esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 2 Materials and Methods
### Description of the Subsea Monitoring System
Before installation at the Grup Balfego facilities, the stereoscopic system underwent calibration in a controlled environment--a swimming pool measuring 12 \(\times\) 6 \(\times\) 2 m. A 1.40 \(\times\) 1.10 m checkerboard pattern was systematically moved from \(-\)45\({}^{\circ}\) to 45\({}^{\circ}\) with respect to the optical axis and placed at distances ranging from 1 to 10 m away from the cameras. The MATLAB Stereo Calibration Application, based on [17; 18], was used to accurately estimate the calibration parameters. Images captured by the stereoscopic system are presented in Figure 2.  
Figure 2: Stereoscopic images acquired with the monitoring system.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Crack Representation
#### 3.5.1 The Level Set Method  
An approach that is much better suited for combination to the XFEM, is the implicit representation of cracks using the level set method [133; 134]. Due to this fact, the method has been extensively used in the XFEM framework in 2D [135] and 3D [136; 137; 138] applications.  
To implicitly represent closed surfaces, such as cracks, two level set functions are needed:  
* The normal level set \(\phi\), defined as the signed distance from the crack surface.
* The tangent level set \(\psi\), defined as the signed distance from a surface that is normal to the crack surface and intersects the crack surface at the crack tip/front.  
The crack surface is then defined as the set of points for which the normal level set is equal to zero and the tangent level set assumes negative values.  
Typically, these level set functions are only computed at nodal points and interpolated for the rest of the domain using the FE shape functions:  
\[\phi=\phi\left(\mathbf{x}\right)=\sum_{\forall I}N_{I}\left(\mathbf{x}\right) \phi_{I},\qquad\psi=\psi\left(\mathbf{x}\right)=\sum_{\forall I}N_{I}\left( \mathbf{x}\right)\psi_{I} \tag{24}\]  
where \(\phi_{I},\psi_{I}\) are the nodal values of the level set functions.  
From the above expressions, spatial derivatives of the level set functions can be conveniently obtained through the spatial derivatives of the FE shape functions. Also evaluation of the enrichment functions can be significantly simplified. More specifically, jump enrichment functions can be directly computed as functions of the first level set, while the polar coordinates of Equation (17), needed for the tip enrichment functions, can be computed as:  
\[r=\sqrt{\phi^{2}+\psi^{2}},\qquad\theta=\arctan\left(\frac{\phi}{\psi}\right) \tag{25}\]  
For the general case of evolving surfaces, level sets are usually updated based on some velocity field by integrating the Hamilton-Jacobi equation. The case of propagating cracks however, requires several additional steps due to the nature of the problem and the fact that cracks are closed surfaces. Firstly, the velocity field, needed to update the crack, is only known at the crack tip/front, thus an additional step is required to extend the field to the whole domain. Then, the crack surface that has already formed should remain unaffected by the level set update, thus the velocity field should be appropriately modified. Finally, an orthogonalization step is necessary to ensure that the two level setsare normal after the update. To simplify the above procedure, several approaches were proposed in the work of Duflot [139] that allowed the update of level set descriptions for cracks without requiring the integration of evolution equations. In Elguedj et al. [140] a similar approach was proposed and applied to dynamic 3D crack propagation. It should be noted that both of these simplified methods rely on some geometric operations and are in fact very similar to methods from the category of the following paragraph.
## 3 Methodology
### Sample and Sampling Techniques  
Sampling has to do with choosing a subcategory of individuals from a statistical population to assess the entire population quickly. In the case of this research, the traffic dataset obtained from Mikros Traffic Monitoring Limited was unlimited. A fraction of four hundred and thirty-four and fourteen traffic data was chosen to easily assess the entire population of the South Africa Transportation Network, and we focused on seven roadsites connected to the N1 Allandale freeway in Gauteng Province due to their higher traffic density and traffic volume. The inputs and outputs variables used in this research are the speed of the vehicles, number of different classifications of vehicles, traffic volume, traffic density, the distance covered by the vehicles, and the time it takes the vehicles to travel the road's length.
