages and the last N pages of documents, as this seems to be more in line with human intuition on how to perform the task. Another interesting direction for future work is to investigate the robustness of these models across different datasets, as their focus on interpage relationships might prove beneficial in scenarios with limited training data.  
We suggest not ruling out clustering entirely, as more variety in documents could both favor clustering and hinder classification.  
**Author Contributions:** Conceptualization, L.B., M.M. and R.v.H.; resources, M.M. and R.v.H.; data curation, M.M. and R.v.H.; software L.B.; formal analysis, L.B., M.M. and R.v.H.; supervision, M.M. and R.v.H.; funding acquisition, M.M.; validation, R.v.H. and M.M.; investigation, L.B., R.v.H. and M.M.; visualization, L.B., R.v.H. and M.M.; methodology, L.B., R.v.H. and M.M.; writing--original draft, L.B.; project administration, R.v.H. and M.M.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported in part by the Netherlands Organization for Scientific Research (NWO) through the ACCESS project grant CISC.CC.016.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Both the data and the code used in this research are publicly available via a GitHub Link ([[https://github.com/irlabamsterdam/PSS_clustering](https://github.com/irlabamsterdam/PSS_clustering)]([https://github.com/irlabamsterdam/PSS_clustering](https://github.com/irlabamsterdam/PSS_clustering))) (accessed on 16 May 2023).  
**Conflicts of Interest:** The authors have no conflicts of interest to declare that are relevant to the content of this article.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **FP** & **FN** & **Total** \\ \hline Normal & 239 & 707 & 946 \\ Top-K & 477 & 477 & 954 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FPs and FNs for start page classification.
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.5 AI Bias, Transparency, and Reproducibility  
Like AI and big data analytics, DT technology faces challenges related to bias, limited transparency, and issues with reproducibility. Ensuring that models are unbiased and explainable is essential to maintain credibility and fairness in healthcare applications. Inconsistent or incomplete data can lead to inaccurate or unreliable DT models [49; 72].
## 4 Discussion
### Performance of the Different Models  
In agreement with recent evidence [9; 17], we observed an increase of model fitness when data were split into a dry and a wet phase, in contrast with the general agreement that LUE can be considered constant in grasslands along the whole growing season. Recent studies addressed this problem by attempting independent LUE estimations from the PRI and SIF [19; 24]. Other models constrain LUE by decreasing a maximum value (LUE\({}_{\text{max}}\)) with meteorological variables (scalars) [11]. In the present study, good results have been achieved by splitting the growing season into a wet and dry phases, reducing model input and further calculations.  
The higher correlation coefficients observed between VIs and midday average fluxes compared to daily average fluxes during the wet phase could be due to the fact that most C uptake occur around midday during that phase of the growing season. During the dry phase however, depending on daily environmental conditions, there can be more variation in the midday average fluxes compared to daily averages. Hence during dry periods, daily averages showed more robust and higher correlations with VIs than midday averages. Overall, despite the differences observed, daily estimates were robust enough in our study to suggest their use instead of midday average flux estimates, being more representative of the total productivity of the ecosystem.  
The validation of the models using the bootstrapping resampling method confirmed the assessment made with the regular accuracy metrics. The narrow 95% confidence intervals obtained forthe \(\mathrm{R^{2}}_{\mathrm{boot}}\) reflects the low variability of the coefficient of determination obtained from the different bootstrapping samples.  
From our carbon flux estimates, we could deduce that during the growing season, midday GPP represents over half of the whole day fluxes. NEE values are negative, which means that at least during the growing season, the grassland of our study area acts as a carbon sink. To our knowledge, there are no studies that estimated carbon fluxes in a karst grassland. However, the average GPP and NEE of \(-711\) and \(-199\) g C m\({}^{-2}\) per growing season respectively are in range with estimates, of \(-867\) and \(-132\) g C m\({}^{-2}\) per season respectively for GPP and NEE, obtained by [71] in a Mediterranean grassland.
## 3 Results and Discussion
### PPF Model's Results  
This section shows the error metric results obtained through the PPFs described in Section 2.1. The most accurate model is the one that was chosen in order to use its predictions as the baseline in developing our IPIF. In addition, the PPF error metrics were compared against the error metrics given in other studies in the literature.
