esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Offline Processing Chain and Cross Crawl Analysis  
The overall ARCOMEM architecture includes several levels [7]. First, in the crawler level, the system decides and fetches the relevant web objects as initially defined by the archivists. In parallel to the crawler level, the online processing level performs time-efficient analysis of the fetched resources to support prioritization of the crawler processing queue. Following that, in the offline processing level, fully-featured versions of the entity, topic, opinion and event (ETOE) analysis and the analysis of the social content operate over the cleansed data from the crawl that are stored in the ARCOMEM Knowledge Base (ARCOMEM KB). These processing tools make use of linguistic, machine learning and Natural Language Processing (NLP) methods in order to provide a rich set of metadata annotations that are interlinked with the original data. The respective annotations are stored back in the ARCOMEM database and are available for further processing and information mining. After all of the relevant processing has taken place, the web pages to be archived and preserved are selected in a semi-automatic way. The selected original pages are transferred to the web archive (in the form of Web ARCHive file format (WARC) files). Finally, the cross-crawl analysis level operates on collections of web objects that have been collected over time in order to register the evolution of various aspects identified by the ETOE and web analysis components. As such, it produces aggregate results that pertain to a set of web objects crawled at different points in time, rather than to a single crawl. Both the web archive and the ARCOMEM KB are accessible for the SARA application to support its functionality.  
The ETOE processing chain within the offline processing and cross-crawl analysis levels of the ARCOMEM architecture is presented in Figure 1. In the following sections, we briefly introduce components responsible for processing text resources.  
**The ARCOMEM KB and Data Model:** The ARCOMEM Knowledge Base (ARCOMEM KB) serves as a central integration point of the chain, enabling its components to store and exchange processing results. The ARCOMEM KB is implemented as H2 RDF [8], a fully-distributed RDF store that combines the MapReduce processing framework with a NoSQL distributed data store. H2 RDF is able to provide a fully-distributed SPARQL query functionality on a virtually unlimited number of RDF triples. The information within the ARCOMEM KB is stored according to the ARCOMEM data model that describes concepts used in ARCOMEM and the relationships among them.
## 4 Localization Based Applications
As well, localization technologies can be used by numerous devices in the future fifth generation cellular system to attain an accuracy of location estimation in the range of centimeter. Basically, in fifth generation cellular networks, it is expected to use precise localization information through all layers of the communication protocol stack [102]. This is due to the prediction of most of the fifth generation cellular user terminals in their mobility patterns knowing that these terminals will be either associated with fixed or controllable units or people [8]. Last but not least, localization is also required for several jobs in cyber-physical systems, like smart transportation systems and robotics in fifth generation cellular system [103,104].
# An Automatic Transformer from Sequential to Parallel Java Code
## 6 Experiments and Results
### Correctness Evaluation  
To prove the validity of our analysis and transformation for the case study, we implemented a test suite that checked whether the generated parallel version kept the same behavior as the sequential one. The JUnit framework was used to write and execute all tests. Table 2 displays the test implemented for the five refactored methods; Description column provides a brief description of what the test checks; Result column shows the value returned by both sequential and parallel executions. Every test has an assertion where the expected value is the output given by the sequential execution of the method, while the actual value is the output of the parallel one.  
For every test that was executed, the result given by the parallel version was the same as the one given by the sequential version. For getUserForAuthor() method, the only one whose return value was a HashMap, all the values in the HashMap were the same in both executed versions. The same goes for books and reviews extracted by the extractFromDataset() method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Time (ms)**} & \multicolumn{2}{c}{**Speed-Up**} \\  & **Sequential** & **Parallel** & \\ \hline extractFromDataset & 36 K & 17 K & 2.11 \\ extractMostReviewedAuthor & 485 & 330 & 1.46 \\ extractLeastReviewedAuthor & 522 & 318 & 1.64 \\ extractAverageReviewedAuthor & 533 & 317 & 1.68 \\ getUserForAuthor & 700 & 486 & 1.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution times for all five methods refactored for the sequential and parallel executions.
