esa-sceva/satcom-chunk-collection huggingface dataset:
Besides, the coverage problem is used to describe the non-destructive requirements of the information about the monitoring region. Therefore, an appropriate sleep scheduling method should not be implemented at the cost of affecting the performance of the system. Generally, the sensor nodes with high power may have more possibility to remain in an active state for monitoring and packet forwarding, and some nodes with low power will be put into the sleep state, under the condition of not affecting the network connectivity and providing sufficient coverage of the target area. On the one hand, by reducing the density of active nodes in the network, it can reduce the redundancy of the sensing data and the wireless communication conflicts and interference [17]. Hence, the energy cost of the whole system will decrease. On the other hand, when some nodes fail due to energy depletion, the adjacent sleep nodes can work instead of the failed nodes to maintain the normal operation of the network [18]. Therefore, the node sleep scheduling method must take into account of the following two basic factors: (1) ensure the approximately full coverage of the target area; (2) ensure that all the working nodes of the communication network are still connected. Furthermore, the goal of topology control is to form an optimized network topology which can extend the life cycle of the network on the condition of ensuring the network coverage and connectivity. In addition, it can improve the performance in aspects of time delay, communication interference, load balance, reliability, scalability, etc. The quality of a network topology is difficult to measure directly by the final goal of topology control. Therefore, it is the first choice for the network design and planning to achieve a good topology in the design of topology control mechanism. In general, the relationship between the various factors of the topology design objectives is complex, so this should be considered and coordinated comprehensively in practice according to the specific application of the network. In particular, due to the fact ESNs are usually applied in harsh environments or a state of emergency, such as military battlefields and disaster relief, there exist some problems about topology control in aspects of dynamic, adaptive, reliability and survivability as follows:  
* The model is too idealistic, and there are many uncertain factors in practical application, which cannot meet the dynamic network topology requirements;
* Lack of effective measurement of the dynamic and self-adaptation network topology;
* Lack of an effective fault tolerance mechanism or method for high reliability and strong survivability in terms of topology control. The rest of this paper is organized as follows: a brief overview of related works is presented in Section 2. Section 3 discusses the system optimization model, and the coverage-preserving control scheduling scheme is proposed in Section 4. The simulation results and performance analysis is presented in Section 5. Finally, Section 6 concludes the paper and discusses some future research directions.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Choice of Working Point and Modulation Amplitude  
With two available working points and a wide range of possible modulation amplitudes, selecting the operating conditions is a multi-dimensional problem. First, we consider both working points separately and find the optimal modulation amplitude for each.  
Figure 9 shows the power of H1 with the sensor operating at WP1. The modulation amplitude ranges from 0 to 2 \(E_{\pi B}\), while the external field varies in the range from 0 to 0.6 \(E_{\pi S}\). H1 exhibits the highest power for \(E_{pp}\) = 1.2\(E_{\pi B}\) and the external field magnitude of 0.5\(E_{\pi S}\), with the latter being equivalent to moving from WP1 to WP2. Nevertheless, it is evident, that for any strength of the external field, the sensor reaches its peak sensitivity with \(E_{pp}\) = 1.2\(E_{\pi B}\).  
Figure 10 shows the variation of the H1 power as a function of the external field for three values of amplitude modulation annotated with white lines in Figure 9. The slopes of the three curves are the same but modulation amplitudes lower than 1.2\(E_{\pi B}\) result in a lower power of H1. Figures 11 and 12 show equivalent results obtained for H2 with the sensor operating at WP2.  
Figure 8: H1 power as a function of the incident electric field.  
Figure 9: H1 power as a function of the external electric field and modulation amplitude. The solid, dashed and dotted lines indicates H1 power at constant amplitude modulation \(E_{pp}\ =\ 2,\ 1.5,\ 0.6\ E_{\pi B}\).  
As we present in Figure 13, which compares the results obtained using the optimal modulation amplitude for each working point, WP1 yields higher sensitivity for lower values of the amplitude modulation and is therefore the preferred configuration for operating the sensor.  
Figure 11: H2 power as a function of the external electric field and modulation amplitude. The solid, dashed and dotted lines indicates H2 power at constant amplitude modulation \(E_{pp}\ =\ 2,\ 1.5,\ 0.6\ E_{\pi B}\).  
Figure 10: H1 power as a function of the incident electric field for fixed values of the modulation amplitude.
