esa-sceva/satcom-chunk-collection huggingface dataset:
# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
## 3 Experiments and Discussion
### Experiment Settings  
We select several strategies to construct the baseline methods to explore the performance details of our algorithm. The following is the detailed construction of strategies in our experiments.  
* OM-TD (TF-IDF [36]): It uses TF-IDF to calculate the similarity of ontology terminological description by the procedure described in [17];
* OM-TD (LSTM [37]): It only uses LSTM to produce ontology terminological description embeddings ignoring ontology network structure information for ontology matching;
* OM-TD (TBERT): It employs our proposed ontology terminological description embedding component without encoding network structure information;
* OM (LSTM + SGAT): It extends the LSTM experiment by applying our proposed SGAT to jointly encoding terminological description and network structure for ontology matching;
* OM (TBERT + GraphSAGE [38]): It combines our proposed TBERT and GraphSAGE to jointly encode a terminological description and network structure for ontology matching;
* OM (TBERT + TransE [39]): It employs TransE, jointly encoding terminological description and network structure for ontology matching. The vectors of nodes are initialized by our proposed TBERT;
* OM (DAEOM): Is our proposed jointly embedding method for ontology matching.  
\"OM-TD\" means only using terminological description for ontology matching. \"OM\" means using both network structure and terminological description. Those methods applied our proposed mapping selection module in final alignment.  
For DAEOM, we use Adam as an optimizer and the configuration of hyper-parameters is listed below: Word vector has the size (\(d\)) 200 and is shared everywhere. We initialized the word vectors from word vectors pre-trained on a combination of PubMed and PMC texts with texts extracted from a recent English Wikipedia dump [40]. All the initial out-of-vocabulary word vectors are sampled from a normal distribution (\(\mu=0,\sigma^{2}=0.01\)). We selected \(\{2,4,6\}\) negative triples sampled for each positive triple. In TBERT, the number of block \(N\) and multi-head \(h_{e}\) in transformer-encoder were 2 and 4. The whole training of TBERT spent 50 epochs. The mini-batch size was set to \(\mathit{Tabatch}=\{100,\mathbf{500},1000\}\). We selected the learning rate \(\lambda_{tb}\) among \(\{0.01,0.001,\mathbf{0.0001}\}\). In SGAT, we set the matching coefficient \(\gamma\) to 10. The output concept vector had size (\(d^{\prime}\)) 25. The SGAT was trained over 100 epochs. The mini-batch size was set to \(\mathit{Sbatch}=1\). We selected the learning rate \(\lambda_{sg}\) among \(\{0.01,\mathbf{0.001},0.0001\}\).  
In order to show the effect of our proposed negative sampling, a symbol \"-\" added to the symbol represented a module that indicates that this module is not equipped with negative sampling tailored for structural relations.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 6 Conclusions  
In this work, a proof-of-concept implementation of the SecAIS in an environment simulating the conventional AIS infrastructure was presented. The cryptographic infrastructure is based on RFC6507 and RFC6508; a third-party online AIS VDM/VDO decoder was used as the conventional AIS device. The main conclusion of this work is that the SecAIS is a viable option for offering authenticated AIS transmissions and ad hoc encrypted AIS transmissions of small secrets (e.g., keys for symmetric ciphers) to the maritime community. Furthermore, this is achieved without modifying the conventional AIS infrastructure or affecting its everyday use at a negligible operational cost.  
Our future work will focus on minimizing the size of the SecAIS data. Since the size of these data depends on the cryptographic parameters used, an obvious solution is to lower the level of security offered by the SecAIS. However, an optimal trade-off between the level of security that the SecAIS offers and its operational cost should be sought.  
Conceptualization, A.G. and S.K.; methodology, A.G.; software, A.G.; validation, A.G.; resources, A.G. and S.K.; writing--original draft preparation, A.G.; writing--review and editing, S.K.; supervision, S.K.; project administration, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded, in part, by the Research Council of Norway, under Project number 310105 \"Norwegian Center for Cybersecurity in Critical Sectors (NORCICS)\".  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Classifier Models
#### 2.5.4 Logistic Regression  
It is a classification algorithm used to assign observations to a discrete set of classes. Unlike linear regression, which outputs continuous values, the logistic regression transform its output using the logistic sigmoid function, to return a probability value that can be mapped to two or more discrete classes.
