esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We evaluated the levels of heavy metals and arsenic (As) species in 11 different types of seaweed collected from major coastal cities in Korea. The concentration ranges of heavy metals in the seaweed were as follows: cadmium (0.023-0.232 mg/kg fresh weight [fw]), and lead (0.025-0.222 mg/kg fw), with most meeting international regulations for edible seaweeds. The amount of total As, however, was high, ranging from 1.020 to 20.525 mg/kg fw. Especially in the case of Sargassum seaweed, the fraction of inorganic As, including arsenate (As [V]) and arsenate (As [III]), which have potent toxicity, ranged from 5.198 to 16.867 mg/kg fw, while other seaweeds, such as _Pryopia_ sp., _Enteromorpha_ sp., _Undaria_ sp., and _Saccharina_ sp., predominantly contained a non-toxic organic As (i.d. arsenosugars). Multivariate analysis revealed that the Sargassum genus group had high levels of inorganic As. Sargassum seaweeds had a high fraction of inorganic As, but most of them are considered inedible seaweeds. Of these, _Sargassum fusiforme_ (hijiki) is widely recognized as an edible seaweed, but the average daily intake is quite low based on statistical data from Asian countries and _S. fusiforme_ is considered a safe food when eaten at the recommended daily intake.  
Keywords:seaweed; heavy metal; arsenic; inorganic arsenic; risk assessment +
Footnote †: journal:
## 3 Direct Numerical Methods for Strongly Disordered Systems
### Kernel Polynomial Method  
The kernel polynomial method (KPM) is a procedure for fitting a function onto an orthogonal set of polynomials of finite order. For the study of disordered systems, the functions which are routinely calculated by the KPM include the DOS and the conductance [42; 135; 136; 137; 138]. These quantities are not representable by smooth functions; indeed, they are often the sum of a set of delta functions. Two outstanding characteristics of fitting such functions to orthogonal polynomials are that the delta functions are smoothed out, and that the fitted function is usually accompanied with undesirable Gibbs oscillations. Different kernels for reweighing the coefficients of the polynomial are devised to lessen such oscillations.  
Here we highlight the main steps for calculating the DOS by the KPM. For such a polynomial expansion it is more convenient to rescale the Hamiltonian so that the eigenvalues fall in the range of \([-1,1]\). We assume that the eigenvalues of the Hamiltonian are properly scaled and shifted to be within this range. The DOS is given as a sum of delta functions,  
\[\rho(E)=\sum_{i}\delta(E-E_{i})\approx\sum_{n=0}^{n_{\max}}g_{n}\mu_{n}T_{n}(E), \tag{13}\]  
where \(g_{n}\) is the kernel function, \(\mu_{n}\) is the expansion coefficient, and \(T_{n}\) is the Chebyshev polynomial. Jackson's kernel is usually used for the \(g_{n}\)[139]. The expansion coefficient is given as \(\mu_{n}=\int_{-1}^{1}\rho(E)T_{n}(E)dE=\frac{1}{D}\sum_{k=0}^{D-1}\langle k|T _{n}(H)|k\rangle\), where \(D\) is the size of the Hilbert space. The efficiency of the KPM is based on a simple sampling of a small number of basic functions instead of the full summation. The \(T_{n}(H)|k\rangle\) for different values of \(n\) can be calculated with the recursion relation of the Chebyshev polynomial. The dominant part in using the recursion relation is the matrix-vector multiplication.  
The Hamiltonian matrix is usually very sparse. For example, the number of non-zero matrix elements for a 3D Anderson model on a simple cubic lattice is seven for each row. This number does not change with system size. The method is rather versatile and can be adapted for almost any Hamiltonian. Unlike the TMM, the KPM can handle long-range hopping and long-range disorder potentials. It can also be used for interacting systems; however, the matrix size grows exponentially [42], limiting practical calculations to a few tens of orbitals.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 2 Literature Review
### Lightweight Messaging Protocol  
Owing to the widespread use of low-power and low-performance devices, including sensors, wearable devices, and IoT sensor devices, systems based on lightweight messaging protocols were extensively studied. These are characterized by small overhead and enable messages to be transmitted to low-power and low-performance devices in low-bandwidth conditions [11; 12]. Transmission Control Protocol (TCP)-based Message Queuing Telemetry Transport (MQTT) [13] and User Datagram Protocol (UDP)-based CoAP [9] are the most widely used.  