## 3 Proposed Scheme  
Smart-city-based IoT applications use a Wireless Personal Area Network (WPAN) comprising IoT devices, sensors, and computing servers. IoT devices face security attacks by different malicious nodes that corrupt the data and increase the chances of collisions while accessing the medium for data transmission. It is hard to identify attacks by malicious nodes when they become a member of the network. In addition, malicious attacks in accessing medium during the contention access period are hard to identify and may cause a collision. That is why contention-free medium access control protocol is preferred over contention-based MAC protocol in wireless sensor-based IoT networks. A proposed time-slot-based medium access control protocol to meet adaptive data requirements (TMPAD) is designed for IoT-based wireless networks in fog-enabled smart cities. The protocol aims to provide a collision-free environment for nodes to transfer varying amounts of data while identifying and preventing malicious attacks. TMPAD's main features include the following:  
* Offers scalability by allowing new nodes to become members of the WPAN in each session.
* Modifies data slots to align with the adaptive data requirements determined by GTS-requesting IoT nodes.
* Computes the trusted data by calculating the trust level of each node.
* Efficiently allocates GTS to accommodate more GTS-requesting nodes with better data transmission and higher mean trust value by applying the TOPSIS algorithm.  
A list of notations used in Sections 3 and 4 of this manuscript, along with their descriptions, are mentioned in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\mathcal{CD}\) & CAP duration \\ \hline \(\mathcal{SD}\) & Time slot \\ \hline \(\mathcal{SD}_{b}\) & Number of bits in a time slot \\ \hline \(GTS_{Rq}\) & Number of GTS required \\ \hline \(\mathcal{DTP}\) & Data transmission period \\ \hline \(T_{Y}^{X}\) & Trust value of node X for node Y \\ \hline \(A^{lf}\) & Trust value calculated for node A \\ \hline \(T_{N}^{lf}\) & Trust value calculated by all neighboring nodes \\ \hline \(\sigma S\) & Sigmoid function to find trust probability of a node \\ \hline \(C_{i}\) & TOPSIS value calculation criteria \\ \hline \(V_{i}\) & TOPSIS value of node i \\ \hline \(TS_{a}\) & Time slots received in session a \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Notations used in this Paper.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 2 Materials and Methods
### Detection of Secretion of Cytokines with ELISA  
VEGF and bFGF are two important cytokines secreted by HUVECs. In our research, the concentrations of VEGF and bFGF in the supernatant were determined with ELISA. After HUVECs were cultured with PAM for 24 h, the supernatant of cell culture was collected by centrifugation, and the operation was performed according to the requirements of VEGF and bFGF ELISA kit (Jianglaibio, Shanghai, China). The strip in the kit was taken out, the standard wells, blank wells and sample wells were set according to the instructions, and 3 multiple wells were set for each group. 50 \(\upmu\)L of sample to be tested and 100 \(\upmu\)L of HRP-labeled detection antibody were added to each well. The reaction wells were sealed with sealing plate membrane, and incubated at 37 \({}^{\circ}\)C for 60 min. After incubation, theliquid was removed from the plate, washing solution was added to each well and washed 5 times.  
Then 50 uL of substrate A and 50 uL of substrate B were added to the well and incubated at 37 \({}^{\circ}\)C for 15 min. Finally, 50 uL of stop buffer was added to each well. The OD value of each well was measured at 450 nm within 15 min. The standard linear regression curve was drawn according to the concentration and OD value of the standard sample, and the concentration of each sample was calculated according to the curve equation. 3 parallel samples were set for each experiment, and each experiment was repeated 3 times to remove outliers to reduce errors.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Experiment Setup and Figures-of-Merit  
Audio data were recorded in 15-minute segments. These are divided into multiple 1-second signal frames, which were then used for feature extraction that served as input to the classifier. Following suggestions from [30; 31], two different experimental setups are considered: \"random-split\" and \"hive-independent\". In the former, the entire dataset from the 10 hives is randomly divided into three parts: 25% is for testing, 20% for validation and the rest is reserved for training. With the latter, a leave-some-hives-out setup was used. In particular, data from seven hives was used to train the models (of which 30% of the training data were set aside for validation), while the remaining three unseen hives were used for testing. This was repeated until all hives appeared in the test set.  
