esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 5 Discussion  
Though this research only considers solar PV renewable energy, it is expected that other renewable energy sources, such as wind power generation, can be added and analyzed in the future. The same model can be used, but this time the inclusion of the output of wind power in renewable energy would be required. The use of net load, instead of actual load power, will help better understand the behavior and effects of PSS in the system.  
For the case study of the Kinmen Island power grid, during low load conditions, the use of the PSS may not be seen as economical. This is mainly because only a few generators were online, and those online generators were of a low fuel cost type. Since almost the same generators were online, the range of the power generation cost was small, and the effect of the charging and discharging of the PSS was barely seen. However, the available full-power range from the PSS (\(\pm\)1.8 MW or 3.6 MW) was used, and the large decrease in cost (however small in range) usually corresponded to the charging of the PSS.  
On the other hand, for case 2, the peak-shaving, as well as the cost saving brought by the use of the PSS, was evident. In the simulations, the PSS helped the system to stabilize, in terms of the reserve, by acting as a load or a source when there was generator turn-on or turn-off. Though the high peak load happened during high PV output, the shaving of the low load condition was seen, where the PSS acted as a load to flatten the net load. Again, the full charging-discharging range of the PSS (\(\pm\)1.8 MW or 3.6 MW) was used in this condition. Furthermore, cost reductions could be seen with the use of PSS.  
In power systems, the PSS has several functions, such as frequency regulator and auxiliary support for intermittent renewable energy. For this case study, not only did the PSS act as a peak-shaving device, but we also saw how the PSS provided support to maintain the spinning reserve limits, which usually happened during the turning on/off of generators. This furthers the maximization of the use of PSS as it may help prevent earlier or later startup or shutdown of generators to maintain the spinning reserve.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Transport Layer  
In the transport layer, the control of congestion and the possibility of its prevention contribute jointly to load-sharing mechanisms to extend the network's lifetime [21]. Transmission power contributes directly to battery lifetime and network throughput. The greater the range of the nodes, the greater the power required for transmission is. Energy control consists the nodes to ensure minimal energy consumption while maintaining network connectivity. Moreover, data transmission is considered one of the most consuming tasks in WSNs [22]. Sensor nodes are used to send scalar values at regular intervals. This poses a problem of energy conservation, especially if the application is to operate for a long time. Therefore, reducing and optimizing the transmission energy would not make any significant benefit to the data processing. On the other hand, the detection of a change in  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Technique** & **Performance** & **Overhead** & **Architecture** & **Design** & **Validation** & **Power Type** \\ \hline Power Mode & \multirow{2}{*}{DFM} & \multirow{2}{*}{++} & \multirow{2}{*}{+-} & \multirow{2}{*}{++} & \multirow{2}{*}{++} & \multirow{2}{*}{++} & Static \& Leakage \\ Based Strategies & & & & & & & & \\ \hline \hline \multirow{6}{*}{Multi Voltage Design Strategies} & \multirow{2}{*}{MVS1} & \multirow{2}{*}{++} & \multirow{2}{*}{+-} & \multirow{2}{*}{++} & \multirow{2}{*}{++} & \multirow{2}{*}{++} & Leakage \& Standby \\  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Impact of energy saving technologies in WSN.  
Figure 1: Parameters Involved within a Low-Power Sensor Node.  
a vital parameter and the real-time processing of the received information in this case is more important.
## 2 Propagation Modeling and Key Rate Estimation
### Key Rate Estimation  
In this paper, we consider the decoy-state QKD in the asymptotic case and finite data size, the secure key rate of decoy-state BB84 QKD in the asymptotic case is given by  
\[R_{\text{GLLP}}=q\left\{-f_{e}\big{(}E_{\mu}\big{)}Q_{\mu}h_{2}\big{(}E_{\mu} \big{)}+Q_{1}[1-h_{2}(e_{1})]\right\} \tag{9}\]  
where \(q\) depends on the implementation (1/2 for the BB84 protocol due to the fact that half of the time [PERSON] and [PERSON] disagree with the bases; if one uses the efficient BB84 protocol \(q\approx 1\) and \(f_{e}\) is the error correction inefficiency function, \(\mu\) is the intensity of the signal state and \(h_{2}\) is the binary entropy function. \(Q_{\mu}\) and \(E_{\mu}\) are the measured gain and the quantum bit error rate (QBER), while \(Q_{1}\) and \(e_{1}\) are, respectively, the gain and error rate of single photon states estimated using the decoy-state method.  