MQTT is a TCP-based lightweight messaging protocol based on the publish/subscribe model. The fixed header length of an MQTT message is two bytes, which is shorter than the fixed header length of the TCP message (20 bytes) and the fixed header length of the UDP message (eight bytes), thereby reducing the length of the entire message. However, the overhead in the handshake process is not suitable for crowdsensing systems, which should establish connection and communicate repeatedly with multiple devices.  
CoAP is a UDP-based lightweight messaging protocol. The fixed header length of a CoAP message is four bytes, which is longer than the two bytes of MQTT, but it can be expected to improve performance compared with TCP and UDP. Unlike MQTT, which requires a central broker based on the publish/subscribe model, CoAP allows inter-node communication without a central broker. In addition, the connection establishment overhead is smaller than that of MQTT, which should undergo a three-way handshake process based on TCP. This feature is suitable for crowdsensing systems [14].  
In this study, we apply CoAP to the communication between user applications and data processing nodes in the proposed system so that the sensing data can be transmitted and received with high efficiency.
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 4 The Utility-Based Ontology Recommendation System
### The Concept Evaluation Component
#### 4.2.1 The Concept Evaluation Criteria  
(a) _The concept context semantic matching \(Sem\_Match(O_{in}c_{in},O,c)\))_: measures the contextual-semantic similarity between two corresponding concepts in two different ontologies. In [28], the contextual-semantic similarity is defined as the similarity in the intentional meanings of these concepts in the context of their ontologies. The authors propose a context-based semantic matching algorithm which extends the AgreementMakerLight (AML) [9], an ontology matching system that generates a set of ontology alignments, to exclude the alignments that do not have evidence for contextual semantic similarity. The basic idea of the context-based semantic matching algorithm [28] is to examine the corresponding semantic relations for the two corresponding concepts such as super-classes, sub-classes, siblings, and equivalent classes. Then, the concept context matching score \(Sem\_Match(O_{in},c_{in},O,c)\) is a numerical value between \"0 and 1\", and is used to measure if the user's input class \(c_{in}\) from the input ontology \(O_{in}\) has a contextual semantic match with its corresponding concept \(c\) in the candidate ontology \(O\).  
(b) _Concept semantic richness (\(Sem\_Rich(O,c)\))_: an evaluation criterion that assesses the level of detail in the representation of knowledge for a specific concept in a given ontology [6; 7; 29]. In our case, we measure the semantic richness of concept \(c\) in ontology \(O\) concerning the following criteria: existence of subclasses, existence of superclass, existence of object properties, existence of definition, and existence of synonyms. The concept semantic richness \(Sem\_Rich(O,c)\) score is calculated using Equation (7):  
\[Sem\_Rich(O,c)=\sum_{i=1}^{n}w_{i}\,*\,r_{i} \tag{7}\]  
where \(n\) is the number of a concept's evaluation criteria, \(r_{i}=1\) if the criterion is fulfilled by the concept \(c\), and \(w_{i}\) is the weight of each criterion and \(\sum_{i=1}^{n}w_{i}=1\).
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 3 Results and Comparisons
### Evaluation Metrics  
The clustering results of the proposed framework are evaluated by Purity, Normalized Mutual Information (NMI), and overall accuracy (OA) scores. Let \(C\) be the set of classes that were obtained from ground reference information and \(\omega\) be the set of clusters obtained from a clustering method/framework.  
