esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The following hypotheses can be put forward:  
* The measuring device will provide sufficiently accurate and reliable measurements of pigment content in plant leaves from existing methods. * The device will be able to measure a wider range of pigments than existing methods. The contribution results can be summarized as:
* A measuring device has been developed that has sufficient accuracy and reliability compared with existing methods of determining the pigment content of plant leaves. * Appropriate regression models were selected for the determination of pigments in plants. They help expand the range of measurement types when assessing the condition of plants. * A selection of technical means of measurement and management has been developed; these have sufficient accuracy and a low cost and are suitable for performing the tasks required to determine the pigments of plants. The device was tested and found to provide sufficiently accurate and reliable measurements of pigment content in plant leaves compared with existing methods. This ensures the quality and reliability of the data collected. Unlike previous methods, the proposed device can measure a wider range of pigments. This expanded capability allows for a more comprehensive analysis of plant pigments and provides a more detailed understanding of their function, distribution, and regulation. The device has the potential to save time and resources in the measurement of pigment content in plant leaves. Its efficiency makes it suitable for practical applications such as crop monitoring, where quick and accurate measurements are essential for effective decision making. This work is organized in the following order: In the \"Materials and Methods\" section, the research objects are defined. A methodology for reference measurements of chlorophyll is presented. The models for determining basic pigments in plants are described. Measuring and controlling the technical means and methods for their analysis are selected, and the statistical methods used are described. In the \"Results\" section, we present an electrical diagram, a block diagram of the software, and a graphical user interface of the proposeddevice. Correction equations for optical sensor data are presented. The results of the literature data analysis are presented. Results from actual plant leaf measurements are also presented. In the discussion part, a comparative analysis is made with accessible literary sources. The results obtained are summarized in the \"Conclusion\". Directions for future research are provided.
## 2 System Architecture and Design
### System Architecture
The number of optical lanes desired should be as high as possible to reduce the cost per Gb/s. Our 2D optical interconnect is designed such that each 4 cells are on a CWDM grid and multiplexed into one of the 16 single-mode fibers, each of which carries a bit rate at a single wavelength of 25 Gb/s; thus, the matrix operates at 1.6 Tb/s (16 \(\times\) 4 \(\times\) 25). The SiP matrix is made up of identical unit cells where each cell is self-contained with its own modulator, laser source and GCs, and each cell in the matrix is optically and electrically isolated from its neighbors.
## 5 Experiments and Results
### Stellar  
#### 5.2.1 Experiments  
We performed experiments in a similar configuration as with Hyperledger Fabric. The setup consisted of 50 sensor nodes, 3 Stellar Core nodes each with a Stellar Horizon companion, 1 aggregator, and 1 client.  
Each sensor generated one log per second, and each log was sent to a peer node, which resulted in a total load of 50 transactions per second. The simulation lasted for seven days, which resulted in 604.8 k transactions per sensor or approximately 30 M transactions in total.  
Sensors simulated readings of either temperature or humidity. For more details, see Section 5.
## 2 Materials and Methods
### Research Methodology
#### 2.3.3 Coupling Coordination Degree Model  
This study used the concept and model of capacity coupling from physics and the multi-system coupling degree formula [41], and constructed a LCCC coupling degree model system. Its functional expression is as follows [42]:  
\[C=\left[\frac{U_{geo}\times U_{ecos}\times U_{econ}\times U_{soc}}{\left(\frac{ U_{geo}+U_{ecos}+U_{econ}+U_{soc}}{4}\right)^{4}}\right]^{\frac{1}{4}} \tag{5}\]  
where \(U_{geo}\), \(U_{ecos}\), \(U_{econ}\), and \(U_{urb}\) represent the carrying capacity index of four subsystems (geological condition, resources and environment, economic scale, and urban construction), respectively, and \(C\) is the coupling degree of the LCCC system, with values between 0 and 1. When \(C\) is 1, the coupling degree of the LCCC system is maximized, and the system reaches a benign interactive state and tends to be orderly. When \(C\) is 0, the coupling degree of the LCCC system is the lowest, and the system is disorderly and chaotic.  
