esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
###### Abstract  
Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency.  
s 2024 11, 11. [[https://doi.org/10.3390/aerospace11030191](https://doi.org/10.3390/aerospace11030191)]([https://doi.org/10.3390/aerospace11030191](https://doi.org/10.3390/aerospace11030191))  
A 1,2
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 2 Islanded Smart Microgrid and False Data Injection Attack
### Islanded Smart Microgrid and FDI  
Recently, cyber-physical systems or CPSs are added and mixed to many sending and receiving networks for monitoring and realizing the behaviors as well as controlling the physical parts [40]. One of the emerging examples of this system is the expansion of the ISMGs. ISMGs are relatively a new kind of power distributed systems which used traditional EPGs and also, ICT infrastructure connected to smart measurements and it may use a variety of specialized devices such as laptops, cell phones, etc., on islands in the oceans.  
A number of ISMG's ingredients permit data systems to analyze forecasting for balancing power generation with power consumption into the system. For instance, online pricing can give valuable insights to energy providers and consumers for managing and controlling demands and power resources.  
Thus, a distributed energy system that manages the processes of production, consumption, transmission and distribution of energy, can make their performance more dynamic and efficient [41]. Nevertheless, the diversity, heterogeneity, and complexity of the control and use of ISMGs pose significant challenges and categories in guaranteeing the whole system's entirety [42]. That is because in ISMGs, network state conclusion and decision making have to be implemented on local smart measurements and devices instead of truly-protected control centers. Therefore, unlike traditional EPGs where most attacks and disruptions diverge at physical accessibility to significant facilities [43], the omnipresence of ISMG's components makes most of the anomalies through cyber infrastructure.  
One example of the malware attacks found on ISMGs is FDI which is able to be used to change the amount of demanding energy and actual generated energy. Therefore, energy and load sharing might be improperly done, leading to additional costs or higherdevastating risks. Trusting these systems and gaining security is vital because, in addition to cyber security, national security is also at stake. Nonetheless, new reactions versus FDIA have focused rather on common electricity network states [42], where FDIAs occur on physical measurement devices instead of on smart equipment [43].  
Regardless of cyber-attacks and ISMG infrastructure distribution design, such methods might not provide complete protection, which can quickly determine for any local or global smart metering device to decide according to system data and status. To answer this challenge, in this paper, a method is proposed to detect FDIAs that is able to be set up and implemented on any smart parts.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Snapshot-Radio SLAM  
The radio SLAM or radio-based SLAM refers to solutions for the joint estimation of various positioning/sensing parameters (e.g., position, velocity, orientation, etc.) of both the target devices and surroundings. Notable surveys about radio SLAM can be found in [29; 31]. Both radio SLAM and snapshot-radio SLAM are currently regarded as complex and challenging research issues. Snapshot-radio SLAM refers to estimators relying on a single snapshot of radio measurements, rather than relying on continuous data collection; this can serve to decrease the power consumption, especially at the side of the mobile device, as well as offer fast real-time positioning and sensing estimates. A basic radio SLAM problem includes at least 7 parameters to be estimated, assuming a single reflection point on a surrounding object, 3D estimates, and desynchronized clocks at the transmitter and receiver; these 7 parameters are the x, y, z coordinates of the mobile target, the x, y, z coordinates of the scatterer or reflector, and the unknown clock bias between the transmitter and receiver clocks. If we assume multiple reflection points (e.g., on RISsurfaces) and also introduce, as unknowns, the device and surrounding objects' rotations (i.e., pitch, roll and yaw in 3D space), as well as the velocities and the wave polarization, one can easily see that the complexity of the estimator increases tremendously. In addition, since snapshot-radio SLAM relies on a single snapshot of data, it has to extract as much information as possible from limited measurements, and this raises issues of robustness under fast-changing environments and limited data. A few of the recent works addressing snapshot-radio SLAM can be found in [34; 35; 40; 65].
