esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The performance of the hybrid filter is analyzed in an MG integrated power system network. The MG employs PV, WT, and BESS. A three-phase hybrid filter is connected as an interfacing device between MG and the grid. The BESS is connected to the DC link of the hybrid filter and is used for regulating voltage and frequency at load terminals. The battery at the DC bus ensures power balance during a change in solar-PV array power and wind power generation with respect to consumer load demand. During periods of high renewable power generation and less load demands, the BESS charges to accumulate surplus renewable energy. Similarly, during less renewable power generation and high load demands, BESS discharges to compensate deficit load demands. The behavior of MG is performed under balanced and unbalanced non-linear loads. The AMDWPT is used as a controlling technique in a hybrid filter, which provides better performance compared to the conventional DWPT technique. The THD percentage is found better in AMDWPT underbalanced and un-balanced non-linear loads. The proposed MPPT technique for PV and WT has worked perfectly under a change in solar irradiation. The performance of the system has been found satisfactory under a change in generation and load perturbations.  
Conceptualization, S.R.D., P.K.R., and A.K.S.; data curation, S.R.D., P.K.R., A.K.S., and T.S.B.; formal analysis, T.S.B., and N.M.K.; funding acquisition, T.S.B., H.H.A., and P.S.; project administration, S.R., T.S.B., N.M.K., H.H.A., and P.S.; investigation, S.R.D., P.K.R., A.K.S., and S.R.; methodology, S.R.D., P.K.R., A.K.S., and S.R.; resources, A.K.S., T.S.B., and N.M.K.; software, S.R.D., and A.K.S.; supervision, P.K.R.; visualization, S.R.D., and T.S.B.; writing--original draft, S.R.D., P.K.R., A.K.S., S.R., T.S.B., and N.M.K.; writing--review and editing, T.S.B., N.M.K., H.H.A., and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 1 Introduction
### Contributions  
Recently, the 3 rd generation partnership project (3 GPP) defined the network data analytics function (NWDAF) in Release 15, which has the role of data collection and processing from different network functions (NFs) towards ML-aided network optimization [16]. NWDAF relies on network function virtualization (NFV), a key enabling technology beyond 5G networks where virtual machines replace basic network operations and hardware elements, leading to improved network deployment flexibility and hardware decoupling. Considering the advances in AI/ML-aided integration in wireless networks and the high interest of academic and industrial stakeholders, as underpinned by the NWDAF-related efforts, this work presents an AI/ML-aided distributed trustable engine (DTE), collecting data from sources across the 6G infrastructure and employing AI/ML modules for anomaly detection purposes, guaranteeing a high security and privacy level and defining appropriate mitigation measures. In addition, the DTE's design supports the creation of high-level intents that can be mapped to relevant mitigation measures to reduce the impact of cyberattacks on the network. More specifically, our contributions are as follows:  
* We present and discuss DTE's architecture and its building blocks, supporting data collection and management, AI/ML model training and deployment, and anomaly detection in accordance with NWDAF specifications.
* To strengthen the privacy level across distributed 6G environments and avoid the drawbacks of centralized ML, an FL extension for DTE's architecture is analyzed.
* The attack classification accuracy of AI/ML-aided anomaly detection is thoroughly evaluated, providing, in detail, an ML model training framework to overcome dimensionality issues of datasets with a high number of features. For this purpose, we analyze and use two open datasets from diverse cyberattacks in the core and edge parts of the network and discuss the performance-complexity trade-off of different ML algorithms.  
