esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Biomedical texts are relatively obscure in describing relations between specialized entities, and the automatic extraction of drug-drug or drug-disease relations from massive biomedical texts presents a challenge faced by many researchers. To this end, this paper designs a relation extraction method based on dependency information fusion to improve the predictive power of the model for the relations between given biomedical entities. Firstly, we propose a local-global pruning strategy for the dependency syntax tree. Next, we propose the construction of a dependency type matrix for the pruned dependency tree to incorporate sentence dependency information into the model to feature extraction. We then incorporate attention mechanism into the graph convolutional model by calculating the attention weights of word-word dependencies, thus improving the traditional graph convolutional network. The model distinguishes the importance of different dependency information by attention weights, thus weakening the influence of interfering information such as word-to-word dependencies that are unrelated to entities in long sentences. In this paper, our proposed Dependency Information Fusion Attention Graph Convolutional Network (DIF-A-GCN) is evaluated on two biomedical datasets, DDI and CIVIC. The experimental results show that our proposed method based on dependency information fusion outperforms current state-of-the-art biomedical relation extraction models.  
biomedical relation extraction; dependency syntax tree; pruning strategy; dependency information fusion; graph convolutional network; attention mechanism Article
## 5 Discussion  
### App Cost and Topic  
Figure 8 shows the price and ratio of PV apps according to their topic. Free apps accounted for 74%, and paid apps for 26% of the 100 analyzed apps. The price of most paid apps ranged within 0.99-10.00 USD, and the most expensive app was 31.00 USD. The cost to use some apps increases through in-app purchases for additional functions, but this part was excluded from the analysis. The costs of external sensors, such as a pyranometer, was also not considered. Nevertheless, considering that the apps provide functions that can partially substitute measuring equipment or analyzers, their prices were considered reasonable.  
As a result of comparing paid apps and free apps, it was found that, in general, paid apps provide various functions or materials more than free apps. In the case of an app that has both the Lite version and the Pro version, there are examples of allowing an external insolation sensor to be used by connecting it to a smartphone instead of inputting statistical value. Some paid app allows reliable NASA weather information to be used instead of entering simple weather information to estimate PV power output. However, considering that the app selected as the representative app in four of the six topics is free, it should be noted that this does not mean that the function or usability of the free app is low-level.  
The interest in energy prosumers, community solar, and micro-grid systems has increased rapidly due to the influence of decentralization energy production policies. There are increasing cases of individuals or local communities producing PV power in unused areas of residential buildings. Therefore, apps associated with PV on roofs or rooftops are  
Figure 7: Representative smartphone application (Solar PV Technology Mobile Course) for education and learning purposes related to PV systems: (**a**) outline of educational course, (**b**) educational material.  
expected to be increasingly used at the individual or local community level. A rooftop PV app with new functions is also expected to be developed in the near future.  
Table 10 summarizes smartphone apps for roof or rooftop PV systems according to their topic. These apps allow the user to remotely extract various characteristics of roof or rooftop, such as shape (pitched or flat), area, tilt, and orientation, using high-resolution satellite images. They can also suggest the optimal layout of solar arrays or installation methods based on the extracted characteristics. The power generation can also be assessed based on PV system capacity and solar irradiance according to the geographic location. Furthermore, they can be used to assess environmental or economic effects over time. Game apps on solar energy topics could provide interesting learning motivation for this field and help users to become familiar with solar PV technology. Given the growing interest in gamification of learning as a new strategy in the field of education, an app for solar energy games could be developed for such purpose. Apps for different types of PV systems (floating, rural, mine, and BIPV) should also be developed.
