esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Herein, we presented all-in-one thermoplastic gel-based EC devices whose components are organic mixed valence compounds belonging to the class of small molecules. In the TV/Fc system we used the thienoviologen as the only electro-active/chromic species while, in the case of TV/FD system we incorporated two electro-active/-chromic species as complementary cathodic (thienoviologen) and anodic (the fluorene-diarylamine derivative) components. Replacement of ferrocene with the MV fluorene-diarylamine allows a more efficient control of the solar radiation over a large part of the vis-NIR spectrum. Cyclic voltammetry and spectroelectrochemistry demonstrate the good electrochemical behavior of the anodic and cathodic compounds, highlighting that low operating voltages (potential difference as 1.4 V \(<\Delta\)V \(<\) 2 V) are enough to reach high optical contrasts both in the visible range and in the NIR region, low response times and outstanding coloration efficiencies. The dual EC response of the Vis-NIR absorbing system coupled with a fine tuning of the coloration at low voltages indicate a great potential for practical use of these systems in many technological fields such as smart windows, electronic displays, optical communication, information storage, and thermal control.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/23/8372/s1](http://www.mdpi.com/2076-3417/10/23/8372/s1)]([http://www.mdpi.com/2076-3417/10/23/8372/s1](http://www.mdpi.com/2076-3417/10/23/8372/s1)), materials: cyclic voltammetry of the ITO/EC/ITO devices containing the Fc/TV and the TV/FD systems (Figure S1), transmittance changes and switching times for the TV/FD and TV/Fc devices (Table S1).  
Conceptualization, Methodology, A.B. and G.A.C.; Investigation, Data Curation, G.A.C., S.C. and A.B.; Writing-Original Draft Preparation, G.A.C.; Writing-Review & Editing, S.C., A.L.C. and A.B.; Resources, Validation, Supervision, Funding Acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Ministero dell'Istruzione dell'Universita e della Ricerca Italiano (MIUR) and the University of Calabria for supporting this project in the framework of the ex 60% budget grant.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample-\(\lambda_{\mu\alpha\xi}\)** & \(k_{e}\)(s\({}^{-1}\) ) & \(I\)\(\varepsilon_{+}\)\(c_{p}\)(0)\({}^{a}\)** & \(\mathbf{R}^{2}\) & \(\mathbf{\chi}^{2}\) \\ \hline TV/Fc-670 nm & 0.64 (0.02) & 1.160 (0.007) & 0.9880 & 0.000888 \\ TV/FD-1240 nm & 0.08 (0.01) & 0.39 (0.02) & 0.9612 & 0.000709 \\ \hline \end{tabular} \({}^{a}\) This has been fitted as a unique parameter.  
\end{table}
Table 1: Fitting parameters for the coloration kinetics at 670 nm and 1430 nm.
# Study on the Ozone Gas Sensing Properties of rf-Sputtered Al-Doped NiO Films
## 4 Conclusions  
Al-doped NiO films were grown by the rf sputtering technique with different oxygen contents in Ar-O\({}_{2}\) plasma, i.e., 2%, 2.8%, and 4%, at room temperature. All films were polycrystalline as revealed by the XRD technique, while their surface was smooth as was confirmed by Scanning Electron Microscopy. Moreover, all films were highly transparent, showing a transmittance of more than 70% in the visible spectrum. The gas sensing performance of the films against ozone was studied at different operating temperatures, namely 25 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, 110 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C. All films were sensitive to ozone of different concentrations. Specifically, films grown with 4% O\({}_{2}\) in plasma were able to detect ozone at an ultra-low concentration of 10 ppb at a low operating temperature of 80 \({}^{\circ}\)C. Thus NiO:Al films can be potentially used as a sensing element for ozone gas sensors.  
Conceptualization, A.P. and V.K.; methodology, E.G.; data curation, E.G.; writing--original draft preparation, E.G.; writing--review and editing, V.B., G.K. and E.A.; supervision, E.G.; project administration, V.B. All authors have read and agreed to the published version of the manuscript.  
Part of this work supported by the project \"Quality of Life\" (MIS 5002464) which is implemented under the \"Action for the Strategic Development on the Research and Technological Sector\", funded by the Operational Programme \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
Not applicable.  
