esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Scheduling with Reinforcement Learning
## 4 Scheduling System with Reinforcement Learning
### Methodology  
The development process of this work was divided into four main phases: design and planning of the work to be developed; the development of the system itself; comprehensive system validation; and collection of results. These phases are based on the hybrid cascade model proposed by [PERSON] [51]; with it, it is possible to take advantage of the structure offered by the cascade models in conjunction with the rapid prototyping of evolutionary models [51].  
A traditional cascade approach begins by defining the system requirements that shall serve as the basis for software design. In the implementation phase, each previously designed component is converted into a unit that can be tested later. In evolutionary development, an implementation of the system is made right from the start. This is then evaluated and iteratively the system is refined to a satisfactory level. This requires the specification of requirements, development and validation to occur simultaneously.  
The methodology employed in this paper follows an hybrid approach as it combines the two discussed models: cascade and evolutionary. It follows, in part, the cascade model because it began with the specification of requirements that serve as the basis for the implementation of the system, then moved on to the development phase of the system. Once this phase was over, the overall system was tested and, finally, the relevant statistical results and values were collected. However, at a lower granularity level an evolutionary model [51] was applied. The initial version of each component was tested as it was developed (as if it were a prototype). Then, each version of the components was worked on in an iterative manner, enhancing the previous versions. At last, the final version was built and combined in the overall system. The development process of this work, from the initial research to the achievement and analysis of the results, is split into four phases: design, development, validation and results. These are detailed on the following paragraphs and illustrated in Figure 2.  
The design phase is the initial phase of the project. Thus, it is not surprising that in it there are activities such as the revision of the state-of-the-art of the scientific areas involved, namely the reinforcement learning algorithms and the training environments of agents that use these algorithms. In the machine learning module, the design phase consisted then in the revision of the state-of-the-art mentioned, which was previously published [2]. This served as the basis for specifying the training environment of the agent to be developed and in the decision of the learning algorithm to use-proximal policy optimization (PPO) [52]. For the scheduling module this phase served to concretely define each of the components of the problems: jobs, the operations that compose each job, the machines where the operations are performed and the decoder of academic instances of these problems.
## 5 Proposal of New Spatial-Temporal Parameters for Optimal Level of Service for Low-Cost Carriers
Furthermore, for gate holdrooms, the segment related to the seating area remained unchanged, since the size of the seating area is defined by the size of the seat, which is from 1.8 m\({}^{2}\) to 2.2 m\({}^{2}\); therefore, these values remained the same in the new guidelines. Standing space in gate holdrooms was also reduced by 0.2 m\({}^{2}\) per passenger, while for customs control a saving of 0.3 m\({}^{2}\) per passenger was proposed. For baggage reclaim facilities, a saving of 0.2 m\({}^{2}\) per passenger was also proposed, which is related to the required number of carousels in the baggage reclaim area. In order to further clarify the savings by applying the newly established LOS guidelines to the spatial requirements of different airport terminal facilities of the passenger terminals, two airport terminal facilities were analyzed: check-in and gate holdrooms. For example, if we look at the check-in segment, a larger number of passengers can be accommodated on the same area if the new (LCC) parameters are taken, which also meets the requirements of LCC passengers who are willing to use less space per person. Also, if we take into account the consideration of the required area, it is possible to reduce it by a certain percentage compared to the demand during peak hour. The same analogy can be applied to other subsystems (immigration/emigration control, security control, etc.). Due to the differences in approach, gate holdrooms were taken as the second example. Namely, users stated that they value a lower price more compared to the larger available space while the passenger is (standing) in the waiting room. In order to obtain results in terms of greater utilization of the area, the number of passengers and the planned standing area within the departure holdroom are entered as input parameters. For example, if the planned standing area is 513 m\({}^{2}\), according to the IATA guidelines, a maximum of 428 passengers can be accommodated (taking the current optimal level). However, according to the new (LOS) optimal level guidelines, it is possible to accommodate more passengers, specifically 513 passengers. Compared to the IATA LOS guidelines, 85 more passengers can use the (standing) space of this facility. Based on the obtained results, an analysis of the level of service (LOS) at the airport terminal facilities for low-cost carriers (LCCs) was conducted, and a hierarchical AHP decision-making model was developed, with associated criteria and sub-criteria in determining the optimal level of service for low-cost carriers. A hierarchical AHP decision-making model represents the basis for the establishment of new spatial-temporal parameters for the optimal level of service (LOS) for low-cost carriers (LCCs).