* Purity is an external evaluation criterion of cluster quality. It is the most common metric for clustering results evaluation, defined as: \[Purity(\omega,C)=\frac{1}{n}\sum_{i}\max_{j}|\omega_{i}\cap C_{j}|,\] (3) where \(n\) denotes the number of data points in the image. The range of purity metric is \((0,1)\), where 0 and 1 represent the lowest and highest cluster quality.
* NMI is a normalization of the mutual information score (MI), where \(MI\) is obtained as: \[MI(\omega,C)=\sum_{i}\sum_{j}p(\omega_{i}\cap C_{j})\log_{2}\frac{p(\omega_{i} \cap C_{j})}{p(\omega_{i}).p(C_{j})}.\] (4)\(P(\omega_{k})\), \(P(C_{j})\), and \(P(\omega_{k}\cap C_{j})\) are the probabilities of a data point being in cluster \(\omega_{k}\), class \(C_{j}\), and in the intersection of \(\omega_{k}\) and \(C_{j}\), respectively. The NMI score is defined, as follows: \[NMI(\omega,C)=\frac{MI(\omega,C)}{\max[H(\omega),H(C)]},\] (5) where \(H(\omega)=-\sum_{i}p(\omega_{i})\log_{2}p(\omega_{i})\) and \(H(C)=-\sum_{j}p(C_{j})\log_{2}p(C_{j})\) are the entropies of \(\omega\) and \(C\), respectively. The larger NMI value indicates higher clustering accuracy.
* OA is the number of correctly classified pixels in \(\omega\) divided by the total number of pixels.
## 5 The Proposed Hybrid Scheme
### UAV Station Model  
All UAVs carry a global positioning system (GPS) and an inertial measurement unit (IMU) to uninterruptedly monitor the position and location of neighboring UAVs in the proposed scheme. The position and location of UAV are then updated in the medium access control (MAC) table.
## 3 Comparison among the Routing Protocols and Open Issues
The protocols based the diffusion mechanism of the whole network will sharply reduce the utilization efficiency of network resource. This problem will become more obvious in large-scale WSNs. A typical solution is the virtual backbone network routing technique. For instance, a protocol named clique clustering (CC) for backbone formation is proposed in [51], which aims to efficiently deal with those network dynamics that are typical of large-scale WSNs. Through the backbone network, some suitable sub-networks are chosen for constructing communication network, and the backbone nodes belonging to the sub-network are used to maintain routing information and capture the topology construction of the whole network. These behaviors aim to reduce the routing overhead and save network resources at utmost, and adapt the route changes which come from the energy depletion of the nodes. According to the discussion of the characteristics of the routing protocols in large-scale WSNs, there exist open issues which are worth focusing on. * Through making the complexity of the routing protocol reduced or not related to the network size, the routing protocol will appear to be much more scalable. * The hierarchical routing protocol is a mainstream method to solve the scalability problem of large-scale networks, but the factors affecting the cluster formation and cluster-head communication are worth reconsidering in future. * An efficient flooding scheme is challenging in large-scale WSNs. * The virtual backbone technique can efficiently enhance the utilization of the network resource, which deserves to be further investigated.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 5 Conclusions  
This paper aims at evaluating the effects of preheating temperature and binder aging on the activation of RA binder. On the basis of the results shown in the paper, the following conclusions can be drawn:  
* Since the air voids and splitting tensile strength at 15 \({}^{\circ}\)C results are easy to obtain and presented a good correlation with binder activation in this study, it is recommended to use the two indicators to measure the activation of RA binder.
* The preheating temperature was found to have a significant effect on the activation of RA binder. As the preheating temperature increases to 180 \({}^{\circ}\)C, RA binder from all the sources can be highly activated. However, the strength of specimens decreased significantly at 120 \({}^{\circ}\)C, indicating that the activation degree of RA binder is quite low under the limiting preheating temperature (120 \({}^{\circ}\)C) of RAP according to the standard. In the current preheating method used for RAP, most of the RA binder is not activated.
