esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Landsat-8 and PlanetScope NICFI Satellite Data  
Surface reflectance data were acquired from the Landsat-8 OLI and PlanetScope NICFI products for the period from September 2020 to May 2022. The atmospherically corrected surface reflectance data from the Landsat-8 OLI sensor are included in Google Earth Engine (GEE)'s USGS Landsat-8 Level 2 Collection, and these images contain four visible, a NIR, and two SWIR bands [43]. Landsat images are selected to form a time series by choosing images where cloud cover does not obscure the blowdown event within each month. In typical cases, Landsat-8 may have several consecutive months where the blowdown event cannot be observed due to cloud cover. In this study, cloud masking using the quality assessment (QA) band was applied to the Landsat-8 images. Similarly, the PlanetScope NICFI monthly mosaics are derived from surface reflectance data from the PlanetScope Dove constellation, having three visible bands and a NIR one [31]. The PlanetScope NICFI data have been subjected to a series of pre-processing, including atmospheric correction, cloud masking, stitching, and seamline removal. Additionally, the PlanetScope NICFI data were already normalized using BRDF-corrected Sentinel-2 data. This strategy reduces the variability added by variable view-illumination conditions, resulting in more reliable and  
Figure 1: Study sites with related blowdown events found in the Amazon Forest. The background image corresponds to the areas covered by rainforests and other land covers in the Amazon Forest, according to the Tropical Moist Forests product [42].  
consistent reflectance measurements [37]. Band information and resolution parameters for both datasets are shown in Table 1. Satellite data were acquired using the GEE platform [44].
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Signal Processing  
#### 2.1.1 Negative Pressure Wave  
When a leak happens along the pipeline, the liquid density of the leak point declines immediately due to the fluid medium losses and the pressure drops. Then the pressure wave source spreads out from the leak point to the leak upstream and downstream ends. Taking the pressure before the leak as the reference criterion, the wave generated by such a leak is called the negative pressure wave. When the negative pressure wave reaches the pipeline terminal end, it will cause the drop of first the station inlet pressure and then the station outlet pressure. The pressure reduction signal is collected by pressure sensors installed on both ends of the pipeline. As the locations of the leak are different, different time difference of the negative pressure wave are captured. Based on the different time difference that pressure sensors on both sides detect, pipeline length and negative pressure wave velocity, the leak point can be determined [15, 16]. The negative pressure wave method is not dependent on system hardware and it also does not need to set up the mathematical model, which means less calculation costs. However, it requires that the leak be quick and abrupt, since especially in the case of a slow leak, there is no obvious negative pressure wave.
# Optimal Routing in Urban Road Networks: A Graph-Based Approach Using [PERSON]'s Algorithm
## 5 Analysis of Results  
The algorithm was applied to data from the city of Sarajevo (Figure 4), and to evaluate its performance, a comparison was made with the values provided by the Google Maps service. The base map was obtained from the OSM service, supplemented with the necessary missing line elements and data required for calculations in the main program code. The described program codes created in AutoLISP were used as tools for this supplementation.  
In Sarajevo Canton, which is a larger territorial and organizational unit, a total of 29 counters are distributed, while, within the city zone, there are 20 counters, the results of which were used in this study. The base map consists of 46,497 lines (graph edges), which, according to their topology, provide 44,301 nodes within an area of 11.8 km \(\times\) 6.5 km. Nodes are created by connecting at least two and no more than five lines. They are divided into fourteen functional categories with corresponding speeds, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Functional Rank** & **Vmax [km/h]** \\ \hline secondary\_link & 40.000 \\ unclassified & 40.000 \\ trunk & 80.000 \\ living\_street & 30.000 \\ motorway\_link & 70.000 \\ secondary & 60.000 \\ track & 40.000 \\ service & 30.000 \\ tertiary & 50.000 \\ residential & 40.000 \\ primary & 60.000 \\ primary\_link & 40.000 \\ motorway & 110.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Functional rank of streets.  