Experiments show that AI/ML-aided anomaly detection and the adoption of the NWDAF logic by the DTE allow the efficient training of ML models and the exploitation of their strong classification capabilities. In this manner, DTE provides highly accurate real-time inference, thus enabling network operators to safeguard their infrastructure and impose relevant mitigation measures, following the principles of intent-based networking. We believe that the proposed distributed AI/ML security solution and its FL-based extension can facilitate cooperation among mobile network stakeholders, as it can overcome potential barriers related to user and commercial data privacy. More importantly, it can enable the smooth integration of AI/ML-aided cyberattack detection and mitigation in 6G networks, accommodating the needs of heterogeneous services and network nodes with different capabilities.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Internet of Things (IoT) and Industrial Internet of Things (IIoT) Technologies  
The advancement in IoT has lessened direct human intervention with objects that are capable of sharing information among themselves and the environment. Designed with sensing and actuating capabilities, these objects have become smart, considering their ability to communicate and autonomously react to real-time or physical events through a unifying infrastructure [49; 50]. The interactions of sensors with Radio Frequency Identification (RFID) and the introduction of Internet Protocol (IP), which enables the identification of nodes and their location as well as easy routing on the Internet [51], have transformed WSNs into IoT. Figure 5 shows the forecasted increase in the IoT devices by 2030, however, this is an estimate as signifies by the asterisk. As the number of IoT devices grows tremendously to about 50 billion worldwide [2], the IPv4 identification process will be exhausted, and IPv6 becomes useful for supporting this increase in the IoT paradigm.  
Industrial IoT is simply the introduction or application of IoT in industries, especially in manufacturing [20; 50]. IoT connectivity is mostly consumer-grade and is used for building automation, human wearables, and messaging, etc., while in IIoT, the connectivity is secured and targeted at the automation of industrial processes such as in defense, aerospace industries, etc. [50]. Table 2 further shows the comparisons between IoT and IIoT based on their respective characteristics [21].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Data Volume** & **Connectivity** & \begin{tabular}{c} **Exchange of** \\ **Information** \\ \end{tabular} & **Market Segment** & **Criticality** & **Impact** \\ \hline IoT & Big data & \begin{tabular}{c} Consumer grade, e.g., \\ smart homes, \\ entertainment, etc. \\ Business to consumers, \\ business to business to \\ consumers \\ \end{tabular} & \begin{tabular}{c} Service providers, \\ consumers, limited \\ enterprises, and \\ small businesses \\ \end{tabular} & \begin{tabular}{c} Not stringent \\ (Excluding medical \\ applications) \\ \end{tabular} & Revolution \\ \hline IIoT & Specific and limited data & \begin{tabular}{c} Secured, e.g., health care, \\ energy, etc. \\ \end{tabular} & \begin{tabular}{c} Business to business \\ only \\ \end{tabular} & \
\begin{tabular}{c} Enterprises and \\ Industries \\ \end{tabular} &
\begin{tabular}{c} Critical for \\ timing, reliability, \\ privacy, and \\ security \\ \end{tabular} & Evolution \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of IoT and IIoT characteristics.  
Figure 5: Forecast of the number of IoT-connected devices by 2030 [2].
## Materials and Methods
### Analysis of the Effect of Analog Output Converter Resolution on Deviation of Output Voltage  
The conceptual basis of stepless control through the use of an inverter can be outlined as follows. A standard inverter was equipped with a manual control to adjust frequency with an analog potentiometer. Each inverter may be adjusted using an external signal. Both voltage (mV) and current (mA) signals may be used and the signals can be received using an AO (analog output) converter connected to a microcomputer. This interface may be used at various resolutions, as both 8-bit and higher resolutions are possible (Table 1). The typical AO output is 5000 mV. A minimal step in the voltage was calculated at this stage for other resolutions of the AO interfaces. For example, minimal steps of 19.608 mV and 4.888 mV were achieved at resolutions of 8 and 10-bits, respectively. This is an absolute error of output voltage.  
On the basis of Table 1, the deviation \(r_{e}\) of the voltage signal generated by the AO converter for the 8, 10, and 12-bit transmission rates were estimated using the following equation:  
\[r_{eAO}=a_{eAO}/U\times 100\% \tag{1}\]  
The values of \(r_{eAO}\) decrease significantly with the voltage growth (Figure 1). The graph (Figure 1) displays a range of up to 1000 mV or 20% of the maximum value of a voltage of 5000 mV.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bit** & **Resolution** & **Minimal Step of Voltage, mV** \\ \hline
8 & 256 & 19,608 \\
10 & 1024 & 4888 \\
12 & 4096 & 1221 \\
14 & 16384 & 0.0297 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Influence of analog output (AO) converter resolution on minimal stepping of voltage (absolute error).  
