esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 4 Challenges and Potential of Power Industry Optical Wireless Communications
Therefore, in the next power wireless channel characteristic study, light must consider electric model, and the integrated lambert the diversity of the respective characteristics of the beam, carried out a series of specific power optical wireless channel measurements, and then through data fitting, statistical analysis methods, for instance, constructing can face a series of typical scenario high reliable power wireless optical channel model, which provide a basis for comparison in a variety of technical scheme evaluation. In the reliability of power optical wireless link, the existing technical scheme and prototype system still have great room for improvement. Especially in the part of optical wireless link, as the light source often needs to meet the requirements of lighting function, the semi-power of the beam is relatively large, which makes the optical signal emitted by the light source unable to focus on the local spatial position where the data receiver is located [134; 135; 136]. At the same time, a considerable part of thesignal power is projected to the space where there are no users, resulting in the actual waste of data capacity and reducing the overall energy efficiency of the system. In future research, beam forming technology can be tried to be introduced into the field of power wireless optical technology [72; 137]. With the user location information fed back by the uplink, the remote data source loads the user data stream onto multiple light sources adjacent to the user space location by selecting the power line path. At the same time, the algorithm is designed to scale the above light source to different degrees, and on the premise of maintaining the same energy consumption level, light signal energy is concentrated in the area where the user is, so as to effectively enhance the user's business experience and improve the overall energy efficiency of the system. Another outstanding disadvantages of OWPT is that it cannot be transmitted over a long distance. Because of its mobility and flexibility, UAV is used in the new technologies such as monitoring and emergency rescue and military applications. The endurance of UAV is always an urgent problem to be solved. The possibility of using OWPT technology to power UAV systems has recently been recognized as a very attractive approach that could lead to alternative and effective charging solutions [84; 137; 138; 139; 140; 141]. Since the OWPT technology does not require electricity, and then it makes future communications technology possible to a large extent. In addition, there are few researches on OWPT, so it is a feasible scheme and has a broad prospect to consider the combination of OWPT technology and power industry.
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 2 Proposed Multi-Lane SerDes Receiver Architecture
### Proposed Multi-Phase MDLL  
Figure 9 shows the block diagram of the multi-phase multiplying delay-locked loop (MDLL) [38; 39] used in this paper. The proposed multi-phase MDLL consists of a phase detector (PD), a charge pump (CP), a loop filter (LF), a voltage controlled delay line (VCDL), a divide-by-10 divider, and a select logic. The MDLL uses a four-stage differential delay line to generate eight-phase clocks (p0 to p7), each with a phase difference of 45 degrees. The proposed MDLL generates 6.25 GHz eight-phase clocks multiplied by ten times using a reference clock of 625 MHz. The lock time of the MDLL is about 350 ns. The proposed MDLL consumes about 24.2 mW of power at 6.25 GHz and achieves a peak-to-peak (p-p) jitter of 10.5 ps. The active area of the proposed MDLL is only 170 \(\upmu\)m \(\times\) 80 \(\upmu\)m. The MDLL core consumes 19.7 mW, and the multi-phase clock distribution across the four CDRs consumes about 4.5 mW. The shared MDLL is located in the center of the four CDRs to minimize the length of the clock distribution. The clock distribution of multi-phase high-frequency clocks is a critical task due to problems such as noise coupling, clock skew, and the power consumption of the clock tree. However, since the overall size of the proposed quad-lane receiver architecture is very small and the clock distribution length in one direction is shorter than 230 \(\upmu\)m, noise coupling and power consumption increase problems can be minimized.  
Figure 8: Frequency response of the proposed CTLE for the different V\({}_{\text{CTRL}}\) voltages.