To further explore coordination of the overall LCCC system based on the coupling degree model, which only reflects the correlation degree among the subsystems, this study used the coordination degree model. Its formula is as follows [43; 44]:  
\[O=\sqrt{C\times T} \tag{6}\]  
\[T=\alpha U_{geo}+\beta U_{ecos}+\theta U_{econ}+\lambda U_{urb} \tag{7}\]  
where \(O\) is the degree of coordination, \(T\) is the evaluation index of LCCC, and \(\alpha\), \(\beta\), \(\theta\), and \(\lambda\) are the weights of the four subsystems (geological condition, resources and environment, economic scale, and urban construction), respectively. These weights are different from those determined by the above entropy method, and were determined subjectively because of their compensatory effect on the LCCC system. This study emphasized the equal importance of the four subsystems, therefore, \(\alpha=\beta=\theta=\lambda=1/4\).
# Super-Frequency Sampling for Thermal Transient Analysis
## 3 Experimental Setup  
As a proof of concept, the thermal transient and impedance of a surface-mounted device (SMD) diode (Vishay DO-219 AD) soldered to an FR-4 printed circuit board was analyzed by super-frequency sampling. Figure 3 shows a sketch of the experimental setup. The board is directly screwed to an aluminum base plate. The heating current, supplied by a programmable power supply (Hameg HMP4040), is switched by an n-channel power MOSFET (Fairchild BUZ11_NR4941), which is controlled by a frequency generator (Hameg HMF2550). To determine the ohmic loss, the voltage drop across the diode is measured by a voltmeter (Keithley 2700) in a 4-wire configuration.  
The surface temperature of the diode is measured by a thermography system with an InSb focal plane array (InfraTec GmbH, Dresden, Germany, ImageJIR(r) 8380S). The thermographic camera has a full-frame resolution of \(640\times 512\) pixels with a sampling rate of 200 Hz. When using the quad-frame setting (\(160\times 128\) pixels), the sampling rate increases to 1000 Hz. For a sufficient resolution of a thermal transient, the sampling period must be 10 to 15 times smaller than the smallest time constant of the response [11]. For semiconductor components, typical time constants are greater than one millisecond. Consequently, the target sampling rate is approximately \(100\,\mathrm{\SIUnitSymbolMicro s}\), and thus, a speed-up factor of \(N=10\) is required. The modulation frequency is set to \(\tau_{\mathrm{set}}=20\,\mathrm{s}\,+\,1\cdot 10^{-4}\,\mathrm{s}\). The applied current is set to \(0.9\,\mathrm{A}\) with a duty cycle of 50 %. The resulting voltage drop across the diode is found to be in the range between \(0.983\,\mathrm{V}\) and \(1.017\,\mathrm{V}\). To measure the system in a quasi equilibrium state, the periodic excitation is started a few minutes prior to the temperature recording.
## 5 Simulation Results and Discussion
### Effects of the Nodes' Movement Speed  
In this section, we compare different protocols' performances with nodes' movement speed changes between 1 and \(\sim\)20 m/s in the test phase. There are 100 nodes in the network. The packet send rate is 1 Hz and the initial energy is 1000 J. The simulation results are shown in Figures 5-7.  
Figure 5 presents the effects of different nodes' movement speeds on the average E2E delay. It is obvious that the metric of DSEGR and QNGPSR outperform GPSR and FEQSAI because QNGPSR and DSEGR can reduce the probability of reaching void areas by two-hop neighbor estimation. The average E2E delay of QNGPSR is basically similar to that of DSEGR. As the speed increases, the metric of QNGPSR and GPSR first increases and then decreases; this is because they increase average hop counts. However, once the speed reaches a threshold value, which leads to link-stability decreasing dramatically and packet loss increases, the E2E delay decreases accordingly. The average E2E delay of FEQSAI is the largest since it involves online learning, resulting in the delay being very large at the beginning. It can be seen that, although the maximum average E2E delay difference between DSEGR and QNGPSR is 8 ms at a speed of 1 m/s, the PDR of DSEGR is 26% higher than QNGPSR at a speed of 1 m/s, which is presented in Figure 6. Moreover, the DSEGR has a slight advantage over QNGPSR at 5 m/s and 10 m/s.  
Figure 6 shows the effects of different nodes' movement speeds on the packet delivery ratio. As the speed increases, the PDR of the DSEGR significantly outperforms QNGPSR, GPSR, and FEQSAI. DSEGR considers link stability and predicts the residual of the neighbor node in the reward function, which can greatly reduce the packet loss rate. The main influence factor is mobility when nodes' speeds are high; thus, unstable links seriously affect the PDR. We can see in the figure that the DSEGR can still achieve higher PDR when the node speed is 20 m/s. At low speeds, the nodes' residual energy is the main influence factor. However, the DSEGR protocol can achieve a higher PDR. Figure 7 further proves that the time of the first dead node is late for the DSEGR when nodes are at low speeds.  