# Bit Streaming Processing Algorithms for Intelligent Hardware Converters
## 3 Results
### Application of the Developed Modules
The solution of this problem by the traditional method requires the use of calculators such as counters to determine the code values of the duration of PWM signals, multiplier, divider, and subtractor. These elements are available in the Quartus II library of parameterized modules. However, when selecting a chip of the CPLD class as a device, the compilation of the project finishes with error messages. To compare the traditional and bitstream approaches, we compiled a HDL description with FPGA selecting. The library elements were configured in a combinational way, and registers were not used. The results of the bit-parallel and bit-stream methods comparison are shown in Table 2.  
A hardware cost analysis shows the cost-effectiveness of the bit-stream implementation. The frequency characteristics were analyzed using Time Quest Timing Analyzer. The maximum frequency f\({}_{\text{max}}\) for the bit-parallel method was 35.94 MHz. For the bit-stream method, the maximum frequency was 111.73 MHz, but the processing period was related to the bit rate, and for the 10-bit device version the frequency was defined as f\({}_{\text{max}}/2^{10}\), or 109 kHz. The conversion accuracy is determined by the bit rate of the device and can be corrected for additional bits if necessary.
## 4 Results
### Water Chemistry  
The physico-chemical parameters and major ion chemistry of groundwater are reported in Table 1. pH values ranged from circum-neutral to alkaline (in the range from 5.9 to 8.1); dissolved oxygen (DO) was in the 1.1-8.5 mg/L range, indicating that the spatial structure of the aquifer ranges from hypoxic conditions, with DO content strongly below saturation due to an oxygen consumption rate exceeding the supply of oxygenated water, to slightly undersaturated in atmospheric oxygen. Electrical conductivity (EC) data spanned a broad range of values (between 187 uS/cm and 7460 uS/cm); total dissolved solids, TDS, were between 112 and 4265 mg/kg. The major ion composition of groundwater plotted in the Piper diagram (Figure 5) indicates a Ca-HCO\({}_{3}\)-type composition for most groundwater samples; some samples belong to the Mg-HCO\({}_{3}\) (W-27, W-36), NaHCO\({}_{3}\) (W-48), Ca-Cl (W-41, W-168), Ca-SO\({}_{4}\) (W-30, W-46) and Na-Cl (W-5) hydrofacies. These data suggest the possible effects of cation exchange in the aquifer. However, for the W-5 sample, belonging to the coastal area, the possible role of seawater intrusion has been addressed by coupling chloride and boron content, assumed as conservative tracers. In particular, the B content in W-5 was 600 ug/L. Considering the average seawater composition, about 13% of the seawater component for both elements was obtained. Although further investigations are required, the data suggest that the Na-Cl hydrofacies of W-5 is attributable to marine intrusion. The concentrations of As, Ba, Fe, Mn, Tl and Zn are reported in Table 2. Among trace metals, Fe and Mn ranged from \(<\)15 to 14,290 and from \(<\)1 to 1770, respectively. High Fe and Mn concentrations are commonly found in groundwater in these settings and have been attributed to geogenic sources. Barium ranged between 0.5 and 1800 \(\upmu\)g/L; Zn ranged between 1.7 and 4800 \(\upmu\)g/L. Thallium was present in very low concentrations in all samples, indicating that the contamination deeply affecting the Baccatoio stream water did not reach the depths of the groundwater. Arsenic reached the exceedingly high value of 1274 \(\upmu\)g/L. It must be noted that these elements reflect the mineralization and past mining works' acid
## 3 Background
### Blockchain
#### 3.1.2 Blockchain and Network  
Mobile edge computing (MEC) integrates mobile access networks and Internet services by sinks computing to the edge of networks [51]. Therefore, privacy protection here is essential, which prompted the authors to use the blockchain with heterogeneous MEC. They proposed a (BlockTC) to solve routing trust and privacy problems. The BlockTC will provide a solution of trusted routing for cross-domain routing by using backup dual links and an accommodative bloom filter (ABF). The authors note that the combination of blockchain and distributed multi-domain networks can protect privacy for MEC systems.  
