esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Endocrine-Disrupting Compounds EDCs
### Paraben Degradation  
Parabens are an example of very popular preservatives used in both personal care and pharmaceutical products. Parabens are alkyl esters of \(\varrho\)-hydroxybenzoic acid; their widespread use is mainly due to their broad spectrum of antibacterial, antimicrobial, and antifungal activities, as well as their stability over the pH range and good water solubility [55; 56; 57]. Parabens began to be used on a large scale in the cosmetics industry in the 1930s and 1940s. Today, the presence of parabens in cosmetic ingredients is a very controversial topic. In 2007, parabens were added to the list of compounds that have a negative effect on the endocrine system, however, the literature reports on this topic appeared much earlier [58].  
Gamma radiation was first used to determine the radiation resistance of methyl-paraben used in pharmaceutical products undergoing radiation sterilization [59]. It was found that for a dose of 5 kGy, used in sterilization, methylparaben does not undergo any transformation either in solid form or in aqueous solution with an initial concentration of 10 uM. Only for high doses above 50 kGy was a slight decomposition in aqueous solution observed. A much higher degradation efficiency for MP was obtained for aqueous aerated solutions with an initial concentration of 3.29 uM where all of the MP was degraded after the 50 Gy dose was applied [60]. In the case of an aqueous solution of butylparaben with an initial concentration of 1 mM, a dose as high as 90 kGy was needed to achieve 90% mineralization [61]. The use of radiation pre-treatment significantly increased the biodegradability of an aqueous solution of methyl 3,5-dichloro- 4-hydroxybenzoate (MDHB) [62]. The obtained data indicate that the effectiveness of MDHB and total organic carbon (TOC)  
Figure 2: The first step of the % \({}^{\bullet}\)OH-induced degradation of BPA [15].  
removal in the irradiation process increased with the absorbed dose, regardless of the initial concentration of MDHB (in the range 1-10 mg dm\({}^{-3}\)) at the absorbed dose of 800 Gy. However, the maximum TOC removal efficiency was only 15.3%.  
Based on the identified by-products, a suggested degradation pathway was presented using the combined irradiation and biological treatment process (Figure 3). Furthermore, the maximum TOC elimination efficiency was 70.1% within 240 h, which suggested that some by-products may have stability, and more research is needed to determine the fate of by-products generated during the combined irradiation and biological treatment process. It has been shown that 4-hydroxybenzoate (4-HBA) shows much less radiation resistance than its ethyl ester 4-hydroxybenzoic ethyl ester (4-HBAEE) [63]. A dose of 1.4 kGy is needed to achieve 50% decomposition for 4-HBA, while for the same amount of 4-HBAEE this increases to 3.2 kGy due to the protective effect of the ester bond prior to decarboxylation.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 8 Challenges and Future Directions
### Scalability and Complexity  
One of the most significant challenges in detecting anomalies in blockchain networks is scalability. As the volume and complexity of blockchain data increase, traditional detection methods may struggle to keep pace. Moreover, the heterogeneous nature of blockchain structure consists of various layers and protocols, thus adding complexity. Addressing scalability and complexity issues necessitates a shift towards dynamic trends that can adapt to evolving network systems and accommodate diverse data sources [30].  
To mitigate scalability and complexity challenges, future research should embrace the synergy of graph learning and neural network methodologies. Graph learning provides a robust framework for modeling blockchain networks by leveraging their structures. Incorporating neural network techniques such as graph neural networks or LSTM can uncover complex patterns, offering insights into anomalous behaviors while circumventing scalability constraints.  
To enhance the ability to detect unusual events, a symbiotic relationship between data mining techniques and the inherent network's resilience must be established. Leveraging unsupervised learning methods, and exploring novel avenues in deep learning approaches, we can uncover latent anomalies within the blockchain ecosystem, enhancing its overall integrity and reliability for future endeavors.
