esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proactive Geospatial Information Services (PGIS)
### Linked Services in PGIS  
To maximize the usage of the geospatial information services, an important idea is to link all the services together. Figure 2 illustrates the connections among the above mentioned four types of services. For the data services in the middle of Figure 2, the inputs are the data collected from sensor web services and the user demands from the social services, and the outputs are the commands to the processing services and the derived results to the social services. For the sensor web services, they can be controlled by the users in the social services and out collected data to the data services. The processing services deal with data from the data services and output the processing results to the data  
Figure 1: Workflow of PGIS.  
services or to the users in the social services directly. The social services manage data such as reading, editing, and writing data, control the sensor web services and provide data as a part of sensor web, and share the data and knowledge in the social environment.  
The services can also be linked on different levels. By connecting the data services with the sensor web services, users can become aware of the environment in near real-time. By connecting the data services, sensor web services, and processing services together, users can analyze near real-time data and discover useful information and knowledge. By connecting the data services, sensor web services, processing services, and social services altogether users are both data providers and knowledge providers through the service chain.  
Figure 3 gives an example illustrating the connections of the four types of services. Assuming there is a fire accident in a suburban area. The users in the social service layer send data requirements to the sensor web services to ask for related data, such as satellite images of the fire domain and the nearby traffic data. This step is labeled as 1 in the figure. Then, the sensors in the sensor web services will acquire related data in near real-time and send them to the data servers. This step is labeled as 2 in the figure. The data services will then store, describe, and publish data in a well-defined standard manner, and then send data to the processing service layer. This step is labeled as 3. Finally, the processing services process data by service chain in a distributed way, and send the final results to the users in the social service layer, which is labeled as 4. These four steps form a closure circle in response to the geospatial information services in a fire accident.  
Figure 2: Linked services in PGIS.
## 1 Introduction  
In the field of surface roughness, roughness parameters are used to evaluate the surface roughness [1, 2]. From the roughness profile, roughness parameters are calculated. The roughness profile is calculated from the primary profile (measurement data) and the mean line (shape and waviness component). Then, applying the low-pass filter to the primary profile, the mean line is obtained. In addition, subtracting the mean line from the primary profile, the roughness profile is obtained. The Gaussian filter (GF, ISO 16610-21 [3] and JIS B 0634 [4]) and spline filter (SF, ISO 16610-22 [5, 6]) are used as one of the standard low-pass filters. However, when these filters apply to the primary profile that includes outliers such as spikes, the waviness and shape components cannot be extracted accurately. To solve this problem, a robust filter that extracts the mean line without the effect of the outlier is proposed. The standard for robust Gaussian filter, ISO 16610-31: Robust profile filters: Gaussian regression filters (GRF) [7, 8, 9, 10], was established and published in 2016. However, ISO/ TS 16610-32 [11], a robust spline filter (RSF) standard in the technical specifications stage, has a serious deficiency. Therefore, it was withdrawn in 2015.  
ISO 16610-31, which was an already established standard, has problems. It only shows the method and a few examples, such as normal roughness and spike. Therefore, the known characteristics of this filter are insufficient. Moreover, the data of robustness to spike showed as a robustness example does not include the roughness component in the primary profile. In roughness measurement, the roughness component is always included. The presence or absence of the roughness component may change the characteristics of the filter. If the characteristics are different, the no roughness component case information is useless; on the contrary, it will only cause confusion in the measurement sites.  
ISO 16610-30 [12] is the robust filter basic concept standard. ISO 16610-30 shows spikes, steps, and slopes as outlier examples: the robust filters should behave robustly to these outliers. However, ISO 16610-31, a robust filter standard, can only behave robustly to the spikes and cannot behave robustly to steps and slopes [13]. The specifications required in the robust filter basic concept standard are not satisfied in the actual robust filter standard, resulting in inconsistencies between standards. Therefore, ISO 16610-31 needs to update the standard.  
In this way, a filter that cannot satisfy the specifications required by the basic concept standard may be established as a standard. Therefore, additional validation and clarification of the characteristics of the filters that have already been proposed are important for the establishment of standards while also avoiding confusion in filter selection at the measurement sites [13; 14; 15].  
Therefore, this paper clarifies the characteristics of the SF-based robust filters, summarizes the points to be considered when using RSF, and identifies the criteria for RSF selection.