###### Abstract
[PERSON] and [PERSON] [11] mentioned that DL has been called the technology that will change the world and affirm that Recurrent Neural Networks (RNNs) and CNNs are the most widely used DL models. The continuous development of DL has led many researchers to adopt this technology for building risk evaluation models [9]. Long Short-Term Memory (LSTM) is also applied to many diverse learning problems that differ significantly in their scale and nature from the initially tested problems [12]. An LSTM model can store previous data and predict future risk trends, making it widely applicable in risk forecasting. It is crucial to note that the RBFNN is part of the conventional Feed-Forward Network (FFN) variety [13], which is a universal approximation function. It is worth mentioning that RBF has greater precision in describing the relationships between risk factors and accident frequency. Moreover, the network structure, primarily denoting the number of nodes in hidden and input layers, is a crucial aspect of neural network model development, given its significant impact on generalization performance. The RBFNN proved to have a significant advantage in approximating, classifying, and speeding up processes [14]. This study aims to prove that RBFNN learning is faster than DL when only three levels (an input, hidden layer, and output layer) are applied. This allows for a dynamic dataset to be used under changing conditions and for faster validation to obtain new prediction models. Moreover, predictions made using the RBFNN are easily auditable, and the results can be comparable to those achieved with DL. A key objective of this work is to demonstrate that efficient, fast, and comparable results can be obtained using simple architectures, such as the RBFNN, by comparing and evaluating these approaches in traffic accident risk-level prediction and the implications of using a dynamic dataset. We use the term dynamic dataset [15] to refer to a process that incorporates new data of driving characteristics collected at specific time intervals from vehicle agents, processed using relevant mechanisms and algorithms, and finally added to the main dataset, making the process continuously changed. This driving dataset was developed due to this research, and its effectiveness was proven by implementing the models presented in this work. This work follows the structure outlined below: Section 2 analyzes related work, Section 3 presents the materials and methods used, Section 4 compares the model's results, Section 5 presents the discussion, and Section 6 shows the conclusions.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 5 Results and Discussion
### Predictive Performance
#### 5.3.2 Classifiers' Performance  
In this section, we discuss the experimental results of the tested algorithms: EE, IF, LOF, and OSVM. First, we used the default hyperparameters for the classifiers to analyse the impact on the predictive performance of changing the time-window. Table 3 presents the average accuracy, precision, recall, F1-score, specificity, and AUC in all datasets for each classifier varying the time-window size. The best result for each metric and classifier is highlighted. It is possible to see that the LOF algorithm had the best results, followed by OSVM, IF, and EE considering the F1-score. Although precision is around 70% in the LOF classifier, the other metrics present satisfactory results. Still bearing the F1-score in mind, the best time-window size for this algorithm was 30 s, followed by 1 s with a small difference between them. Then, we chose the 1-s one. Although it is not the best time-window, by choosing the 1-s one, botnets would take less time to be detected. After having defined the best time-window, we evaluated the amount of legitimate data needed to yield good predictive performance. First, we fixed the time-window to 1 s and used the default hyperparameters for each classifier. Then, samples ranging between 10% and 90% (10-point step) of the legitimate data were extracted to make the training dataset. In other words, at each iteration, for each profile-infection combination, we sampled a percentage of all legitimate data and induced the behaviour models on it. After that, the classifiers were tested using the remaining legitimate data and all botnet data. All performance metrics presented in Table 4 had very similar results, showing that the classifiers can use the smallest tested fraction of legitimate data to model legitimate behaviour and still reach high predictive performance. This also indicates that all legitimate data are very similar. Thus, the sample size of 0.1 (10%) was chosen. \begin{table}
\begin{tabular}{l c c c c} \hline
**Feature** & **CPU Utilisation (\%)** & **Memory Utilisation (GB)** & **Number of Tasks Running** & **CPU Temperature (\({}^{\circ}\)C)** \\ \hline Values & 0–100 & 0–1 & 0–300 & 0–100 \\ \hline \end{tabular}
\end{table} 
Table 2: Features and their value ranges.
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 2 Related Works
To the best of our knowledge, no other research work in the literature proposes and studies the performance of HWNs that employ MPTCP (as a transport layer protocol) in order to provide timely and reliable data transfer for smart grid distribution level applications.