The authors declare no conflict of interest.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 4 Deployment of AccFlow
### Virtual Centralized Controller  
In order to deal with large numbers of flows, we can also adopt the concept of a virtual centralized SDN controller. Specifically, multiple processors can serve as the conceptual centralized controller. Each processor keeps its routing table and tackles a certain number of flows. In order to tolerate individual processor failures within the distributed virtual centralized controller system, we can adopt the Paxos protocols [28]. In fact, the B4 architecture [14], a worldwide large-scale SDN data center network built by Google, also adopts the concept of virtual centralized controller by clustering their networks to deal with millions of flows traversing Google's data centers. By embracing these techniques to solve the scalability problem, AccFlow can be deployed in real networks. We present a straightforward deployment architecture in Figure 7.  
We consider deploying AccFlow on both core routers and border routers. Typically, border routers are responsible for dealing with the inter-domain flows such as BGP sessions [29] whereas core routers carry the traffic across the AS and may execute a particular traffic engineering policy such as MPLS [30]. Consider the situation where a remote legitimate client and the attacker are sending their traffic to the AS through an undeployed border router \(R_{1}\). Since the attacking flow \(D\) exhausts the bandwidth of the victim border router \(R_{1}\), the client's flow \(A\) is throttled to zero throughput (flow \(A\) is not able to traverse \(R_{1}\) in Figure 7). Attacking flow \(D\) continues to propagate in the AS until it counters a deployed core router \(R_{2}\) which drops its packets to save the network resources for the legitimate flow \(C\) (attacking flow \(D\) is not able to traverse \(R_{2}\) in Figure 7). When the AS deploys AccFlow on its border router \(R_{3}\), it protects the inter-domain traffic \(B\) from being attacked so that \(B\) can safely enter the AS. Apart from launching inter-domain attacks, the attacker can also compromise the nodes within the AS to generate local DoS attacking flows, such as the attacking flow \(E\). The deployed core router \(R_{4}\) can protect the network from such an attack. Furthermore, the deployed routers can stop the local attacking flows from leaving the AS to attack remote sites. To sum up, AccFlow is compatible with the existing security protocols and is incrementally deployable without disruption to the existing network infrastructure.
# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 2 Related Work
### Benchmarking ECG DBs  
The MIT-BIH Normal DB [4; 5] comprises ECG signals recorded from 18 subjects, all in good health. The MIT-BIH Arrhythmia DB [6; 7] comprises ECG signals recorded from 47 subjects. The Arrhythmia DB was developed for arrhythmia detection. ECG data were recorded in two channels while the subjects were walking. The MIT-BIH ST Change DB [8] comprises ECG signals recorded from 28 subjects over varying time spans. To create a DB with signals recorded over extended periods, data from five of these subjects were recorded during an exercise stress test.  
The QT DB [9] comprises ECG signals representing various QRS and ST changes. This DB encompasses 105 records of ECG signals sourced from a total of seven DBs, including 15 MIT-BIH Arrhythmia records, 6 MIT-BIH ST Change records, and 10 MIT-BIH Normal Sinus Rhythm records. The Abdominal and Direct Fetal ECG DB [10] contains 5 min signals recorded from five pregnant women. The signals were recorded at a sampling rate of 1 kHz, with each record containing four abdominal signals from a pregnant woman and one simultaneous recording of fetal ECG signals from the fetal scalp.  
The PTB Diagnostic DB [11] contains data obtained from Frank XYZ leads in addition to the standard 12-lead positions, totaling 14 leads. ECG data were recorded at a maximum sampling rate of 10 kHz, featuring 290 subjects inclusive of both able-bodied subjects and heart disease patients. The ECG-ID DB [12] contains 310 lead I records gathered from 90 subjects. The signals were generated at a sampling rate of 500 Hz. The number of signal measurements for each subject ranged from 2 (collected on a single day) to 20 (collected periodically over six months).  
