esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems.  
Keywords:insular electrical systems; renewable energy integration; electric vehicle batteries; power smoothing; V2G +
Footnote †: journal: _Sciencedence_
## 2 Study Area and Datasets
### Study Area  
The study area (Figure 1) is located in the inner part of Dublin Bay on the east coast of Ireland. The region investigated is a C-shaped inlet, a designated UNESCO Biosphere and covers an area of approximately 50 km 2(10 km long and 5 km wide) with water depths ranging from 0-15 m. The bay is joined and bisected by the river Lifkey in the south and north. The intertidal region extends in a broad arc around most of the bay, giving a total intertidal area of some 20 km 2; in a relatively flat topography interrupted by tidally controlled related features, such as drainage channels and inlets. Beyond the intertidal region, the topography of the bay is characterized by a gentle E-W regional slope not exceeding 0.5. Near the northern shoreline, off the Howth peninsula, slopes can be, locally, steep up to 5. The Lifkey channel extends ca. 6 km E-W and has a U-shape section, with a width between 300 and 1200 m, heights ranging from 6-8 m and wall slopes averaging 1-3. A number of man-made structures such as pipelines, buoysand anchorage sites are present in the study area described in the nautical charts. Descriptions of Dublin Bay and its intertidal environment are given in earlier papers by several authors [41, 42].  
The study area has been chosen as a representative test with similar characteristics in terms of geomorphology (e.g., C-shaped bay, estuaries) and socio-environmental factors (e.g., dense city, sea level rise) to other embayment areas worldwide. Data availability issues with recent hydrographic bathy2 metry surveys together with suitable satellite imagery have also been a key factor.
## 3 Research Process
### Data Acquisition and Preprocessing
#### 3.2.2 Data Preprocessing  
In this paper, we take the titles, abstracts, and keywords of the paper dataset, as well as the titles and abstracts of the patent dataset, as the text analysis corpus for the LDA topic model [33]. After removing the literature with incomplete information, we obtained 56,715 valid scientific papers and 32,807 patents. Subsequently, we extract the text analysis corpus required for the LDA topic model to construct the initial corpus. To enhance the accuracy and reliability of the topic analysis and improve data quality, it is necessary to perform data preprocessing on the text corpus. The specific steps are as follows:  
Figure 2: Statistical chart of paper and patent data quantities.  
Preprocessing: Remove numbers, punctuation marks, and symbols, etc., from the text. The purpose is to eliminate noise and reduce computational burden. At the same time, convert the words in the text into singular and lowercase forms, and conduct preprocessing operations such as word segmentation and part-of-speech restoration to enhance the accuracy and efficiency of text processing.
2. Construct the feature word list: Use natural language processing toolkits to perform feature word extraction and other operations on the initial corpus. Subsequently, perform deduplication and filtering operations to obtain the final feature word list and use it as the input for the LDA topic model.
3. Constructing the invalid word list: To reduce the interference of high-frequency invalid words and improve the accuracy of topic recognition, construct an invalid word list for the target domain based on a general stopword list. Specifically, examine the results after initial LDA topic model clustering, select words with a high frequency but no practical significance, and incorporate them into the general word list. Subsequently, repeatedly iterate the above process to form the final invalid word list.
4. Stage division: Considering the large time span of literature data, global data analysis may lead to the submergence of important topics, making it difficult to deeply analyze the association between science and technology in the field of cybersecurity. Therefore, following a time series, divide the literature data from 2005 to 2024 into ten stages, with each stage lasting two years.
## 4 Application in Tunnel Smooth Blasting
### 3D Numerical Modelling of Smooth Blasting Tunnel  
The 3D numerical model of the tunnel with bedded joints is shown in Figure 9. The average dip direction and dip of the parallel joints is \(-22^{\circ}\angle 56^{\circ}\) relative to the direction of the tunnel, and the spacing between the two adjacent joints is 1.5 m within the rock mass of sandstone. In order to maintain the progressing level of the running in hydrocode, the thickness of the entire model, as well as the length of all the boreholes, is set as 20 cm. The tunnel design contour is semicircular in shape, with a radius of 6.93 m. A total of 150 boreholes are arranged in the tunnel face with diameters of 4 cm; the detailed layout for the boreholes is shown in Figure 10. The emulsion explosive has a diameter of 3.2 cm and a length of 20 cm. It was assumed that all the explosives are charged in the center of the boreholes, and decoupling charge means is adopted in this practical blasting. The spacing between  
Figure 8: (**a**) Tunnel face of Zigaojian Tunnel. (**b**) Visible bedded joints.  