Figure 1 shows the low deviation at higher voltages and particularly high deviation values within the low voltage range, especially for the 8-bit resolution of AO. This error is much lower at higher resolutions of AO, due to the step changes in the voltage of the AO converter, especially at low voltage output.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 4 Hyper-Parameters Optimization
### Mutation methods  
The mutation probability plays a crucial role in maintaining genetic diversity within the population and avoiding premature convergence to suboptimal solutions. A higher mutation probability can introduce more variation, potentially leading to better exploration of the solution space, but it may also disrupt good solutions. Conversely, a lower mutation probability helps preserve beneficial traits but risks the algorithm becoming stuck in local optima. The following were tested: variant mutation and random resetting mutation. Figure 7 illustrates a graph comparing the fitness scores of the best routes obtained using two different mutation methods: variant mutation and random resetting mutation (described in Section 3.3.4). The experiment varied mutation rates from 0 to 1 in increments of 0.1, with a population size of 1000 and 500 charging station points to increase complexity.  
The results indicate that the variant mutation method (in blue) consistently achieved superior fitness scores compared to the random resetting mutation (in orange). This performance gap is particularly notable at higher mutation rates, where the variant mutation method excelled significantly. Conversely, the random resetting mutation demonstrated inconsistent performance across mutation rates, making it difficult to determine an optimal rate for this approach.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 4 Results
### Reflectance-Based Results at SDSU  
There were three successful ground-based collects for Landsat 9 during the period from November 2021 to June 2023 using the SDSU vegetated sites in Brookings. The average values are shown in Figure 15, where the format is identical to Figure 14. The uncertainty bars are based on the current uncertainty estimate for the reflectance-based approach of \(\pm\)2.5% (k = 1) [17]. In OLI bands 1 and 2 (coastal aerosol and blue), there is a higher bias between the OLI and the field measurements, and this is mainly due to the very low surface reflectance of vegetation in these two bands coupled with atmospheric effects that occur for a surface reflectance of <0.2 in these spectral regions. An example of the typical surface BRF as a function of wavelength for Railroad Valley, Ivanph Playa, and an SDSU vegetated site is shown in Figure 16.  
Figure 14: A summary of the Landsat 9 OLI radiometric calibration results using 12 comparisons with the RadCaTS (November 2021 to June 2023). The results are shown as the ratio of the TOA quantities for each data product (e.g., spectral radiance or reflectance) to those determined using the RadCaTS.
## 2 Materials and Methods
### Remote Sensing Product  
The Global Land Surface Satellite (GLASS) product [13; 49; 50] was utilized as the representative remote sensing carbon flux product. This comprehensive product incorporates 14 different land variables, encompassing the prevailing LUE models presently accessible. The GPP variable obtained from GLASS offers a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days. For the purposes of this study, data spanning from 1999 to 2018 were employed for evaluation.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology
### Caching Mechanism
We utilize Mesh Info API for retrieving grids that correspond to the given parameters, as shown in Table 8.  
The curl command for requesting the simple mesh ID is as follows. curl -X GET -header 'Accept: application/json' '[[http://133.130.90.193/api/uti1/mesh?lower=34.57354@%20136.671033&upper=35.424788%20137.83](http://133.130.90.193/api/uti1/mesh?lower=34.57354@%20136.671033&upper=35.424788%20137.83)]([http://133.130.90.193/api/uti1/mesh?lower=34.57354@%20136.671033&upper=35.424788%20137.83](http://133.130.90.193/api/uti1/mesh?lower=34.57354@%20136.671033&upper=35.424788%20137.83)) 8116&landuse=landuse H21010@foratio=20'  
An example response of a grid that corresponds to the conditions is presented as follows:  
{ \"cached\": 0, \"center\": \"34.583333333 137.025\", \"date\": \"\", \"frequency\": 0, \"id\": \"51377002\", \"landuse\": \"landuse_H21010@\", \"percent\": \"27.0\", \"simpleID\": \"12701202\", \"timestamp\": 0 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline lower & 34.573540 136.671033 & Lower corner of Aichi prefecture \\ \hline upper & 35.424788 137.838116 & Upper corner of Aichi prefecture \\ \hline Land use & landuse\_H210100 & The parameter is landuse\_H210100, which indicates paddy field \\ \hline ratio & 20 & The grids that have 20\% of farmland in Aichi prefecture are expected \\ \hline \hline \end{tabular}
\end{table} 
Table 8: List of Mesh Info API parameters for pre-caching script. The information shows that the grid, simple mesh ID 12701202, contains 27 percent of paddy field. The number of grids, which contain more than 20 percent of paddy field in Aichi prefecture, was counted as 1471. After receiving the list of grids, getObservation is issued for requesting the data of those grids. The sequence of data and reaction of each component is described in Figure 4.  