# Anatomical-Based Customized Cervical Orthosis Design in Automation
## 2 Materials and Methods
### The Design Workflow of Cervical Orthosis  
The fully automatic orthosis design software was implemented using Microsoft Visual Studio 2015 with the C++ programming language. Figure 2 shows the automatic workflow for the customized cervical orthosis. We used a 3D scanner (GoIScan 50, Ceaform, QC, Canada) to scan the neck region of the subject. When scanning, the subject sits comfortably against the back of the chair and tucks their chin with the eyes closed, ensuring the ear canals are directly above the shoulders. The scanning area includes the upper chest and back, the entire neck, and the partial head region next to the neck. We pre-set an anatomical neck feature template for neck features matching between the template and the subject by the maximum bipartite matching algorithm [14]. A Hungarian algorithm was then applied to obtain a perfect match with minimum weight after distance normalization between  
Figure 1: (**a**) The designed cervical orthosis. (**b**) The anatomical features below the cervical orthosis.  
every pair of features [15]. These features serve as reference points to determine the neck coordinate and circulated connection to form two centerlines of the front and back pieces of the orthosis. The orthosis prototype is formed by expanding a given width and thickness on the surface of the neck model. Boolean difference and smoothing operations are applied to the front and back pieces for refinement before 3D printing. They were fabricated using a fused deposition modeling (FDM) 3D printer with polylactic acid material. It was then padded below the front piece above the subsurface of the clavicles and the back piece behind mastoid processes to prevent pressure ulcers. Four Velcro accessories were glued tightly on the upper and lower lateral sides of the front and back pieces, allowing opening and closing when wearing and untying the orthosis.  
Along with the fundamental theory, by importing the scanned cloud points, our developed software embedded a fully automatic process for neck feature identification, coordinate systems' transformation, the orthosis template, normalization, renormalization, and customized cervical orthosis prototyping. The developed software gives adjustable parameters for only one orthosis thickness and seven allowance settings between the user  
Figure 2: The design workflow for cervical orthoses.  
and the orthosis, including anterior, posterior, and lateral orthosis; mandibular angles, mastoid processes; clavicles; and prominent verteb regions. The design of the new type of orthosis adopts the advantages of the currently available and popular cervical orthosis, Vista Multipost Collar (Aspen Medical Products, Irvine, CA, USA), in support and stabilization but improves lightweight, ventilation, and strength of cervical orthosis.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### The Complexity Analysis  
The CARA algorithm proposed in this article is a distributed one-hop routing algorithm. After a node is deployed, its location remains fixed. Each node calculates its routing evaluation parameters. The evaluation parameter reflects a certain aspect of the node's state, regardless of the number of nodes, so its time complexity is constant. When making routing decisions, the decision node establishes judgment matrix \(D\). The decision matrix \(D\) is related to the number N of forwarding nodes. In the routing decision-making process, the time complexity of the normalization is \(O(N)\). The weight vector computes in constant time. Both of the ideal values and \(R_{j}\) execute in \(O(N)\) time. Thus, the time complexity of the CARA algorithm is \(O(N)\). For spatial complexity, the neighbor information table and decision matrix maintained by each node are related to N, which is also \(O(N)\). To sum up, the CARA algorithm occupies less memory space, offers faster decision-making speed, and has lower computing and storage requirements.
## 3 Learning with Corrupted Data
### Experiment Design
#### 3.1.1 NLP Supervised Learning (NLP-SL)  
The first experiment focuses on the GLUE benchmark tasks. Firstly, a BERT model is pre-trained using Wikitext and Bookcorpus as the base model; then the base model is frozen. We add classification head on top of the base model to fine-tune it on eight tasks: CoLA, SST-2, MRPC, STSB, QQP, MMLI, QNLI, and RTE using GLUE dataset. The GLUE input sentences are corrupted by replacing certain words with the [UNK] token, while the labels \(y\) remain uncorrupted. This type of data corruption is commonly encountered in natural language processing tasks. For instance, when digitizing text corpora, some words may become indistinguishable and are thus masked as unknown, whereas the associated labels are typically unaffected. Evaluating the amount of knowledge learned by a model remains a subjective challenge. In this experiment, performance is measured using classification-related accuracy metrics. Specifically:  
* The Matthews Correlation Coefficient (MCC) is used for CoLA;
* The average of Pearson and Spearman correlation coefficients is used for STSB;
* Test accuracy is used for the remaining tasks.  