# Online Learning-Based ANN Controller for a Grid-Interactive Solar PV System
###### Abstract  
The technology transformation of industry 4.0 comprises computers, power converters such as variable speed devices, and microprocessors, which distract from the quality of power. The integration of distribution-generation technologies, such as solar photovoltaic (PV) and wind systems with source grids, frequently uses power converters, which increases the issues with power quality. DSTATCOM is the FACTS device most proficient in recompensing current-related power quality concerns. A model of DSTATCOM with an ANN controller was developed and implemented using a backpropagation online learning-based algorithm for balanced non-linear loads. This algorithm minimized the mathematical burden and the complications of control. It demonstrated a dynamic role in improving the quality of the power at the grid. The algorithm was implemented in MATLAB using an ANN model controller and the results were validated with an experimental set-up using an FPGA controller.  
power quality; distributed generation; solar PV; DSTATCOM; ANN controller +
Footnote †: journal: Applied Sci.  
**Citation**: [PERSON]; [PERSON].  
The DG structure consists of a non-conventional energy resource, a DC/DC chopper, and an inverter. The voltage source converters (VSCs) use controllable switches that are controlled by various pulse generators [4; 5]. Maximum power is extracted from the sun or the wind, and made constant using a DC-DC converter [6]. The power generated from renewables is given to the grid using grid-tied inverters. In a grid-tied system, however, the inverter has numerous supplementary roles to perform. Under normal scenarios, the inverter sustains the battery in a mode of full charge for use throughout power interruptions. When the grid is disconnected, the grid-tied inverter impeccably steps in to convert DC power from both the solar and battery sources into operational AC power to sensitive loads, so that they run without any interruption [7].  
The power electronic-based converters and non-linear loads pollute the system with multiple power quality challenges, such as harmonics [8]. The grid-tied inverter works as a DSTATCOM and can mitigate the harmonics at the source side, keeping it purely sinusoidal [9]. Many conventional devices such as SVC, STATCOM, and UPQC [10; 11; 12; 13; 14; 15; 16], as well as advanced control techniques such as ANN and Fuzzy, are available in the literature, but due to their limitations, they are not flexible or simple to use [17; 18; 19; 20; 21]. Control of the grid-interactive VSC using an adaptive ANN model with online training is discussed in this paper. The VSC, connected via shunt to the system, was acting as a DSTATCOM. The performance of this grid-interactive solar-distributed generation system was analyzed using a three-phase balanced non-linear load. The simulation results were validated using an experimental set-up, and the THD was determined for the above-mentioned system.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 6 Wearables and Wireless Body Area Networks (WBAN)
### Energy Harvesting (EH)  
Energy harvesting as a battery alternative has been discussed in Section 5.2. However, EH in the context of wearables encounters new challenges, such as critical reliability level, expected tiny size and position in/on the human body, limited exposure to energy sources such as solar energy, and so on. Accordingly, we revisit EH and review some EH studies in the context of wearables. The three most prominent techniques include photovoltaic cells attached to a wearable (energy is accumulated from environmental illumination), thermoelectric nanogenators (the energy source is the heat produced by the human body), and kinetic energy harvesters (energy is created by natural body motion). All energy harvesting cases pose a fundamental challenge of effective energy management, which involves continuous decision making regarding how much energy to spend on sensing, measurements, on-board classification, and transmission (see also Section 5.2).  
We present examples for each of the techniques mentioned above in the context of wearables. Thermoelectric energy-harvesting units exploiting self-generated human heat are suggested to be fabricated straight into the textile of the garments [193]. Energy harvesting for activity-aware wearables is designed by [PERSON] et al. [194]. The idea is to remove the accelerometers, which consume about 80% of the battery. Instead, the authors propose to employ kinetic energy harvesters, which will convert human motion into electrical power. Additionally, to compensate for the accelerometers' removal, the input and output of the kinetic energy harvester would be analyzed by a specialized activity classifier to track and identify the human activity (i.e., to perform the primary task of the wearable). Light-based energy-harvesting wearables are discussed by [PERSON] et al. [195]. It is noteworthy that as the available luminosity can be highly unpredictable, the overall functioning, data-gathering, and transmission process would imply an optimization problem. Henceforth, the same authors suggest a protocol to optimize the number and accuracy of interpretation of human gestures by an energy-harvesting wearable device under an energy budget, [PERSON] et al. [196]. To this end, they constructed an analytical model that characterizes energy consumption based on experimental data and formulized the optimization problem. [PERSON] et al. [197] suggest incorporating energy harvesting as part of MAC 802.15.6. The proposed MAC modification includes the usage of some body sensors as relays. The managing part at a hub (e.g., a smartphone) sets the optimal relay charging times to perform the data transmission by relay effectively. A source and the relays send their energy-harvesting updates within the request for cooperation (RFC) packets. The charging times are calculated according to the amount of energy at the source and relays and are updated in accordance with previous packet transmission success or failure.