the explosive and borehole is filled with air. The ALE/JH-2 programmed algorithm [53] is adopted to simulate the rock breaking process induced by blasting. The rock is modeled using Lagrangian material while the air and explosives are modeled by ALE. The finite element meshes used for discretization of the rock are tetrahedron shaped in 3D space; the average element size is 3.0 cm around the boreholes and gradually increases outwards. The rear boundary of the whole model is constrained in XOY plane and defined as the non-reflection boundary, which means that all the waves can penetrate the boundary without reflection just as in transmission in continuous media. The detailed parameters of the sandstone are listed in Table 1 and are obtained using the computational and extrapolated methods proposed before. For the properties of the joints, automatic surface-to-surface contact is adopted in the hydrocode, and it is assumed that the joint surface has no cohesive strength, the dynamic friction coefficient is 0.30 and the tensile strength is zero [62]. The parameters of the emulsion explosives are listed in Table 2, where 'E. 1' and 'E. 2' represent the explosives charged in the inner cut boreholes and the periphery boreholes, respectively, and the initial detonation positions are at the back of the boreholes. The threshold value of the effective strain is set as 1.0. Once this value is exceeded, the local element would be deleted in order to prevent excessive large deformation during the running of the numerical model. The detailed blasting process is shown in Figure 11; different colors represent different degrees of rock, which will be elaborated later in this study.  
Figure 10: Layout of the drilling boreholes.  
Figure 9: Numerical model with joints of bedded tunnel.
## 2 System Description  
This section describes the proposed system in terms of its architecture and presents the theoretical models for the emitter and receiver, i.e., modulation and demodulation. The proposed system architecture is presented in Figure 1.  
The system's main components are the emitter and the receivers. The emitter is an m-CAP digital modulator, which aggregates \(m\) data sources into a modulated signal, where each data source is modulated into one band. The m-CAP signal is transmitted by a ceiling LED light fixture, being propagated in the VLC channel, assumed to be in a line-of-sight configuration. The optical signal is then captured by the IoT node devices. Each IoT node listens to a single band, and the demodulation is performed in the analog domain, resorting to a quadrature mixer and filtering, which will be detailed in Section 2.2.  
Although the m-CAP frequency spectrum is typically contiguous, as in Figure 2, guard bands may be created by suppressing the even bands (gray/dashed), decreasing the interference between bands. Despite the spectral efficiency degradation being halved, the overall performance of individual bands will benefit from the same receiver filter specifications. This ensures simple implementations at the receiver side, which results in low-cost, low-power, and small-sized VLC-based IoT devices. As an alternative, when using a raised cosine filter in the emitter, the bandwidth of each band could be compressed by adjusting the filter roll-off factor. The relation between the roll-off factor and the guard bands will be discussed later in Section 2.3.2. Nevertheless, guard bands degrade the spectral efficiency, as well as the available orthogonal channels, limiting the maximum number of connected devices. Regarding the spectral efficiency, several IoT applications require a limited data rate. In fact, current RF technologies used in the IoT were designed with data rates as low as 100 bit/s, as in the case of SigFox(r). Therefore, considering the available LED bandwidth, spectral efficiency degradation is the price to pay to meet the IoT requirements, allowing simple devices to operate in a multiple-user scenario. Concerning the maximum number of connected devices, m-CAP could be combined with TDM, such as the active bands in Figure 2 alternating in the time domain, in two distinct time slots: in the first time slot, the green bands would be active, and in the second, only the gray  
Figure 1: Proposed m-CAP VLC-based IoT system architecture using digital modulation and analog demodulation.  
bands would be modulated. Therefore, the maximum number of connected devices would increase by a factor of two.
## 2 Materials and Methods
### Data Acquisition  
EEG signals were recorded using the g.USBamp system (The manufacturer of this device is g.tec medical engineering GmbH, Schiedlberg, Austria). EEG electrodes were positioned in accordance with the international 10-20 system. The reference electrode (A1) was placed on the unilateral earlobe of the subject, while the ground electrode was located at Fpz, on the prefrontal cortex. Since SSMVEPs elicit the strongest responses in the occipital region of the brain, six electrodes--O1, Oz, O2, PO3, POz, and PO4--wereselected for data acquisition in this area, as illustrated in Figure 2. EEG signals were sampled at 1200 Hz, followed by preprocessing steps that included a 4 th-order Butterworth notch filter (48-52 Hz) to eliminate powerline interference and an 8 th-order Butterworth band-pass filter (2-100 Hz) to suppress low- and high-frequency noise. The filtered signals were subsequently downsampled to 240 Hz. Throughout the recording session, electrode impedance was maintained below 10 k\(\Omega\).
