esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Rheological Behavior of AlBA/TA Hydrogel Thread  
In attempting to obtain an AlBA hydrogel, the AlBA concentration was increased to 3 wt% to avoid the formation of persistent bubbles. As a result, the system became much more viscous instead of gelation as expected. However, to transform the concentrated solution of AlBA into a hydrogel, the classical route of alginate gelation with CaCl\({}_{2}\)[20] was replaced by a new approach, in which polyphenol molecules were used as crosslinking agent for AlBA chains. Thus, AlBA was mixed with pyrogallol, pyrocatech, or TA at a final polyphenol concentration of 0.01 M. In the case of the AlBA/TA mixture, the gelation occurs immediately after TA addition, while for AlBA/phenol derivative systems a decrease in the overall viscosity was noticed. As shown in Figure 2b, as a result of rheological measurements, the values of loss modulus of the AlBA 3 wt% solution were higher than those of storage modulus over the entire range of the operating frequency (0.1-10 Hz), indicating a prevalent viscous component of the viscoelastic behavior. Similarly, but at generally smaller values, the system's AlBA/phenol derivatives roughly exhibited the same shift between moduli, showing a prevailing viscous component of the viscoelastic behavior. As a result, mixing AlBA with compounds carrying a single \"cis-diol\" moiety per molecule did not lead to intermolecular aggregation/crosslinking to make the initial solution a hydrogel. TA, having at least two \"active cis-diol\" functionalities per molecule, played the effective role of crosslinker between AlBA polymers, with direct consequences on the gel-like character of the final system revealed by the higher values of storage modulus in comparison with those of loss modulus (Figure 2b). To optimize the process of hydrogel formation by using TA as crosslinker, a number of AlBA/TA mixtures were prepared by varying AlBA concentration (1, 2, 3 and 4 wt%) at the same TA content (0.01 M). In fact, at a concentration of 1 wt% AlBA, no gelation occurred, but at 2 wt% AlBA, the AlBA/TA mixture became a hydrogel. As the AlBA concentration increased, the number of polysaccharide-like molecules involved in crosslinking via TA also increased, so that at 3 and 4 wt% AlBA, the hydrogels displayed relatively higher values of storage modulus (Figure 2c). However, it was noticed that the 4 wt% AlBA/0.01 M TA mixture exhibited a non-uniform appearance owing to persistent air bubbles inside the system during hydrogel formation and manipulation (Figure 2d). Taken together, 3 wt% of AlBA and 0.01 M of TA were suitable for the facile fabrication of uniform hydrogel-based threads.
# Hybrid Deep Reinforcement Learning for Pairs Trading
## 3 Related Work  
### Algorithmic Trading  
Considerable studies have been conducted on algorithmic trading, including on supervised learning-based methods [27; 28; 29; 30; 31; 32] and reinforcement learning-based methods [33; 34; 35; 36]. Supervised learning-based methods predict stock prices but do not decide trading actions. Reinforcement learning-based methods can decide trading actions by learning a profitable trading policy. [PERSON] and [PERSON] [33] proposed a deep reinforcement learning method that uses a candlestick (or K-line) as a summary of price movements. In this method, a candlestick is decomposed into the lengths of the upper shadow line, lower shadow line, and body. After applying clustering to each component, the cluster centers and body color are used to represent the input state for reinforcement learning. [PERSON] et al. [34] pro posed a time-driven feature-aware jointly deep reinforcement learning algorithm, which uses a gate structure, gated recurrent unit (GRU) [37] network, temporal attention mechanism, and auto-encoder that predicts the next closing price, where the encoding part of the auto-encoder is used for state representation. [PERSON] et al. [35] proposed an imitative deep reinforcement learning method that uses a demonstration buffer and behavior cloning for imitation learning. [PERSON] and [PERSON] [36] proposed a practical algorithmic trading method called SIRL-Trader, which achieves good profit using only long positions. SIRL-Trader uses offline/online SRL, imitative reinforcement learning, multistep learning, and dynamic delay.  
In this work, we study a pairs-trading problem based on hybrid reinforcement learning, which is a fundamentally different problem from those of the above studies. This fundamental difference raises new challenges, such as how to define action spaces and reward functions for pairs trading, how to clone the behavior of a prophetic pairs-trading expert, and how to hybridize two reinforcement learning algorithms, as described in Section 4.