## 3 Results
### Radiation Dose  
By applying different image scanning speeds, the radiation dose delivered was progressively and significantly lower for the EOS-Fast protocol (KAP 258.6 mGy cm\({}^{2}\)) compared with the EOS-Medium protocol (KAP 387.89 mGy cm\({}^{2}\), \(p<0.001\)), and even more when compared with the EOS-Slow protocol (KAP 517.19 mGy cm\({}^{2}\), \(p<0.001\)) (Figure 2). Moreover, the EOS-Fast radiation dose was also significantly lower than the median radiation dose of conventional radiographs (644.7 (599.4-651.97) mGy cm\({}^{2}\), \(p=0.004\)) for all phantom configurations (Figure 3). Figure 4 shows a case from this study.  
Figure 1: Intermodality and interobserver reliability of insert length measurements among different modalities and among different observers: (**A**) Intermodality reliability between EOS imaging system and computed tomography (CT) scanning. The mean of the measurements obtained with EOS and CT for each insert is reported on the x-axis; the difference between the two measurements is reported on the y-axis. (**B**) Intermodality reliability between the EOS imaging system and conventional X-ray. The mean of the measurements obtained with EOS and X-ray for each insert is reported on the x-axis; the difference between the two measurements is reported on y-axis. (**C**) Interobserver reliability between measurements obtained by two different observers with the EOS imaging system. The mean of the measurements is reported on the x-axis; the difference between them is reported on the y-axis.  
Figure 2: Bar graph illustrating the mean radiation dose by modality. CR = conventional radiographs and EOS = EOS imaging system at three different speed settings (speed 2 = 15.2 cm/s; speed 3 = 11.4 cm/s; speed 4 = 7.6 cm/s).
## 3 System Model Design
### Calculating Mobile Similarity of Node and Narrowing the Node Range by a Single Threshold
#### 3.2.2 Reducing Node Range by Setting Threshold  
By setting a single threshold, nodes with a node similarity value higher than the threshold are left, and nodes with a node similarity value lower that the threshold are deleted. First, we will combine the result values of the similarity of two nodes and combine them with appropriate weights to get \(M\!N\!S\):  
\[M\!N\!S=\delta\bullet\!N\_similarity+\mu\bullet\!M\_similarity \tag{9}\]  
\[Q_{\textit{leave}}=Q_{[Q_{\textit{M\!N\!S}}>\widetilde{MS}]} \tag{10}\]  
\(M\!N\!S\) is the value of the node similarity of each node, \(Q_{\textit{leave}}\) represents the remaining node, \(Q_{\textit{M\!N\!S}}\) represents the \(M\!N\!S\) value of the node, and _Threshold_ is the experimentally calculated threshold. From experiment, we set the appropriate threshold to narrow the node range. The purpose of this operation is to first calculate the attribute distance for the next step of the model. If all nodes are calculated, then the workload will be very large, and the system processing will be hindered. Secondly, for nodes with too low node similarity, its message forwarding success rate is also very small. Therefore, we will eliminate these nodes in advance, which will make the system more efficient.
# GIS-Based Environmental Monitoring and Analysis
## 1 Introduction  
The environment is an important aspect of sustainable development. Environmental degradation, global warming, forest degradation, agricultural crop production shortfalls, and glacier coverage changes are severe problems that require us to take immediate action. Timely and effective monitoring of the environment enables us to further our knowledge regarding the current status of these conditions, and GIS is a powerful tool that provides vital support for the achievement of sustainable development goals.  