Given the Olympic history of the city of Sarajevo, Path 1 was chosen to connect the base of the mountain where the bobsleigh competition was held with the Olympic Museum. In the total sample tested, the root mean square error between the [PERSON] driving time (\(Y_{i}\)) and the GM driving time (\(Y_{p}\)) was RMSE = 0.85, according to Equation (13).  
\[RMSE=\sqrt{\frac{\Sigma\left(Y_{i}-Y_{p}\right)^{2}}{n}} \tag{13}\]
## 3 The Proposed FedSAGE Framework for Federated DGA Detection  
Building upon the challenges identified in Section 2, we propose FedSAGE (**F**ederated **S**election and **C**ustering for **A**nomaly and **G**enerative-domain **E**valuation), a novel framework that uniquely combines unsupervised latent space-based client clustering and computation-aware selection. Unlike prior works that rely on auxiliary datasets or parameter-based similarity, FedSAGE requires no labels and minimizes communication cost by leveraging the latent representations of a shared VAE. This section details the architectural and algorithmic design of our approach.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 6 Related Studies Based on Multi-Band and Multi-Media Communication Technologies
### Multi-Band Techniques for Adaptation Layer  
In this multi-band technique, existing methods, such as OFDM, FDMA, or OFDMA, can be used to divide the frequency in underwater communication. In [58], OFDM has many advantages for underwater communication schemes. Limited underwater acoustic bandwidth can be utilized efficiently by the use of OFDM. OFDM makes effective use of the spectrum by allowing overlay between sub-carriers. The introduction of guard time with cyclic prefixes reduces inter symbol and inter carrier noise significantly; hence, the modulation scheme is robust against ISI and ICI. By dividing the wide band frequency selective channel into narrowband smooth fading sub channels, OFDM is more robust in terms of frequency fading. It is computationally-resourceful thanks to the IFFT and FFT methods which implement the modulation and demodulation functions respectively. In addition to the advantages of the basic OFDM scheme, the performance of the communication scheme for underwater channels can be further improved in following ways. By the introduction of appropriate pilot carriers, effects due to channel distortion can be corrected. The introduction of Forward Error Correction (FEC) coding and interleaving can expand the reliability and performance of the communication scheme by significantly reducing the Bit Error Rate (BER).  
The OFDMA-based MAC protocol is constructed on the OFDMA technology, which splits an accessible channel into a several orthogonal sub-channels, called \"subcarriers\". We use this technology to allow concurrent sessions through subcarrier sharing among the nodes that are in communication with each other. Each nearby pair of nodes uses a subcarrier or a pair of subcarriers to send data. This used set of subcarriers is, therefore, kept for the pair until they relinquish it clearly. The timeline is separated into slots, each of length Ts, where transmissions start only at the beginning of each time slot. Synchronization is assumed to be done through a one-hop transmission from the base station. The objective is to achieve optimal sharing of the available subcarriers; optimal here means the best distribution of the available subcarriers among network nodes which results in the minimum transmission power consumption subject to a minimum required throughput level [58]. Figure 2 shows the scheme for OFDM-based underwater communication. This method can be applied to our multi-band techniques for splitting the frequency band.  
The parameters used for OFDM-based communication are bandwidth, carrier spacing, carrier frequency, sampling frequency, cyclic prefix duration, symbol duration, total symbol duration, subcarriers, etc.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## Materials and Methods
### Bistatic GB-SAR Focusing Algorithms
#### 2.2.2 Back-Projection Algorithm  
For a point target \(P(x_{c},\,y_{c})\) in Figure 1a, the received signal at a given center position \(x\) between the two antennas separated by the baseline \(B\) is  
\[s(x|x_{c},\,y_{c})=e^{i\phi(x)}=e^{-j\frac{2\pi}{\lambda}(r_{1}+r_{2})},\,|x| \leq X/2, \tag{4}\]  
where  
\[r_{1}=\sqrt{\left(x-x_{c}-B/2\right)^{2}+y_{c}^{2}},\]  
\[r_{2}=\sqrt{\left(x-x_{c}+B/2\right)^{2}+y_{c}^{2}}. \tag{5}\]  
Here, \(X\) is the coherent integration length in azimuth direction, which is a function of range \(r\),  
\[X(r)=\frac{\lambda}{L}r-B<X_{max}. \tag{6}\]  
The coherent integration length is less than the rail scan length, i.e., \(X_{max}=6\) m, in this experiment.  