This mechanism enhances the response time even for the first request in the study area. With pre-caching, the first request in the study area ensures response performance. The pre-caching mechanism can be expanded to other areas based on disk space and the admin's decisions.
# Multilevel Regularization Method for Building Outlines
## 2 Materials and Methods
### Local Regularization of Building Outline-Based Convex Hull Grouping
#### 2.3.3 Regularization of Line-like Node Set Using Least Squares Method  
The least squares method is an algorithm for fitting \(n\) points to minimize the overall error of their distances to a fitted line. It can be used to fit the boundaries of building outlines with noise. The calculation process assumes that the equation of the fitted line for point-like set \(V_{i}=\{V_{i0}\), \(V_{i1}\), \(V_{i2}\), \(V_{i3}\), \(\ldots\), \(V_{in}\}\) is \(y=kx+b\), and it seeks the values of \(k\) (Equation (7)) and \(b\) (Equation (8)) that minimize the residual sum of squares (RSS) (Equation (6)).  
\[RSS=min\sum_{i=0}^{n}e_{i}^{2}=\underset{a,b}{min}\sum_{i=0}^{n}\left[y_{i}- \left(kx_{i}+b\right)\right]^{2} \tag{6}\]  
\[k=\frac{n\sum_{i=0}^{n}x_{i}y_{i}-\sum_{i=0}^{n}x_{i}\times\sum_{i=0}^{n}y_{i}} {n\sum_{i=0}^{n}x_{i}^{2}-\left(\sum_{i=0}^{n}x_{i}\right)^{2}} \tag{7}\]  
\[b=\frac{\sum_{i=0}^{n}y_{i}-k\sum_{i=0}^{n}x_{i}}{n} \tag{8}\]  
when \(n\sum_{i=0}^{n}x_{i}^{2}-\left(\sum_{i=0}^{n}x_{i}\right)^{2}=0\), the slope \(k\) of the straight line becomes infinitely large. In this case, the equation of the line is \(x=\frac{\sum_{i=0}^{n}x_{i}}{n}\).  
Then, the perpendicular feet of all points in the set \(V_{i}\) onto the fitted line are solved, and the two points with the highest and lowest \(y\) values (or, if the \(y\) values are equal, those with the highest and lowest \(x\) values) are determined. The two points are grouped into a new set to replace point set \(V_{i}\). The specific process is illustrated in Figure 10.  
Figure 9: Schematic of point set categorization, Inside the green line indicates the individual collection of nodes after splitting. (**a**) Point-like node set; (**b**) Line-like node set; (**c**) Area-like node set.
## 2 Related Work
### Spatial Pyramid Pooling Structure  
Spatial pyramid pooling (SPP) is a technique for mapping an input image of any size into a fixed-size feature vector in a convolutional neural network that can adaptively split it into a pyramid and perform pooling at each split level. The network can handle images of any size and scale.  
[PERSON] et al. [29] incorporated an SPP module into a convolutional neural network for the first time. The SPP module divides the feature map into multiple grids at different scales and performs a pooling operation on each grid. Features of different scales were combined to improve the accuracy of the model. The SPPF [50] module greatly improved the computational speed by using a single pooling layer instead of multiple pooling layers in the SPP module. It incorporated two pooling operations of different sizes to obtain more contextual information. To further increase the calculation speed, the SimSPPF [20] module used the ReLU activation function, instead of the SiLU activation function, in the SPPF module. To improve the feature transfer capacity of the SPP module, cross-phase partial (CSP) coupling (SPPCSPC) was proposed [21]. However, the calculation speed decreased. In convolutional neural networks, SPP technology offers an effective solution for processing images of arbitrary sizes and scales and has been continuously optimized and improved.