Here, we follow the standard channel model using a known channel transmittance \(\eta\) for key rate estimation. Specifically, when the photon number \(i\) of each pulse follows a Poisson distribution with a WCP source at intensity \(\mu\) set by Alice, the transmittance, yield \(Y_{i}\), gain \(Q_{i}\) and QBER \(e_{i}\) are  
\[\begin{array}{l}\eta_{i}=1-(1-\eta)^{i},\\ Y_{i}\approx Y_{0}+\eta_{i}=Y_{0}+1-(1-\eta)^{i},\\ Q_{i}=Y_{i}\frac{\mu^{i}}{\pi}\mathrm{e}^{-\mu},\\ e_{i}=\frac{\phi Y_{0}+\epsilon_{d}\mu}{Y_{i}},\end{array} \tag{10}\]  
where \(Y_{0}\) and \(e_{0}\) are the dark count rate and the error rate of background, respectively, and \(e_{d}\) is the misalignment error. Then, the overall gain \(Q_{\mu}\) and QBER \(E_{\mu}\) for the intensity \(\mu\) are  
\[\begin{array}{l}Q_{\mu}=\sum\limits_{i=0}^{\infty}Y_{i}\frac{\mu^{i}}{\pi}e ^{-\mu},\\ E_{\mu}=\sum\limits_{i=0}^{\infty}e_{i}Y_{i}\frac{\mu^{i}}{\pi}e^{-\mu}\end{array} \tag{11}\]The misalignment error comes from the long-distance QKD experiment reported in Reference [10], and the channel transmittance is assumed as a given value. However, the modified misalignment error \(e_{d}=\sum\limits_{k=1}^{3}sin^{2}\Delta\theta_{k}\) (\(k=1\), \(2\), \(3\)) can be acquired when the misalignment error occurs in three channels for the measurement-device-independent (MDI)-QKD protocol in References [32, 33]. Thus, we can calculate \(e_{d}\) through \(e_{d}=sin^{2}\Delta\theta\) in only one channel for our decoy-state BB84 QKD protocol once the rotation of polarization angle \(\Delta\theta\) is obtained. Moreover, it can be seen from Equation (7) that, for the linearly polarized quantum light after transmission in a turbulent atmosphere channel, the polarized light component in the total light intensity will be reduced (i.e., \(P(\rho_{0};z)<1\)). Thus, the practical transmittance can be expressed as \(P(\rho_{0};z)\eta\).
## 3 State of Art: An Overview on Safety for AVs (Risks, Drawbacks, and Benefits)
Applying these concepts to road design leads to a change in the typical paradigms of the road infrastructure design, considering, for example, all the work necessary for the implementation of those roadside electronic devices that communicate with AVs (i.e., cameras, pavement sensors, and electronic booths). All these elements must be taken into account in urban and land use planning, as they can affect parameters such as urban density, land use, impact on the environment, used materials, and emissions, to name a few. Table 1 lists some of the possible governance strategies adopted to manage AV-related risks on the transport network. Figure 3: Hercules logistic AV used during COVID-19 pandemic to deliver goods [12]. Electronic and Information-Technology (IT) applied to AVs also constitute a potential risk in terms of safety. Automatic vehicles adopt electronic platforms and computer processors (Hardware, or H/W), along with dedicated software programs and specific code lines (Software, or S/W). Like any other technological device, these systems are subject to malfunction or failure risks, with possible consequences to the entire surrounding environment. In case of the automated systems' failure, an up-to-date backup plan managed by human beings should always be available and ready to be put into action, involving trained staff (traffic managers and constables, to name a few). In addition, additional issues may appear due to the potentially inaccurate interaction between computerized and non-computerized elements, such as pedestrians or bicycles, as the surrounding context of an AV is often unpredictable [18]. For instance, some recent studies have focused on AVs' interaction with ramp metering [19]. AV models are based on mathematical algorithms that can be translated into software [20], and dedicated studies have been developed over the last decades. Different models bear different pros and cons and should be examined singularly. Another issue concerns security, as IT systems can potentially get hacked and hijacked [21]. An adequate level of cyber-security is, therefore, necessary [22]. Since AVs work with automated systems, a well-calibrated model should guarantee precise and optimized vehicle flows, leading to improved travel times, lower traffic congestion, reduced levels of pollution, and higher safety standards. As an example, Table 2 shows how applying fitting algorithms to AVs' interaction with ramp metering can lead to improved Levels of Service (LOS), lower levels of fuel consumption (g/kg of fuel) and emissions (grams) in the road network [23]. Acronyms reported in Table 2 are Carbon Monoxide (CO), Volatile Organic Compounds (VOC), Nitrogen Oxides (NOx), referring to the quantity of Vehicles (VEH).