## 2 Materials and Methods
\begin{tabular}{c c c c c c} \hline \hline
**Term** & ***1** & **25E/75P** & **50E/50P** & **75E/25P** & **25E/75W** & **50E/50W** & **75E/25W** \\ \hline \(\Delta T_{\mathrm{bp}}\) [\({}^{\circ}\)C] & & 21.71 & & 18.84 & \\ \(\Phi\) & 25\%/75\% & 50\%/50\% & 75\%/25\% & 25\%/75\% & 50\%/50\% & 75\%/25\% \\ \(\rho_{\mathrm{l}}\) [kg/m\({}^{3}\)] & 796.33 & 792.86 & 789.39 & 944.28 & 891.50 & 838.71 \\ \(c_{\mathrm{p1}}\) [KJ/(kg\({}^{\circ}\)C)] & 2.619 & 2.560 & 2.500 & 3.821 & 3.415 & 2.958 \\ \(h_{\mathrm{fg}}\) [KJ/kg] & 804.44 & 843.55 & 882.99 & 2120.44 & 1768.50 & 1372.26 \\ \(\epsilon^{\mathrm{*2}}\) [\({}^{\circ}\)C] & 90.9 & 85.58 & 81.53 & 86.6 & 82.3 & 79.9 \\ \(\sigma\) [mN] & 24.9 & 23.7 & 22.6 & 39.5 & 30.2 & 25.5 \\ \(D_{0}\) [mm] & 2.20 & 2.21 & 2.20 & 2.34 & 2.27 & 2.24 \\ \(V_{0}\) [m/s] & 0.7 & 0.7 & 0.7 & 0.7 & 0.7 & 0.7 \\ \(Wc\) & 34.4 & 36.2 & 37.6 & 27.4 & 32.8 & 36.1 \\ \(Bo\) & 0.379 & 0.400 & 0.413 & 0.321 & 0.373 & 0.404 \\ \hline \hline \end{tabular}  \({}^{a}\)  
\({}^{b}\)  
\({}^{c}\)  
\({}^{d}\)  
\({}^{e}\)  
\({}^{f}\)
exposure time of 3 \(\upmu\)s. A macro lens (Nikon AF 200 mm f/4D, Nikon, Tokyo, Japan) is attached to the camera via an extension ring, achieving an image resolution ranging from 11.4 to 11.8 \(\upmu\)m/pixel. Illumination for imaging is provided by a 640 nm laser (CAVILUX Smart, Cavitar Ltd., Tampere, Finland). Image processing is performed using Davis V8.0 software and a customized Python V3.8.5 program. The diameters of the binary droplets vary between 2.20 mm and 2.34 mm, yielding Weber numbers (\(\textit{We}=\rho_{1}\textit{V}_{0}\textsuperscript{2}D_{0}/\sigma\)) between 27.4 and 37.6 and Bond numbers (\(\textit{Bo}=\rho_{\text{B}}\textit{R}_{0}\textsuperscript{2}/\sigma\)) between 0.321 and 0.413. It is observed that the Weber number remains under the splashing threshold at normal surface temperatures [37]. The effects of gravity are negligible in this study due to small Bond numbers. The error in determining the droplet boundaries is no more than \(\pm\)1 pixel during image processing. Therefore, the measurement error uncertainty of droplet size and centroid position is \(\pm\)0.012 mm. The impact velocity of the droplet is calculated using the change in droplet centroid position within 5 frames divided by the interval time. The temporal uncertainty for velocity computation is twice the exposure time. Utilizing the error propagation equation, the maximum relative uncertainty for velocity, Weber number, and Bond number are ascertained to be 1.39%, 3.32%, and 2.18%, respectively. The errors for other variables are calculated based on the standard deviation from the statistical results of multiple experimental outcomes.