## 3 Results  
### Object Oriented Classification with Random Forest  
Previous research has shown that although RF can handle high dimensional data, classification accuracy remains relatively unchanged when only the most important predictor variables are used [80, 81]. Therefore, we ran the RF classification several times and recorded the importance ranking of the less important variables of each iteration. In addition, a correlation and collinearity analysis was performed, leaving a set of only the most important and uncorrelated variables. Four data layers were removed from our data layer stack used in the final classification because they were mostly redundant and did not offer new information for classification: PlanetScopes's Band 2 and its NDVI, as well as the fused Band 6 and 7 (Red Edge). Table 7 summarizes the final layer stack used, as well as the MDA and MDI scores for each layer. Table 8 details the variable importance for sorting each of the land cover categories. These metrics suggest that for most land cover categories, elevation was the most important variable for their classification. All used PlanetScope layers were also consistently useful for classification, as were the fused NIR and Narrow NIR bands. The fused NDVI and NDA were particularly helpful for differentiating the _Cedrela_--cedar cover. The classification method yielded a cumulative classification error ranging from 2.76% for Floreana to 6.2% for Santa Cruz (Table 9). Permanent crops were the most commonly misclassified cover, as it was sometimes misclassified as a transitory crop or as mixed forest. Table 9 summarizes the error rates, as well as the most common classification errors (>0.1 error rate) revealed in the confusion matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Data Layer** & **Mean Decrease Accuracy** & **Mean Decrease Gini** \\ \hline Orthophoto & Elevation & 95.59 & 94.96 \\ Orthophoto & Slope & 62.39 & 63.39 \\ PlanetScope & B1—Blue & 63.00 & 69.28 \\ PlanetScope & B3—Red & 49.06 & 59.35 \\ PlanetScope & B4—NIR & 57.42 & 69.64 \\ S2 + PS & B2—Blue & 42.37 & 48.39 \\ S2 + PS & B3—Green & 50.15 & 57.28 \\ S2 + PS & B4—Red & 48.47 & 49.41 \\ S2 + PS & B5—Red Edge & 52.94 & 44.28 \\ S2 + PS & B8—NIR & 54.23 & 60.81 \\ S2 + PS & B8A—Narrow NIR & 57.88 & 65.54 \\ S2 + PS & B11—SWIR & 54.79 & 50.34 \\ S2 + PS & B12—SWIR & 55.04 & 48.65 \\ S2 + PS & NDVI & 54.59 & 62.92 \\ S2 + PS & NDVRE & 55.79 & 58.22 \\ S2 + PS & SI & 60.47 & 46.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Data layer stack and variable importance.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
###### Abstract  
Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine's vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.  
misalignment; fault prediction; combined prediction; multivariate grey model; quantum genetic algorithm; least squares support vector machine +
Footnote †: journal:
###### Abstract  
With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety.  
InSAR; D-InSAR; single-pass; repeat-pass 2020
## 5 Interactive Function Development and Plug-In Application Interface Design
### Interactive Function Development for Unity platform  
For more adequate applications of model information, the Unity platform was selected in this study to further develop the interactive function and the design of the user interface. By focusing on enhancing the link between the user and the information, development was divided into three functional projects for functional development and interface design, namely the \"visual information list\", \"interactive information list\", and \"interactive animation\".  
Taking users' implementation of the integration platform project at the restoration site as an example (Figure 7): (1) The user can review the information of any restored components or components awaiting restoration in the project through the \"visual information list\" option (refer to the circle number 1 marked in Figure 7), conduct comparisons of the actual components at the restoration site, and browse components at various stages of restoration. (2) When the user finds any error in the component information and has to revise the component information or add restoration suggestions, the user can input and store the updated information content through the text box in the \"interactive information list\" (refer to the circle number 2 marked in Figure 7), and the updated information will be automatically uploaded in real-time to the cloud service framework (WebAPI). The restoration information may be added or overlain continuously through such lists and may be compared with the restoration information of the same component at different stages in the list. Upon completion of information updating on the platform, the user updates the Revit model information through the plug-in programming interface (iData+) of the platform administrator. (3) Since the Revit platform only has the function of viewing the model, this platform provides the application of the \"interactive animation\" option (refer to the circle number 3 marked in Figure 7). In addition to providing simple animation of the camera path, it also provides interactive animation of the disassembly and assembly of building components, which is more in line with the needs of restoration designs for historic buildings and assists in the communication between the design end and the construction end. In the future, its function development may be customized according to the user's needs.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications
### Intelligent Industrial Automation  
Founded on the principles of supply chain management and optimization, autonomous machinery, additive manufacturing processes, data analytics, and the IoT, Industry 4.0 [99] has ignited the shift toward industrial automation. Conversely, the emerging Industry X.0 paradigm [100] aims to synergize industrial automation's diverse facets by integrating artificial intelligence. Interconnected factories are central to this vision as crucial hubs of significant big data, pivotal for guiding decision-making processes. Thus, it can be expected that future factories will require assured high-throughput connectivity for thousands of devices, frequently with sub-millisecond latency.  