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
###### Abstract
The proposed approach is most similar to the SVO in [15]. A KLT tracker [16] is also used in a similar way to avoid both feature detection and matching in a new frame, but with several important distinctions that are summarized in the following steps:  
1. By analyzing the relationship between the motion experienced by the feature, the average disparity, the translation velocity, the yaw angle, and the adaptive window size for the AKT must by necessity be jointly determined, which can significantly improve the tracking accuracy in the presence of scale distortion and affine transformation. 2. The AKT tracks the inherited features between only the left images of two consecutive frames, and the SKM is performed in a new stereo frame, which can avoid computationally expensive feature detection and feature-based stereo matching processes as much as possible. 3. To limit the drift error, an effective veer chain matching (VCM) scheme is introduced. 4. A systematic evaluation using the KITTI dataset [17] was performed. The experimental results show that the proposed SVO can achieve better real-time performance in comparison to the other state-of-the-art approaches without deteriorating the localization accuracy. The rest of the paper is organized as follows. Section 2 concisely outlines the proposed SVO, which is explained in detail in Section 3. Section 4 outlines several comparative experiments that were conducted to demonstrate the effectiveness of the proposed approach, and the conclusions are made in Section 5.
## 4 Conclusions  
In this paper, a preliminary study about the evaluation of the learning quality (as perceived by the learner) is provided. The models that we developed are able to predict a binary variable--namely a pass/fail evaluation--by combining multiple features that are related to the e-learning platform experience of the learner, so at this stage, the perception refers to whether a student passes the course or not. The work has been focused to propose a learning analytics model and identify a set of parameters for performing automatic learners' assessment. We focused on supervised learning techniques developing ensemble classifiers and testing them on a benchmark dataset, namely OULAD, using specific learning features that have been grouped and extracted from the dataset. Our learning model takes different variables into account, ranging from the learner's score to his/her interactions with the platform resources as well as with other learners, showing its effectiveness in the prediction of the final assessment of the learner. The results shown in this paper suggest the applicability of the approach on a larger scale. To this end, we also developed a Moodle plugin that is able to periodically extract the input features of our model from the e-learning platform in an automatic way for training and evaluation purposes. Future directions of this research will be devoted to larger experimentations directly on Moodle platforms with a high number of active learners aimed at evaluating their performance over time and trying to give useful feedback about the quality of the learning process itself. In addition, having access to structured data for each student and for each subject will also enable the exploitation of individual analytics (or cluster of analytics) for multiple purposes such as better understanding whether a student passes or fails/withdraws or evaluative ones to influence how a curriculum is designed or delivered. As soon as a greater number of data will be available, other methodologies could also be developed and tested to integrate and improve this approach (e.g., trying to distinguish and identify the factors that lead to a failure separately from the ones that lead to withdrawal), such as deep learning techniques.  
Conceptualization, V.R. and R.M.; methodology, V.R. and R.M.; software, A.C.; validation, G.D. and C.P.; writing--original draft preparation, V.R., A.C. and R.M.; writing--review and editing, all authors; supervision, E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.  
This work is within the project STELLE--Satellite Technology to Enabling new Learning and Lessons Environment ([[https://business.esa.int/projects/stelle](https://business.esa.int/projects/stelle)]([https://business.esa.int/projects/stelle](https://business.esa.int/projects/stelle))).  
Not applicable.  
The authors would like to thank Proton Group S.p.A. for their support.  
The authors declare no conflict of interest.