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Projected Rainfall Erosivity and Soil Erosion Rates in Kenya  
Tables 14 and 15 give the statistical details and comparisons of the baseline rainfall erosivity, with projected future values computed from the CMIP5 models under the two RCPs. For the 2080s period, all the GCMs, except MIROC-5 RCP 2.6, revealed an increase in precipitation, with CCCMA_CANESM2 RCP 8.5 recording the highest rise of 85.2% for the entire country. Similarly, only MIROC-5 RCP 2.6, MIROC-5 RCP 8.5 and CSIRO_MK3.6.0 RCP 2.6 GCMs resulted in a precipitation decrease in all of the 2030s projections. Nationwide, there was a general upward trend in rainfall erosivity in all the GCMs in both periods, except for MIROC-5 RCP 2.6 (2030s) and MIROC-5 RCP 8.5 (2080s), which showed \(-\)3.9% and \(-\)8.2% decreases, respectively (Figures 10 and 11). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landform Type** & **Area (10\({}^{4}\) ha)** & **MARE (Baseline)** & **MASLR (Baseline)** \\ \hline Alluvial plain & 526.9 & 802.3 & 0.59 \\ Badland & 38.9 & 576.8 & 0.35 \\ Coastal plain & 84.1 & 3013.8 & 1.59 \\ Complex & 117.7 & 398.7 & 1.63 \\ Depression & 132.1 & 1627.3 & 3.42 \\ Escarpments & 24.2 & 4428.5 & 26.51 \\ Foot slope & 211.6 & 1611.9 & 4.08 \\ Mount. Foot ridges & 441.7 & 3133.3 & 17.71 \\ Mountains & 259.4 & 5202.6 & 19.01 \\ Plain & 3010.2 & 1315.4 & 1.62 \\ Plateaus & 795.0 & 2904.6 & 7.61 \\ Valley & 35.0 & 780.7 & 1.94 \\ Volcanic craters & 98.9 & 1911.8 & 9.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Baseline soil erosion rates per landform types in Kenya. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Type** & \begin{tabular}{c} **Area** \\ **(10\({}^{4}\) ha)** \\ \end{tabular} & \
\begin{tabular}{c} **MARE (Baseline)** \\ **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **MASLR (Baseline)** \\ **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \end{tabular} \\ \hline Acrisols & 5.8 & 4150.6 & 7.84 \\ Andosols & 108.9 & 5363.7 & 17.67 \\ Arenosols & 258.7 & 1994.6 & 1.63 \\ Cambisols & 225.7 & 2718.9 & 9.62 \\ Ferrasols & 312.7 & 4838.6 & 12.38 \\ Fluvisols & 21.5 & 576.0 & 1.08 \\ Gleysols & 68.2 & 1487.3 & 1.80 \\ Lithosols & 756.9 & 1616.5 & 5.44 \\ Luvisols & 1110.2 & 1649.4 & 1.73 \\ Nitosols & 379.4 & 5223.6 & 22.69 \\ Planosols & 22.2 & 4226.2 & 13.14 \\ Regosols & 620.7 & 957.0 & 2.85 \\ Solonchaks & 371.2 & 604.1 & 0.77 \\ Vertisols & 178.3 & 2296.7 & 4.43 \\ Xerosols & 252.6 & 969.9 & 1.59 \\ Yermosols & 1082.8 & 594.6 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Baseline soil erosion rates under different soil types in Kenya.
## 5 Conclusions and the Future Work  
In this paper, we propose a model for entity linking in Chinese short text based on normalized RoBERTa sentence vectors and label embedding, which alleviates the semantic sparsity problem of Chinese short texts by making full use of semantic information. The proposed model employs the flow model to post-process the sentence vectors for fully utilizing the semantic information in the limited text. In addition, we further enhance the entity-linking performance by entity typing with label embedding. The experimental results on CCKS2020 and CCKS2019 datasets demonstrate that our model outperforms existing entity-linking methods and some commonly utilized deep learning approaches.  
Figure 8: Comparison results for different sentence vectors on CCKS2020.  
Figure 9: Comparison results for different sentence vectors on CCKS2019.  