## 4 Challenges
### Authentication Delay  
Most of the reviewed trust models imply the use of cryptography-based node authentication. However, the lengthy signature verification process may affect the calculation of the trust value [41]. To mitigate the risk, [PERSON] et al. [41] proposed the sharing of authentication information between neighbors rather than authenticating every received message. The approach aims to achieve a balance between the security and efficiency needs of the WSN. However, this approach assumes that neighboring nodes can be trusted to share accurate authentication data, which introduces potential vulnerabilities. For example, malicious nodes could manipulate this process by providing false authentication data to undermine trust evaluation or prioritize their own messages.  
Future models could dynamically adjust authentication probabilities based on real-time network conditions. For instance, historical trust scores, packet loss rates, and environmental data could be used to predict high-risk nodes or events, enabling a smarter and more adaptive selective authentication approach.
## 1 Introduction
[28] used SDP for the visual reconstruction of original signals, combined with a multi-covariance Gaussian process regression method for tool wear monitoring, showing that this method can accurately recognize tool wear states under the same conditions. [PERSON] et al. [29] proposed a rolling bearing fault diagnosis method based on empirical mode decomposition and SDP, decomposing the vibration signals of rolling bearings through SDP to convert each intrinsic mode function component into a fused image, using an improved Manhattan distance for image classification diagnosis. The SDP transformation can convert discrete data of different signals into visual images according to their time series, providing a better representation of data and thereby mining more patterns and features [30; 31]. The effectiveness of the SDP transformation is mainly limited by the selection of the lag coefficient \(l\) and the aperture gain \(\xi\), which are difficult to set through subjective experience [32]. To fully exploit the superiority of SDP in multi-channel fusion, this paper uses the maximization of the difference degree of template images of various wear states as an indicator, utilizing the global optimization capability of the differential evolution (DE) algorithm to search for parameters. On this basis, a residual network with a fusion attention mechanism is used for transfer learning on images under the source condition, building a generalized milling tool wear state recognition model to identify the wear state of milling tools under variable conditions, verified through milling tool wear experiments.
## 4 Numerical Simulations and Discussion
### Comparison with Other AOA Methods
#### 4.3.4 Execution Time  
The speed of computation is a crucial factor for any application, hence the time for execution of the PDDA method is compared with state-of-the-art AOA methods in identical conditions. Since the computation complexity shown in table 4 mainly depends on the number of the sensors (M), the comparison of the execution time has been implemented by changing M = [10, 20,..., 100] and keeping the other simulation parameters constant. A MATLAB simulation was run with one hundred iterations (i.e., K = 100) for each method, and the average times of execution at each M recorded using tic and toc functions. The angular range of interest is [\(-90^{\circ}\), \(90^{\circ}\)] with interval grid \(\delta\) = 0.5\({}^{\circ}\), \(D\) = 6, SNR = 10 dB and \(N\) = 100. For the OGSBI and Root-SBL methods, the tolerance error and maximum number of iterations are set at 0.001 and 500 respectively, while the other simulation parameters for these are as given in [35,38] respectively. All the experiments have been carried out in MATLAB R2016-b on a PC with a Windows 8.1 operating system, processor: Intel(R) Core (TM) i7-4790 CPU @ 3.6 GHz, with 32 GB installed RAM. It is obvious from Figure 10 that the PDDA method is fastest. Minimum Norm, MUSIC and Capon give reasonable speed, whereas the Root-BSL and OGBSI techniques are the slowest. This is because their computational complexity not only depends on the number of sensors but also on the other parameters such as tolerance error and maximum number of iterations, which are required to find the optimum solution. The exact execution time of each method may differ from these results according to the specifications of the computer and situations which are associated with program running but the relative behaviour for each algorithm should be the same.  
Figure 9: Error probability vs RMSE, \(M\) = 8, \(D\) = 2, \(N\) = 10, SNR = 1 dB, CC = 0.95.