For model evaluation, two figures-of-merit were used: mean absolute error (MAE) and the root mean square error (RMSE) obtained from the estimates and computed as follows, respectively, for a sample size of \(M\):  
\[\text{MAE}=\frac{\sum_{n=1}^{M}|y_{i}-\hat{y}_{i}|}{M}, \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{n=1}^{M}\left(y_{i}-\hat{y}_{i}\right)^{2}}{M}}, \tag{10}\]  
where \(y\) is the observed value and \(\hat{y}\) the prediction.
## 5 Thermal Analysis
### Thermal Model-Lunar Environment
[55] suggests taking a convective heat transfer coefficient, \(h\), between 5 and 25 W/m\({}^{2}\)K for free convection in air. Since the convection will be low due to the reduced gravity on the Moon, the coefficient of 5 W/m\({}^{2}\)K is considered here. \(\circ\): \(x\) = \(L\) (where \(L\) = 0.4 m): Radiant heat emitted from the outer surface as by the Stefan-Boltzmann law, \(dT/dx=-\varepsilon\sigma T^{4}(x=0.4)\), where \(\sigma\) is the Stefan-Boltzmann constant of 5.670374419 \(\times\) 10\({}^{-8}\) Wm\({}^{-2}\)K\({}^{-4}\), \(\varepsilon\) and is the material's emissivity which is assumed to be 0.9 for highlands regolith here. \(\circ\): \(x\) = \(L\): During the day, two cases are considered here with a maximum solar heat flux of 600 and 1300 W/m\({}^{2}\) respectively. The value of 1300 W/m\({}^{2}\) represents the approximated maximum flux, and 600 W/m\({}^{2}\) accounts for regions with increased lunar albedo and lower Sun elevation with respect to the surface. Depending on the Moon base position, the true solar flux will be a periodic function with maximum amplitude of approximately 1300 W/m\({}^{2}\) during the lunar day period followed by a null solar flux during the lunar night. The cycle has a period of 28 Earth days.
## 2 Related Work
### Set-Preference Learning  
A few lines of work propose to infer preferences of items from the preferences of item sets [5; 6; 7; 8; 9]. The most relevant one is [5], which collects user set-level ratings by sending emails to users. The sets are randomly constructed to a fixed size (five items in this case) from the rated movies in the user's history. Noteworthily, constructing sets from users'previously rated items does not change the setting of SPL. Still, the goal is to reduce item ratings from set ratings, though the item ratings will be used to evaluate the accuracy of item-rating prediction. To infer item-level preferences, the author of [5] proposed three different methods:  
* _average rating model_ (ARM) [5] assumes that a set-based rating reflects users' average ratings for all the items. The estimated rating of user \(u\) for set \(S\) is given by the average of all predicted item ratings: \[\hat{r}_{u}^{s}=\mu_{u}^{s}=\frac{1}{|S|}\sum_{i\in S}\hat{r}_{ui}\] (1)
* _variance offset average rating model_ (VOARM) [5] captures the preference diversity of items in the set. Besides estimating the mean of the set rating, it also estimates the variance. Therefore, the final prediction is given by: \[\hat{r}_{u}^{s}=\mu_{u}^{s}+\beta_{u}\sigma_{u}^{s},\] (2) where \(\beta_{u}\) is the personalized factor; \(\mu_{u}^{s}\) is mentioned in Equation (1) as the mean of the ratings of all items, and \(\sigma_{u}^{s}\) is the standard deviation of the ratings of items in the set \(S\), which is given by: \[\sigma_{u}^{s}=\sqrt{\frac{1}{|S|}\sum_{i\in S}(\hat{r}_{ui}-\mu_{u}^{s})^{2}}.\]
* _extremal subset average rating model_ (ESARM) [5] estimates the ratings of extremal subsets and predicts set rating with weighted aggregation: \[\hat{r}_{u}^{s}=\sum_{i=1}^{2n_{u}-1}w_{ui}e_{ui},\] (3) where \(e_{ui}\) is the average rating of the \(i\)-th extremal subset.  
The method proposed by [9] constructs groups of items to efficiently elicit the initial preferences of cold-start users. Then, a new user's interest in the individual items is generated through the average preference of users who share similar tastes for the item groups. Differently from [9], SPL focused on a more general case: (1) rather than constructing sets with similar items, SPL does not require homogenous items, and (2) SPL aims to develop fully personalized recommendation methods that are not limited to cold-start recommendation.  