* The artificial RAP produced through the accelerated aging method can be used to represent RAP with different oxidation levels and investigate the influence of aging on binder activation. Regarding the RAP preheated at a high temperature (180 \({}^{\circ}\)C), the aging of RA binder does not show any negative effect on binder activation. However, when the preheating temperature is low (120 \({}^{\circ}\)C), the aging of RA binder shows a significant adverse effect on binder activation. According to the presented test results, it is recommended to increase the preheating temperature of RAP during the production process to improve the activation of RA binder, especially when the oxidation level of RA binder is high.  
Conceptualization, L.L.; methodology, L.L. and Y.M.; validation, Y.M.; formal analysis, L.L. and Y.M.; investigation, L.L. and Y.M.; resources, L.L.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, L.L. and Y.M.; visualization, Y.M.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support obtained from the National Natural Science Foundation of China (Project No. 51778483).  
The authors declare no conflict of interest.
## 4 Result  
The results of implementing a smart grid demand scheme using power line communication (PLC) technique have demonstrated significant advancements in grid management and consumer engagement. By employing PLC, which utilizes existing power lines for data transmission, seamless communication between grid components is achieved, leading to enhanced demand-side management. The scheme's outcomes showcase improved energy efficiency, as real-time data exchange facilitates accurate monitoring of electricity consumption patterns, allowing consumers to make informed decisions about their usage. Additionally, PLC-enabled demand schemes enable utilities to implement dynamic pricing strategies based on peak and off-peak hours, encouraging consumers to shift their consumption to times of lower demand, thus alleviating strain on the grid during peak periods as shown in Figure 11. The results underscore the potential for reduced energy wastage, lowered electricity bills for consumers, and optimized grid performance. Ultimately, the combination of smart grid demand schemes and PLC technology holds promise in creating a more responsive and energy-efficient electricity distribution network. The implementation of a smart grid demand scheme using power line communication (PLC) technique has yielded noteworthy outcomes that redefine how electricity consumption is managed and optimized. Through PLC, which harnesses power lines for data transmission, the integration of communication and energy distribution has brought about transformative results. This approach has fostered a two-way flow of information between consumers and the grid, enabling real-time monitoring of energy usage and allowing consumers to actively participate in load management as shown in Figure 12.  
One of the remarkable results is the increased grid efficiency and stability. The instant communication facilitated by PLC empowers utilities to anticipate and respond promptly to fluctuations in demand, thereby avoiding overloads and minimizing the risk of blackouts. Moreover, the PLC-based demand scheme has enabled utilities to implement demand response initiatives with precision. During peak usage periods, consumers receive signals to reduce their electricity consumption, contributing to a more balanced load distribution and reducing the need for additional power generation as shown in Figures 13 and 14.  
Figure 11: Classification of a surface plot of the maximum grid power transmission (Watts) variance on the left and the minimum grid power transmission (Watts) variance on the right, as functions of SNR parameters and time.  
Figure 12: The visualization of minimum SNR and maximum SNR congestion control of data and frequency for power transmission to a smart grid.  
Figure 13: Maximum processing time of PLC technique. The curves are made by changing the sampling frequency and the observation interval length C.
## 4 Results and Discussions
### Performance Analysis of Disease Risk Detector  
When trained on a shared GPU from COLAB, the sequential training of the disease risk detector with 5-fold cross validation with the MobileNetV2 architecture took approximately 4.5 h with 45 epochs for each fold on average. The disease risk model's performance results with respect to metrics considered on the test model dataset are shown in Table 3. The test dataset consists of 485 images in total, with 361 unhealthy tongue images and 124 normal, healthy tongue images. Concise ROC Curve Figure 4, for the 5-folds and ensemble techniques with a mean value, indicates the model's appreciable performance for binary classification.  