The back-projection algorithm (BPA) is performed by a convolution of the received signal and a reference function so that the focused signal is obtained by  
\[g(x)=\int_{-\infty}^{\infty}h^{-1}\big{(}x^{\prime}-x\big{)}s\big{(}x^{\prime }\big{)}dx^{\prime}, \tag{7}\]  
where \(h^{-1}(x)=s^{*}(x)\), the conjugate of (4). Analytical evaluation of (7) results in a sinc function [10] that defines the azimuth resolution. For a bistatic GB-SAR system, the azimuth resolution if further restricted to the overlapping portion of the Tx and Rx antenna footprints by  
\[\delta x_{B}=\frac{\lambda r}{2X}=\frac{\lambda r}{2(\lambda r/L-B)} \tag{8}\]  
For a monostatic case (\(B=0\)), the azimuth resolution of strip-map mode has a maximum value of \(\delta x=L/2\), i.e., a half of the azimuth real aperture. However, the resolution becomes poorer for a bistatic GB-SAR configuration with increasing \(B\), due to  
Figure 2: Range resolution ratio of bistatic and monostatic radars (\(\delta r_{B}/\delta r\)) with different baselines \(B=0\) m, \(0.5\) m, and \(1\) m, respectively.  
the reduced overlap of the Tx and Rx footprints. A squint operation of Tx and Rx for better overlap of the footprints would be preferable to enhance the azimuth resolution.
## 1 Introduction
### Related Works
In general, vehicular networks are multiple-agent systems possessing collective behavior among the agents, resulting in uncertainties. The incoordination between the agents leads to oscillations in actions and hinders the overall performance. For this reason, multi-agent RL algorithms have evolved. Multi-agent deep Q-learning algorithms are proposed for optimizing offloading and resource management in the MEC network in [35]. Despite the enhanced computational efficiency and reduced communication latency contributions of MEC, its computing resources are restricted. Therefore, vehicles with huge unused resources in urban areas are utilized for offloading computing tasks attempted through vehicular fog-edge computing [36]. In addition, the vehicular environment is highly dynamic; hence, the training and testing phase possesses a deviation in its environment. In these aspects, the recent DRL algorithms create discrepancies implying indecisive behavior in a rapidly changing environment. To circumvent all the mentioned constraints, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) among MARL schemes is broadly accepted. The centralized training and distributed execution nature of MADDPG means that every agent gathers knowledge about the others in the training phase and implements the actions independently from the gained knowledge. However, complications emanate as the critic's intervention develops proportionally to the number of agents also accompanied by incoordination among the agents. In rapidly changing dynamic scenarios, infeasibility in allocating unique resources to each vehicle and the implications of spatially distributed vehicles have contributed to the DIRAL mechanism to enhance PRR performance [37]. The conventional optimization techniques fail to satisfy the expectations of a highly scalable network with channel uncertainties and, hence, distributed resource allocation algorithms have manifested [38]. The resource management framework is modeled as a multi-agent system in which the platoon leader acts as an agent and takes action purely based on channel information gathered locally [39]. The platooning of vehicles is a crucial application of URLLC architecture. The aggregation of time-sensitive safety messages and traffic updates is incredibly significant. The timing accuracy of these messages is framed as Age of Information (AoI). The DQN-based dynamic environment learning method is demonstrated to forecast optimal MCW for intelligent vehicular nodes, guaranteeing age fairness in V2I communications [40]. The AoI minimization problem in the AB-EH-CRN Network is handled using the HAEP-DDPG algorithm, particularly evolving to the continuous and discrete action space combinations [41]. The performance optimization of AoI-aware radio resource allocation has been investigated in a Manhattan V2V network recently [42]. However, the proposed scheme focuses on delivering CAM messages with 99% transmission probability by guaranteeing minimized time lapse in the platooning URLLC application.
## 3 Full Characterization of the V2G
### Social Dimension  
The Social Dimension seeks to expose all factors that somehow change the way of life of people affected by ESS. This dimension is divided into four attributes, each with its sub-attributes (refer to Figure 4):  
Figure 4: Tree of social dimensions (Source: the authors).  
* Jobs The Jobs attribute is divided into two sub-attributes:
* Direct jobs: The number of direct jobs generated during the construction, operation, and maintenance of the V2G.