# Plant Design for the Conversion of Plastic Waste into Valuable Chemicals (Alkyl Aromatics)
## 2 Materials and Methods
### Selection of Catalyst  
Catalyst contact during the pyrolysis of plastic waste can be broken down into two distinct categories: liquid-phase contact and vapor-phase contact. In a liquid-phase reaction, melted polymers encounter a fine powder-type catalyst with a large external surface area in which the polymer chain is broken down into light products [15]. The catalyst is the key ingredient in this process; hence, its price is proportional to the volume used. After the polymer fragments are cracked, they are linked to the catalyst contained in the fluidized bed reactor, which reforms the polymer fragments produced by heat degradation. The two processes use different types of catalysts, which are classified as zeolite, oxide, porous, fluid catalytic cracking (FCC), and reform catalysts, among others. Heavy hydrocarbons are typically cracked using acidic catalysts, such as alumina, silica-alumina with mesopores, and zeolite with microspores [16]. The professional petroleum industry is their main place of employment. While the catalyst's chemical characteristics are certainly important, the catalyst's physical features have a much larger impact on catalytic activity and product selectivity. Land area, pore size, tunable pore size, and pore structure are examples of these features. Platinum-supported alumina is used as our catalyst, since it maximizes the production of alkyl aromatics [17]. In addition, it has everything needed to improve both product and process quality.
## 5 Detection Methods: Modulation Spectroscopy for Wideband Absorption
### Varied Modulation Amplitude  
A modulation amplitude setting always confronts multi-spectrum with different widths, which creates a significant dilemma. Any single modulation amplitude cannot cover a large spectral width difference, e.g., a difference of the half width at half maximum is more than 50%. A practical way to achieve this is to use a varied modulation amplitude for multi-spectrum detection, namely WMS with varied modulation amplitude (WMS-VMA) [162].  
The WMS-VMA was realized mainly by a homemade digital lock-in amplifier (DLIA), which performs the modulation, demodulation of WMS-1f and WMS-2f, and generation of reference signal by an integrated field-programmable gate array-based circuit. The DLIA generates an arbitrary waveform signal by direct digital frequency synthesis. The sine waveform with varied amplitude is prepared in advance and stored in the DLIA's random access memory, and then read out to control the sequence using the frequency controller integrated in the DLIA. The method has been verified by a multiparameter optical sensor with typical broadband spectroscopy [162].
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
The basic architecture of a WSN includes the sensor or sensors that convert the measured variables (temperature, current, voltage,...) into electric signals, the power management module, the energy storage unit [21], the energy harvesting, and the communications module, which are represented in Figure 2.  
This work reviews and analyzes the state-of-the-art and recent progress in EH systems for WSNs intended for transmission lines while identifying challenges and research needs. This topic has a huge impact and expansion potential, but this area is still in its infancy due to the specificities and complexities of transmission lines. The information condensed in this review paper can also be applied to other applications, such as power transformers, distribution switches, or medium- and low-voltage power lines. The knowledge presented in this work has been compiled from the latest technical and scientific literature, including  
Figure 2: **(a)** Basic structure of a wireless sensor node (WSN). **(b)** Structure of a wireless sensor network, adapted from [22]. journal papers, review articles, conference works, doctoral theses, technical reports, and white papers. The rest of the article is organized as follows. Section 2 reviews ultra-low-power energy harvesting technology for wireless sensor networks. Section 3 reviews the state-of-the art batteryless energy harvesting systems. Section 4 reviews the state-of-the-art of energy harvesting methods for transmission lines. Section 5 discusses the main characteristics of the analyzed energy harvesting methods. Section 6 identifies the challenges and research needs related to this topic, and finally, Section 7 concludes this review paper.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 5 Usage Scenarios
### Vehicle to RSU Temporary Connection  
In this scenario, one vehicle discovers one nearby RSU and requests data. Vehicles can connect to any node of the IPFS network. The point of entry is not important, and vehicles do not need to know the name or location of the data before connecting and doing a request. When the organization of the content is done using semantic structures and linked data, the relevant search parameters are not where the information is located but its meaning and context.  