## 2 Materials and Methods
### Statistical Analysis
#### 2.3.3 Standardized Multivariate Linear Regression Model  
Unlike the Pearson correlation method, the Standardized Multivariate Linear Regression (SMLR) method uses Gaussian normalized values in place of the original ones. Thus, the effects of changes in different factors can be compared. The SMLR model separates the relative contribution of each factor. The coefficient before each variable represents its importance in partitioning the SMLR model, and the fitting coefficients (R) of SMLR represent the combined effects of driven factors to GPP. The SMLR model is expressed as follows:  
\[\frac{GPP-\overline{GPP}}{\sigma_{GPP}}=\beta_{0}+\beta_{1}\frac{ED_{1}- \overline{ED_{1}}}{\sigma_{1}}+\ldots+\beta_{n}\frac{ED_{n}-\overline{ED_{n}} }{\sigma_{n}}, \tag{7}\]  
where \(ED_{i}(i=1,2,\ldots,n)\) are influencing factors; \(\overline{GPP}\) and \(\overline{ED_{1}}\) are multi-year mean values of \(GPP_{GS}\) and influencing factors, respectively; \(\sigma_{GPP}\) and \(\sigma_{ED}\) are standard deviations of \(GPP_{GS}\) and influencing factors, respectively, during the study period; and \(\beta_{1},\beta_{2},\ldots,\beta_{n}\) are regression coefficients that represent the importance to \(GPP_{GS}\). Factors with a larger coefficient are assumed to contribute more to the variation in \(GPP_{GS}\), and the one with largest coefficient was identified as the dominant factor to \(GPP_{GS}\) in our study area during 1982-2015.  
Before using the SMLR model, the Variance Inflation Factor (VIF) is calculated to exam multicollinearity among the influencing factors. The F-test is conducted on the model, while the \(t\)-test is conducted on the coefficients before each influencing factor. Significant tests were those below the 0.05 significance level.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 3 Forecast Error Reduction by Adaptive Filtering  
If \(x(n)\) is the time series of a forecast variable at a given cell, the reduction of RCM forecast error by filtering process aims at obtaining a filtered version \(y(n)\), mathematically described by the so-called difference equation  
\[y(n)=\sum_{l=0}^{N}w_{l}x(n-l) \tag{2}\]  
where \(w_{l}\) are the \(N\)-order filter coefficients, \(y(n)\) and \(x(n)\) are the output and input signals, and \(l\) is the coefficient index that also indicates the \(l\)-th past sample of the input signal. The \(n\)-th iteration of this process is illustrated in Figure 2. The sliding windows passes over previous samples of the input signals--over \(x(n),\ldots x(n-3)\) in the figure--and the filter order is \(N=3\). In this case, we consider a digital filter with finite impulse response (FIR) [30] that produces the output \(y(n)\) as in Equation (2).  
Figure 2: Filtering process employed to reduce climate forecast errors.  
In the proposed approach, the filter is designed using adaptive filtering techniques. The general framework of adaptive filtering is shown in Figure 3, where \(n\) is the iteration number, \(x(n)\) denotes the forecast time series, \(y(n)\) denotes the adapted filter output signal, and \(d(n)\) is the NCEP reanalysis considered the reference time series [24]. The error signal \(e(n)\) is computed by subtracting \(y(n)\) from \(d(n)\), which subsidies the adaptive algorithm to appropriate updating of the filter coefficients. The coefficients \(w_{l}\) are adjusted iteratively to reduce the error. Hence, the filter is adaptive.  
The core idea is to find the optimum filter that, when it is applied to the forecast climate time series, its output becomes more adherent to the observational data--in this case, the NCEP reanalysis. It is expected that the adapted filter based on past data from Eta RCM forecast and NCEP reanalysis should lead to forecast accuracy improvement of the Eta model's future forecasts. One notes the dependence on past data for the filter training process. For the adaptive filter design, several factors need to be considered, such as filter length and adaptive algorithm.  
In addition, the statistical behavior of climate times series depends on the geographic location of the evaluated region. As a starting point, one highlights that it is unlikely to find one single optimum filter for the whole domain. Hence, data partitioning and clustering are required for adequate filter adaption. In doing so, one obtains filters that are applicable at specific spatial domains (forecast cells), and the filters inherently consider prognostic evolution, a temporal aspect. We apply the K-means, an unsupervised learning algorithm, to obtain the number of filters to use. The next section describes the specific methodology that we adopt.
