esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Extraction and Purification of the MMPB Product
An important fraction of MMPB is either not extracted, degraded, or not retained by the cartridge used. Another possibility is the application of an on-line SPE method for the purification of the sample after Lemieux oxidation. Unlike the classical SPE methods for cartridges (off-line SPE), the on-line-SPE is directly coupled with HPLC, saving time on the analysis compared to the off-line methods. [PERSON] et al. [64] compared three on-line SPE columns (C18(r), BetaBasic-18(r), and Hyper-Sep Retain PEP(r)) on environmental surface water samples. Some peak-tailing was observed with the Hyper-Sep(r), while the C18(r) could not efficiently withstand a large number of consecutive injections. Due to the difference in pH between the samples (pH around 2 after the oxidation step) and the solution used during the washing step, 0.5% \(v/v\) formic acid was added to the mobile phases of the on-line SPE. According to the authors, this washing step is the most critical as it allows the desalting and removal of interfering matrix components. Therefore, the optimized method corresponds to a sample loading of 5 mL and a wash with 3 mL, both at a flow rate of 2500 mL/min. By using this on-line SPE on water samples, MMPB was analyzed in just 8 min with a limit of quantification (LOQ) of 0.5 ng/L. Another derivative of the SPE method that could be applied to total MCs is solid phase micro-extraction (SPME). This method uses a fused polymer-based fiber as an adsorption medium for the analyte [82]. SPME was first developed with fish tissues (_Goodea_ sp. ; _Oncorhynchus nerka; Perca flavecs_) spiked with MCs and then applied to environmental fish samples [61]. Due to the detection mode used (GC-MS), the purified extracts had to be derivatized to form mMMPB. Three fiber types (Supelco, Inc., Belfort, PA, USA) were compared: polydimethylsiloxane/divinylbenzene (PDMS-DVB), divinylbenzene/Carboxen(tm)/polydimethylsiloxane (DVB-CAR-PDMS) and polyacrylamide (PA). Among these three types of fiber, the best linearity between the peak area and concentration of MMPB was obtained with PDMS-DVB, and an LOQ of 0.14 ug/g was calculated. Following purification, it is possible to add an intermediate step before analysis and to concentrate the extracts by evaporating under nitrogen [29; 70] or argon flow [28] at different temperatures from 25 to 40 \({}^{\circ}\)C [29; 70; 83].
## 3 Results and Analysis
### Comprehensive Impact Analysis
Figure 10: The comparison results of conventional quantitative parameters under the influence of different clusters when there are 30 natural fractures in the region (**(a)** number of damaged interface elements versus time; **(b)** fracture area versus time; **(c)** fracture aperture at injection point versus time; **(d)** maximum fracture aperture versus time; **(e)** fracture volume versus time; and **(f)** ratio of tensile failure versus time) (C1, C3, C6, C9, and C12 represent fracturing scenarios involving 1 cluster, 3 clusters, 6 clusters, 9 clusters, and 12 clusters, respectively). Figure 9: Fracture morphology under diverse cluster fracturing scenarios when there are 20 natural fractures in the region (**(a)** is a 3D view and **(b)** is a top view) (C3, C6, and C9 correspond to fracturing scenarios involving 3 clusters, 6 clusters, and 9 clusters, respectively). The process of fracture morphology changes when the fracture number is 30 is shown in Figure 11. As the number of fracturing clusters increases, the main artificial fractures in the reservoir show an increasing trend. Unfortunately, when fracturing 1 cluster, 1 main fracture was formed, when fracturing 3 clusters, 3 main fractures were formed, when fracturing 6 clusters, 6 main fractures were formed, and when fracturing 12 clusters, 12 main fractures were formed. However, when fracturing nine clusters, only three main fractures were formed. We speculate that this is due to the large number of fractures near the wellbore generated in the simulation model of nine-cluster fracturing, indicating that implementing nine-cluster fracturing under the conditions of this model will make it difficult to achieve economic fracturing effects. Figure 11: Fracture morphology under diverse cluster fracturing scenarios when there are 30 natural fractures in the region (**(a)** is a 3D view and (**b**) is a top view) (C1, C3, C6, C9, and C12 represent fracturing scenarios involving 1 cluster, 3 clusters, 6 clusters, 9 clusters, and 12 clusters, respectively). network of fractures [42; 43]. Comparing the simulation results under different numbers of natural fractures and clusters in this section, as the number of natural fractures increases, artificial fractures may become more complex. For example, when the simulated target block contains 40 natural fractures, even under single-cluster fracturing conditions, more complex fracture shapes are formed. Meanwhile, when the simulated target block contains 20 natural fractures, even under the conditions of fracturing of nine clusters, it may be difficult to form a complex artificial fracture network. Figure 13: Fracture morphology under diverse cluster fracturing scenarios when there are 40 natural fractures in the region (**(a)** is a 3D view and (**b**) is a top view) (C1, C3, C6, C9, and C12 represent fracturing scenarios involving 1 cluster, 3 clusters, 6 clusters, 9 clusters, and 12 clusters, respectively).