For example, a vehicle is approaching an intersection with the intention to turn right. Before arriving, the vehicle does an IPFS connection to get recent sensor information and decide the optimal lane and speed to use during the maneuver. If it is not connected to any peer yet, first it will need to discover the IPFS address of the nearest peers and connect to any of the discovered RSUs. Once connected, the next step is to get the RSU basic information. This information is a JSON document  
Figure 6: Graphic summary of the four different usage scenarios.  
that should include at least the type of RSU, its location, its real-world address, and its capabilities. JSON is the syntax used to represent semantic data structured using RDF. The root document should never include a large quantity of information, but one or more links to additional documents instead. Vehicles can follow these links to have access to all the information published by the RSU. IPFS links are not URLs but hashes (CIDs). CIDs are unique for all the network and can be used to link to data published by any node in the network.  
By default, RSUs should try to keep a local copy of all the linked documents, unless they do not have enough storage space. This problem could be quite common if the IPFS network is used to store large quantities of data, like video feeds produced by traffic cameras. In that case, an RSU linking to this data could decide to keep only copies of the most recent data. In case a vehicle requests access to data not available in the RSU, the DHT search would return the addresses of other nodes that still keep copies of the older data. Of course, these nodes do not need to be RSUs too, and could in fact be at any location. This opens the door to use remote cloud storage-dedicated nodes that would be only used to keep copies of old and rarely accessed data.
## 6 Calibration in a Stochastic Environment Under Sensor Nonlinearities
### Assumptions and General Aspects  
We adopt the following set of additional assumptions:  
A5: Instead of \(z_{t}^{j}\) and /or \(\Delta z_{t}^{j}\) sent by the node \(j\), node \(i\) receives the signal \(z_{t}^{j}+\eta_{t}^{ij}\) and/or \(\Delta z_{t}^{j}+\eta_{t}^{ij}\), where \(\{\eta_{t}^{ij}\}\) are i.i.d. random sequences, independent of \(x_{t}\) and measurement noise, satisfying \(E\{\eta_{t}^{ij}\}=0\) and \(E\{(\eta_{t}^{ij})^{2}\}=(\sigma_{t}^{ij})^{2}<\infty\), \(i,j=1,\ldots,n\).  
A6: Measurement noise sequences \(\xi_{t}^{i}\) from (1) are mutually independent i.i.d. random sequences, independent of \(x_{t}\) and communication noise, and satisfy \(E\{\xi_{t}^{i}\}=0\) and \(E\{(\xi_{t}^{i})^{2}\}=(\sigma_{\xi}^{i})^{2}<\infty\), \(i=1,\ldots,n\).  
A7: The step size sequences of the algorithms satisfy \(\delta_{t}^{i}>0\), \(\sum_{t=0}^{\infty}\delta_{t}^{i}=\infty\) and \(\sum_{t=0}^{\infty}(\delta_{t}^{i})^{2}<\infty\).  
Assumption A7 is typical for stochastic approximation algorithms, enabling the successful asymptotic suppression of noise influence, e.g., [49; 60].  
In order to indicate a direct way of extending the results obtained in the preceding sections to the stochastic case, we have to recall that our calibration algorithms can be formally considered to be time-varying linear systems, containing the following: (1) stochastic inputs (measurement and communication noises); (2) deterministic inputs (nonlinearity terms). Responses to these inputs are to be superimposed onto each other.  
The following lemma can be directly applied in an attempt to reconsider the above-presented results in a case when \(\delta_{t}^{i}\) is not constant, but satisfies A7 instead of A1.  