## 2 Materials and Methods
### Vehicles  
#### 2.2.1 Energy Storage Vehicles  
As stated before, energy storage locomotives and multiple units are not a new concept. First attempts are far from being considered \"prototypes\" due to the number of units, lifetime, and services that they developed during their careers. These precedents are interesting from an engineering point of view. In the 20 th century, some manufacturers started to apply energy storage in railways. A good example is German Railways and their battery multiple units (BEMU) called \"ETA\" (i.e., Elektrischer Triebwagen mit Akkumulatoren, in German [10]) being developed by Prussian and German railways. The series are 177, 178, 179, or 180 (manufactured from 1907 to 1937) and, later, series ETA 150.0 and 150.5 (around 100 units, further renamed as series 515, built from 1953 to 1957). The target for their services reached 100-150 km with the same battery for older units, and it was extended to 300-350 km with ETA 150.5 (storage capacity of batteries around 520 kWh). Some of those units remained operative until the early 90s. Another interesting example of freight units is Fieless Steam locomotives. Their duties usually were switching on industrial (chemical) plants. In this case, these locomotives avoid the danger of fire and explosion of conventional steam (notice that early designs started 100 years ago). These locomotives stored steam in a large boiler, and this steam came from steam power plants needed in this industry sector. The last class of those units was built by Henschel in the early 50s. Some units remained in service until 1995 and can be considered, to some extent, as a more successful ancestor of compressed air storage systems (CAES) in PPS. Figure 2 shows these interesting and representative units.  
Figure 1: Characteristics of the track: (**a**) Elevation profile and maximum speed on the Cartagena–Murcia track; (**b**) Electrified and track gauge and zones (single-double-triplex). Acronyms for main train stops: MC (Murcia del Carmen); ER (El Regüeron); RS (Riquelme–Sucina); BA (Balsicas); TP (Torre–Pacheco); and CT (Cartagena, Passenger PS, and freight FS station). The main line (MC–ER) has two branches (CT and Alicante; see figure arrows and links in red).  
Figure 2: Some examples of first energy storage railway units (recreation). Fieless locomotive Henschel D-fl/25/3.5 (1); ETA 177 (2), ETA 515 (3).
## 4 University Staff Allocation Planning--Case of Course Cast
### Method Verification Based on Historical Data
(or teachers) so that they could replace an absent colleague? The method presented in this work is dedicated to resolving such questions. In order to illustrate its applicability, let us consider datasets collected in Tables 6-8, for which competency framework \(G_{OPT}\) robustness (\(R_{\mathcal{P}}^{\mathcal{Z}}(\omega)=1\)) to teacher \(P_{18}\): [PERSON]'s absence is sought. In other words, the question is:  
_Does there exist a minimal competency framework \(G_{OPT}\) of FECS that guarantees robustness value \(R_{\mathcal{P}}^{\mathcal{Z}}(\omega)=1\) in the event of absence of teacher \(P_{18}\): [PERSON]?_  
The method has been implemented in the LINGO environment (Intel i7-4770, 8 GB RAM). The optimal solution, \(G_{OPT}\) (source: GitHub), was obtained in under 1 s. In competency framework \(G_{OPT}\), there is an acceptable teacher allocation, \(X\), allowing other teachers \(P_{29}\), \(P_{41}\), \(P_{44}\) and \(P_{7}\) to conduct courses \(Z_{47}\), \(Z_{48}\), \(Z_{74}\), \(Z_{106}\), \(Z_{121}\), \(Z_{211}\) and \(Z_{125}\), which were initially assigned to the absent teacher \(P_{18}\):  
* Course \(Z_{47}\) (50 h, 10 tasks)
- Course \(Z_{48}\) (75 h, 15 tasks)
- Course \(Z_{74}\) (80 h, 16 tasks)
* Course \(Z_{106}\) (15 h, 3 tasks)
- Course \(Z_{211}\) (20 h, 4 tasks)
* Course \(Z_{121}\) (30 h, 6 tasks)
* Course \(Z_{125}\) (75 h, 15 tasks)  
In the new competency framework, \(G_{OPT}\), teacher \(P_{7}\)is assumed to take over a single competency in conducting classes from course \(Z_{125}\). The \(G_{OPT}\) competency framework ensures the continuity of classes in the absence of teacher \(P_{18}\): [PERSON] without having to employ an additional replacement tutor. The illustrated option of supplementing competencies in the scope of conducting classes from course \(Z_{125}\) by teacher \(P_{7}\) is one of several solutions obtained, others being, for example:  
* Teacher \(P_{22}\) : [PERSON] complements the competency from course \(Z_{125}\),
* Teacher \(P_{43}\): [PERSON] complements the competency from course \(Z_{125}\), etc. The solutions presented above include cases requiring filling in for competencies in only one course. In practice, it emerges that a sufficiently early supplementation of competencies by one teacher would protect FECS against the effects of resignation from the work of teacher \(P_{18}\): [PERSON]. In general, employee absences are usually unexpected (unplanned), which curbs predicting which teachers and in what number will be absent. Therefore, to provide the framework with suitable absorption qualities, absences of several teachers must be simulated. Examples of the synthesis of the FECS competency framework guaranteeing robustness to this type of interference are presented in the next section.