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### 4.2.5 Stablecoin  
Stablecoin is a cryptocurrency pegged to a stable asset or basket of assets, reducing price volatility. This stability makes them suitable for various DeFi applications, including lending, borrowing, and trading. Stablecoin is a digital currency designed to maintain a stable value and purchasing power by incorporating an economic framework [93]. Noncustodial stablecoin are a specific type of stablecoin that aims to keep a stable value relative to a target currency, typically USD, through the implementation of various economic mechanisms [52]. These stablecoins are available in the DeFi space, allowing investors to earn returns on their cryptocurrency investments while minimizing the volatility and its negative impacts. Stablecoins offer a reliable solution to the price fluctuations commonly found in the DeFi ecosystem, allowing for fast and low-cost fund transfers and creating a more stable market environment.  
We briefly summarize the two most used methods of stabilizing cryptocurrencies:  
* Fiat-Collateralized: In the fiat-collateralized mechanism, stablecoins are pegged to a fixed amount of fiat currency, usually USD, and this is achieved through a network of banks that hold the fiat collateral. This approach is not decentralized, and stablecoins like USDT and USDC are examples of this mechanism [55].
* Crypto Asset-Collateralized: In the context of stablecoins, a common stabilization mechanism involves using a crypto asset as collateral to back each unit of the stablecoin. Stablecoin requires a mechanism to protect against fluctuations in the value of the collateral. One example of such a stablecoin is _DAI_, which is pegged to 1 USD. To obtain a newly created _DAI_, a user must provide a greater amount of cryptocurrency as collateral than the value of the _DAI_ requested [41].
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### RF Technologies
#### 2.2.4 Monopoles at 2 GHz  
A major cause of antenna underperformance in WBANs is antenna detuning due to proximity to the human body. Shielded monopole antennas could overcome this issue if the ground plane is aligned with the body. For this research, path loss measurements are performed with radially symmetrical shielded monopoles, tuned at 2 GHz. This frequency was selected because it results in a considerably smaller antenna size in comparison to 420 MHz or 900 MHz antennas. The 2.4 GHz ISM band was avoided to prevent interference during our measurements. Furthermore, the 2 GHz monopoles are compatible with the antenna arrays used in this research, for which the frequency selection was driven by component specifications.  
The design of the antenna is presented in Figure 6a: A 37.5 mm monopole sticks out of a 72.0 mm FR4 disk with a double-sided copper ground plane. The antenna is equipped with an SMA connector. For on-body applications, a 90\({}^{\circ}\) SMA connector is used, allowing close-to-skin mounting, as depicted in Figure 6b.  
Three identical shielded monopoles were manufactured, allowing 2-port measurements of the reference scenario, and 3-port measurements of configurations with on-body repeaters. For all S-parameter measurements, the VNA described above is used with 50 ohm ports. Each configuration is measured 50 times with a frequency span of 500 MHz-3 GHz with 501 frequency steps (sweep time 5.3 ms). The S-parameters are then used as an input in the path-loss models. Figure 6c presents the monopole's S\({}_{11}\) parameters in free space, and mounted on the body. In the free space scenario, resonance can be observed at 2 GHz, and the intended \(-10\) dB S\({}_{11}\) value is obtained. When the antenna is mounted on the body, the resonance peak shifts towards 1.91 GHz. However, due to the wide resonance peak, the S11 value at 2 GHz is still \(-9.5\) dB.  
Figure 6: (**a**) Radially symmetrical shielded monopole with antenna dimensions (mm). (**b**) Reference scenario with two on-body monopoles, connected with 90\({}^{\circ}\) SMA connectors. (**c**) Measured Power reflection coefficient (S\({}_{11}\)) of the monopole in free space (solid black) and on the body (dashed black).  
==WARNING: Truncated because of repetitions==
: email: [https://www.sslodowska-Curie](https://www.sslodowska-Curie).  
++  
antennas and providing their technical advise. The authors would also like to thank [PERSON] and [PERSON] from University of Lille for their support and advice in developing the Body Coupled Communication solution. The authors would like to thank [PERSON] for proofreading the manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 5 Experiments and Results
### VPR Test Result
We can observe that the VPR works well as the location predicted is not far from the ground truth. The average VPR location error is about 0.45. Since the images dataset is discrete, there are some errors in the VPR location. Therefore, VPR can only obtain approximate positioning results but not completely accurate positioning results. In addition, VPR predicts a point, and a continuous walking path cannot be obtained. It cannot be correctly positioned in the place without the images. In summary, it is difficult for VPR to perform independent and accurate positioning, and other algorithms are required to correct the VPR.