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology
### Completely Mixed Activated Sludge Process  
A completely mixed aeration tank means that the waste water is fully mixed with the original mixture after entering the aeration tank. Therefore, the composition, F/M value and quantity and quality of microbial community of the mixture in the tank are completely uniform. The position of the whole process on the sludge growth curve is only one point. This means that the biological reaction is the same in all parts of the aeration tank, and the oxygen absorption rate is also the same. This process is characterized by a strong ability to withstand impact load, and the ability of the mixed liquid in the tank to dilute the wastewater and weaken the peak load. And because the aerobic requirement of the whole tank is the same, it can save power. The aeration tank and sedimentation tank can be built together for easy operation and management. However, the continuous inflow and outflow of water may cause short circuits, and the process is prone to sludge swelling and other problems [23]. The technological process is shown in Figure 2. For the treatment of municipal wastewater, the BOD load (Ns) is 0.2-0.6 kg BOD5 / (kg MLSS-d), the volume load (Nv) is 0.8-2.0 kg BOD5 / (m\({}^{3}\cdot\)d), the sludge age (mean residence time of biosolids) (\(\theta\)r) is 5-15 days, the concentration of suspended solids (MLSS) is 3000-6000 mg/L, the concentration of volatile suspended solids (MLVSS) is 2400-4800 mg/L, the sludge reflux ratio (R) is 25-100%, and the aeration time (t) is 3-5 h. The removal rate of BOD, COD  
Figure 1: Process flow chart of pushed-flow activated sludge process.  
and TSS reaches more than 85% [24]. Since the completely mixed aeration tank requires continuous water inflow and outflow, and the tank is fully and evenly mixed, there is the problem that the sludge resources cannot be better treated, and it is easy to cause sludge expansion. At the same time, it is not easy to adjust the treatment method after certain changes in sewage quality making the recycling of sludge resources is especially difficult. Compared with the traditional activated sludge process, it may be more efficient in energy utilization. However, the poor utilization of sludge is a shortcoming.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 5 Grid System of Marine Vehicles  
The implementation of medium-voltage direct current (MVDC) systems is increasingly pertinent in ship power systems due to the presence of pulsed and other dynamic electrical loads. A robust management system is essential to regulate the MVDC bus voltage, ensuring stable operation amidst the sporadic introduction of impact loads. An advanced intelligent coordination algorithm has been proposed to mitigate the effects of these pulsed loads and optimize power distribution among storage units. The effectiveness and accuracy of these algorithms have been rigorously tested and validated within a MATLAB/Simulink environment [26; 47; 48; 49; 55; 56; 57; 58]. Unlike traditional AC systems, which are often bulky and inflexible, DC systems alleviate synchronization issues and eliminate the need to manage reactive power. This makes DC systems particularly suitable for isolated power systems and specialized applications such as shipboard microgrids. Given these advantages, the MVDC system is regarded as the optimal solution for fulfilling the complex demands of onboard power systems. Consequently, addressing the control challenges associated with MVDC is crucial, especially considering the variety of power sources and the presence of transient shock loads. Control techniques must be tailored to these diverse conditions to ensure the system's effective and safe operation [44; 50; 51; 52; 53; 54; 55; 56]. Current literature highlights that gas-based generators in hybrid ships often fall short of delivering the peak currents required by the network. There is a critical need to enhance naval platforms' flexibility, efficiency, and performance. Research findings suggest several promising advancements facilitated by medium-voltage direct current (MVDC) systems. The University of Texas has developed a megawatt-level MVDC/high-frequency alternating current (HFAC) power system testbed. This system can operate at clock speeds up to 40 MHz on an FPGA board, enabling real-time visualization of power system dynamics. Additionally, a real-time digital simulation module is employed for conducting hardware-in-the-loop (HIL) testing, further augmenting the development and validation of advanced naval power systems [56; 57].