# Water Jacket Systems for Temperature Control of Petri Dish Cell Culture Chambers
## 3 Results and Discussion
### Analyzing the Effect of Recirculating Water and Surrounding Air Parameters  
Next, we characterize the thermal performance of the system with respect to the flow rate and temperature of the recirculating water (serving as the heat source), as well as the temperature and free convection coefficient of the surrounding air (serving as the heat sink), as discussed below.  
_Water flow rate:_ Figure 3a shows the temperature profile across the bottom surface of the Petri dish (referred to as \(T_{\text{sample}}\)) when varying the flow rate of the water recirculating through the water jacket (\(Q_{\text{water}}\)) from 15 to 120 mL/min. The temperature contours across the middle cross-section of the Petri dish at various water flow rates are presented in Figure S2. Increase of \(Q_{\text{water}}\) enhanced the convective heat transfer through the water jacket. However, the majority of the heat left via the outlet port of the water jacket without being injected into the Petri dish. Under these conditions, despite increasing the average temperature of the sample inside the Petri dish, the magnitude of the temperature gradient along the radial axis of the Petri dish did not change significantly.  
_Water inlet temperature:_ Figure 3b presents the temperature profile across the Petri dish when changing the temperature of the water entering the water jacket (\(T_{\text{water}}\)) from 15 to 45 \({}^{\circ}\)C. The temperature contours at various water inlet temperatures are presented in Figure S3. Increase of \(T_{\text{water}}\) enhanced the conductive heat injected into the Petri dish via its sidewalls, which consequently increased the temperature gradient along the radial axis of the Petri dish. Decrease of \(T_{\text{water}}\) below the ambient temperature reversed the conductive heat from the sample into the water jacket, leading to negative temperature gradients along the radial axis of the Petri dish.  
_Ambient temperature:_ Figure 3c shows the temperature profile across the Petri dish when varying the temperature of surrounding air (\(T_{\text{air}}\)) from 24 to 16 \({}^{\circ}\)C. The temperature contours at various ambient temperatures are presented in Figure S4. Decrease of \(T_{\text{air}}\) enhanced the convective heat losses at the free surfaces of the Petri dish, and consequently increased the temperature gradient along the radial axis of the Petri dish.  
_Convection coefficient:_ Figure 3d presents the temperature profile across the Petri dish when changing the free convection coefficient of surrounding air (\(h_{\text{air}}\)) from 5 to 20 W/m\({}^{2}\)K. The temperature contours at various convection coefficients are presented in Figure S5. Increase of \(h_{air}\) enhanced the convective heat losses at the free surfaces of the Petri dish and, accordingly, increased the temperature gradient along the radial axis of the Petri dish.
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.4 Decision Trees (DTs)  
Decision Trees (DTs) are widely used in food science for their simplicity and interpretability. DTs split data into subsets based on feature values, creating a tree-like structure where nodes represent decision rules, and branches represent outcomes [117]. Food quality assessment is the field in which DTs can be applied in HSI to evaluate different quality parameters, with high accuracy.  
Research using DTs coupled with NIR-HSI for the classification of black tea quality reached an accuracy of 93.13% using the fine tree model, which evidently proved the capability of DTs to manage complex, multi-factorial datasets on their own [118]. In addition to that, DTs proved to be very resilient in agricultural applications, as evidenced by their capability to estimate firmness and soluble solids in apples using hyperspectralimages with R\({}^{2}\) values of 0.881 and 0.679, respectively [119]. While decision trees bring in these advantages, more sophisticated models, such as random forests or gradient boosted trees, would easily outperform in a noisy dataset or with higher demands on accuracy.
## 4 Experiment Description and Results Analyses  
### Test Site and Operation Scheme of the DE-GSHP  
A test was carried out to evaluate the performance of the DE-GSHP under real working conditions. The test site was located in a seasonally cold region of China. In the study area, winter lasts from November to February and the mean daily temperature varies between \(-8\)\({}^{\circ}\)C and \(4\)\({}^{\circ}\)C. January is the coldest month, with the lowest temperature reaching \(-19.8\)\({}^{\circ}\)C. The surface layer of the foundation bed at the test site is filled with 0.5 m thick non-frost-heave Group A filling material, and the lower part is a 0.1 m thick medium-coarse sand cushion layer. A test setup was created by filling a quadrangular frustum pyramid with a height of 3.2 m and a top width of 1.6 m. Figure 6 schematically illustrates the experimental facilities with the DE-GSHP at the center of the bench. The thermal conductivity, density, and specific heat of the soil were 1.74 W/(m\(\cdot\)K), 1690 kg/m\({}^{3}\), and 1800 J/(kg\(\cdot\)K), respectively.  