Figure 5: Performance comparison of the end-to-end delay among GPSR, QNGPSR, DSEGR, and FEQSAI with different node movement speeds.  
Figure 7 depicts the effects of the different nodes' movement speeds on the time of the first dead node. As can be seen, the nodes in DSEGR die later than those in QNGPSR, GPSR, and FEQSAI. The reason for this is that DSEGR has an energy prediction model, which can predict the remaining energy of neighbor nodes to balance the energy. Nodes moving at low speeds may lead to certain nodes acting as central nodes. As a result, the central nodes will die early due to node energy exhaustion. With increasing speed, the relative displacement between nodes changes more obviously, and the links become unstable. There are no so-called central nodes; thus, the energy consumption of the nodes will become more balanced, and the time of the first dead node is postponed.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 2 Data
### NSIDC Snow Depth Data Product and Ship-Based Observational Snow Depth Data  
To retrieve the snow depth on sea ice from the HY-2B SMR data and perform a comparative analysis with frequently used snow depth products, the GCM-W1 AMSR-2 snow depth data product released by the NSIDC was utilized (Figure 1d,e). The NSIDC AMSR-2 snow depth has a spatial resolution of 25 km and a temporal resolution of 1 day.  
Ship-based snow depth observational data were used to validate the HY-2B SMR-derived snow depth. These snow depth data were collected during the 35 th Chinese National Antarctic Research Expedition (CHINARE-35). During CHINARE-35 (from November 2018 to January 2019), a camera and a known-size reference ball were fixed on the R/V XueLong to record sea ice conditions and the snow depth on sea ice along the ship's track, which followed a standardized photogrammetric method (Figure 2). As the ship sailed through the ice, sea ice bended and reversed, and the process was captured on the camera observing at a frequency of 14 frames per second. Pixel values of snow depth could be obtained from the image. To reduce errors, we selected three places (\(L_{12}\), \(L_{34}\), and \(L_{56}\)) and calculated the average value to represent the real pixel value of snow depth. The pixel value of the reference ball diameter was also obtained (\(L_{\text{AB}}\)). Combined with the actual diameter of the reference ball (\(D_{\text{ball}}\) = 30 cm), the actual snow depth (\(D_{\text{snow}}\)) could be calculated through the following equation: \(D_{\text{snow}}=\frac{1}{3}\left(L_{12}+L_{34}+L_{56}\right)\times\frac{D_{ \text{ball}}}{L_{AB}}\). The error of this ship-based observed snow depth was less than 7% [11]. Due to environmental factors such as photography failure during the ship's voyage and the quality of the photographic images, a total of 1248 ship-based snow depth measurements, distributed in various sectors of Antarctica, were obtained in this study for evaluating the accuracy of the HY-2B SMR derived snow depth.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sensor** & **SMR** & **AMSR2** \\ \hline Satellite & HY-2B & GCM-W1 \\ Launch date & 25 October 2018 & 4 July 2012 \\ Altitude (km) & 973 & 700 \\ Incidence Angle (\({}^{\circ}\)) & 53 & 55 \\ Swath Width (km) & 1600 & 1450 \\ Scan Period (s) & 3.8 & 1.5 \\ \hline  & 6.93 V/H, 0.5 & 6.93 V/H, 0.4 \\ Center Frequency (GHz) & 10.65 V/H, 0.6 & 10.65 V/H, 0.7 \\ Polarization, & 18.7 V/H, 0.5 & 18.7 V/H, 0.7 \\ Temperature Sensitivity (K) & 23.8 V, 0.5 & 23.8 V/H, 0.6 \\  & 37 V/H, 0.8 & 36.5 V/H, 0.7 \\  & - & 89 V/H, 1.2 \\ \hline \end{tabular}
\end{table}
Table 1: Main characteristics of HY-2B SMR and GCM-W1 AMSR2.