Baseline scores (\(\frac{1}{2}\) for binary classification and \(\frac{1}{3}\) for three-way classification) are subtracted from these metrics. The final model score is computed as the average of the scores across all tasks. To ensure consistency, hyperparameters such as the number of training epochs and learning rates are tuned using uncorrupted data and kept fixed for all subsequent experiments (see Table A1). Model convergence for these experiments is illustrated in Figure 1. For clarity, this experiment is referred to as the \"Natural Language Processing--Supervised Learning (NLP-SL)\" experiment in the following sections. Note that the base model is used only for sentence embedding and is frozen; the goal is to test the model's ability to extract knowledge from corrupted data and store it in the feed-forward network.  
Figure 1: Model convergence in two experiments. **(a)** NLP-SL experiment: The \(x\)-axis represents training steps, and the \(y\)-axis shows validation accuracy. **(b)** Signal-RL experiment: The \(x\)-axis denotes training episodes, and the \(y\)-axis indicates episode reward. Training configurations are detailed in Table A1.  
For the NLP-SL task, two types of data corruption are introduced:
* **Data missing**: Each word in the training samples has a probability \(p\) of being replaced with a [MASK] token.
* **Inserting noise**: Each word in the training samples has a probability \(p\) of being replaced with a randomly selected word from the vocabulary.
## 1 Introduction  
Nowadays, the Electricity Generating Authority of Thailand (EGAT) uses conventional methods (such as over-current, voltage, and differential relays) to diagnose power transformer faults. However, there are some events that cause unnecessary operation of the differential protection. At the same time, the cause of the differential signal is larger, even though there is no internal fault. In addition, there are several decision algorithms that have been developed for relay protection to prevent mistake tripping. Thus, the unnecessary operation of the protective equipment under different non-fault conditions is solved by several methods, including magnetizing inrush current, ratio mismatch, through-fault current line, etc.  
This research presents a new decision algorithm of protective relay development to discriminate between external and internal faults. The simulation system is modeledin the alternative transients program/electromagnetic transients program (ATP/EMTP), which is the popular power system transient program. The simulated current waveforms are extracted using the DWT, which extracts the high-frequency component. The DWT coefficients of the first scale are investigated and used to detect faults in this research. As the data and training time of the PNN are less compared to the BPNN, the PNN is chosen in this algorithm. Although the PNN has not been fully evaluated, the PNN has many advantages, such as fast training, flexible adding or removing data from a dataset, training without storing data for a long time, etc. These are useful for distinguishing between external and internal faults using a DWT and PNN. The suggested algorithm's validity is investigated using various fault inception angles, fault locations, and faulty phases. The decision algorithm is built in detail and put to use in a number of case studies based on Thailand's power transmission and distribution infrastructures.  
The contributions of this paper are summarized as follows:  
* Design a new decision algorithm for protective relay to discriminate between internal and external faults.
* Study the simulation model in the ATP program to understand and consider the issues in the study.
* Analyze the decision algorithm's performance to determine the most suitable fault-detection methods.  
This study is organized as follows: in Section 2, relevant research has been described in order to broaden the perspective of understanding and consider the issues in the study more clearly. In Section 3, the wavelet transforms are explained to understand the analysis process. In Section 4, the simulation model is made in the ATP program and generates the resulting signal for wavelet analysis. The fault-detection algorithm is explained in Section 5, and the decision algorithm is presented in Section 6. The results are compared between three fault detection methods in two cases to find the most suitable method. Finally, the conclusions are presented in Section 7.