During the experiment, a thermocouple (PT100) with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C was utilized to measure the temperature. Three thermistor cables were installed to the right of the DE-GSHP, and their horizontal spacing was 0.25 m. In the present study, 12 temperature sensors were used to measure the temperatures at different points. Five sensors were installed from 0.0 m to 1.0 m, while seven sensors were installed from 1.2 m to 3.2 m. The experimental values were recorded every 10 min by a data acquisition system.  
In this study, the operation modes of the DE-GSHP were selected to acquire a constant heat-supplying temperature. Specifically, the heat-supplying temperatures at the mid-position of the condenser for four groups were fixed at 30, 45, 60, and 75 \({}^{\circ}\)C, respectively, by using the temperature control method of return difference. The return difference was 5 \({}^{\circ}\)C. The experimental duration of each scheme was 24 h. The experiment was conducted in winter and the average daily temperature was below \(-\)5 \({}^{\circ}\)C. Observations commenced on 21 February 2018. At the end of one group of tests, the ground temperature was returned to the original level, after which, the next group of tests began.
## 2 Full DC Microgrid System Modeling
### Microgrid System Modeling  
To keep the power balance and common DC bus voltage stable, a proportional-integral (PI) controller is introduced to calculate the power needed to be compensated for by the public grid and BS for the on-grid operating mode and by the DG and BS for the off-grid operating mode. The power balance expressed by Equations (1)-(3) is introduced to keep the common DC bus voltage \(v_{DC}\) at the reference voltage represented by \(v_{DC}^{*}\). The stability of the \(v_{DC}\) represents the power quality to supply the DC load directly. One notes that the load can also be assumed, such as a predefined load integrated with the DC/DC or DC/AC converter.  
\[\Delta p=p_{PV}-p_{L}-p_{PI} \tag{1}\]  
\[p_{PI}=K_{P}(v_{DC}^{*}-v_{DC})+K_{I}\int{(v_{DC}^{*}-v_{DC})} \tag{2}\]  
\[\Delta p=p_{BS}+p_{G}-p_{DG}+p_{SC} \tag{3}\]  
where \(p_{PV}\) is the power of PV sources; \(p_{L}\) is the DC load power; \(K_{P}\) and \(K_{I}\) are the PI controller coefficients; \(\Delta p\) is the compensation power by the public grid, BS, DG, and SC; \(p_{G}\) is the public grid power, which represents the power injection when \(p_{G}\) is positive and the power supply when \(p_{G}\) is negative; \(p_{BS}\) is the storage power that represents BS charging when \(p_{BS}\) is positive and BS discharging when \(p_{BS}\) is negative; \(p_{DG}\) is the DG supply power which is only zero or positive; \(p_{SC}\) is the SC power that represents SC charging when \(p_{SC}\) is positive and SC discharging when \(p_{SC}\) is negative.  
In Equations (1) and (2) the PV sources generate power, the load consumes the power from the PV, and the PI controller power keeps the common DC bus stable. The excess or insufficient power \(\Delta p\) is compensated for by the public grid, BS, DG, and SC in Equation (3). When \(\Delta p\) is positive, representing that the PV sources generate excess power in Equation (1), the excess power should be injected into the BS, the public grid, or the SC in Equation (3). When \(\Delta p\) is negative, representing that the PV power generation is not enough to supply the load and to compensate for the common DC bus in Equation (1), the insufficient power should be supplied by the public grid, BS, DG, and SC in Equation (3). The DG only supplies power, therefore, the \(p_{DG}\) should be subtracted in Equation (3).  
Figure 1: Overview of full direct current (DC) microgrid.
## 2 Energy Management and Review of Surveys
### Communications Protocols  
[PERSON] et al. [35] categorized the applicable communication technologies in MGs as wired (including PLC, ethernet, and fiber optics) and non-wired (including Bluetooth, Wi-Fi, ZigBee/6 LoWPAN, Cellular, and LPWAN).  