###### Abstract  
Measurement-device-independent quantum key distribution (MDI-QKD) is innately immune to all detection-side attacks. Due to the limitations of technology, most MDI-QKD protocols use weak coherent photon sources (WCPs), which may suffer from a photon-number splitting (PNS) attack from eavesdroppers. Therefore, the existing MDI-QKD protocols also need the decoy-state method, which can resist PNS attacks very well. However, the existing decoy-state methods do not attend to the existence of PNS attacks, and the secure keys are only generated by single-photon components. In fact, multiphoton pulses can also form secure keys if we can confirm that there is no PNS attack. For simplicity, we only analyze the weaker version of a PNS attack in which a legitimate user's pulse count rate changes significantly after the attack. In this paper, under the null hypothesis of no PNS attack, we first determine whether there is an attack or not by retrieving the missing information of the existing decoy-state MDI-QKD protocols via statistical hypothesis testing, extract a normal distribution statistic, and provide a detection method and the corresponding Type I error probability. If the result is judged to be an attack, we use the existing decoy-state method to estimate the secure key rate. Otherwise, all pulses with the same basis leading to successful Bell state measurement (BSM) events including both single-photon pulses and multiphoton pulses can be used to generate secure keys, and we give the formula of the secure key rate in this case. Finally, based on actual experimental data from other literature, the associated experimental results (e.g., the significance level is 5%) show the correctness of our method.  
decoy state; measurement-device independent; quantum key distribution; photon number splitting attack; statistical hypothesis testing Article
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### Mobility and Transmission Power Maintenance  
Sink mobility brings new challenges to routing and data dissemination in large sensor networks. Once any sink has changed its location, all sinks in the border-based sink groups would need to re-build their transmission power. It is important to note that only the sinks in the same group (previously and currently) as the mobile sink would need to take part in transmission power adjustment. It can be seen that our scheme is also suited for mobile networks.  
Figure 10: Illustration of radio radius calculation.  
Transmission power maintenance is the process of real-time adjustment of the transmission power for some special conditions, such a a routing failure; at this time alternate routing or a router discovery mechanism need to start for setting up new transmission power allocation for all or some sink nodes. The MSCWSNs-PC protocol since the process of establishment of the radio radius is based on the local connectivity state of adjacent sink nodes, transmission power maintenance can be attributed to the issue of how frequently power control assistant messages (PCAMs) are periodically exchanged.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Rules of Collision Avoidance  
Vessels shall comply with specific rules when carrying out collision avoidance. That is, the COLREGs. The regulations are divided into six categories from Part A to F and include 41 regulations. This chapter describes encounter situations and collision avoidance customs classified by the COLREGs:  
* Head-on: This is the situation that shall be deemed to exist when a vessel sees the other ahead or nearly ahead. In this case, the vessels shall alter their course to starboard so that each shall pass on the port to port.
* Crossing: This is the situation that shall be deemed to exist when a vessel crosses the others. In this case, the vessel with the other on her own starboard side shall keep out of the way.
* Overtaking: This is the situation that shall be deemed to exist when a vessel shall be overtaking when coming up toward the other from a direction more than 22.5\({}^{\circ}\) abaft her beam. In this case, any vessel overtaking any other should keep out of the way of the vessel being overtaken.  
Some reports numerically determine the three types of encounters. Table 1 is a table that summarizes the variables used to determine encounter situations in previous studies and their values. The definitions of terms used in Table 1 are as follows:  
* The term \"Relative Bearing\" refers to the measurement of the azimuth between the heading of both ships clockwise from the heading of the OS to the heading of the TS [22].
* The term \"Heading\" refers to the direction in which a vessel is pointed at any given moment, expressed as the angular distance from 000 degrees clockwise through 360 degrees [22].
* The term \"Encounter angle\" refers to the angle measured by transposing the heading of the TS to the starting point of the heading vector of the OS [23].  
Figure 1: Flow charts for the development of determining encounter situation awareness classification models.  