The best model is saved for each fold of cross-validation. Predictions of each fold on a test ensemble dataset with 485 images are combined to form an ensemble. Implementation of the Random Forest ensemble is done by picking random samples and features to build  
Figure 4: Receiver operating characteristics curve (**a**) 5-fold cross-validation models. (**b**) Ensemble techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{2}{*}{Fold-1} & Diseased & 0.99 & 0.91 & 0.95 & \\ \cline{2-6}  & Normal & 0.78 & 0.97 & 0.86 & \\ \hline \multirow{2}{*}{Fold-2} & Diseased & 0.97 & 0.94 & 0.96 & \\ \cline{2-6}  & Normal & 0.85 & 0.90 & 0.88 & \\ \hline \multirow{2}{*}{Fold-3} & Diseased & 0.98 & 0.98 & 0.98 & \\ \cline{2-6}  & Normal & 0.93 & 0.94 & 0.93 & \\ \hline \multirow{2}{*}{Fold-4} & Diseased & 0.97 & 0.94 & 0.95 & \\ \cline{2-6}  & Normal & 0.84 & 0.92 & 0.88 & \\ \hline \multirow{2}{*}{Fold-5} & Diseased & 0.98 & 0.91 & 0.95 & \\ \cline{2-6}  & Normal & 0.79 & 0.95 & 0.86 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics for the 5 folds of cross-validation of the disease risk model.  
many decision trees on a bootstrapped dataset through a repeated process. Prediction of new data is accomplished by bagging operations, wherein the final decision is the most common decision amongst all decision trees.  
The Soft Majority Voting Ensemble aggregates the generated predictions from the five folds to create a new prediction matrix. The category with the maximum value is considered the final prediction for each sample. In this particular case, the ensemble of 5-fold predictions with two classes does not show enhanced performance; F1 scores for Random Forest and Soft Majority Voting are comparable to the individual fold F1 score as depicted in Figure 5.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 3 Results and Discussion  
### Displacement Time Series  
The displacement time series computed from the 100-Hz GCER observations are shown in Figure 2. The displacements measured with the LDT (refers to True in Figure 2) are considered the truth and are also shown in the figure for comparison. The time series can be divided into three parts, namely stationary (07:00-08:26), quasi-static (08:26-08:28), and sinusoidal (08:28-08:38) motions. The first part also includes the time for the PPP convergence. The quasi-static motion simulated permanent deformation and the sinusoidal motion may correspond to vibrations.  
Figures 2a and 2b show that the PPP and PPP-AR solutions took about 15 min to converge to a centimeter level. After that, the PPP, PPP-AR, and RTK techniques all captured both the stationary and dynamic displacement signals well. In general, as expected, the RTK solutions are the best. The PPP-AR and PPP results show a sub-c  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **PPP** & **PPP-AR** & **RTK** \\ \hline Frequency & Dual & Dual & Dual \\ Sampling rate & 0.01S (100 Hz) & 0.01S (100 Hz) & 0.01S (100 Hz) \\ Elevation mask & 10\({}^{\circ}\) & 10\({}^{\circ}\) & 10\({}^{\circ}\) \\  & Zenith: GPT2w & Zenith: GPT2w & \\ \multirow{2}{*}{Tropospheric model} & Mapping function: & Mapping function: & \\  & VMF1 [38] & VMF1 [38] & \\ Ionospheric model & IF & IF & \\ \multirow{2}{*}{Orbit} & Final products from & Rapid products from & Broadcast \\  & GFZ (5 min) & WUM (1 min) & ephemerides \\ \multirow{2}{*}{Clock} & Final products from & Rapid products from & \\  & GFZ (30 S) & WUM (30 S) & Broadcast ephemerids \\ Estimator & Kalman filter & Kalman filter & Kalman filter \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of PPP, PPP-AR, and RTK data processing.  
Figure 2: Displacement time series computed from the GCER observations and (top) PPP, (middle) PPP-AR, and (bottom) RTK methods.  
level accuracy. Further analysis of the performance of the ultra-high-rate GNSS will be given in the following sub-sections.