###### Abstract  
A comparative study of the performance of a communication system for IoT applications is presented. The experiment is based on the bit error rate, which is obtained by varying the distance between two transceiver modules, each attached to a microcontroller Arduino Uno. Four scenarios are considered for our experimentation. Each scenario is mainly characterized by interchanging radiator elements which are attached to the transceiver modules. For this, two antennas are designed and implemented: a modified shape-optimized Landstorfer Yagi-Uda antenna and a printed turnstile antenna. The measurements show good agreement, with simulations having gain values of about 9 dBi and 3 dBi for the quasi Yagi-Uda structure and the turnstile antenna, respectively. System performance tests are conducted to compare the performance of the commercial solution at various distances to custom-designed antennas. These tests aim to evaluate the improvement achieved using a new set of antennas. The key to this solution is the use of a high-directivity antenna for data transmission and a circular polarized omnidirectional antenna for reception, which shows an improvement of around 60% in terms of the bit error rate during data transmission compared to the pair of commercial antennas included in the RF module.  
IoT; Landstorfer antenna; turnstile antenna; BER; performance; wireless +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### Network Elements  
ZigBee networks support hundreds of devices and should thus be suitable even for large deployments. They can be organized into star, tree or mesh topologies. The ZigBee standard is based on the two defined IEEE 802.15.4 device classes including _Full-Function Device_ (FFD) and _Reduced-Function Device_ (RFD) and proposes three different types of devices: ZigBee coordinator, ZigBee router and ZigBee end devices (Fig. 8). A unique FFD _ZigBee coordinator_ manages the network by supervising the network formation as well as information storage, and bridges it with others ZigBee networks. The _ZigBee routers_ are complementary to the network manager and also FFD devices with additional routing capabilities, responsible for linking group of devices and supporting multi-hop communications. _ZigBee end devices_ are either RFD or FFD. They transmit the collected sensor or actuator data to a unique FFD including router or coordinator functionality. Consequently, a FFD becomes the master of RFDs organized according to a star topology. Furthermore, the ZigBee specifications introduce a _trust center_ to manage the keys and the end-to-end configuration. Only one center trusted by all devices should be active and be associated with all network devices.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
###### Abstract  
Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method.  
phase unwrapping (PU); multi-baseline PU; two-stage programming approach; Bayesian filtering; robustness +
Footnote †: journal: Applied Physics Letters  
1
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Measurement of Voltage, Current, and Power of PD  
#### 3.1.1 Open-Circuit Voltage  
Human motion is a relatively complex motion as the generated peak to peak voltage (V\({}_{\text{PK-PK}}\)), or peak voltage (V\({}_{\text{PK}}\)) is not constant nor purely sinusoidal. Therefore, measuring such PD voltage is difficult, particularly when cycling and walking motions were performed at different speeds. For this reason, the OC voltage of PD was measured using two devices, namely voltmeter (FLUKE 115) and oscilloscope (TBS 1052B):  
* When the PD is subjected to external excitation, the positive and negative terminals were connected to a voltmeter. This device measures only the root mean square (RMS) value of the PD voltage. Therefore, the measured RMS voltage was converted into peak voltage (V\({}_{\text{PK}}\)) by using the Equation (4).  
\[\text{V}_{\text{PK}}\ =\sqrt{2}*\ \text{V}_{\text{RMS}} \tag{4}\]
* To verify the measurement, the peak voltage (V\({}_{\text{PK}}\)) waveform was observed in the oscilloscope.  
In this study, the voltage obtained using both methods is found to be the same.
## 1 Introduction
### Research Literature Review
For instance, some studies have explored the use of machine learning algorithms that can adapt to different operating conditions and transformer characteristics, thereby reducing the dependency on extensive training data and computational resources [28; 29]. Additionally, advancements in signal processing techniques, such as wavelet transforms and S-transforms, have shown promise in accurately distinguishing between inrush currents and internal faults, even in the presence of low harmonic content [30; 31]. Moreover, hybrid approaches that combine traditional protection methods with modern artificial intelligence techniques are being investigated to provide a balanced solution that enhances both reliability and security. These hybrid methods aim to optimize the performance of differential relays by integratingreal-time data analysis and adaptive learning capabilities, which can dynamically adjust to changing system conditions and fault scenarios [32; 33]. As a result, while harmonic blocking and restraint techniques have been the cornerstone of differential protection, their limitations necessitate the adoption of advanced technologies. The integration of artificial intelligence and sophisticated signal processing methods holds great potential for improving the accuracy and reliability of transformer protection systems in modern power grids.