Although our model achieves advanced results, it still has some limitations. Since our model is based on the recognized short texts for entity linking, the effectiveness in practical applications will be limited by the accuracy of the recognized entities. In contrast, directly linking unstructured short texts can eliminate erroneous referents, which is the direction of our future work. In the future, we will combine named entity recognition with entity linking for joint learning to directly obtain the recognized referred and linked entities. Furthermore, our entity-linking method will be further applied to the expansion of knowledge graph and intelligent systems of question-answering.  
Conceptualization, L.G. and L.Z. ([PERSON]); methodology, L.G.; software, L.G.; validation, L.G. and L.Z. ([PERSON]); formal analysis, L.G.; investigation, L.G.; resources, L.Z. ([PERSON]) and J.H.; data curation, L.G.; writing\(-\)original draft preparation, L.G.; writing\(-\)review and editing, L.Z. ([PERSON]); visualization, L.G.; supervision, L.Z. ([PERSON]) and J.H.; project administration, L.Z. ([PERSON]) and J.H.; funding acquisition, L.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The research was supported by the Soft Science Research Program of Zhejiang Province under Grant [No.2021C25051] and Zhejiang Province Key Research and Development Project under Grant [No.2020C03071, No.2021C03145].  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 3 Experiment and Result
### Testbed Preparation  
As shown in Figure 4, the PRONOSTIA platform consist of a rotating part, a loading part, and a measurements part. The rotating part has an asynchronous motor with a speed reducer and two shafts. The power of the motor is 250 W, and the speed can reach 2830 rpm. After the transmission, the speed of the secondary shaft will work under 2000 rpm. The loading part is the key part of the whole system which is used to accelerate damage to the bearing. The radial force, acting on the bearing, could reach values up to 5000 N, which will significantly reduce this bearing's life duration. The measurements part is responsible for collecting original data. There are two kinds of sensors in the system including vibration and temperature. The vibration sensors are located on the vertical axis and horizontal axis, respectively. The temperature sensor (PT100) is attached to the outer ring of the bearing. The vibration signal is sampled at 25.6 kHz, while the temperature is sampled at 10 Hz.  
Figure 5 shows the vibration signal gathered during the overall run-to-failure experiment. The testbed was shut down when the amplitude of the vibration reached 20 g, which is the general ending criterion for all the bearing experiments. It is worth noting that the vibration signal is not continuously sampled during the experiment period. As shown in Figure 6, a vibration sample is collected after each specific time interval. The length of the sample is 2560 points, and the time interval is 10 s in each experiment.  
Figure 3: The workflow of dynamic maintenance scheduling.
## 2 Materials and Methods
### Design of Proposed Control System
#### 2.3.3 Design of State-Dependent Weighting (SDW) Matrix of State \(\mathbf{Q}\)  
First, the SDW matrix of state is expressed as  
\[\mathbf{Q}=\text{diag}\Big{(}\ \ \mathbf{Q}_{q_{e}}\ \ \mathbf{Q}_{\omega_{e}}\ \ \mathbf{Q}_{\delta_{e}}\ \ \mathbf{Q}_{\hat{\delta}_{e}}\ \ \mathbf{Q}_{\hat{\delta}_{e}}\ \ \mathbf{Q}_{\hat{\delta}_{e}}\ \Big{)} \tag{30}\]  
Figure 8: Sigmoid function dependent on the singularity parameter.  
where \(\mathbf{Q}_{q_{e}}\) is a SDW of quaternion error \(\mathbf{q}_{e}\), \(\mathbf{Q}_{\omega_{e}}\) is a SDW of satellite angular velocity error \(\mathbf{\omega}_{e}\), \(\mathbf{Q}_{\delta_{e}}\) is a SDW of gimbal angle error, and \(\mathbf{Q}_{\delta_{e}}\) is a SDW of gimbal angular velocity error. By setting \(\mathbf{Q}_{q_{e}}\) as large, agility increases, and by setting \(\mathbf{Q}_{\omega_{e}}\) as large, the convergence to a target attitude angle increases. Although one study [21] shows that it is possible to achieve both agility and convergence by making the SDW of state state-dependent, in this paper, we use constant values for simplicity.  