Geographic Information Systems (GIS) have extensive applications in environmental monitoring, data analysis, and advanced geosciences. They facilitate effective land use management, soil monitoring, digital soil mapping, and the analysis of erosion, landslides, terrain slopes, and vegetation. In forestry, GIS supports forest monitoring, fire management, the creation of digital elevation models, and the assessment of flammability indices. This technology is also invaluable in managing natural resources and analyzing natural disasters such as floods, droughs, and earthquakes, contributing to disaster risk reduction. The integration of technologies like LIDAR, remote sensing, 2D and 3D analyses, GeoAI, smart city applications, GIS software and devices, spatiotemporal patterns, WebGIS, and mobile and distributed GIS enables advanced data processing and analysis. In precision agriculture and offshore aquaculture, GIS optimizes production processes. For water and ocean monitoring--including oceanography, sea-level rise, land-ocean interactions, coastal environment monitoring, coastal changes, erosion, and water quality--GIS provides essential analytical tools. Additionally, these systems are employed in monitoring climate change, biodiversity loss, air quality, and particulate matter (PM) levels. GIS-related image processing techniques, big data management, data mining, multi-temporal analysis, decision support, and visualization techniques play a crucial role in effective environmental management.
## 3 Results
### Dynamic Functional Connectivity  
With resting-state fMRI, the SZ patient group demonstrated a significantly decreased mean strength across time windows (dFC-Str) between the left cuneus (CUN.L) and right caclarine (CALR) (\(p=0.00000568\)), between the right cuneus (CUN.R) and the right caclarine (CALR) (\(p=0.0000156\)), between the right lingual gyrus (LING.R) and right cuneus (CUN.R) (\(p=0.0000198\)), and between the right-middle temporal gyrus (TPOmid.R) and right cuneus (CUN.R) (\(p=0.0000489\)) with \(t\) values \(-4.79\), \(-4.54\), \(-4.48\), and \(-4.25\), respectively. (Figures 2 and 3, Table 3, Movie S1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Diagnosis (DSM Code)** & **Number** \\ \hline \hline Dementia of the Alzheimer’s type, with late & 1 \\ onset, with delirium (290.3) & 3 \\ Disorganized type (295.1) & 3 \\ Catatonic type (295.2) & 1 \\ Paranoid type (295.3) & 41 \\ Residual type (295.6) & 12 \\ Bipolar type I (295.7) & 1 \\ Depresses type (295.7) & 1 \\ Schizoffective Disorder type (295.7) & 5 \\ Undifferentiated type (295.9) & 5 \\ Bipolar Disorder type I, Most Recent Episode & 1 \\ Mixed, In Full Remission (296.4) & 1 \\ Unspecified type schizophrenia chronic state & 1 \\ (295.92) & \\ _Healthy Controls:_ & \\ Major Depressive Disorder, Single Episode, In & 1 \\ Partial Remission (296.26) & \\ Depressive Disorder type, Not Otherwise & 1 \\ Specified (311) & \\ Other Healthy Controls (none) & 72 \\ \hline \hline DSM, Diagnostic and Statistical Manual of Mental Disorders. & \\ \end{tabular}
\end{table}
Table 2: Primary diagnosis information for schizophrenia patients and healthy controls.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Connections Time/Windows** & **44 s (22 TRs)** & **60 s (30 TRs)** & **100 s (50 TRs)** & **150 s (75 TRs)** & **290 s (145 TRs)** \\ \hline CUN.L-CALR & \(6.17\times 10^{-6}\) & \(5.68\times 10^{-6}\) & \(6.13\times 10^{-6}\) & 6.33 \(\times 10^{-6}\) & - \\ CUN.R-CALR & \(1.53\times 10^{-5}\) & \(1.56\times 10^{-5}\) & \(4.60\times 10^{-5}\) & - & - \\ LING.R-CUN.R & \(1.89\times 10^{-5}\) & \(1.98\times 10^{-5}\) & \(2.40\times 10^{-5}\) & \(2.12\times 10^{-5}\) & - \\ TPOmid.R-CUN.R & \(4.72\times 10^{-5}\) & \(4.89\times 10^{-5}\) & \(2.58\times 10^{-5}\) & \(2.80\times 10^{-5}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The group difference of dFC-Str (four connections) for different window sizes (Movie S1).  
In contrast to mean strength between the groups, variability (dFC-SD and ALFF) within all temporal windows did not present significant differences (\(p>0.05\)) between the SZ patient group and the HC group. Furthermore, there were no significant differences in the dwelling times of all states between the two groups. The significant level here was FDR-corrected \(p<0.05\).