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Magnetic Nanoparticles
#### 3.5.1 Enzyme-Based Immunosensors
MBs were functionalized with the corresponding capture antibodies, then added in a bacteria suspension for capturing. Then, GOx-Ab conjugate was employed to label the MBs-Ab-cell complexes, which was then magnetically collected on the electrode. Glucose added in solution led to local production of gluconic acid, which was monitored by measuring the impedance of the solution (related to the local pH decrease and ionic strength increase). The LoD was ca. 100 CFU mL\({}^{-1}\) and the detection range between 1000 and 2000 CFU mL\({}^{-1}\). Also in 2016, demonstrating that this electrochemical immunostaining is viable in integrated devices for practical applications, [PERSON] et al. [151] reported a fully-integrated microfluidic platform using MB-labeled capture antibodies and HRP-labeled signaling antibodies. The MB-labeled antibodies were first magnetically collected on the electrode, then the sandwich assay was performed (Figure 27C). Upon addition of H\({}_{2}\)O\({}_{2}\) and TMB in solution, TMBOx was amperometrically reduced on the working electrode. Transferrin was detected with a LoD of 0.03 ng mL\({}^{-1}\). At last, very recently in 2017, [PERSON] et al. [152] proposed an amperometric immunosensor for the quantification of TGF-\(\beta\)1 using a sandwich-type architecture with MBs functionalized with capture anti-TGF and signaling antibodies labelled with a poly-HRP commercial polymer. Conversely to the previous reference of [PERSON] et al. they magnetically collected the immunocomplexes on MBs after the sandwich assay. Amperometric measurements were carried out at \(-\)0.20 V by adding H\({}_{2}\)O\({}_{2}\) onto the electrode surface in the presence of HQ as redox mediator. Calibration showed linearity between 15 and 3000 pg mL\({}^{-1}\) and a LoD of 10 pg mL\({}^{-1}\). Figure 27: **(A)** Architecture of the magnetic Fe\({}_{3}\)O\({}_{4}\)-based immunosensor. MBs were decorated with GOx, signaling Abs and Fc-labeled \(\beta\)-CD. Capture Abs were grafted on GR sheets; **(B)** DPV at pH 7.0 Dependency of the DPV response towards different avian leukosis virus concentrations. Reprinted from [149], Copyright 2013, with permission from Elsevier; **(C)** Design, fabrication, and detection principle of the MB-based microfluidic electrochemical system; **(a)** Immunosensing principle with MB collection and sandwich capture of transferrin; **(b)** The microfluidic sensing chip, (i) photograph of the chip with an integrated microelectrode; (ii) photograph of the microelectrode; (iii) photograph of the reaction chamber with oxidized TMB, (iv) transfer of oxidized TMB to the detection chamber; **(D)** Amperometry measurement at \(-\)100 mV for different standard transferrin concentrations. Adapted from [151], licensed under a Creative Commons Attribution 4.0 International License.
## 4 Structural Analysis of the Reference COB Segment
### Loads and Boundary Conditions
During both the NO and OP loading scenario, as prescribed by [15], the same EM loads were considered. In particular, only the ferromagnetic loads contribution was considered, since the Lorentz's forces one is negligible. Instead, this assumption cannot be done for the UVDE loading scenario and the Lorentz's forces needed to be taken into account. So, as already done in [18], the EM loads related to two different time steps were considered for the thermomechanical evaluation under the UVDE loading scenario: the time steps corresponding to the maximum of the radial forces and moment, t = 11.52 s and t = 11.585 s, respectively. Thanks to a purposely developed computational procedure, the EM loads, calculated separately through a dedicated electro-magnetic analysis, were applied to the node mesh of the COB segment model. In particular, this procedure allowed matching the cloud of points on which the EM forces are calculated to the structural analysis mesh. Since the former was much coarser than the latter, the developed procedure was capable of assigning the EM forces calculated at the point (x, y, z) to the closest node of the structural analysis mesh. Since the EM model took into account the presence of breeder and DWTs, which are absent in the COB segment FEM model, the EM values in that regions were allocated to the nearby Eurofer portion in order to conservatively consider their effects.