## 2 Fifth-Generation Network Slicing and Related Work
### Related Work Employing Markov Decision Process  
Efficient resource allocation policies among network slices have a direct impact on resource utilization. Active research efforts are currently focused on the effective management of network resources using the network slicing technique. MDP, a widely utilized mathematical approach to optimization issues, is also employed in our study. The Table 2 summarizes various recent studies that have applied this method.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref.** & **Approaches Involving MDP** & **Reward Function** & **Action** \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{Deep reinforcement learning (DRL)} & Resource efficiency, edge computing performance assessment, QoS maintenance & Service request arrivals within a cluster of fog nodes overseen by an edge controller \\ \hline \multirow{3}{*}{[41]} & \multirow{3}{*}{Federated DRL} & QoS satisfaction, network load for each MVNO & Coordinated by the InP controller \\ \cline{2-3}  & & Income from one transaction, net income from all transactions, benefits achieved by a user in each transaction, transaction cost generated by each transaction & An economic model initiates dynamic pricing interactions, leading to the redistribution of resources \\ \hline \multirow{3}{*}{[43]} & \multirow{3}{*}{Double DQN, Dijkstra algorithm, binary search-assisted gradient descent} & Minimization of the number of accepted service requests with higher priority and minimization of the total cost \\ \cline{2-3}  & & Resource utilization, QoS violation penalty & A resource management agent monitors the status of network slices and MEC nodes and makes decisions on adjusting the allocated resources \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of selected works employing a Markov decision process (MDP) for resource capacity planning in network slicing.
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD
#### 5.3.4 Time Delay of PCO  
Inter-satellite measurements are based on the phase center of the transceiver antenna, while the antenna is installed based on its geometric center. During the measurement process, the inconsistency between the antenna's phase center and its geometric center introduces an error known as the time delay of PCO.  
The time delay of PCO can be corrected by the factory default values, which were calibrated by the satellite manufacturer and have been released on the Beidou official website [69]. Alternatively, it can be determined through in-orbit calibration. This method generally utilizes the observation data between the globally uniformly distributed ground stations and the satellites and solves the antenna phase center deviation parameters through network estimation [70].
## 2 Materials and Methods
### UV Sources  
The mitigation of targeted gases was investigated using 4 different light sources (UV-A: 367 nm and UV-C: 254 nm, 222 nm or 185 + 254 nm, Supplementary Materials Figure S5). Here, 2 low-pressure mercury sources (American Ultraviolet Co, Lebanon, IN, USA) were used, both of which emit strongly at 254 nm, but 1 additionally contains a small 185 nm component because the bulb is made from special materials that allow transmission of that line. The emission spectrum of low-pressure Hg lamps is well known, and these sources both also contained small emissions at 365 nm and other wavelengths common to all of these bulbs. Nonetheless, we refer to these as 254 nm or (185 + 254) nm light sources. An excimer source (Ushio America Inc., Cypress, CA, USA) emitting at 222 nm was the third source. The fourth source was an LED with emission centered at 367 nm lamps (T8 LED, Eildon Technology, Shenzhen, China), near the 365 nm range that Hg lamps commonly were used for, but without disadvantages of Hg-based lamps.  
The UV-A LED (367 nm) consumed 16 W per lamp. Here, 2 UV-C (254 nm and 185 + 254 nm) bulbs were mounted in 1 15A UV fixture (American Ultraviolet Co, Lebanon, IN, USA). The power consumption of 1 15A UV fixture was 30 W. The UV-C excimer (222 nm) lamp was a single-bulb system with a customized power supply and its nominal power consumption of 300 W.
## 2 Materials and Methods
### Knees Estimation  
The premise of this work is to diagnose the real aging cycle or capacity fade curve of the battery in second life by comparing it against known aging trajectories or predefined cycles, where the combinations of the stress factors leading to the trajectories are known, to estimate the contribution of each combination to the real aging cycle. Known aging trajectories are obtained from accelerated test data, including first and second life, for which an accurate window for second life must be previously estimated. In this work, we employ Bayesian Change Point (BCP) detection, a statistical method used to identify points in a sequence (rate of change), allowing for the detection of knee or elbow points in the capacity fade curve [21]. BCP, unlike clustering, accounts for order and temporal continuity. It has a probabilistic interpretation that supports uncertainty quantification and efficiently detects subtle changes or shifts.  
In BCP, the time series is divided into segments, each with its own statistical properties, such as mean and variance. The change point is the unknown time at which these properties change. The Bayesian approach provides a probability distribution for possible change points or knees, considering the uncertainty in the estimate. The likelihood of the observed data is calculated given that there is a knee at some point, and it is assumed that the change is uniform prior. The posterior distribution is obtained using Bayes' Theorem, which combines the likelihood with the prior. The simplified sequence calculation for BCP is shown as follows [21; 22]:  
* Given a time series \(y_{1},\ y_{2},\ldots,y_{T}\), the goal is to find one or more change points \(\tau_{1},\ \tau_{2},\ldots\), where the statistical properties of the data change.