**Lemma 3** ([64]).: _Let a real number sequence, \(\rho_{t}\geq 0\), satisfy the following inequality:_  
\[\rho_{t+1}\leq(1-\gamma_{t})\rho_{t}+\gamma_{t}p_{t}, \tag{27}\]  
_where \(0\leq\gamma_{t}\leq 1\) and \(0\leq p_{t}\leq p\). Then,_  
\[\rho_{t}\leq\rho_{0}\prod_{k=0}^{t-1}(1-\gamma_{k})+p[1-\prod_{k=0}^{t-1}(1- \gamma_{k})].\]  
_If, in addition, \(\limsup_{t\to\infty}p_{t}\leq p\) and \(\sum_{t=0}^{\infty}\gamma_{t}=\infty\), then \(\limsup_{t\to\infty}\rho_{t}\leq p\)._  
Generalizations of Lemma 3 to time-varying vector-matrix recursions can be performed in a straightforward way, e.g., [50; 51]. After additional technicalities, it is possible to prove that the above-derived results related to \(\delta_{t}^{i}=\delta\) can be essentially extended to a case when \(\delta_{t}^{i}\) satisfies A7, without any significant changes occurring in the already-adopted assumptions. Therefore, in the following text, we present the main results obtained through applying the analyzed calibration algorithms to linear sensor models in the presence of measurement and communication noise, assuming \(v_{t}^{i}=0\) in (1).
## 1 Introduction  
Mud volcanoes are relatively understudied features within the field of geology. They resemble true igneous volcanoes in shape but have distinct underlying processes and characteristics. They are the largest surface expression of methane-rich fluids that migrate through neotectonic faults or fractures [1] and are scattered across various areas around the world, with their distribution being controlled by tectonic or geological factors [2]. However, the complex mechanisms and processes involved are not perfectly understood [3, 4].  
The formation of a mud volcano involves the extrusion of a mixture of gases, liquids, and solids [5]. This extrusion is primarily driven by the upward migration of pressurized subsurface fluids, typically a slurry of fine sediment and water, occasionally mixed with hydrocarbon gases such as methane. These materials are forced upward through geological faults and fractures.  
It is unclear how many mud volcanoes there are on Earth, although the total number is over 600 just for onshore [6]. Mud volcanoes can appear both onshore and offshore.  
Despite their geological significance [5] and associated hazards [7, 8, 9] that include the (relatively low) seismicity, the potential violent release of hydrocarbons and mud, the degradation of soil and quicksand effect, and episodic dissociation of gas hydrates, mud volcanoes are relatively understudied compared to other geological phenomena.  
One reason for the relative lack of intensive study is the remoteness and challenging accessibility of many mud volcano sites. Many of these formations are located in regions that are difficult to reach, such as offshore locations or within rugged terrains. This presents logistical challenges for scientists aiming to conduct regular and detailed studies. Furthermore, mud volcanoes are complex systems that involve a range of geological and biochemical processes, making them challenging to address holistically. They often involve the interaction of lithosphere, hydrosphere, atmosphere, and potentially even biosphere components [10], all of which must be accounted for in order to understand the entire system.  
However, geophysical surveys can reveal at least some parts of the plumbing system of mud volcanoes. This is what was attempted here. We aim not necessarily to provide a better understanding of the entire mud volcano system but to image some of the subsurface features and extent of the Paclele Mici volcano with a true 3D geoelectrical survey and demonstrate the feasibility of this FullWaver methodology in the study of mud volcanoes in general. The core of our work here utilizes the V-FullWaver equipment from IRIS instruments, a distributed survey equipment system for deep resistivity and induced polarization surveys which is designed to be used for 2D, pseudo 3D, or full 3D surveys.
## 3 System Hardware and Software Structure
### Hardware and Software for Vehicle Perception System  
To comprehensively evaluate the driver's perceptual and control capabilities, it is essential to conduct a thorough and precise observation and understanding of the external environment. RPAM utilizes a vehicle perception system based on external sensors and GPS positioning to recognize and cognitively comprehend the surroundings.  
The vehicle perception system consists of two main components: (1) a high-precision map system and (2) a comprehensive environmental perception system. The high-precision map provides static road construction information for the RPAM, and the comprehensive environmental perception system is specialized in analyzing extravehicular point cloud data generated by an LiDAR system to identify dynamic information related to traffic participants. The detailed implementations of each unit are described below.
## 2 Materials and Methods
### Preliminary Optimization Parameters for Fiber-Constrained Flexible Actuators
#### 2.2.2 Preliminary Selection of the Fiber-Constrained Annular Flexible Actuator  
Previous research has shown that fiber inclination has a significant effect on the bending and twisting of flexible actuators. In order to investigate the effect of fiber inclination on the deformation of the flexible actuator, fibers with inclination angles of \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\) and \(60^{\circ}\) were bound to the above strip of flexible actuator on the basis of the fiber spacing setting being at 2 mm; the deformation of the flexible actuator under different fiber inclination angles was then obtained.