## 4 Comparison Results
### Analyzing Local Metrics on Real-World Datasets
#### 4.3.4 Distance to Decision Boundary  
A key application of decision maps is to show how close a point on the map is to the actual decision boundary of the studied classifier. For this, the 2D distances in the map should depict the corresponding nD distances as closely as possible. Figure 8 (left) shows, for each map pixel, its distance to its closest decision boundary in the high-dimensional space, computed using Equation (6). In this figure, we can see that our expectations align with the observations--pixels close to the decision boundaries in the map are dark (meaning that they mapped points close to the actual decision boundaries in nD), whereas pixels deep in the decision zones are bright (meaning that they mapped points far away from the nD decision boundaries). Yet, we can also see variations in these distances across the decision-map methods. The SDBM and DV display patterns closer to the desired outcome compared to the DBM. DV shows a wider area with low distance values, whereas the SDBM shows a more concentrated area. This follows the patterns observed in the gradient maps (Figure 6), where DV expanded the gaps between point clusters, whereas the SDBM compressed them. The DBM exhibits a complex pattern, where certain zones display low distance values, even for pixels located in the center of the decision zones. These zones are particularly noticeable in the upper areas of the HAR and FashionMNIST datasets, as well as the leftmost area of the MNIST dataset. Interestingly, these zones (circled with red dotted lines in Figure 8, left) are also NDZs, which coincide with the zones of low \(S\) (see Figure 7). This correlation indicates reduced confidence in the inverse projection in those zones. The importance of confidence in extrapolation is underscored by these findings and is discussed further in Section 4.3.5.
# Biocompatible Dispersants for Carbon Nanomaterials
## 6 Biocompatible Dispersants
### Cellulose
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num.** & **Dispersant** & **CNM** & **Description** & **Application** & **Ref.** \\ \hline \multirow{3}{*}{1} & Type I/II NCC & SWCNT & SWCNTs dispersed with varying ratios of types I and II NCC & Stability testing; in-vitro bioactivity, biocompatibility studies & [38] \\  & & & NCC & studies & \\  & & & & & \\  & & & & & \\  & & & & & \\
2 & CNFs, NCC & SWCNT & SWCNTs functionalised with CNFs and NCC & Hydrogel and nano paper preparations; investigating the mechanisms of cellulose dispersants & [37] \\  & & & & & \\
3 & Methylcellulose & Graphene nanoplatelets & Graphene nanoplatelets & Aqueous graphene dispersions for reinforcing cement & [42] \\  & & nanoplatelets & methylcellulose & cement \\  & & & Sulfate, carboxylate, amino-silane functionalised NCC and CNF materials used to disperse MWCNTs & Dispersions used to produce highly stable composite electrodes & [43] \\  & & & & & \\
5 & Carboxymethylated CNFs & SWCNT & Carboxymethylated CNFs from softwood pulp used to disperse SWCNTs & This dispersion was utilised to produce conductive films, anisotropic microscale fibres, and aerogels & [44] \\  & & & & \\
6 & NCC & MWCNT graphene & Non-covalent functionalization with NCC & Heat transfer fluids & [5] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cellulose-based CNM dispersions.
# A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals
## 2 System Design and Implementation
### Water Consumption Estimation  
To best create a situation that does not inhibit watering behavior in livestock or wildlife, the system has up to multiple individual water troughs to provide adequate access and volume to collect data from multiple animals simultaneously. Calculating the change in volume during a drinking event is the best option to determine water consumption. In the experiment site, each trough in the drinking station has a 416 L capacity. The dimensions of these troughs are 124.5 cm in length, 84 cm in width, and 53 cm in depth. Equation (1) describes the formula utilized to calculate consumption \(\Delta V\):  
\[\Delta V=\frac{\pi\cdot a\cdot b\cdot(h_{1}-h_{2})}{1000} \tag{1}\]  
where \(a\) is the short axis, which is half of the width, and \(b\) is the long axis, which is half of the length. Change in depth is determined, where \(h_{1}\) is the height reading at the end of a drinking event and \(h_{2}\) represents the height reading at the beginning of the drinking event. Equation (1) is applied to the system to measure the water consumption based on  
Figure 3: The flow chart of the control program for sensing, processing, and data storage.  
the readings of the water level sensors. The distance sensor has a resolution of 0.1 cm. As the animal drinks, the sensor records the increased distance from the sensor to the surface of the water. Based on the above parameters, the conversion based on this formula is that every 0.1 cm change in depth equates to 0.82 L consumed.
# Thinfilm Hybrid Nanostructures: A Perspective to Subcycle Opto-Electronics and Coherent Control
## 2 Methods
### Plasmonic field Evolution in a Thin Gold Nano Disk after Ultrafast Excitation  
The absorption resonance of a disk is mainly determined by its diameter [36]. A variation results in a resonance shift either to shorter wavelength (decreasing diameter) or longer wavelength (increasing diameter). In the case of very thin disks the absorption is also shifted with increasing disk diameter to shorter wavelength. The resonance shift follows an exponential behaviour corresponding to the aspect ratio of thickness and diameter.  