* Bayesian methods aim to compute their posterior distribution given the observed data: \[P(\tau|y_{1}:T)=\frac{P(y_{1}:T|\tau)P(\tau)}{P(y_{1}  
\(\theta_{i}\): Parameters for segment \(i\); Each segment is assumed to follow a known distribution. In our R code, the time series is loaded, and the BCP analysis proceeds to detect change points or knees between a threshold onward (set to 0.85) and a change point threshold (set to 0.5) to frame second life. The first knee detection is identified by finding the index where the posterior probability exceeds the threshold of 0.5. After detecting the first knee point, a new subset of data is prepared, and BCP is applied again to this subset. The second-life time series is then obtained by extracting the data between these two knees.
## Data and Methods
### LDAS-Monde System Components
#### 2.1.3 ERA-5 Atmospheric Reanalysis  
ERA-5 [44] is the fifth generation of European re-analyses produced by the ECMWF and a key element of the EU-funded Copernicus Climate Change Service (C3S). ERA-5 important changes relative to ERA-interim former ECMWF's atmospheric reanalysis include (i) a higher spatial and temporal resolution as well as (ii) a more recent version of ECMWF Earth system model physics and data assimilation system (corresponding to ECMWF's cycle CY41R2, [[https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation](https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation)]([https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation](https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation)), last access June 2018). It makes it possible to use modern parameterizations of Earth processes compared with older versions used in ERA-interim. For instance, in addition to being applied to satellite observations, a variational bias scheme is also applied to aircraft and surface ozone and pressure data. ERA-5 also benefits from reprocessed data sets that were not ready yet during the production of ERA-interim. Two other important features of ERA-5 are the more frequent model output and improved model spatial resolution, going from six-hourly output in ERA-interim to hourly output analysis in ERA-5, and from 79 km (horizontal dimension) and 60 levels (vertical dimension) to 31 km and 137 levels in ERA-5. Finally, ERA-5 also provides an estimate of uncertainty through the use of a 10-member ensemble of data assimilations (EDA) at a coarser resolution (63 km horizontal resolution) and three-hourly frequency. ERA-5 is foreseen to replace ERA-interim re-analysis. All ERA-5 atmospheric variables were interpolated at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution. A bilinear interpolation from the native reanalysis grid to the regular grid was made.
## 3 QualiChain Architecture, Pilot Concept, Research Questions, and Execution Methodology
### QualiChain Architecture
#### 3.1.1 QualiChain Technical Architecture  
In Figure 1, an overview of the QualiChain technical architecture is presented, including the layers, the modules, and the provided services within the layers, along with the communication among the different modules.  
Specifically, the platform is composed of six main layers (Academic Verification and Accreditation, Knowledge Graph, Knowledge Extraction, Identity and Access Control Management, Analytics and Decision Support, and the Front End) that are summarized below.  
* The Academic Verification and Accreditation layer is at the top of the architecture and includes services for access to the blockchain and the Distributed File Storage. This layer only communicates with the Knowledge Graph that is described next.
* The Knowledge Graph layer is the central data repository of the QualiChain platform. The QualiChain Ontology is used to store the data, while the QualiChain Knowledge Graph Query Engine enables querying the QualiChain Ontology. The QualiChain Knowledge Graph layer communicates with every other layer of the QualiChain Platform.
* The Knowledge Extraction layer includes services for data acquisition, annotation, and knowledge extraction from data (e.g., skill extraction from job posts). It interacts only with the Knowledge Graph layer for storing the extracted knowledge.
* The Identity and Access Control Management layer communicates with all other layers of the QualiChain platform to ensure that only authenticated users will have access to the requested resources.
* The Analytics and Decision Support layer includes an analytics module that consists of the following set of services: CV customization, Profiling, Course Recommendation, Curriculum Design, Recruitment and Internal Allocation, and Career Advisor, as well as a module for Multi-Criteria Decision Support with several available methods. This component communicates with the Knowledge Graph to read and write data, as well as with the Front End.  
* Finally, the Front End includes the QualiChain platform user interface, as well as a Visualization Engine that enables complex data visualizations. It communicates with all services, providing the end user with an intuitive way to take advantage of all the offered services.  
All these layers were deployed independently and communicate with each other through a Microservices architecture implemented in the QualiChain Mediator Component. Finally, the Front End communicates with the other components through APIs.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Reviewed Literature Statistics  
Upon examining the 61 articles included in this review, relevant statistics were obtained. Figure 7 presents the distribution of papers per year from 2018 through 2022 as well as early work from 2023.  