## 4 Results
### Topology Optimisation  
A computational algorithm is set up to maximise part stiffness for a fixed target mass. Firstly, sunder iterations between optimisation and structural validation lead to the establishment of target mass value, as depicted in Figure 7 (top). First computations are based on a rough mesh in order to reduce computational cost whereas subsequent models are refined by creating a finer mesh in order to obtain more accurate results.  
TO is run on four design spaces, ranging between the design space shape constrained by the original part profile and an enlarged design space in order to study the ideal shape from a structural point of view. Figure 7 (bottom) shows firstly the enlarged design space (a), which corresponds to a volume that includes the original can opener model but is limited on three sides by the functional elements, which cannot be modified. Then, the top-limited design space (b) considers only the volume below the upper face of the original model while, conversely, the bottom-limited one (c) cannot exceed the can opener's lower profile. Finally, the fully limited design space (d) cannot exceed the original part  
Figure 6: Structural model for shape computation and simulation.  
Figure 7: (Top): Topology optimisation refinement of the concept (**a**) and (bottom): a comparison of the enlarged (**a**), the top-limited (**b**), the bottom-limited (**c**) and the fully limited (**d**) design spaces.  
profile. The design space should bring to the result the best structural performance. It is fundamental to preserve the lower profile (i.e., the fulcrum), since it is given by the part envelope during the can opening. Conversely, for the upper shape, further constraints are not necessary, and, in this way, optimisation can be less constrained, so both results from design space (c) and (d) are feasible. TO setup is based on a target mass around 25 g, and a process-related constraint of minimum thickness for computed elements of 2.5 mm.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 5 Conclusions  
The monitoring of cropland abandonment in hilly areas is a key but difficult task in the land-cover change field, and there is a lack of reliable and efficient approaches to monitor cropland abandonment in hilly areas on large scale. In this study, we presented an approach combining Sentinel-1 and Sentinel-2 imageries and incorporating them into the GEE platform to determine cropland abandonment on the basis of annual classification. Firstly, cloud interference removal, linear interpolation, and SG filtering were applied to synthesize the eligible indices with monthly intervals. Secondly, high-quality samples were generated using superimposed Google imagery, existing classification products, and developed visualization tools of GEE. Then, the RF classifier was used to perform pre-classification and secondary classification, resulting in annual land-cover distribution maps. Lastly, cropland abandonment of rainfed cropland and irrigated cropland was mapped in the study area as per the designated definition of cropland abandonment. Our work sheds some light on mapping the cropland abandonment in hilly areas on a global scale.  
S.H., conceptualization, data curation, methodology, writing--original draft, and writing--review and editing; H.S., conceptualization, funding acquisition, methodology, writing--review and editing, and project administration; W.X., conceptualization and funding acquisition; Z.Y., writing--review and editing; M.Y., investigation and data curation; J.Z., investigation; J.Q., conceptualization. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Fund of China (Grant No. 41401659), the Science and Technology Department of Sichuan Province (Grant No. 2015 JY0145), and the Natural Resources Department of Sichuan Province (Grant No. kj-2021-5).  
Sentinel-1, Sentinel-2, Google image, Global Forest Change (GFC) land cover, the JRC annual water surface data, the ESA WorldCover 2020 land cover, and global ESRI 2020 land cover data are available via the Google Earth Engine platform. The Global land cover product with a fine classification system at 30 m (GLC_FCS30) in 2020 is available via the Earth Science Big Data Science Engineering Data Sharing Service System. The China Land Cover Dataset (CLCD) in 2020 is available via the website [[https://zenodo.org/record/5210928#.YqB5](https://zenodo.org/record/5210928#.YqB5) whpByUl]([https://zenodo.org/record/5210928#.YqB5](https://zenodo.org/record/5210928#.YqB5) whpByUl), accessed on 22 June 2022.  
The authors declare no conflict of interest.