Enabled by symbiotic technology, the deployment of industrial IoT facilitates real-time monitoring, predictive maintenance, automation, and the realization of smart factory systems, revolutionizing manufacturing processes for enhanced efficiency, productivity, and scalability, with symbiotic connections with diverse radio systems, combining a variety of manufacturing processes, thereby raising efficiency and scalability to a comprehensive level, and ensuring the safety of safety-critical systems and operators. This kind of solution allows the digitization and automation of the manufacturing industry, promoting efficient, intelligent, and sustainable practices [67; 91].  
Intelligent driving and industrial transformations drive essential 6G needs, culminating in uMUB, uHSLLC, and uHDD service categories. These emerging services necessitate holistic communication, sensing, and computation integration, inspiring the fusion of photonics and AI [18].  
The URLLC service classes encompass a wide range of use cases within upcoming ITS, future industrial facilities, and the management of unmanned swarms of aerial vehicles or robots. Within the realm of ITS, the integration of sensors, cameras, radars, AI/ML, and V2X technologies finds prominence. V2X assumes a pivotal role by furnishing AI-equipped vehicles with contextual information from smart roads, automated highways, and intelligent autonomous intersections.
## 2 Signal Model  
Consider a multi-input, multi-output joint radar-communication system, which is equipped with a uniform linear array (ULA), consisting of M transmit antenna sensors, as shown in Figure 1. Moreover, the radar and communication receivers consist of \(M\) transmit antenna elements. The antenna array transmits an integrated waveform for radar target detection that is also decoded at the communication end for detecting encoded information.  
Let \(s_{m}(n)\in\mathbb{C}\) denote the \(n\)th sample of a discrete waveform, consisting of \(N\) samples, emitted by the \(m\)th antenna, where \(m=1,\ldots,M\) and \(n=1,\ldots,N\). Let \(\mathbf{s}(n)\) denote a vector that collects the \(n\)th samples of the waveforms transmitted by all antennas, i.e., \(\mathbf{s}(n)=[s_{1}(n),\ldots,s_{M}(n)]^{T}\). Then, the far-field waveform in the direction \(\theta\) is given by  
\[\mathbf{x}(n;\theta)=\mathbf{a}^{H}(\theta)\mathbf{s}(n) \tag{1}\]  
where  
\[a(\theta)=[1,e^{-\jmath 2\pi dsin(\theta)/\lambda},\ldots,e^{-\jmath 2\pi(M-1)dsin( \theta)/\lambda}]^{T} \tag{2}\]  
is the transmit steering vector, with \(\lambda\) being the wavelength and \(d\) the inter-element spacing between the individual antenna elements. Let \(\mathbf{S}=[s_{1},\ldots,s_{N}]\) be the \(M\times N\) space-time transmit waveform matrix.  