Finally, the SDW matrix of state is expressed as  
\[\begin{split}\mathbf{Q}_{q_{e}}&=\text{diag}\bigg{(} \begin{array}{cccc}0&1&1&1\end{array}\bigg{)}\;\times\;10^{6}\\ \mathbf{Q}_{\omega_{e}}&=\text{diag}\bigg{(}\begin{array}{cccc}5&5&5\end{array} \bigg{)}\;\times\;10^{6}\\ \mathbf{Q}_{\delta_{e}}&=\text{diag}\bigg{(}\begin{array}{cccc}1&1&1&1\end{array} \bigg{)}\\ \mathbf{Q}_{\delta_{e}}&=\text{diag}\bigg{(}\begin{array}{cccc}0&0&0&0\end{array} \bigg{)}\end{split} \tag{31}\]
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 2 Materials and Methods
### Experimental Procedure  
Two collector systems of the same dimension were separately used for the SAD and SD drying processes. The first experiment, hereafter called 'Performance of SAD drying,' concerns the comparison of the required drying times and vitamin C retention of SAD relative to SD and OSD. For this experiment, OSD and SD were carried out during the day time between 9:00 a.m.-5:00 p.m. when enough solar radiation was available. The SAD was applied for five additional hours in the evening until 10:00 p.m., with the adsorption system in use. Full night experiments were not feasible in view of safety regulations. Before each experiment, the SD and SAD were allowed to run for 30 minutes to reach a steady state before loading the trays that contain the yam cubicles to be dried each day.  
About 200 grams of yam ('Dente') cut (\(3\times 3\times 1\) cm) with initial moisture content between 1.63-2.13 kg water/kg dry matter was placed on each of the three successive trays stacked in the same chamber of external and internal dimensions \(70\times 45\times 50\) cm and \(60\times 45\times 40\) cm, respectively. The air flow was in the upward direction from the bottom part of the drying chamber beneath the trays made of wire mesh. At the end of each day, the yam samples were cooled and placed in zip-lock polyethylene bags and stored at room temperature. The next day, the samples were unpacked and placed in the trays to continue drying. This process was repeated on subsequent days until the product was sufficiently dried. At low moisture contents, the benefit of air dehumidification at night becomes minor. Hence, in one experiment, after two day-night cycles, the drying was continued by the solar system only. The vitamin C content was measured for the dried samples.  
The next experiment, called the 'Drying different yam cultivars' experiment, concerned the drying behavior of different cultivars and the effect of different drying methods on the composition, color, and vitamin C. The procedure is similar to the one described for the first experiment. One hundred grams (100 g) each of yam cultivar cut (\(3\times 3\times 1\) cm) with an initial moisture content between 1.55-2.06 kg water/kg dry matter depending on the variety were all loaded on one tray and the same was loaded on the tray stacked below it as a replicate. Yam samples from tree cultivars ('Pona', 'Lili', and 'Dente') of _D. rotundata_ were bought from a yam farm.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Metrics and Compared Algorithms  
The event detection accuracy and overall system cost are used as evaluation metrics for the evaluation of the algorithms. The accuracy could be further divided into recall rate (\(p1\)) and precision (\(p2\)), which are defined as follows:  
\[p1=\frac{n_{0}}{n_{0}+n_{1}};\ \ p2=\frac{n_{0}}{n_{0}+n_{2}} \tag{17}\]  
where \(n_{0}\) is the number of detected true events, \(n_{1}\) is the number of undetected true events and \(n_{2}\) is the number of detected false events. Precision (also called positive predictive value) is the fraction of relevant events among the retrieved events, while recall (also known as sensitivity) is the fraction of relevant events that have been retrieved over the total amount of relevant events.  
Nodes sense the data and send them to the RSUs, and events are verified by the ITS servers. The overall cost contains the cost of sensing at local nodes, computation at nodes, RSUs and the ITS center and data transmissions among nodes and RSUs. When nodes work in HCS mode, they generate more data and hence need to upload a larger amount of data to the RSUs. For simplicity, we use the total number of messages sent as an indicator for the overall cost, and the average time spent on detecting the events is also considered. The average results of five simulations are presented in this section.  