The CSU_ECG DB [13] comprises ECG signals recorded in both static and dynamic situations. ECG data were obtained from 506 subjects engaged in four static and three dynamic situations, and the DB was established by acquiring data across three sessions. ECG lead I was recorded at a sampling rate of 2000 Hz using a Biopac MP160 instrument (Biopac Systems Inc., Goleta, CA, USA). Table 1 shows a summary of the benchmarking ECG DBs. In Table 1, the benchmark DBs built for user recognition research include ECG-ID and CSU_ECG DB.
## 3 Background
### Types of Graph Neural Network
#### 3.3.1 Gcn  
The GCN [33] is one of the most basic graph neural networks designed to operate on graphs. As the name suggests, it is inspired by the CNN model. GCN can be understood as performing a convolution in the same way that traditional CNN performs a convolution-like operation when operating on images. Fundamentally, a GCN takes as input a graph together with a set of feature vectors where each node is associated with its feature vector. The GCN is then composed of a series of graph convolutional layers that iteratively transform the feature vectors at each node. The GCN layers use the message-passing mechanism previously mentioned to aggregate information from neighboring nodes and reflect it into the current node's representation. This same procedure is carried out at every node. The output of each GCN layer serves as the input data for the subsequent GCN layer. Consequently, the graph data are transformed into new embedding through the layers, and the final neural network layer utilizes these embeddings to address tasks such as node classification and graph classification. The GCN model is described as shown in Figure 1.  
The hidden states of nodes at each layer are made up of two consecutive processes: aggregation and update. This is where the idea of 'convolutional' comes into play. The hidden states of each GCN layer can be updated through the following formula:  
Figure 3: Message -passing mechanism in GNN.  
\[\mathbf{H}^{(l)}=\sigma(\mathbf{A}\mathbf{H}^{(l-1)}\mathbf{W}^{(l-1)}+\mathbf{b}^{ (l)}), \tag{1}\]  
where  
**A**: the adjacency matrix of the graph;  
**H**: the node feature matrix;  
**W**: the GCN layer's weight matrix;  
**b**: the bias number;  
\(\sigma\): the activation function.  
At each node updated in the \(l\)th layer, the information is updated based on the connections between neighboring nodes with that node, which can be seen as the'mask' of a node. This mask plays a role similar to the concept of a kernel in a CNN model. The nodes of the graph are sequentially updated by sliding these'masks' over each vertex and performing information aggregation right there. This aggregation is typically achieved by multiplying two matrices \(\mathbf{H}\) and \(\mathbf{W}\), as outlined in the formula above. However, at its core, it still embodies the essence of 'convolution' in aggregating information from each node's neighbors. This is the reason behind the name of the GCN.
## 3 Mechanism analyses of scale effects  
In order to analyze the mechanism of scale effects, firstly, we need to abstract the retrieval process from reality, which is concluded as follows:  
\[products=f\,(measurements) \tag{1}\]  
where measurements refer to the physical quantity measured by remote sensing; \(f\,\)refers to the retrieval model that is used to estimate products from measurements; products are the characteristic parameters of land surface, such as biophysical (e.g., leaf area index, fraction of photosynthetically active radiation absorbed by vegetation) or geophysical variables (e.g., albedo, emissivity). The measurements, retrieval model and products may not be the same at different scales. So they can be considered as scale-dependent. The relationships are demonstrated in Figure 1.  
Here, \(r_{n}\) or \(<\)\(R\)\(>\), \(f\) or \(F\) and \(p_{n}\) or \(<\)\(P\)\(>\) represent the measurements, retrieval models and products at the local or large scale, respectively. Apparently, if the retrieval models at  
If we assume the retrieval models are the same at any scale, there are two ways to estimate measurements or retrieve products at a large scale. One is to aggregate measurements and products directly using local scale data, thereby producing average measurements \(<R>_{2}\) and distributed products \(<P>_{1}\). The other is to use \(<R>_{2}\) and \(<P>_{1}\) through the retrieval model and the inverse model to generate the corresponding ones, thereby producing lumped products \(<P>_{2}\) and equivalent measurements \(<R>_{1}\). It is difficult to determine which are best. We can only select the appropriate one by real situations. For example, the goal of scaling for leaf area index (LAI) is to make the values derived from coarse resolution sensor data equal to the arithmetic average of values derived independently from fine resolution sensor data [21]. If the retrieval model is proposed at local scale and the products estimated are associated with the unit area, such as LAI, \(<P>_{1}\) may be more suitable because it follows the law of conservation of matter. Otherwise, \(<P>_{2}\) may be more advisable. The product of temperature is an example. Here is the other thing we need to pay more attention to. The aggregation may not be area-weighted, the aggregation of radiance in a heterogeneous terrain region should consider both the area and the local slope angle effects [22]. Besides, not all the aggregation is scientifically reasonable. As the aggregation of temperature follows neither the law of conservation of energy nor the law of conservation of matter, consequently, it may not make sense. The discrepancy between \(<R>_{1}\) and \(<R>_{2}\), and \(<P>_{1}\) and \(<P>_{2}\) may be the focus of scale research. From the discussion above, the research on scale effects and scaling in remote sensing should begin around the points of view of measurements, retrieval models and products.  