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 2 Materials and Methods  
### Overall System Composition  
The loss detection system for rapeseed cleaning is composed of three parts: a signal acquisition device, a signal processing circuit and a loss detection and counting system for rapeseed kernels. The block diagram of the loss detection system is shown in Figure 1 (Figure 1 was created using Microsoft Visio 2019). The signal acquisition device is a cleaning loss sensor composed of a piezoelectric ceramic and an inductive plate. The core function of the signal processing circuit is to use the energy distinction method to identify the signals of rapeseed kernels and impurities. During operation, the electrical signal of shock vibration generated by the cleaning material striking the inductive plate is processed through an amplifier circuit, a detection circuit and an in-phase integral circuit. The signal energy is compared with the pre-established energy distinction threshold for rapeseed kernels and impurities through a triple voltage comparison circuit, and signals corresponding to rapeseed kernels, short stems and carob shells are identified. They are converted into three different types of pulse signals, and then the number of falling edges of the pulse signals is analyzed using a single-chip microcomputer to identify and detect the number of lost kernels. Through the TAOJINGCHI communication protocol, the detection result is transmitted to a serial screen for display.
## 2 Methodology
### Modelling and Validation  
The UH-60A Black Hawk helicopter rotor is selected as the baseline. The rotor load modelling and power analysis are performed based on the informative experimental data [39]. Table 1 shows the attributes of the UH-60A Black Hawk helicopter. The rotor of the UH-60A helicopter uses two airfoils: SC-1095 and SC-1094R8. The look-up table of aerodynamics coefficients (\(C_{L},C_{D}\), and \(C_{M}\)) for the airfoils is deduced based on wind  
Figure 3: Forces and moments for the trim solution.  
Figure 2: Reference frames for the blade load prediction.  
tunnel test measurements [40]. Figures 4 and 5 shows the pre-twist distribution, airfoil layout and platform of the blades obtained from Ref. [41].  
The rotor power is the product of the shaft moment and rotor speed.  
\[\left\{\begin{array}{c}P=P_{Induced}+P_{Profile}=(M_{Z-Induced}+M_{Z-Profile}) \omega\\ C_{P}=P/\rho\pi R^{2}(\omega R)^{3}\\ C_{P-Induced}=P_{Induced}/\rho\pi R^{2}(\omega R)^{3}\\ C_{P-Profile}=P_{Profile}/\rho\pi R^{2}(\omega R)^{3}\end{array}\right. \tag{2}\]  
where \(P\) is the rotor power, \(P_{Induced}\) and \(P_{Profile}\) are the induced power and profile power, \(C_{P()}\) is the coefficient of power, \(\rho\) is the density of the atmosphere, and \(\omega\) is the rotor speed.  
The validation analysis is conducted. The predictions of rotor power while using established methods are compared with the flight test data of the UH-60A Black Hawk helicopter in Figure 6. Generally, a good consistency is observed for the advanced ratio from 0.05 to 0.35 and weight coefficients (\(C_{W}\)) of 0.0065, 0.0074, 0.0083, and 0.0091.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Data** \\ \hline Rotor Radius(m) & 8.1788 \\ Rotor Speed(rad/s) & 27.0 \\ Number of Blades & 4 \\ Blade Chord Length (m) & 0.5273 \\ Blade Airfoil & SC1095 \& SC1094R8 \\ Hinge Offset(m) & 0.381 \\ Root Cutout(m) & 1.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline helicopter attributes.  
Figure 4: Pre-twist distribution of the UH-60A blade.  
Figure 5: Layout of SC1095 and SC1094R8 airfoils on the UH-60A blade platform.  
Four flight conditions are considered in this study to investigate the generalizability of segment twist control configuration for ATR in rotor power reduction, as shown in Table 2. The weight coefficients of 0.0065 and 0.0083 are determined, representing the typical-thrust flight condition and the medium-thrust flight condition, respectively. The advanced ratios of 0.25 and 0.35 are selected to represent the cruise flight condition and the high-speed flight condition, respectively.