[PERSON] et al. [34] introduced an advanced wireless technology, called LoRa (Long Range), to establish a communication network for interoperable smart microgrids (ISMs).  
The system networks were also categorized as: personal area network (PAN), local area network (LAN), metropolitan area network (MAN), and wide area network (WAN).  
[PERSON] et al. [12] presented an overview of the communication technologies: GSM (900-1800 MHz, 14.4 Kb/s, 1-10 km), GPRS (900-1800 MHz, 170 Kb/s, 1-10 km), 3G (1.92-1.98 GHz, 2 Mb/s, 1-20 km), 4G (2.11-2.6 GHz, 100 Mb/s, 1-10 km), 5G (3-90 GHz, 10 Gb/s, >1 km), WiMAX (2.5-5.8 GHz, 75 Mb/s, 10-50 km), PLC (1-30 MHz, 2-3 Mb/s, 1-3 km), Zigbee (800 MHz-2.4 GHz, 250 Kb/s, 30-50 m), and Bluetooth (2.4-2.483-GHz, 2.1 Mb/s, 0.1-1 km).
# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 3 Methodology  
### Problem Definition  
Knowledge Graph: We defined the knowledge graph \(G=(E,R,A,V)\), where \(e\in E\), \(r\in R\), \(a\in A\), and \(v\in V\) denote entities, relationships, attributes and corresponding attribute values, respectively. For example, the author of the book _How the Steel Was Tempered_ is [PERSON], and its publication year is 1932. This can form a relational triple How the Steel Was Tempered-Author-Nikolai Ostrovsky and an attribute triple How the Steel Was Tempered-Publishing Year-1932. The entities contained in it are _How the Steel Was Tempered_ and [PERSON], the relationship is the author, the attribute is the publishing year, and the attribute value is 1932. The number of hops in the knowledge graph refers to the number of relationships (edges) from one entity to another in the knowledge graph. Each relationship (edge) represents a \"hop\", so the number of hops is a way to describe the distance between two entities. For example, the number of hops from an entity to its nearest neighbor is \(1\). \(N^{h}(e)=\{(r_{i},e_{i})\}_{i=1}^{|N^{h}(e)|}\) denotes the set of all h-hop neighbors of entity \(e\), where \(h\) is the number of hops, and the i-th neighbor contains a neighbor relationship \(r_{i}\) and the corresponding entity \(e_{i}\), where \(\left|N^{h}(e)\right|\) denotes the number of \(N^{h}(e)\). \(N(e)\) denotes the set of all neighbors of entity \(e\). \(A(e)=\{(a_{i},v_{i})\}_{i=1}^{|A(e)|}\) denotes the set of attributes of entity \(e\), and the \(i\)-th attribute contains a name \(a_{i}\) and the corresponding attribute value \(v_{i}\), where \(\left|A(e)\right|\) denotes the number of \(A(e)\).  
Entity Alignment: Given two knowledge graphs \(G\) and \(G^{\prime}\) and a set of already aligned entity pairs \(\mathrm{P}=\{(\mathrm{e}\sim\mathrm{e}^{\prime})\}\), we aimed to derive a ranking function \(\mathrm{f}:\)E\(\times\mathrm{E}^{\prime}\rightarrow\mathbb{R}\) to compute the similarity scores between the two entities, based on which we ranked the correctly aligned entity \(e^{\prime}\) to any queried entity \(e\) as high as possible among all the entities in \(E^{\prime}\). A ranking function makes it possible to score entity pairs. Such a function needs to accept input features, calculate similarity scores, and output similarity scores. The term \"ranking\" refers to the use of this score to find the best matching entity. The method used to derive the ranking function is very important because it directly affects the accuracy and effect of the entity-alignment task.