###### Abstract  
Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.  
WSN; energy efficiency; energy center; PSO; mobile sink; network lifetime +
Footnote †: journal: Sensors
## 2 Background
### Federated Learning
* **Hyperparameter Sensitivity**: poor choices for the proximal term coefficient can adversely affect model performance and convergence. * **Communication Overhead**: the approach requires multiple rounds of communication between clients and the server to reach consensus. * **Reliance on Global Model Quality**: the effectiveness of the proximal terms is contingent on the quality of the global model. Given these challenges, careful tuning and a deep understanding of the specific application context are essential for optimizing performance. Federated buffered asynchronous aggregation (FedBuff) is another FL approach that employs ensemble-based aggregation [51]. FedBuff is designed to enhance efficiency and performance in scenarios with limited connectivity and variable data distribution. Instead of sending model updates to the server after every communication round, nodes can buffer several updates, reducing the frequency of communication and improving adaptability to non-IID data. Despite its advantages, FedBuff also has limitations, including the following:* **Increasaeed Latency**: buffering can introduce delays in model updates. * **Local Memory Constraints**: nodes may face challenges due to limited memory capacity. * **Outdated Local Models**: buffered updates may lead to stale model information. * **Risk of Overfitting**: extended local training without server feedback can result in overfitting. Nonetheless, it can be noticed that FL approaches suffer from high communication overhead and the need to run a large number of communication rounds (steps) to reach the desired accuracy. Improving accuracy is found to be proportional to the number of communication rounds. Additionally, in general, FL algorithms assume that nodes are deployed with direct communication access to the server. Therefore, the FL algorithm does not perform any in-network communication and thus does not take advantage of en-route processing capabilities to further improve the efficiency of FL algorithms. Table 1 compares and contrasts between the three FL algorithms mentioned in this paper, namely FedAvg, FedProx, and FedBuff. The authors in [51] introduced FedBuff, a federated learning algorithm that enhances communication efficiency and robustness in asynchronous settings by utilizing a buffering mechanism for aggregating client updates. The method is particularly effective in federated learning scenarios with 100 devices where client updates may be delayed or asynchronous. By storing and aggregating updates in a buffer, FedBuff allows for more efficient synchronization and reduces the number of rounds needed to achieve convergence. In their experiments with non-IID data on the CIFAR-10 dataset, FedBuff required around 250-300 rounds to reach 50% or above accuracy, which is faster than FedAvg, which needed more than 400 rounds in similar settings. This improvement is due to the buffering technique that mitigates the impact of straggling clients and varying client latencies.
# Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
## 3 Aligning COURAGE Entities to the CIDOC CRM Ontology
### Mapping of Classes  
This section explains the preliminary investigations and decisions for the mapping. The first step was to decide upon which data needed to be converted into CIDOC CRM. We had to filter out private and personal data such as e-mail addresses or institutional budget numbers. Data for statistical purposes (e.g., approximated numbers and types of items in collections) and data very specific about collection operators (e.g., number of online visitors, type of catalogue) were also omitted. Finally, we decided to include facts into the mapping process, which may be useful for historical research and fall into the scope of CIDOC CRM. The selected main classes include person, organization, collection, and featured item. Additionally, the auxiliary classes describing the roles of persons and organizations in the history of collections (e.g., founder, owner, etc.) should be mapped as well.  
The second step was to select the target CRM classes for mapping. The selected COURAGE main classes had straightforward equivalents in CRM: E21 Person, E74 Group, E78 Collection, and E22 Man-Made Object. During the mapping of the rest of the classes, some extensions were necessary. In general, we tried to avoid the creation of new classes as much as possible. It occurred only if there was no equivalent class in CIDOC-CRM and it was a major concept for the context.  
The first question was how to represent our specific type categories in CIDOC-CRM. As the E55 Type class is used to classify CIDOC-CRM instances, it was used extensively in our diagrams. For clarity, we decided to create different subtypes of E55 Type, namely E55.1 Professor, E55.2 Educational Background, E55.3 Operational Type, E55.4 Organisational Type, E55.5 Geographical Scope, E55.6 Topic, E55.7 Item Type, E55.8 Gender, E55.9 Name Type. These classes helped us to create each entity type once and use it later with other instances. This way, we could use the vocabularies developed in COURAGE in the CRM descriptions.  
The second extension was initiated by the fact that certain roles had a single candidate class in CIDOC CRM: E87 Curation Activity. COURAGE differentiated between content creator, operator, collector, stakeholder, and supporter roles regarding collections. In order not to lose this important information, subclasses of E87 curation activity were created and used for the mapping, including E87a content creation activity, E87b operator activity, E87c collection activity, E87d stakeholder activity, E87e support activity.