## 2 Related Work
### Advances in Entity Linking, Enrichment and Representation  
Over the years, various entity enrichment approaches have been introduced, aiming at augmenting the usefulness of the extracted triples, including entity linking and polarity detection processes before representing them through a graph database. Linking the identified entity mentions in text to anontology or dictionary is considered an essential step in creating informative triples, with various knowledge bases (KBs) pertaining to general knowledge being employed for this purpose based on morphological similarity, such as DBpedia [53], Freebase [54], and Yago [55]. There are cases, however, where many of the entities in these general-interest KBs are irrelevant for certain applications, therefore domain-specific ontologies for semantic information brokering, based on inter-ontology relationships such as synonyms, hyponyms, and hypernyms of the extracted entities are used [56]. In order to further increase the links between morphologically dissimilar extracted entities and KB-related objects, neural-based methods are also implemented, exploiting word embeddings to represent semantic spaces [57; 58], also allowing for domain-agnostic entity resolution [59]. With regard to sentiment analysis (neutral vs. emotionally loaded) and polarity (positive vs. negative) detection of a text [60], lexicon-based [61], ML-based [62], and neural-based [63] classifiers are commonly used to identify the polarity of a relation within a sentence. In the field of graph representation, the two main implementations of graph models include resource description framework (RDF) triple stores [64] and labeled property graphs (LPG) [65]; both provide ways to explore and graphically depict connected data.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Features of TD-Light Propagation Including Penetration Depth, Optical Pathlength, etc.
#### 5.1.2 Mean-TOF, Partial Pathlength, and Sensitivity for the Head Model  
[PERSON] et al. [126] investigated light propagation in models of the adult head theoretically using MC simulations as well as solving the DE and experimentally using measurements of TR reflectance, \(R(t)\), from phantoms. Four different models of adult brain were constructed to see the effects of the SD distance on the mean-TOF, \(<\!\!t>\), and partial pathlength of the \(i\)-th layer, \(<\!\!l_{i}\!\!>\), and the sensitivity distribution for CW on the SD distance, and to see the effect of the CSF layer on \(<\!\!t>\) and \(<\!\!l_{i}\!\!>\). More realistic head models were used to extend the study [127].  
[PERSON] et al. [128] theoretically studied light propagation in a realistic adult head model constructed from MRI data using a hybrid DE/radiosity technique and a MC simulation to obtain \(R(t)\), \(<\!\!t>\), their TR-sensitivities, \(H_{R}(t)\) and \(H_{<\!\!t>}(t)\), and \(<\!\!l_{i}(t)\!\!>\). They found that \(H_{<\!\!t>}(t)\) has larger values than \(H_{R}(t)\) in deeper regions.  
[PERSON] et al. [129] studied \(R(t)\) from a multi-layered model to estimate the changes in \(R(t)\) due to absorption changes in the layers from \(<\!\!l_{i}(t)\!>\) calculated by MC simulations. Their results were verified by experiments using two- and three-layered phantoms and by in vivo experiments.
## 3 Research Models  
Prior studies commonly use GARCH models to examine dynamic conditional variances and DCC models to examine dynamic conditional correlations. Nonetheless, the pure time-varying process involved in these models cannot identify nonlinear volatility jumps and state-dependent correlations [32; 33]. In this section, we propose a regime-switching approach to handle these nonlinearities and test our hypotheses regarding the intensity and direction of the volatility spillover effect. For comparative analysis purposes, we start this section by first introducing the conventional time-varying approaches (i.e., GARCH and DCC) as a benchmark against our proposed regime-switching approach.
## 2 Related Works
### State of the Art for Ego-Motion Estimation  
The current state-of-the-art in ego-motion estimation has presented significant advancements, particularly through the use of deep learning techniques. Approaches based on Convolutional Neural Networks (CNNs) have shown promising results in accurately estimating the motion of vehicles in multiple environments. For example, [19] proposed a novel CNN architecture designed to capture spatial and temporal features from consecutive image frames, achieving notable improvements in the accuracy of motion estimation. This model demonstrates the effectiveness of using deep learning to address the complexities associated with real-world scenarios.  
However, traditional methods such as visual odometry have been widely employed to estimate ego-motion. These methods typically rely on feature extraction techniques such as SIFT [20] and ORB [21], which provide robust performance in detecting and matching keypoints across frames. However, they can suffer from limitations under challenging conditions, such as low texture or significant motion blur. Despite their strengths, reliance on feature matching can result in inaccuracies, particularly when the environment lacks distinctive features.  
Recent studies have also explored the integration of IMU (Inertial Measurement Unit) data with visual input to enhance ego-motion estimation. For example, ref. [22] demonstrated that the combination of IMU data with visual odometry results in improved accuracy and robustness. This hybrid approach capitalizes on the strengths of both sensors, compensating for the weaknesses of visual-only methods in fast-moving or visually sparse environments. Similarly, recent advances in automotive radar-based systems have demonstrated their potential for robust and accurate estimation of ego-motion [23; 24; 25]. Furthermore, event-based methods such as those discussed by [26] highlight innovative approaches to optical flow and ego-motion estimation, pushing the boundaries of existing technologies.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Evaluation Metrics
#### 3.6.6 Mean Square Error (MSE) and Root Mean Square Error (RMSE)  
The Mean Squared Error (MSE) is a widely used performance metric that quantifies the average discrepancy between the observed values and the model's predicted values. MSE is calculated by taking the average of the squared differences between the predicted and observed values, providing a measure of the model's overall accuracy. This metric is particularly useful for assessing the effectiveness of regression models, where the goal is to minimize the error between the predicted and actual values.  