Figure 1: The relationship of measurements, retrieval model and products at different scales.
## 3 Results and Discussion
### Comparison with MODIS Albedo Product
Among the same data shown in Figure 8, the albedo data for the forest pixels were extracted based on the land cover map from the National Land Cover Database 2011 (NLCD 2011) [38] and their relative albedo accuracies against MODIS products were plotted in Figure 9. The HJ albedo estimation generally improves by more than 5% as the view zenith angle increases, which is similar to the findings from the validation against ground measurements (see Figure 7). At the same time, the bias remains stable relative to the relative RMSE (RRMSE) in HJ albedo. The RRMSE keeps decreasing until the view zenith angle reaches 20\({}^{\circ}\), although most of the decrease occurs when the view zenith angle becomes larger than 5\({}^{\circ}\). Nevertheless, this demonstrates the unique advantage of wide-swath satellite data in capturing the surface BRDF information and thus the albedo estimation compared with nadir view satellite sensors.
## 3 Case Study
### Investigation Findings  
In this section, we summarise the key findings from the investigations that were carried out without specifying the causes. Insights from these findings provide the analysis to unpack the direct and contributing causes, which will be outlined in the next section.  
The location of origin for the fire was confirmed through the investigations carried out. A high-pressure filter and hydraulic pump had melted in the transverter section of the machine, where the fire was intense. This was consistent with the observations made by the operator at the time of the incident. These machines are equipped with automated fire suppression systems, which should be able to detect and suppress fires in areas with a high likelihood of fires. The fire suppression systems should also have the means to manually activate the suppression system in the event of a fire, provided the system fails to automatically activate and discharge. In this particular case, the system was unable to detect the fire. However, the operator managed to manually activate the fire suppression system, but it was unable to extinguish the fire after discharging. It was discovered that there were no fire suppression system spray bar nozzles directed to this area of the machine. Inspections carried out on the fire suppression system installation on the other model B machines showed inconsistencies in nozzle orientation.  
Investigations carried out on another model B machine indicated that there were electric cables close to hot components in the transverter areas. Thermal imaging was carried out on the components with temperatures that would compromise the insulation integrity of these cables. These cables were intertwined with hydraulic hoses, which showed signs of previous leaks and spills. Other cabling inconsistencies such as excess cables and incomplete cable installations were also noticed. Furthermore, several additional systems had been installed on these machines, which were not included in the original design of the machine. Management of these changes did not involve the Original Equipment Manufacturer (OEM), and the commissioning of these alterations were improperly managed.  
After investigating why the operator was unable to fully extinguish the fire with a portable fire extinguisher, it was noted that the operator was a learner operator and had not received any firefighting training. It was noted that the portable fire extinguisher was unable to effectively extinguish the fire in this area with the covers in place. This also raised interest to understand why the operator had to leave the machine unattended to physically look for the supervisor to report the incident. It was noted that the operator did not have any communication device that would allow him to immediately communicate to trigger emergency response teams.