Let \(\mathbf{x}_{R}=[x_{R}(0),\ldots,x_{R}(N-1)]^{T}\) be the desired radar waveform and \(\mathbf{x}_{C}=[x_{C}(0),\ldots,\allowbreak x_{C}(N-1)]^{T}\) be the desired communication waveform. The transmit waveform matrix \(\mathbf{S}\) is designed such that the \(\mathbf{x}_{R}\) is synthesized in radar direction \(\theta_{R}\) and \(\mathbf{x}_{C}\) in communication direction \(\theta_{C}\), respectively, where \(\theta_{R}\
eq\theta_{C}\), i.e.,  
\[\mathbf{a}(\theta_{R})\mathbf{S}=\mathbf{x}_{R}^{T} \tag{3}\]  
and  
\[\mathbf{a}(\theta_{C})\mathbf{S}=\mathbf{x}_{C}^{T}. \tag{4}\]  
Equations (3) and (4) can be combined as  
\[\mathbf{A}^{H}(\Theta)\mathbf{S}=\mathbf{X}, \tag{5}\]  
where \(\mathbf{A}(\Theta)=[\mathbf{a}(\theta_{R}),\mathbf{a}(\theta_{C})]\) and \(\mathbf{X}=[\mathbf{x}(\theta_{R}),\mathbf{x}(\theta_{C})]^{T}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline DFRC & Dual-Function Radar Communication \\ EW & Electronic Warfare \\ FANET & Flying Ad hoc Network \\ FFRED & Far-Field Radiated Emission Design \\ GA & Genetic Algorithm \\ IO-AW & Iterative Optimization with Amplitude Weighting \\ IRCS & Integrated Radar and Communication System \\ ISAC & Integrated Sensing and Communication \\ LFM & Linear Frequency Modulation \\ MIMO & Multi-Input Multi-Output \\ OFDM & Orthogonal Frequency Division Multiplex \\ PAPR & Peak-to-Average Power Ratio \\ PRI & Pulse Repetition Interval \\ QPSK & Quadrature Phase-Shift Keying \\ RCC & Radar-Communication Coexistence \\ SER & Symbol Error Rate \\ ULA & Uniform Linear Array \\ WS & Waveform Synthesis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.
## 3 Method
### Combination Function  
Combination function analyzes the rating-comment matrix and captures the sentiment bias between the two by presetting three scenarios. The exact flow of the combinatorial function is shown in the Figure 3.  
In the combination function, we build the sentiment analysis module and denote each user's sentiment vector by \(v_{s}\). We use \(R_{c}\in\mathbb{R}^{n\times m}\) as the user-item rating matrix, where each column is extracted as a rating vector \(v_{r}\). We then use a simple but efficient function to build a joint vector \(v_{e}\) with \(v_{r}\) and \(v_{s}\):  
\[v_{e}=v_{s}\oplus v_{r} \tag{9}\]  
where \(\oplus\) is a combinatorial function of the SCTD. \(v_{r}\) is a \(q_{r}\)-dimensional vector with entry \(r_{ui}\). \(v_{s}\) is a \(q_{s}\)-dimensional vector with entry \(s_{ui}\). \(v_{e}\) is an n-dimensional vector, where \(q_{r}\) equals the number of items evaluated by the user. In practice, \(q_{r}\geq q_{s}\). We assume that \(q_{r}=q_{s}\). When a user rates an item and also writes a review, we can obtain the rating \(r_{ui}\) via \(r\) and calculate the corresponding sentiment score \(s_{ui}\) via SCS. We define the difference between \(s_{ui}\) and \(r_{ui}\) as the opinion bias \(Ob\). We want to enhance expressiveness through reviews and ratings while filtering out useless reviews. Thus, the combinatorial function \(\oplus\) can be generalized to the following three cases.  
Figure 2: SCS module.  
Auttofill: When the user \(u\) only rates or comments one item \(i\), this time can cause gaps in the data and affect the accuracy of the combined function. To avoid this, \(\oplus\) fills \(r_{ui}\) or \(s_{ui}\).
* Selective Fill: If the user \(u\) has rated and commented on the item \(i\), then a Drop judgment will be made first. If neither the user nor the item is deleted, \(\oplus\) will be filled using a fixed weighted linear function \(v_{ei}=\varepsilon_{ui}+(1-\varepsilon)r_{ui},\varepsilon\in(0,1)\).
* Drop: If \(Ob_{ui}<\mu\), it is not processed. If \(Ob_{ui}\geq\mu\), \(\oplus\) decreases the user's sentiment rating \(s_{ui}\) for item \(i\). If \((drop_{u}/q_{s})<\iota\), the same is not processed. If \((drop_{u}/q_{s})\geq\iota\), \(\oplus\) will remove user \(u\). \(drop_{u}\) is the number of deleted comments. \(\mu\) and \(\iota\) are predefined thresholds.  
According to the processing corresponding to the above situation, the enhanced rating matrix \(R_{c}\) is constructed, which is expressed by the following formula:  
\[R_{c}=CF(v_{s},v_{r}|\beta) \tag{10}\]  
where \(\beta\) represents \(\varepsilon,\mu,\iota\).