* Quality and safety: This sub-attribute characterizes V2G based on the type of labor needed for its operation. It assesses the level of specialization required and the overall safety of the job in terms of its viability.
* Perception of Comfort The Perception of Comfort attribute is defined by four sub-attributes all related to people's \"feelings\" concerning V2G. These are:
* Noise pollution: This attribute can be defined by the amount of dB emitted by the V2G at a comfortable distance (this varies according to the system location).
* Visual pollution: This sub-attribute is quite subjective, as it depends on the perception of each person; it can be determined through local interviews asking what people think about the implementation of a system that visually modifies the environment they live in.
* Olfactory pollution: This sub-attribute is quite subjective, as it depends on the perception of each person; it can be determined through local interviews asking what people think about the implementation of a system that olfactorily modifies the environment they live in.
* Thermal pollution: This sub-attribute is defined by the emission of heat by the energy storage system and whether this heat is sufficient to alter the environment around it.
* Impacts on the Development  
The Impacts on the Development attribute refers to factors that can both positively and negatively impact social development activities in the region. This attribute has two sub-attributes:  
* Impacts on infrastructure and economic activities: This item measures the impacts caused by the creation of new infrastructures and the development of new economic activities (creation of new businesses) due to the implementation of V2G.
* Human Development Impacts: This value is known as the regional HDI, an item that can be evaluated considering the improvements or negative effects related to V2G.
* Social effects due to environmental imbalance These fall into the following sub-attributes:
* Impacts on health--considering the elimination of automobiles, the combustion within the studied city would experience health improvements due to the reduction of greenhouse gas emissions.
* Impacts on agriculture.
## 4 Results and Discussion  
Multiple measurements of different surface topographies are performed to validate the performance of the custom-built MA. In the following, the investigations are divided into two parts. At first, measurements are performed to validate the function of the CSI with a focus on the MA. These measurements are performed on a vibration damped table without applying external vibrations during the measurement. In addition, the measurement process in the second part is disturbed by an externally stimulated vibration and carried out without vibration damping. This part two deals with the capability to correct disturbances in CSI data sets.
## 2 Methods and Materials
### Temperature Correction Models  
Several temperature correction models have been published. In one approach, a linear relationship applicable to electrolytic solutions can be used to describe the temperature effect on concrete resistivity [30; 31]:  
\[\rho_{T}=\rho_{T_{0}}[1+\alpha(T_{0}-T)] \tag{2}\]  
where \(\rho_{T}\) (\(\Omega\).m) is the resistivity of the material measured at a temperature \(T\) (\({}^{\circ}\)C), \(\rho_{T_{0}}\) (\(\Omega\).m) is the resistivity of the material measured at a reference temperature \(T_{0}\) (\({}^{\circ}\)C), and \(\alpha\) (\({}^{\circ}\)C\({}^{-1}\)) is a temperature coefficient depending on the nature of the material. Values of \(\alpha\) for concrete have been reported ranging from 0.022 to 0.035 \({}^{\circ}\)C\({}^{-1}\)[30; 31]. Some authors advocate that a linear law such as Equation (2) is the most convenient to standardize resistivity measurements in practical applications [16; 20].  
Figure 1: Schematic diagram of the PCB sensor with 19 electrodes (E1 to E19).  
In another approach, Arrhenius types of relations [16; 20; 31; 32; 33] are used as a popular method to describe the effect of temperature on electrical resistivity:  
\[\rho_{T}=A\exp(\frac{E_{a}}{RT}) \tag{3}\]  
where \(\rho_{T}\) (\(\Omega\).m) is the resistivity of the material measured at a temperature \(T\) (K), \(A\) (\(\Omega\).m) is the nominal resistivity when \(T\) (K) \(\rightarrow\infty\), \(R\) = 8.314 J mol\({}^{-1}\) K\({}^{-1}\) (the universal gas constant), and \(E_{a}\) (J/mol) is the activation energy for resistivity which quantifies the amount of thermal energy required to promote a mole of the ions in concrete pore solution from equilibrium state to activated state [34]. Values of \(E_{a}\) have been reported to range from 16.9 kJ/mol to 42.77 kJ/mol [4; 16], depending on the degree of saturation and the microstructure properties [35]. It was emphasized that using a constant value of \(E_{a}\) for all types of concrete could lead to misleading results [31; 36].