# The Rise of Quantum Information and Communication Technologies
## 3 Management and Control of QKD Networks
* QKDN controller control and management--this is in charge of the overall control and management of the functional elements in the QKDN controller and communicates this with functions in other layers, such as the QKD module, QKD link, KM, and the QKDN manager. Two scenarios involving the QKDN controller have been investigated. The first one is the provision of a key relay route and the second one is the rerouting of key relay. Both scenarios are compliant with the procedures recommended by ITU-T Y.3804, illustrated in Figures 3 and 4. The QKDN manager supports, for selected scenarios, the following fault management functions:  
Figure 4: Key relay rerouting procedure (source: ITU-T Y.3804). Figure 3: Key relay route provisioning procedure (source: ITU-T Y.3804). * Fault monitoring, detection and visualization;
* Fault diagnosis and remedy action definition;
* Support to the QKDN controller for the rerouting control of key relay as needed in the case of faults. In the example of the use case, two scenarios involving the QKDN manager have been investigated. The first one is QKD link failure (channel loss) and the second one is key relay failure in KM. In compliance with the procedures recommended by ITU-T Y.3804, illustrated by Figures 5 and 6, for both scenarios, the following apply:  
* The QKDN manager receives the alarm notification from the faulty resource;
* The QKDN manager applies the rules for identifying the correct healing actions;
* The QKDN manager communicates said actions to the QKDN controller (e.g., key relay path rerouting);
* The QKDN controller actuates said actions. The QKDN manager supports, for selected scenarios, the following configuration management functions:  
* QKDN resource configuration and provisioning;
* QKDN resource status monitoring;
* QKDN resource inventory management;
* QKDN topology management;
* Support to the QKDN controller for the provision of key relay routes. Specifically, the QKDN topology discovery scenario has been investigated, in compliance with the procedure recommended by ITU-T Y.3804, illustrated in Figure 7. Each QKDN resource sends to the QKDN manager configuration information that is used to create the QKDN resource inventory and the QKDN topology. For each QKDN resource (e.g., the QKD module, QKD link, and KM), said configuration information contains the following:  
* A resource identifier;
* A resource state (e.g., in service, out of service, standby, or reserved);
* Attributes related to the specific characteristic of the resource;
* Relationships with other resources. The QKDN manager collects all of this information and generates the QKDN topology, considering both the quantum and the key management layers. The QKDN manager keeps the topology updated, gathering every notification coming from the QKDN resources. The QKDN manager provides updated configuration information to the QKDN controller when required (e.g., in key relay route provisioning and key relay rerouting procedures).
## 5 Guidance of Traffic Parameters for Signal Control Optimization
### Data Support for Intelligent Control Algorithms  
With the development of artificial intelligence technology, data-driven intelligent signal control has become a development trend. Rich parameters extracted from trajectory data provide high-quality inputs for intelligent control algorithms, specifically including:1. Feature engineering for machine learning models: Extracted parameters including link flows, speeds, densities, and travel times can directly serve as feature inputs for machine learning models. Compared with traditional single-flow data, multi-dimensional features can more comprehensively describe traffic states, improving model prediction accuracy and generalization capability. Features include: * Temporal features: Flow and speed variations across different time periods * Spatial features: Correlation parameters of upstream and downstream links * State features: Indicators reflecting saturation levels such as density and queue lengths 2. Application of Bayesian optimization: Average travel time parameters can be combined with Gaussian process-based Bayesian optimization methods to achieve intelligent offset optimization. Advantages of Bayesian optimization include: * Capability to handle nonlinear and non-convex optimization problems * High sample efficiency, suitable for computationally expensive scenarios * Ability to quantify uncertainty, providing robust optimization results 3. State space for reinforcement learning: For more complex adaptive signal control, extracted traffic parameters can constitute the state space for reinforcement learning: * Current state: Real-time density, speed, and queue lengths for each link * Historical state: Parameter change trends over the past several cycles * Predicted state: Future flow predictions based on OD matrices This rich state representation can help agents learn superior control strategies, achieving proactive traffic control adaptable to demand changes. In summary, traffic parameters extracted from vehicle trajectory data provide comprehensive data support for signal control optimization. From coordination control subarea division to specific offsets, from oversaturation state identification to intelligent control algorithm applications, these parameters play important roles in all aspects of signal control. Compared with traditional fixed detector-based methods, trajectory data not only provides equivalent information but also reveals spatiotemporal evolution patterns of traffic flows, providing a solid data foundation for achieving more intelligent and efficient signal control.