The Root Mean Squared Error (_RMSE_) on the other hand is a widely used performance metric in machine learning that measures the average magnitude of the errors between the predicted and observed values. _RMSE_ is a valuable tool for evaluating the accuracy of a predictive model after the algorithm has converged. This metric is also especially useful in regression tasks where the goal is to minimize the error between the predicted and actual values, and it provides a measure of the model's predictive ability [58]. _RMSE_ is calculated by Equation (16) [58].  
\[RMSE=\sqrt{\frac{1}{m}{\sum_{i=1}^{m}}\Big{(}e_{i}-\widetilde{e}_{i}\Big{)}^{ 2}} \tag{16}\]  
where \(e_{i}\) and \(\widetilde{e}_{i}\) represent the original and estimated attribute values, respectively, and \(m\) is the total number of predictions. A larger _RMSE_ value indicates a less accurate model, whereas a smaller _RMSE_ value indicates a higher degree of predictive accuracy.
## 2 Proposed Adaptive Finite Step Length Algorithm  
### HL-RF Algorithm  
Due to its simplicity and efficiency, the HL-RF algorithm [20] is a widely used algorithm to estimate the failure probability. It includes the following three main steps.  
(1) Transform the random variables \(\mathbf{X}\) into standard normal random variables \(U\) by Rosenblatt or Nataf transformation techniques [26], which is expressed as  
\[\mathbf{U}=\Phi^{-1}[F_{\mathbf{X}}(\mathbf{X})] \tag{2}\]  
where \(\Phi^{-1}\) is the inverse cumulative distribution function (CDF) of the standard normal distribution, \(F_{X}\) is the CDF of \(\mathbf{X}\), and \(\mathbf{U}=(U_{1},U_{2},\cdots U_{n})\).  
(2) Find the most probable point (MPP) \(\mathbf{U}^{*}\) by using the following iterative formulation:  
\[\mathbf{U}^{k+1}=\frac{G\big{(}\mathbf{U}^{k}\big{)}-\
abla^{\mathrm{T}}G\big{(}\mathbf{U} ^{k}\big{)}\mathbf{U}^{k}}{\left\|\
abla G\big{(}\mathbf{U}^{k}\big{)}\right\|}\mathbf{ \alpha}^{k} \tag{3}\]  
where \(\left\|\cdot\right\|\) is the norm of a vector, \(k\) is the counter of iteration, \(\
abla G\big{(}\mathbf{U}^{k}\big{)}\) is the gradient of \(G\big{(}\mathbf{U}^{k}\big{)}\), and \(\mathbf{\alpha}^{k}\) is the sensitivity factor that can be calculated by  
\[\mathbf{\alpha}^{k}=-\frac{\
abla G\big{(}\mathbf{U}^{k}\big{)}}{\left\|\
abla G\big{(} \mathbf{U}^{k}\big{)}\right\|} \tag{4}\]  
(3) Estimate the failure probability by  
\[P_{f}\approx\Phi(-\beta) \tag{5}\]  
where \(\Phi\) is the CDF of the standard normal distribution and \(\beta=\left\|\mathbf{U}^{*}\right\|\) is the reliability index.  
However, the HL-RF algorithm may fail to converge for highly nonlinear functions. As shown in Figure 1, assume that \(\mathbf{U}^{k}\) is obtained after the iteration of step \(k\), \(\mathbf{U}^{k+1}\) is determined based on the sensitivity factor \(\mathbf{\alpha}^{k}\) and the limit state surface. It is obviously that the line \(L_{1}\) is parallel to the negative gradient direction at point \(\mathbf{U}^{k}\). If the negative gradient direction at point \(\mathbf{U}^{k+1}\) is parallel to the line \(L_{2}\), we have \(\mathbf{U}^{k+2}=\mathbf{U}^{k}\) and the iterative process is caught in a periodic loop. Several improved algorithms, such as the STM [24], HLRF-BFGS [36], and RHL-RF [27] algorithms, have been developed to overcome the non-convergence of HL-RF algorithm, but more computational efforts are needed.