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 2 Field Test of Vibration Data Acquisition with Portable Devices  
This section first provides an overview of the new substation frame used in the field vibration test. Given the considerable height and large span of the frame, a novel method utilizing MEMS sensors for vibration response acquisition was proposed to simplify the testing protocol. This method was successfully implemented to acquire the dynamic response of the new substation frame.
# Evaluation of the SOA Formation in the Reaction of Furfural with Atmospheric Oxidants
## 3 Results and Discussion
### SOA Size Distributions
In this reaction, due to the formation of preexisting ultrafine particles from the degradation of the OH precursor, it is believed that these act as condensation nuclei that promote the growth of the particles in this process. However, the existence of a nucleation process cannot be dismissed. For the study of the SOA formation in the reaction of furfural with ozone, several individual additions of a small quantity of an O2/O3 mixture were carried out, but only after the addition of a large excess of O3, and particle formation was observed. A similar evolution of the size distribution as in the case of chlorine atoms was observed. The SOA distribution for the reaction of furfural with chlorine atoms is shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Precursor** &
\begin{tabular}{c} [**Precursor** \\ **Oxidant]0/10\({}^{\text{14}}\)** \\ **Molecule cm\({}^{-3}\)** \\ \end{tabular} & \(\psi\)**min** & \(\Delta\)**[ROG]0/10\({}^{\text{-4}}\)** & \(\Delta\)**[ROG]/10\({}^{\text{-4}}\)**\(\mathrm{\SIUnitSymbolMicro g}\)m\({}^{3}\)** & **M0/\(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{3}\)** & **Y** & **Size/nm** \\ \hline Cl2 & 2.43 & 43 & 3.4 & 2.9 & 950 & 0.03 & 107–523 \\ CH3 ONO & 2.43 & 86 & 3.1 & 2.8 & 90 & 0.003 & 69–254 \\ * O3 & – & 90 & 2.5 & 1.1 & 60 & 0.005 & 39–191 \\ \hline \hline \end{tabular}  
* O3 obtained from an ozonizer with a maximum output of 5 g O3/h.  
\end{table} 
Table 2: Experimental conditions employed for SOA study. Aerosol mass (\(\mathrm{M}_{0}\)), aerosol yield (\(\mathrm{Y}\)) and SOA size distribution (diameter) obtained at the end of each studied reaction. A set of experiments at different furfural/oxidant concentrations were carried out in order to study its influence of this parameter on the aerosol yield for the reaction with chlorine atoms. The results are summarized in Table 3.  
In most experiments, aerosol yields in the 5-7% range were obtained. The lowest aerosol yield was obtained in Experiment 5, in which a concentration ratio <1 was employed. Reaction 2, with a ratio close to one, also gave a low value of Y. The aerosol yields obtained in this work for the photooxidation and ozonolysis of furfural are low, but these values are of the same order of magnitude as those reported in the literature for isoprene [22,23] and pinene [24] oxidations. Nevertheless, these values may represent lower limits.
## 4 Edge Cloud-based Next-generation Internet
### Content Overlays  
In our proposed model, surrogate and the storage from different Edge Cloud providers would create an overlay (shown in Figure 5) of a logical content-centric Internet architecture. The model not only creates place holders for content but also provides interfaces to deploy value-added services that use such content. Hence, this model goes beyond the CDN-based content distribution services.  
For successful operation of the model, a control and management layer is required in the middle of the overlay and the infrastructure layer. Three different roles can be identified in support of our architecture, in accordance with the perspective on the virtual network model proposed in [2].  
1. The Infrastructure Provider (InP), owner and administrator of the underlying physical infrastructure of the Edge Cloud. The InP markets the transport of raw bit streams and processing services (_i.e._, slices) to its vendors (e.g., Virtual Network Provider (VNP)). ISPs are obviously included as potential InPs as the InP is responsible for maintaining the physical resources (e.g., routers, switches, surrogates, storage, physical links, _etc._) inside the AS (Autonomous System) it operates. The InP provides the necessary interfaces to the CP through the VNP.