Industrial manufacturing is developing through the integration of the Internet of Things (IoT) and industry, which led the industry of the Internet of Things (IIoT) to provide a general interconnection system [52]. Thus, it is difficult to protect privacy due to the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Blockchain Type** & **Blockchain Network** & **Papers** \\ \hline \multirow{8}{*}{Public permissionless} & Ethereum & [30; 31; 34; 35; 36; 43; 44] \\ \cline{2-3}  & Witnet & [37] \\ \cline{2-3}  & Aeternity & [38] \\ \cline{2-3}  & Truthoein & [39] \\ \cline{2-3}  & Waltonchain & [40] \\ \cline{2-3}  & Dronechain & [41] \\ \cline{2-3}  & OriginStamp & [42] \\ \hline \multirow{8}{*}{Permissioned} & Hyperledger Fabric & [45] \\ \cline{2-3}  & SpeedyChain & [46] \\ \cline{1-1} \cline{2-3}  & Not Available & [47; 48; 49] \\ \cline{1-1} \cline{2-3}  & BlockID & [50] \\ \hline Both & ChainAnchor & [26] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blockchain permissions.  
large number and types of terminals. In addition, the digital identity and data of access devices are semi-transparent to devices in the network, and this is an issue that must be taken care of in the IoT. The Authors proposed novel, private, blockchain-enabled trusted anonymous access (BlockTrust); a blockchain-enabled, tripartite, anonymous-identification trusted-service provision scheme (TriTrustServ) has been proposed to guarantee a balanced tradeoff among credibility, confidentiality, and efficiency. Their results show that using blockchain in their proposal can address anonymous trusted access.  
Software-defined optical networking (SDON) control plane security is essential, especially in the network. The authors have proposed a distributed control architecture for SDON using the blockchain technique (BlockCrtl) to address the security [53]. Their results show that BlockCrtl can detect attacks such as DoS attacks and fault-tolerant control and provides high performance.
## 4 Results
### Comparative Analysis of Model Accuracies in WSD
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Key Strengths** & **Key Weaknesses** \\ \hline BiGRU (Optimized & 84\% & Faster convergence, & RNN-based, may struggle with \\ with ADAM) & 84\% & efficient optimization & long-range dependencies \\ \hline BiGRU (Optimized & 85\% & Focus on important parts of the & Increased complexity, longer \\ with Attention) & 85\% & input, high performance & training time \\ \hline Hybrid BiGRU + BERT & 79\% & Combines sequential and & May not fully leverage the \\  & & contextual learning & strengths of both models \\ \hline Hybrid BiGRU + RoBERTa & 70\% & Combines sequential learning & Model integration issues, \\  & & with robust contextual & lower performance \\  & & understanding & requires optimization \\ \hline Hybrid BiGRU + DeBERTa & 85\% & Superior contextual handling, & Model complexity, \\  & & high performance & requires optimization \\ \hline Hybrid BiGRU + ALBERT & 84\% & Efficient parameter usage, & \\  & & good performance & May not outperform larger models \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary table of enhanced model performances. BERT approach). The goal of combining BiGRU with RoBERTa is to improve BiGRU's sequential learning capability while also utilizing RoBERTa's enhanced comprehension. Although RoBERTa outperforms BERT, its 70% accuracy indicates that it does not enhance BiGRU as well as other transformer models, maybe as a result of problems with model integration or training methodology. With the use of improved position embeddings and disentangled attention, DeBERTa is an improved version of BERT that can handle more challenging language problems. BiGRU and DeBERTa are combined in the hybrid technique to collect contextual information and sequential dependencies. The performance of BiGRU optimized with attention mechanisms is mirrored by its accuracy of 85%, indicating that DeBERTa's sophisticated capabilities enhance BiGRU. An abbreviated, parameter-efficient variant of BERT is called ALBERT. Using effective embeddings and parameter sharing, BiGRU and ALBERT seek to lessen the computational load while preserving high performance. The hybrid BiGRU + ALBERT model performs well, leveraging both the sequential learning capability of BiGRU and the efficiency of ALBERT, as evidenced by its 84% accuracy. Any NLP issue is approached as a text-to-text problem by the flexible transformer model T5. The goal of combining BiGRU with T5 is to combine the sequential learning of BiGRU with the potent text-to-text generating capabilities of T5. However, the hybrid BiGRU + T5 model may not be in line with the task at hand, as shown by its 58% accuracy. Combining T5 with BiGRU may result in less-than-ideal performances for tasks that are better suited for classification because T5 is essentially a text generation model. Prediction uses classification results as additional context to predict the correct sense given the original sentence, as outlined in Table 6.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Sea Surface Drifter Data  
In situ measurements of sea surface currents were obtained from autonomous Lagrangian drifting buoys that are passively transported by the ocean surface currents [46; 47]. During the drifting buoy evolution, the data on the position are interpolated at uniform intervals (\(\sim\)30 minutes) relying on the kriging interpolation method developed by [47]. The velocities are finally computed via a finite-difference method of the interpolated positions and provided with six-hourly temporal resolution. The data covering the period of our study have originally been provided by the Italian Institute of Oceanography and Experimental Geophysics (OGS) for the purposes of the ESA-CIRCOL project. The timeseries are accessible via [[http://doi.org/10.6092/7a8499](http://doi.org/10.6092/7a8499) bc-c5 ee-472c-b8b5-03523d1e73e9]([http://doi.org/10.6092/7a8499](http://doi.org/10.6092/7a8499) bc-c5 ee-472c-b8b5-03523d1e73e9), last accessed 14 January 2022); buoy-derived surface current values are only retained if the buoy is equipped with a drogue: a device that guarantees the buoy evolution to be driven by the ocean currents rather than by surface winds [48].
# Production of Photovoltaic Electricity at Different Sites in Algeria
## 2 Materials and Methods
### Radiation Model Used: Mathematica Model in Horizontal Plan  
To simplify the modelling of our problem, we have assumed that the day studied is characterized by a clear day and an average sky.  
In this study, we used the PERRIN DE BRICHAMBAUT model [5], which calculates the global radiation as the plural of two radiations: direct and diffuse in the horizontal plane.  
\[\text{G}=S+\text{d} \tag{1}\]  
\[S\text{: direct radiation} \tag{2}\]  
\[S=A\sin(h)\exp\frac{-1}{C\text{ }\sin(h+2)} \tag{3}\]  
\[\text{d: diffuse radiation} \tag{4}\]  
\[\text{d}=\text{B}(\sin(h))^{0,4} \tag{5}\]  
\[h\text{: angular height (}^{\circ}\text{).} \tag{6}\]  
A, B and C are the constant that depend on sky nature:  
A = 1300 B = 87 C = 6 for very clear sky  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Geographic Coordinates** & **Algers** & **Batna** & **Ouagrla** & **Bechar** & **Sidi Bel Abbes** \\ \hline Latitude & 36.75 & 35.55 & 31.94 & 31.61 & 35.19 \\ Longitude & 3.04 & 6.17 & 5.32 & \(-\)2.21 & \(-\)2.62 \\ Altitude & 0 & 1048 & 150 & 747 & 483 \\ Albedo & 0.2 & 0.25 & 0.35 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geographic data of study sites [5].  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Temperature** & **Algers** & \multicolumn{3}{c}{**Batna**} & \multicolumn{3}{c}{**Ouagrla**} & \multicolumn{3}{c}{**Bechar**} & \multicolumn{3}{c}{**Sidi Bel Abbes**} \\ (\({}^{\circ}\)C) & 1/1/2022 & 1/7/2022 & 1/1/2022 & 1/7/2022 & 1/1/2022 & 1/1/2022 & 1/7/2022 & 1/1/2022 & 1/7/2022 \\ T max & 18.6 & 23.9 & 18.8 & 35.8 & 20 & 46 & 20.3 & 41 & 22 & 34.6 \\ T min & 14.3 & 20.8 & \(-\)0.5 & 16.4 & 4.5 & 29.6 & 2 & 30 & 0.9 & 16.7 \\  & \multicolumn{3}{c}{**Monthly temperatures: January and July 2022**} & & & & & & & \\  & January & July & January & July & January & July & January & July & January & July \\ T max & 18.5 & 27.2 & 15.7 & 35.7 & 21.2 & 42.6 & 19.5 & 41.8 & 18.7 & 36.9 \\ T min & 13.9 & 22.4 & 1.5 & 16.2 & 6.9 & 27.5 & 4.3 & 28.6 & 4.2 & 18.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature data of study sites [6].  