The distribution of programmable devices used in the reviewed articles is presented in Figure 8. The results show that a significant portion of the implementations were done using software switches (40%). These switches are typically used for prototyping and proof of concept purposes. Hardware devices, including switches, FPGA boards, and smart NICs, accounted for a combined usage of 55%. This indicates that the majority of research work is geared towards deployment in real-world networks. It should be noted that 4.9% of the papers did not provide any information regarding the device used.  
Figure 9 shows the distribution of papers based on the architecture section where the applications have been deployed. It is evident that the majority of solutions, representing 51%, have been implemented in the edge-to-core section of the architecture. This indicates that most of the research found in this review is focused on the transport network. In addition, Figure 10 illustrates the distribution of articles based on their specific use case implementations, revealing that tunneling and forwarding, alongside network slicing, emerge as the most frequently adopted use cases. Moreover, the survey foun  
Figure 8: Distribution of implemented programmable devices from reviewed articles.  
Figure 7: Distribution of reviewed articles per year.  
made their used code available on the GitHub platform. Further details can be found in Table 13 from Appendix A of this paper.
## 3 Results and Discussion
### Model Parameter Determination and Statistical Analysis
[22] describing dual substrate kinetics of ammonia oxidation by [PERSON] model, with R\({}^{2}\) > 0.98 and adj R\({}^{2}\) > 0.98 and low standard errors (RMSE) < 0.61. Additionally, [PERSON] et al. [23] reported a high correlation between Haldane prediction and experimental data with adjusted R\({}^{2}\) (0.995), \(K_{s}\) (2.997 \(\pm\) 0.041 mg/L) and \(K_{i}\) (64.736 \(\pm\) 0.023 mg/L). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Kinetic Parameters**} \\ \cline{2-7}  & \(V_{m}\) & \(K_{s}\) & \(K_{i}\) & \(n\) & **Reference** & \\ \hline \multirow{3}{*}{Haldane} & 0.323 & 9.65 & 152.40 & - & [19] & SNaD under high phenol concentrations using & \\  & 0.45 & - & 23.94 & - & **This study** & **SNaD in CN\({}^{-}\)** \\  & & & & & & (_Haldane model without substrate inhibition_) \\ \hline \multirow{3}{*}{Rate law} & - & - & 0.047 & 1 & [20] & Degradation of ammonia nitrogen under high \\  & - & - & \(6.29\times 10^{-5}\) & 2.27 & **This study** & **SNaD in CN\({}^{-}\)** \\ \hline \multirow{3}{*}{Andrews} & 0.0485 & 28.63 & 24.284 & - & [20] & Denitrification with nitrate inhibition \\  & 0.36 & 13.22 & 27.54 & - & **This study** & **SNaD in CN\({}^{-}\)** \\ \hline \hline \end{tabular} \(K_{s}\) is pollutant inhibition constant, \(K_{i}\) is the saturation constant, \(C_{s}\) is the concentration of the pollutant, \(n\) is the model fitting constant and \(V_{m}\), is the maximum specific degradation rate of the pollutant.  
\end{table} 
Table 3: Kinetic parameters obtained from different studies assessing nitrification and aerobic denitrification.
## 3 Results
### Organic Filters
#### 3.1.2. UVA Filters  
* Benzophenones: benzophenone-3, (2-hydroxy-4-methoxyphenyl) phenyl- methanone; 2-benzoyl-5-methoxyphenol; 2-hydroxy-4-methoxybenzophenone; (2-hydroxy-4-methoxyphenyl) phenyl methanone; 4-methoxy-2-hydroxybenzophenone; advastab 45; ai3-23644; anurvex; b3; benzophenone. Widely used, although less and less since it is one of the worst chemical filters that exists, especially BP-3 or Oxypbenzene, which has multiple endocrine disrupting effects, is bioaccumulative, persistent in the environment, is associated with photoallergic reactions, is absorbed in significant amounts through the skin, passes into breast milk. There is evidence of neurotoxicity and ecotoxicity affecting many aquatic species [15; 68]. With the current data, it should be prohibited. The CDC found it in the bodies of 97% of Americans tested in one study. Always avoid. (EGW 8 fair)
* Anthranilates: They are weak UVB absorbers and mainly UVA absorbers, but less effective than benzophenones.
* Avobenzene (Butyl Methoxydibenzoylmethane, Parsol 1798) Today one of the safest chemical filters. Allergies rare. It degrades easily in sunlight, losing its effectiveness, although it can be stabilized with other safe substances such as Octisalate. Ecotoxin. The most recommended by the EWG. (EWG 2 limited)
* Mexoryl SX: Tetraphthalidine sulfonic acid dialcamphor: Broad UVA absorber with efficacy similar to avobenzene. It is also considered safe. (EWG sec 2 limited)  
Figure 2 shows how UV filters can affect the human body.  
Figure 2: How human body is affected by UV filters.