2. The Virtual Network Provider (VNP) who gathers resources from one or more InPs and builds a virtual network, which is composed of virtual nodes and links. Thus, the VNP hides the details of the InPs and provides logical interfaces to the CPs to deploy their content. A VNP works as a broker by offering different network services, such as routing, QoS, _etc._, to the CPs. The VNP negotiates with the InP for maintaining the guaranteed level of infrastructure services.
3. The CP who deploys and maintains the (possibly distributed) applications of the surrogate and also the storage in different Edge Clouds. They are plugged in the interfaces that the VNPs provide. The VNP always offers interfaces to the CP in such a way that the surrogates are deployed in convenient locations at the edge.  
Figure 5: Roles and operation of the content distribution architecture.  
A simplified operational architecture is shown in Figure 5, where the VNP builds the virtual network by assembling physical services from four InPs. On top of the virtual network, surrogate and the storage build the overlay of the content-centric Internet architecture. Although only one VNP and one CP are shown in Figure 5, multiple VNPs and CPs may exist in parallel.  
In terms of number of entities and their roles, our proposed model has similarities with Cabernet [20], however the multi-functions of the Edge Cloud, complete with the introduction of surrogates, and the functionalities and location of the service providers make a big difference in between them.  
Note that there are alternatives to such a scenario, in terms of who would own the edge Clouds and operate them, but these considerations are beyond the scope of this paper.
# A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications
## 3 Results and Discussions
### Proposed Antenna Gain and Efficiency (%) at Different Stages  
The gain (dBi) and percentage efficiency of the proposed antenna is also taken into account and considered at all four cases. The brief description of the gain is tabulated in Table 7 which shows the gain of the proposed antenna in all four cases at their resonance frequencies. It is seen that the proposed antenna possesses an acceptable gain. Also, a brief description of the antenna efficiency (%) is tabulated in Table 8 and plotted in Figure 17, which shows the percentage radiation efficiency of the proposed antenna in all four cases at their resonance frequencies. It shows that antenna efficiency (%) is very stable in each case at the resonance frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Freq. (GHz)** & 2.7 & 4.2 & 8.4 \\ \hline
**RL (dB)** & \(-\)35 & \(-\)17 & \(-\)19 \\ \hline
**BW (MHz)** & 1100 & 800 & 700 \\ \hline
**Gain (dBi)** & 1.43 & 1.80 & 1.70 \\ \hline
**Dir. (dB)** & 1.52 & 2.02 & 2.02 \\ \hline
**Eff. (\%)** & 94 & 89 & 84 \\ \hline
**VSWR** & 1.4 & 0.9 & 1.02 \\ \hline \multirow{2}{*}{**App.**} & Maritime/ & Long Distance & X-band/Satcom \\  & Radiolocation Service & Comm. & App. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summarized results of Case 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Case 1** & & **Case 2** & & **Case 3** & & **Case 4** \\
**Freq.** & **Gain (dBi)** & **Freq.** & **Gain (dBi)** & **Freq.** & **Gain (dBi)** & **Freq.** & **Gain (dBi)** \\ \hline
2.7 & 1.8 & 1.9 & 1.55 & 2.7 & 1.35 & 2.7 & 1.43 \\
4.4 & 1.9 & 4.1 & 1.7 & 4.1 & 1.7 & 4.2 & 1.80 \\
8.1 & 2.01 & 5.4 & 0.64 & 5.4 & 0.65 & 8.4 & 1.70 \\
9.4 & 1.41 & 6.6 & 0.94 & 6.9 & 1.53 & - & - \\ - & - & 8.6 & 2.34 & 8.8 & 2.5 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Gain (dBi) in all four cases at resonance frequencies.  
Figure 17: Radiation efficiency (%) of the proposed antenna in all cases.