###### Abstract  
In this study, CdS/porous g-C\({}_{3}\)N\({}_{4}\) heterostructures were successfully synthesized via in situ co-precipitation to efficiently degrade tetracycline hydrochloride (TCH) under visible light. The heterostructures, particularly at a 2:1 mass ratio of CdS to porous g-C\({}_{3}\)N\({}_{4}\), demonstrated significant improvements in both adsorption and photocatalytic performance. The adsorption and degradation rates increased 4-fold and 9.64-fold, respectively, compared to pure porous g-C\({}_{3}\)N\({}_{4}\), with optimal removal rates achieved at a catalyst dosage of 0.2 g/L. Detailed mechanistic studies revealed that photogenerated holes (h\({}^{+}\)) and superoxide radicals (\(\cdot\)O\({}_{2}^{-}\)) were the primary active species driving the degradation process, while hydroxyl radicals (\(\cdot\)OH) played a minimal role. The composite material also maintained over 70% degradation efficiency after five cycles, indicating excellent stability. This research presents a promising route for the photocatalytic treatment of wastewater containing persistent organic pollutants, offering practical insights into dosage optimization, reaction kinetics, and mechanistic pathways that enhance performance.  
CdS; porous g-C\({}_{3}\)N\({}_{4}\); photocatalytic; tetracycline hydrochloride; wastewater treatment; photogenerated charge separation +
Footnote †: journal: Applied sciences
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
###### Abstract  
The exploitation of renewable energy sources in the building sector is a challenging aspect of achieving sustainability. The incorporation of a proper storage unit is a vital issue for managing properly renewable electricity production and so to avoid the use of grid electricity. The present investigation examines a zero-energy residential building that uses photovoltaics for covering all its energy needs (heating, cooling, domestic hot water, and appliances-lighting needs). The building uses a reversible heat pump and an electrical heater, so there is not any need for fuel. The novel aspect of the present analysis lies in the utilization of hydrogen as the storage technology in a power-to-hydrogen-to-power design. The residual electricity production from the photovoltaics feeds an electrolyzer for hydrogen production which is stored in the proper tank under high pressure. When there is a need for electricity, and the photovoltaics are not enough, the hydrogen is used in a fuel cell for producing the needed electricity. The present work examines a building of 400 m\({}^{2}\) floor area in Athens with total yearly electrical demand of 23,656 kWh. It was found that the use of 203 m\({}^{2}\) of photovoltaics with a hydrogen storage capacity of 34 m\({}^{3}\) can make the building autonomous for the year period.  
hydrogen storage; building electrical needs; Power-to-X-to-Power; dynamic analysis; zero-energy building +
Footnote †: journal:  
0000-0002-119-223][PERSON]  
122
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Traffic Optimization  
By maximizing the effectiveness and use of network resources, traffic optimization assures seamless traffic data flow. Packet forwarding, load optimization, and QoS delivery are the three basic categories under which traffic engineering optimization falls.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 4 Datasets  
This study determines RSL and ASL trends by using both TG (13 stations) and SA (10 missions) time series over the Baltic Sea for the period of 1995-2022. The beginning of the time series, 1995, considers the availability of reliable SA datasets.