The characteristic of the [PERSON] and [PERSON] (16) criterion is that the angle classified as the head-on is the largest, and a safe zone is set according to the angle of the meeting. Unlike other models, the [PERSON] et al. (17) model and [PERSON] (18) model are characterized by numerically distinguishing between overtaking and overtaken. In addition, the two models added the quarter-lee situation in the crossing situation. [PERSON] et al. (20) have the smallest angle classified as a head-on. By introducing the concept of safe distance, logic was added not to classify it as an encounter situation if there is no risk of collision. In this study aspect, the angle at which the relative bearing and OS look at the TS was used to determine encounter situations. The aspect is the relative bearing in which the TS looks at the OS. The aspect is positive if the OS is on the starboard side of the TS and vice versa.
## 3 Results and Discussions
### Mass Balance
vulgaris_ only presented a biodegradation contribution on the last day. As previously explained, _C. vulgaris_ potentially releases AEOM at the end of its exponential growth phase, which promotes E degradation. Comparing our results is challenging because E is one of the least-studied oestrogens in this context, with limited previous research studies available. Additionally, among the existing studies, E is often examined as a degradation product of 17\(\beta\)-estradiol. For example, [PERSON] and [PERSON] (2019) found that adsorption accounted for 10% of total E removal using _S. obliquaus_ and _C. vulgaris_, identifying biodegradation as the primary removal mechanism for both species. The biodegradation of E by _S. obliquaus_ was 77%, whereas _C. vulgaris_ degraded only 38% within 5 days [84]. As explained earlier, these results are only partially aligned with ours, primarily due to differences in cultivation. Figure 6: Contribution (%) of biosorption (red), bioaccumulation (blue), and biodegradation (green _Scenedesmus_ sp. and orange _C. vulgaris_) in BPA removal by _Scenedesmus_ sp. (**a**) and _C. vulgaris_ (**b**). Error bars represent \(\pm\) standard error of the mean (\(n=3\)). Contribution of abiotic degradation is not plotted because it is negligible.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Antarctica Project
#### 3.5.1 Scope and Objectives  
The Spanish Antarctic Station Juan Carlos I, on Livingston island, is only inhabited during the Austral summer, but remote sensors are measuring throughout the entire year. Much of this information must be processed in almost real time, so it cannot wait until the following Antarctic campaign. This information is transmitted to the OE in Spain through a satellite link. Currently, only part of the recorded geomagnetic data from just one of two variometers in operation can be sent to the OE by satellite link and made available using the facilities provided by the INTErnational Real-time MAGnetic observatory NETwork (INTERMAGNET).  
The main objective of the Antarctica project was to deploy a trans-equatorial long-haul HF link, considered as an alternative to satellite or a complementary backup system in case of satellite link failure, to send data from several sensors in Antarctica to Spain. To fulfill this purpose, other major objectives have been derived: (a) obtain an historic series of data of the ionospheric channel for almost a solar cycle to characterise and understand this ionospheric channel and (b) define and evaluate HF data modem waveforms: this information will be used to specify both a robust and a high throughput modulation scheme depending on the status of the ionospheric channel. For more information about the motivation and the development of the project the reader is referred to Reference [57].
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
###### Abstract  
Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and machine learning. Firstly, CEEMDAN is used to decompose the original traffic flow time series to obtain multiple component sequences with huge complexity differences. In order to measure the complexity of each component sequence, the permutation entropy of each component sequence is calculated. According to the permutation entropy, the component sequence is divided into three types: high-frequency components, intermediate-frequency components, and low-frequency components. Secondly, according to the different volatility of the three types of components, the high-frequency components, intermediate-frequency components, and low-frequency components are predicted by long short-term memory (LSTM), support vector machine (SVM), and k-nearest neighbor (KNN), respectively. Finally, the accurate traffic flow prediction value can be obtained by the linear superposition of the prediction results of the three component prediction models. Through a measured traffic flow data, the combined model proposed in this paper is compared to the binary gray wolf algorithm-long short-term memory (BGWO-LSTM) model, the improved gray wolf algorithm-support vector machine (IGWO-SVM) model, and the KNN model. The mean square error (MSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error (MAE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error (RMSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indexes of the combined model are far smaller than those of the other single models. At the same time, the decision coefficient (R\({}^{2}\)) of the combined model is also closer to 1 compared to the other models, indicating that the prediction result of the combined model is the closest to the actual traffic flow.  
short-term traffic flow; CEEMDAN; long short-term memory; support vector machine; KNN +
Footnote †: journal: Medical  
of realizing the real-time prediction of short-term traffic flow is much greater than that of medium-term and long-term prediction of traffic flow. However, in order to realize the real-time guidance of traffic in intelligent transportation systems, it is necessary to publish traffic information in good time, which requires the study of traffic characteristics on short-term scales [3].