## 4 Simulations and Analysis
### Verification of the DOA Deduction through the Spatial Response Power  
In this section, the effectiveness of the DOA deduction by the spatial response power is verified. Figure 5 shows the histograms and the errors of the DOA of the target source varying with two kinds of errors of the initial DOA of the target source, i.e., \(5^{\circ}\) and \(10^{\circ}\). The other simulation settings were the same as in Section 4.2.  
**Case 1:** Figure 5a shows the statistical histogram of the maximum value distribution of the spatial response power after the first iteration. We can find that the maximum value is located at \(71.1^{\circ}\), rather than at \(75^{\circ}\) of the initial DOA of the target source. This confirms the effectiveness of the procedures of the backward correction of the steering vector of the target source and the DOA deduction through the spatial response power. Furthermore, Figure 5b shows that as the number of iterations increases, the error of the derived DOA of the target source gradually decreases; this indicates that the iteration of the DOA deduction can effectively reduce the error of the estimated DOA of the target source.  
**Case 2:** Figure 5c shows that an error of \(10^{\circ}\) can also be effectively reduced to \(2.1^{\circ}\) after the first iteration. Similarly, Figure 5d also shows that as the number of iterations increases, the error of the derived DOA of the target source gradually decreases. This also indicates that the proposed method is applicable to the case that the DOA of the target source is seriously mismatched.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 6 Online Anomaly Clustering  
In this section, we propose an integration of NARX-SCAPA and the ECDF-based fuzzy c-means clustering, so that anomalies can be clustered online. Such access to real-time clustering of anomalies can help decision-makers to diagnose and act on a system more effectively by making associations with previous anomalies.  
Figure 8: Fuzzy c-means clustering with ECDF on fragmented segments. (**a**) Time series. (**b**) Residual series.  
We test the real-time clustering of collective anomalies with clusters from fuzzy c-means clustering using ECDF in a simple clustering/allocation scheme. First, we evenly divide the 20-day time series data into a training group and a testing group. Next, we detect the collective anomalies within the entire training group using the NARX-SCPA framework, and based on the results, we generate anomaly clusters with the ECDF-based fuzzy c-means clustering offline. Then, we implement the same anomaly detection method on the testing group in real time, and when a new anomaly sequence is fully detected, it is assigned to the nearest cluster in terms of the Euclidean distance.  
The results of such a clustering/allocation scheme are compared to the clustering results of fuzzy c-means clustering using ECDF with all anomalous time series segments from the 20-day simulated time series. It can be found in Figure 9 that the performance of the clustering/allocation approach is similar to the clustering performance of fuzzy c-means clustering with ECDF, with Rand indices of 0.69 versus 0.65. It suggests that such a clustering/allocation scheme is capable of online anomaly clustering with fuzzy c-means clustering, Euclidean distance, and ECDF. However, it is also worth noting that an increase in the amount of training data in the clustering stage can help the clustering with more generalised high-quality clusters that can accommodate more possible individual differences between the anomalies of the same type.
## 3 Results and Discussion
### Characterisation of Biosorbents
While these three C-H peaks are clear for RS, the peaks for asymmetric and symmetric C-H stretching are merged for WW. The peak at 1730 cm\({}^{-1}\) is associated with the stretching of C=O resulting from carbonyl and carboxylic acid groups esters in RS [78] and WW [79]. The wide band at around 1600 cm\({}^{-1}\) could be assigned the aliphatic and/or unsaturated aromatic compounds [78], methoxy stretching vibration of lignin [81], and/or ketonic component of hemicellulose [80]. The symmetric and asymmetric stretching of COO- bonds were assigned by the peaks at 1592 cm\({}^{-1}\) and 1428 cm\({}^{-1}\), respectively [82; 83]. The peaks at 1460 cm\({}^{-1}\), 1428 cm\({}^{-1}\), 1367 cm\({}^{-1}\), 1320 cm\({}^{-1}\), 1027 cm\({}^{-1}\), and 877 cm\({}^{-1}\) are all associated with the stretching and bending vibrations of -CH\({}_{2}\), -CH\({}_{3}\), CH, -OH, and C-O bonds in cellulosic structures and aliphatic chains in lignocellulosic biomass [78; 80; 84; 85]. The peaks between 1070 and 1275 cm\({}^{-1}\) can be assigned to the stretching vibration C-O, C-O-C, and C-OH from the main source of cellulose, hemicellulose, and lignin [78; 80; 85; 86]. The substituents of aromatic rigs (C-H stretching) could be observed at the range of 750-950 cm\({}^{-1}\)[78; 80; 85]. The above results indicate that cellulose, hemicellulose, and lignin are the main component biosynthesis and the functional groups are mainly provided by cellulose [87]. Figure 1: FTIR analysis of biosorbents: LD, CH, HH, RS, and WW.