# Analysis of 'Pre-Fit' Datasets of gLAB by Robust Statistical Techniques
## 1 Introduction
FSDA is distributed through GitHub [[https://github.com/UniprJRC/FSDA--accessed](https://github.com/UniprJRC/FSDA--accessed)]([https://github.com/UniprJRC/FSDA--accessed](https://github.com/UniprJRC/FSDA--accessed)) on 15 May 2021-- and the MATLAB community [[https://www.mathworks.com/matlabcentral/fileexchange/72999-fsda](https://www.mathworks.com/matlabcentral/fileexchange/72999-fsda)]([https://www.mathworks.com/matlabcentral/fileexchange/72999-fsda](https://www.mathworks.com/matlabcentral/fileexchange/72999-fsda)) (accessed on 15 May 2021). A complete documentation with examples, datasets and tutorials is available at [[http://rosa.unipr.it/FSDA](http://rosa.unipr.it/FSDA)]([http://rosa.unipr.it/FSDA](http://rosa.unipr.it/FSDA)) (accessed on 15 May 2021). A version of the suite with selected functionalities is also available in R at [[https://cran.r-project.org/web/packages/fadaR/index.html](https://cran.r-project.org/web/packages/fadaR/index.html)]([https://cran.r-project.org/web/packages/fadaR/index.html](https://cran.r-project.org/web/packages/fadaR/index.html)) (accessed on 15 May 2021) and in SAS, upon request to the JRC co-authors. )_ The analysis of pre-fit datasets reported in this article can be considered as our first attempt to apply robust statistical techniques as provided by the FSDA toolbox to PVT estimation. After a description of the typical data and the problems to be faced in Section 2, with a first essay employing the traditional robust estimator _least median of squares_ (LMS), we proceeded with the use of other FSDA functions for outlier detection still applied to relatively large samples of data. We did not use the full potential of FSDA, however, with the aim of comparing the results obtained at the beginning of Section 2 with LMS, we exploited _least trimmed squares_ (LTS) and _forward search_ (FS). In Section 3, a more precise analysis is done by considering samples whose cardinality is coherent with practical use: relatively short _time windows_ of ten samples. In Section 4, the results of our analysis are summarized together with an indication of the directions of future work. Figure 1: Raw actual pre-fit residuals recorded by station KIR0 on 4 November 2018. The pre-fit residuals for all satellites are depicted in blue color, while pre-fit residuals for GPS satellites 3 and 32 are depicted in red and black, respectively, to highlight some outliers.
###### Abstract  
Building information modeling (BIM) has been widely applied in conjunction with the industry foundation class (IFC) for buildings and infrastructure such as railways. However, a limitation of the BIM technology presents limitations that make designing the three-dimensional (3D) alignment-based information models difficult. Thus, the time and effort required to create a railway track model are increased, while the reliability of the model is reduced. In this study, we propose a methodology for developing an alignment-based independent railway track model and extended IFC models containing railway alignment information. The developed algorithm using BIM software tools allows for a discontinuous structure to be designed. The 3D alignment information connects different BIM software tools, and the classification system and IFC schema for expressing railway tracks are extended. Moreover, the classification system is fundamental for assigning IFC entities to railway components. Spatial and hierarchical entities were created through a developed user interface. The proposed methodology was implemented in an actual railway track test. The possibility of managing IFC-based railway track information, including its 3D alignment information, was confirmed. The proposed methodology can reduce the modeling time and can be extended to other alignment-based structures, such as roads.  
alignment; building information modeling (BIM); industry foundation class (IFC); interoperability; parametric modeling; railway track +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Main Results and Discussion
#### 4.2.2 Different Length of Observed Time Series  
For a more comprehensive evaluation, Table 2 lists the performance of the PCC module with different lengths of the observed time series. As the table shows, the PCC module improves the performance of the different backbone models across varying observation lengths. The improvements are more significant in shorter observed time series. This indicates that PCC can help to reduce the model's dependence on the length of the observed series. For instance, the DLinear model with PCC achieves better performance based on 8 h of observed time series than the original model based on 32 h of observation. Thus, our PCC module can help the model to capture the underlying patterns more effectively in the data scarcity scenario, leading to more robust and reliable results on the weather forecasting task with less data storage.