There are also some works in the multi-agent systems community focusing on representing set-level preferences [6; 7; 8]. Differently from SPL, these methods aim to find the optimal subsets by partial order over the power set, which requires item-level feedback. In comparison, SPL only assumes the availability of set-level feedback, so the partial order cannot be obtained.
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 1 Introduction
The above research has achieved certain results in the field of insulator state detection, but it generally focuses only on a single insulator self-explosion and pays less attention to insulator breakage and flashover, which makes the inspection process smaller detection range, while insulator breakage and flashover will likewise threaten the stable operation of the transmission line, coupled with the aerial insulator of a small scale, a variety of angles, a complex background, and a lot of obstructions, the above methods are difficult to meet the actual needs during an inspection. To automate the defect detection of insulators in transmission lines and alleviate the significant workload faced by human inspectors, this study introduces an advanced deep learning-based method tailored for insulator state detection. The proposed approach is designed to achieve high-precision identification of common insulator defects, including self-explosion, breakage, and flashover, which are critical for maintaining the safety and reliability of power transmission systems. By leveraging innovative techniques, this method effectively addresses key challenges such as accurately detecting small targets and distinguishing obscured objects in complex environments. Furthermore, the improvements in detection accuracy and reliability ensure that the proposed method can serve as a robust solution for real-world applications, potentially enhancing the efficiency and precision of transmission line maintenance operations.
## 6 Discussion
### Determination of Parameter Estimation Methods  
There are several parametric methods for estimating shear strength parameters via direct shear tests on rock joints. However, since the test data for the engineering cases studied in this study exhibit limited specimens and non-discrete characteristics, it is necessary to compare different methods for estimating shear strength parameters based on physical tests and to analyze the accuracy of various data processing models. Thus, this study uses four sets of data from specific engineering cases to estimate the shear strength parameters of cohesion (\(c\)) and internal friction angle (\(\varphi\)) via the least squares method, point group center method, and stochastic fuzzy mathematics method. The corresponding estimation results for the four sets of data are presented in Table 10. Considering the computational complexity, engineering applicability, and stability of parameter estimation, the least squares method is selected for parameter estimations.
## 2 Theory and Derivation  
### Image Transmission Mechanism of Hexagonally Aligned Fiber-Optic Imaging Bundles  
A fiber-optic imaging bundle consisting of tens, or even hundreds, of thousands of multi-component glass fibers with thick cores and thin cladding was arranged identically on the input and output end faces, as shown in Figure 1. Most of the fiber filaments in the middle were kept free and loose to ensure flexibility. The ideal image transmission mechanism of the bundle [17] is that each fiber in the bundle has good optical insulation so that each fiber can transmit light independently. Each fiber at the input of the bundle can be regarded as a sampling hole, which independently transmits an image element with a certain brightness. The object is imaged on the input side of the bundle and divided into several image elements with different brightness levels by the sampling hole. Due to the correlation between the input and output sides of the bundle, all the images are transmitted separately by each fiber channel and are then recombined at the output into an image identical to the one at the input side.  
The working principle of the hexagonally aligned fiber-optic imaging bundle is shown in Figure 2. The imaging system consists of a front visible imaging system, a relay imaging element (hexagonally aligned fiber-optic imaging bundle), a coupling objective lens, and an area array CCD.  
The detection target was magnified to a certain extent and imaged into the incident face of the hexagonally arranged fiber-optic bundle through the telescope objective. Each fiber end can be regarded as a sampling hole. The image of the object is divided into a number of different brightness image elements by the sampling hole. The image elements transmit signals along the respective channels, which are recombined at the output end into an image consistent with that of the input end. The coupling objective lens couples the image from the fiber-optic beam onto the surface of the CCD sensor if the relative aperture of the area array fiber-optic beam is unchanged, and the fiber-optic core layer diameter is much larger than the Airy spot radius of the telescope system. The incident side of the hexagonally arranged fiber-optic beam is located on the image-side focal plane of the telescope objective, the outgoing side is located on the object-side focal plane of the coupling objective, and the surface CCD sensing plane is located on the image-side focal plane of the coupling objective.