There is not much research directly related to the fog-based data-gathering and event-monitoring in VANETs. For fair comparison with the proposed algorithm, we also conducted another four data-gathering and event detection schemes: (1) NAIVE: nodes sense in HCS mode periodically all the time, and whenever a node encounters an RSU, its data are uploaded to the RSU and then forwarded to the cloud for event detection. (2) PROPHET [38]: a contact history-based protocol. Nodes exchange data according to the data forwarding probability to RSUs based on the contact history; (3) ESSMD [39]: the takes advantage of all chances to upload the data, but reduces the number of event broadcasting when vehicles detect the same event in close proximity. The PROPHET scheme is designed for the delay-tolerant network (DTN) [7], where obviously, VANETs comprise one kind of DTN. PROPHET and ESSMD are classic and provide good comparison to our scheme, which aims to decrease the cost while at the same time achieving an acceptable detection rate.  
Figure 3: Part of the snapshot of the simulation field where six RSUs and 60 vehicular nodes are deployed at Xiamen University campus centered at latitude: 24.4390262, longitude: 118.0977218. Circles denote the nodes and their sensing areas; five-pointed stars denote the RSUs.
## 2 Research Object and Methods
### Numerical Setups  
#### 2.4.1 Computation Domain and Mesh Generation of the Flow Field  
Corresponding to the structure diagram shown in Figure 1, the computing domain of the high-speed pump flow field is also divided into four parts, namely, inlet domain, impeller domain, volute domain, and outlet pipe domain. The influence of both sealing and structural chamfering is ignored in order to improve the simulation efficiency. Combined with the actual inflow situation, the inlet and outlet pipe domain are extended appropriately to reduce the influence of backflow boundary conditions [25].  
Professional mesh generation software ANSYS ICEM 14.1 is used to perform meshing of the computational domains. Due to the irregularity of the flow channel volume, unstructured mesh technology is used to divide the grid, the same as subsequent acoustic mesh generation. In order to eliminate the influence of the number of cells on the accuracy of numerical simulation, the grid independence analysis of head is carried out, the results of which are shown in Figure 4. It can be seen from the figure that when the number of cells is more than 3.18 million, the influence of the increase of the grid number on the head can be ignored. Moreover, places such as the blade wall surface is mesh-encrypted; the final mesh quality of all domains exceeds 0.35. The maximum nondimensional wall distance, \(y+<60\), was obtained in the complete flow field, which means it could satisfy the requirement of all turbulence modeling methods used in this paper. So, meshes with the determined number of 3.18 million cells (as shown in Figure 5) are used for subsequent calculation.  
Figure 4: Analysis of mesh independence.  
Figure 5: Computational domains and generated meshes of the model pump.
## 1 Introduction  
Nowadays, the Internet of Things (IoT) is increasingly essential in our daily lives and appears in many areas, such as smart homes, Industrial IoT (IIoT) or Internet of Drones (IoD). Sensors, devices and applications make our lives easier; however, they also collect sensitive data, which can lead to security problems. Drones are unmanned vehicles that operate remotely or can be autonomous, and they can operate on water, land and air. One type of drones used as a flying vehicle is the unmanned aerial vehicle (UAV), which has gained popularity in recent years due to its affordability, accessibility, and versatility. Originally developed for military purposes, drones have found their way into various civilian applications, such as video recording, agricultural work, sports activities, and territorial supervision. With the ever-increasing demand for drone technology, their usage is predicted to become a part of daily life soon, which is shown by the package delivery services by Amazon, the ambulance drones adopted by ambulance services of the Netherlands, providing essential medicine until the arrival of ambulance personnel, and other commercial drones. The Internet of Drones (IoD) is an architecture designed to provide communication and coordinated access to controlled airspace over the Internet between drones, base stations and users. Figure 1 represents a typical IoD architecture.  
The security of an Internet of Drones (IoD) network is critical, as inappropriate authentication and unauthorized access to the network can result in several security issues, like sensitive data theft, impersonation of the IoD participants, or drone hijacking and interception. Although the potential benefits of such a network are vast, these benefits come with potential risks, which must be addressed through robust and secure schemes and protocols.