# The Attachment of Carbon Nanotubes to Atomic Force Microscopy Tips Using the Pick-Up Method
## 3 Results
### Techniques to Determine CNT Attachment to AFM Tips
#### 3.1.3. Electron Microscopy (EM)  
TEM and SEM are often used to determine CNT attachment but typically requires a high resolution field emission microscope to resolve the attached CNTs, particularly if they are less than 20 nm in diameter. Figure 3a-e are examples of SEM images of CNT-AFM tips produced using the pick-up method. While EM can produce definitive images of the attached CNTs potentially yielding information on tip shape, CNT diameter and CNT length they do require careful edge-on imaging with an experienced operator. The tips that the CNTs are attached to must also be conductive which can be problematic for silicon nitride tips. It should be noted that CNTs on AFM probes are also susceptible to changes in humidity and electrostatic forces [54] which may be experienced in preparing for and executing EM analysis. [PERSON] et al. [54] noted that they could not produce TEM images of their AFM tips which they were confidant had CNTs attached using the pick-up method. They concluded that electrostatic charging was removing the CNTs from the AFM tips. They solved this by using AFM probe containers that were conductive. We have also found that the electron beam itself can, in some instances, cause the CNTs to move or detach from the AFM probe, making SEM imaging difficult and damaging to the CNT-AFM tip. This can be seen in Figure 3f where the CNT was originally in the position of the dotted line but during SEM imaging the CNT moved to its final position indicated  
Figure 2: This figure shows an AVD curve with a CNT attached. Section A is the tip approaching the surface with the amplitude of oscillation of the tip constant. Section B shows the amplitude of the cantilever as the tip begins to the tap the surface and the amplitude decreases. Section C shows where the CNT buckles and the amplitude of the cantilever temporarily increases until the amplitude is again suppressed.  
in Figure 3f. This rendered the CNT tip unusable. It should be noted that for the AFM probes in Figure 3a-e SEM imaging was very stable and multiple images were acquired indicating secure CNT attachment to the AFM tip. These probes were suitable for extensive AFM imaging in standard tapping mode and as the tips they are attached to are conductive then potentially also conductive imaging. For most CNT attachment methods, including the pick-up technique, improved resolution and AVD curves are sufficient to verify CNT attachment has occurred.
## 1 Introduction  
Trace gas analysis is widely applied in environmental detection, atmospheric science, medical diagnoses, and industrial agricultural process control [1, 2, 3]. Laser spectroscopy can provide high precision concentration detection of trace gas with fast response, large range and high resolution among different coexisting species. The most common approaches are based on infrared absorption technologies, such as non-dispersive infrared absorption (NDIR) [4, 5], cavity ring-down spectroscopy (CRDS) [6], cavity-enhanced absorption spectroscopy(CEAS) [7].  
CEAS is an essential technology in laser spectroscopy. In 1998, this technique was proposed to facilitate CRDS by [PERSON] et al. [7, 8, 9]. In principle, CEAS and CRDS enhance the effective absorption path by the relaxing effect of the high-quality passive optical cavity. When the laser meets resonance conditions, the effective absorption path of the absorbing medium increases thousands of times [10]. Unlike CRDS, CEAS obtains the absorption spectrum by measuring the transmitted light intensity of the resonant cavity, and does not require expensive light modulators such as optical switches. Therefore, the CEAS system is compact, cheaper and suitable for commercial application.  
Since the peak stability of resonant light in CEAS technology directly affects the minimum detectable absorption coefficient, the most effective method to improve the sensitivity of CEAS is frequency stabilization technology. Currently, the primary laser frequency stabilization technology mainly includes Pound-Drever-Hall technology [11] and optical feedback frequency locking [12]. This paper presents a compact optical feedback Cavity-Enhanced Absorption Spectroscopy (OF-CEAS) system setup with a high-finesse passive U-shaped cavity. During the study of this system, a special asymmetric etalon effect is observed, which is different from the etalon effect observed in V-shaped cavity CEAS systems during our previous works [13]. These adverse effects limit system performance and application range. In order to reveal the physical mechanism of this phenomenon, the transmission spectrum of each mirror was analyzed and discussed according to the multi-beam interference. Ways to prevent this phenomenon are also proposed. After eliminating the interference of this phenomenon on spectral line measurement, the accuracy is improved, and the OF-CEAS system with the minimum detectable coefficient \(\alpha_{\text{min}}\) = 8.33 \(\times\) 10\({}^{-9}\) cm\({}^{-1}\) was obtained. These works have guiding significance for the performance improvement of the CEAS system.