# PV Benefits Assessment for PV-Powered Charging Stations for Electric Vehicles
## 2 PV Benefits Assessment Methodology
### Phase 1: Maximum Pre-Sizing
#### 2.1.1. _Requisite Data_  
The necessary input data, requested from the user during phase 1, are the following:  
* Available maximum public grid power \(P_{G}\), which corresponds to the maximum value of the power, in kW, that the power grid can supply the future PVCS;
* Typology corresponding to the choice of PV panels installation on the roof or on the car parking shade;
* Type of the charging terminals according to the typology PV panels installations: for a rooftop installation, ground type charging terminals are assigned, and for a shade-type installation, suspended charging terminals are assigned;
* Number of parking spots to be covered \(N_{p}\);
* Available area \(A\) corresponding to the roof area available for the PV panels installation and to the PVCS available area for car parking shade;
* Type of PV panels according to the desired range, i.e., high-range, mid-range, low-range, and the French model, corresponding to monocrystalline panels defined according to PV power at standard test conditions (STC) \(P_{STC}\) and PV conversion efficiency \(\eta\): \(\odot\)  High-range: \(\eta\) = 22.6%, \(P_{STC1}\) = 400 W; \(\odot\)  Mid-range: \(\eta\) = 19.6%, \(P_{STC2}\) = 330 W; \(\odot\)  Low-range: \(\eta\) = 18.4%, \(P_{STC3}\) = 300 W; \(\odot\)  French model: \(\eta\) = 18.1%, \(P_{STC4}\) = 300 W.
* PV orientation \(\Upsilon\) and PV inclination \(\alpha\), which will make it possible to determine the irradiations received by the PV panels;
* Depending on the location, latitude and longitude, the weather data profile is generated through a download link allowing to obtain the solar irradiation data from Photovoltaic Geographical Information System (PVGIS) software [27]. These aforementioned inputs data are entered according to the interface depicted in Figure 2.  
Figure 1. Flow chart of phase 1.  
The five steps of the phase 1 shown on the flow chart schematized in Figure 1 are presented below.
# HCL Control Strategy for an Adaptive Roadway Lighting Distribution
## 5 Conclusions  
This article proposes a design and control strategy for LED human-centric intelligent roadway lighting. First, it is based on the theoretical analysis of zonal flux. A simple and fast secondary optical design method for LED roadway lighting was established, aiming to achieve both road illuminance uniformity and luminance uniformity of the photometric light curve as the design goal. Thus, two types of optical lenses were produced and assembled to a street lamp to meet various lighting needs. In addition, they were equippedwith an imaging luminance meter to detect the luminance level of the road in real time. Then, the return signal was used to adjust the output of the street lamp in a timely manner through the ZigBee wireless transmission control interface to meet the lighting demands, whether the road was wet or dry road. Then, a roadway lighting evaluation in practice was carried out where we found out that the lens designed for luminance uniformity did indeed provide quality road lighting, giving drivers a safer and more comfortable road environment.  
Another future application of this research is for an autonomous car in the future. Taoyuan City in Taiwan has always aimed to be an advanced smart city and a demonstration field for an autonomous car. However, non-uniform luminance will cause not only the zebra effect but also unclear road markings. An autonomous car will configure the path of travel using camera images to check road markings. Based on the result of this research, we can now lower the chances of the zebra effect on the dry and wet road surface by using an ILM to automatically sense and switch optic lenses. With the novelty of this article's findings, we can collaborate and integrate it with the future autonomous car system, which combines luminance sensing with imaging recognition by using an advanced ILM device.  
Conceptualization, C.-H.L.; methodology, C.-H.L., C.-Y.H. and K.-Y.L.; software, C.-H.L. and K.-Y.L.; validation, K.-Y.L. and S.-F.Y.; formal analysis, C.-H.L., K.-Y.L. and C.H.C.; investigation, C.-H.L., K.-Y.L., S.-F.Y. and M.C.H.; resources, C.-H.L., C.H.C. and S.-F.Y.; data curation, C.-H.L., C.-Y.H. and K.-Y.L.; writing--original draft preparation, C.-H.L., C.-Y.H. and K.-Y.L.; writing--review and editing, C.-H.L., C.-Y.H., J.-C.G. and M.C.H.; visualization, C.-H.L., K.-Y.L. and S.-F.Y.; supervision, C.-H.L.; project administration, S.-F.Y., C.H.C. and M.C.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated or analyzed to support the findings of the present study are included this article. The raw data can be obtained from the authors, upon reasonable request.  
The authors declare no conflict of interest.