## 1 Introduction
Then, we discuss orchestrators' issues, which have not been discussed among the players, and propose a new MEC/Cloud Orchestrator architecture. Figure 1: Benefits of MEC compared to cloud systems. This paper additionally proposes a deployment method to enhance the feasibility of MEC applications within this architecture. This deployment method is discussed and detailed in two cases. The first case is that the Cloud Owner has the MEC/Cloud Orchestrator, and the second case is that the Cloud Owner and Private/Local Operator have the MEC/Cloud Orchestrator divided into function levels. The implementation method is designed for each case, and the advantages and disadvantages are discussed. From the end user's perspective, these implementation methods, including SDN/NFV, improve Quality of Service (QoS) and Quality of Experience (QoE), as SDN/NFV contributes to flexible application deployment based on user locations as well as enhanced scalability of E2E network connections [18]. Next, we design the entire system to verify the superior performance of the proposed architecture. The implemented system is deployed as an edge cloud at Ookayama Campus of Tokyo Institute of Technology. A Proof-of-Concept (PoC) testbed for Beyond 5G was constructed by installing and deploying radio units outdoors. The testbed was built with State-of-the-Art (SOTA) production hardware (e.g., 5G, Sub-6/mmWave, fully virtualized). The proposed system with MEC is validated in this PoC field. The verification results suggest the achievement of lower-latency services and further proves that stable communication is enabled. This demonstration provides our readers with an example of the uniqueness of the proposed architecture. The remainder of the paper is organized as follows. Section 2 introduces related works in order to help readers understand SOTA MEC and highlight our contributions. Section 3 presents the overall system architecture. First, it reviews the overall concepts of the proposed orchestration and network architecture. Second, it illustrates the diagrams of each player and explains the target scenarios. Section 4 elaborates on the proposed implementation of orchestration and edge computing. Section 5 describes our testbeds, outdoor PoC, and the initial measurement results. Finally, Section 6 provides concluding remarks.
## 4 Experiment
### Depth Estimation Completeness Analysis  
In light field image depth estimation, completeness metric is used to measure the coverage degree of depth map on target scenes, reflecting how much original scene depth information is contained in depth results [36]. This study calculates completeness by measuring the distance between laser point cloud and estimated depth map, counting the error between each pixel's depth value and ground truth, then defining a threshold \(\tau\), dividing the number of pixels with distances less than the threshold by the total number of ground truth pixels, thus obtaining the depth estimation completeness indicator:  
\[\text{Completeness}= \frac{\Sigma_{\text{p}\in\text{P}}\text{I}(|\text{d}_{\text{p}} -\text{d}_{\text{p}}^{\text{gt}}|<\tau)}{|\text{P}|}, \tag{12}\]  
The closer this value is to 1, the higher the coverage of the reconstructed point cloud on the real scene, indicating better reconstruction completeness.  
From the completeness indicator comparison results in different dataset scenes in Table 7, we can observe that in HR260-Outdoor and R32-Indoor, compared to Zeller and BLADE algorithms, Our algorithm improved the completeness of recovered depth scenes by at least 2.5% and 6.6%, performing better in completeness. In R12-Indoor, Our algorithm's completeness indicator is slightly lower than BLADE algorithm, but still shows about 1% improvement over Zeller. It is worth noting that due to the large number of laser point clouds in R12-Indoor, the scene completeness calculated by all three algorithms is relatively low.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Depth Pixel Density** & **BadPixel(0.5)** & **RMSE** \\ \hline HR260-Indoor & 60.2\% & 14.6\% & 2.7\% \\ R32-Indoor & 51.4\% & 20.5\% & 0.8\% \\ R12-Indoor & 53.7\% & 21.0\% & 0.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative experiments after prior knowledge optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Depth Pixel Density** & **BadPixel(0.5)** & **RMSE** \\ \hline HR260-Indoor & 34.2\% & 5.4\% & 1.4\% \\ R32-Indoor & 38.7\% & 5.5\% & 0.8\% \\ R12-Indoor & 38.1\% & 5.0\% & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative experiment without prior knowledge optimization.