A = 1230 B = 125 C = 4 for medium sky A = 1200 B = 187 C = 2.5 for polluted sky
## 4 Results
### Water and Ice  
The reflected signal intensity of water was measured at 5 \({}^{\circ}\)C to 10 \({}^{\circ}\)C in 10 serial outputs, averaging 50 scans per individual output, using the Thermal Stage. Ice was monitored in 10 serial outputs, also averaging 50 scans per individual output using the Thermal Stage. The temperature of the ice was allowed to rise from \(-\)25 \({}^{\circ}\)C to \(-\)15 \({}^{\circ}\)C. The water and ice reflected signal intensity data are presented in Figure 4. Normalised water reflectivity (compared to air) was in the order 0.03 to 0.07 for wavenumber 23 to 38 (-0.70 to 1.15 THz) and -0.04 for wavenumber 56.5 to 75.0 (-1.70 to 2.25 THz).  
Figure 3: (**a**) Photo and (**b**) schematics of ATR setup with Thermal Stage used for temperature dependent absorbance spectroscopy at the THz/Far-IR beamline with Thermal Sample Stage attached.  
Ice reflectivity showed a tighter grouping at 1.7 to 2.25 THz and a reduction in reflectivity with increasing frequency. There was much greater consistency of data in the 1.70 to 2.25 THz range when compared to the 0.70 to 1.15 THz range. The results at the most reliable point of 0.9 THz in the lower data set and at 2.0 THz are presented in Table 4.  
The measured contrast between liquid water and ice reflected signal intensity at both ranges is broadly in line with the dielectric properties data from [14; 15; 16; 17; 18]. The reflected signal intensity of water and ice show a decline over the 0.70 to 1.15 THz range, but the experimental values are erratic. At 2.0 THz, the reflected signal intensity of water is flat over the measured range and does not change with temperature. The reflectance of ice declines with both frequency and temperature over the 1.70 to 2.25 THz range. The measured reflected signal intensity of ice at 2.0 THz is consistent with a refractive index of 1.83, which is at the upper boundary of the literature derived data.
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 4 Global Network View
### Route Topology  
We proposed the route topology inference (RTI) in [18]. The algorithm framework is shown in Figure 1. Since this method is not limited by the routing protocol adopted by the current network, and only uses the packet tracking hybrid active detection method to construct a transmission path from the source node to the sink, an online global routing topology view for a WSN can be provided and shown.  
The core idea of packet tracing is that, on the transmission path from the source node to sink, the forwarding nodes selectively mark part packets according to the marking rule upon receiving the packets. At the end of the sampling period, the sink can establish trace lists from the source node to the sink by parsing the marked packets. As the sink determines the one-to-one correspondence trace in the trace list according to the packet with the same hop to the source node, it retrieves the trace list from the source node. The trace list returned by mark parsing may be incomplete due to packet loss or insufficiently marked packets. Also, the vacant traces in the trace list can be supplemented by auxiliary inference or active detection [18].  
RTI increases the memory load, though it has a significant advantage in the correctness and convergence of reconstruction. Moreover, the relay node does not need to mark all packets, as it only marks the packets based on conditions, and for one path, each packet is marked only by one relay node.  
The reconstructed WSN routing topology is shown as a graph, where the nodes on each path and links between the nodes are added to the graph, and the source node identifying a path is added to the link also. If a source node has multiple paths to the sink, the remaining nodes or edges are added to the routing topology based on the tracking path after the first path is added, so the marked routing topology view from the source node to the sink is generated.