The electric field presented in Figure 3a at the surface of a gold disk is induced by plasmonic excitation with monochromatic light at the resonance wavelength.  
The near field shows a dipole structure with the polarization axis as the symmetry axis. One half of the disk is negatively charged, the other positively with a smooth transition. The temporal evolution of the integrated field of the two half disks together with the excitation signal is shown in Figure 3b. The driving light field induces a dipolar field that oscillates slightly slower than the driving field. In one plasmonic field cycle the laser electric field builds up, then decreases and reverses to opposite polarity. After one cycle the field returns to the initial polarisation. During the plasmon build up time, the electric field strength grows. The duration of the plasmon build up is almost as long as the driving laser pulse: the maximum of the excitation signal is reached around 10 fs, whereas the plasmonic field still builds up, and reaches its maximum at around 13 fs. The laser field has almost vanished at the maximum of the plasmonic field. After the plasmon build up, the electric field oscillates on the disk and is damped over time.  
The plasmon dephasing takes 32 fs until the plasmonic field has decreased its field strength to \(1/e^{2}\). For the plasmonic response of a single disk follows, that the resonance shifts with decreasing diameter and increasing thickness to shorter wavelength, and the plasmonic field on the disk surface is represented by one dipole. Once excited, the field builds up and is then damped within several ten femtoseconds. As we will see, in the case of the gold-silica-silver nano sandwich system, this behaviour is strongly changed by coupling effects, resulting in the time-dependent evolution of plasmonic surface modes as well as whispering gallery modes.
## 3 Results
### Durability Tests  
3.2.1. Cutting and Tape Test  
An inspection of surfaces after the cutting and tape test revealed that both coatings passed the test since the area involved in detached flakes of coating at intersections was less than 15% (Figure 9-top). Moreover, after the test, the surfaces remained superhydrophobic as highlighted by the residual low wettability of the surfaces after the test (see pictures of water droplets at the bottom of Figure 9).
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 3 Fractional-Order Vibratory System with Viscoelasticity  
First, a viscoelastically damped structure is considered as an example of a fractional-order vibratory system in order to explain LQR control. The equation of motion for forced vibration of a one degree-of-freedom viscoelastically damped system with a fractional derivative is:  
\[\Big{[}mD^{2}+cD^{q}+kD^{0}\Big{]}x(t)=f(t), \tag{11}\]  
where \(x(t)\) is the displacement, \(m\) is the mass, \(c\) is the viscoelastic damping coefficient, \(k\) is the spring constant, \(t\) is time, and \(f(t)\) is the external force. Dividing both sides of Equation (11) by \(m\) leads to the following normal form:  
\[\Big{[}D^{2}+2\zeta{\omega_{n}}^{2-q}D^{q}+{\omega_{n}}^{2}D^{0}\Big{]}x(t)=u( t), \tag{12}\]  
where \({\omega_{n}}^{2}=\frac{k}{m}\), \(\zeta=\frac{c}{2m{\omega_{n}}^{2-q}}\) and \(u(t)=\frac{1}{m}f(t)\).  
It is known that \(q\) has a value of approximately 0.5 for most viscoelastic materials [27], and is therefore fixed at 0.5 in this paper. Consequently, the viscoelastic damping model can be expressed using a one degree-of-freedom equation of motion known as the generalized Voigt model [28]:  
\[\Big{[}D^{2}+2\zeta{\omega_{n}}^{\frac{3}{2}}D^{\frac{1}{2}}+{\omega_{n}}^{2 }D^{0}\Big{]}x(t)=u(t), \tag{13}\]  
where \(u(t)\) is the control input.  
Next, based on the above equation, a fractional-order state equation is derived. Using a sequence of fractional differentiations with a difference in order of \(1/2\), the state vector \(\mathbf{x}(t)\) can be defined as follows:  
\[\