esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
As shown in [27], a deep-learning structure comprises BERT-Base and a final emotional and sentimental analysis classification layer. By considering two datasets of tweets, they attained an accuracy of 92 and 90% for emotion and sentimental analysis, respectively, from which it was possible to conclude that Bert's language modeling power contributes significantly to achieving a good tweet classification. Small datasets were used, and the Bert model could not classify long tweets due to a lower number of attention layers. Since then, new language processing models, including BERT, Roberta, AL-BERT, and T5, increased performance by experimenting with alternative pre-training approaches, updated model topologies, and bigger training corpora. In 2021, [PERSON] et al. developed an ARABERT model for various downstream tasks like Named Entity Recognition (NER) and Sentiment Analysis (SA). They achieved a 92% accuracy for the various downstream tasks [28]. As proposed in [29], the authors performed a self-training of the Arabic dataset that contains different dialects that cause low performance when a new dataset is added. The authors of [30] provided a sequence-to-sequence model that performs a text normalization of the Arabic dialect. The paper proposed an encoder/decoder model where the encoder accepts the input text, builds the features, and then collects the features to generate the final output. As explained in [31], the Naive Bayes model is applied to the spark dataset to collect tokens, and then the polarity terms are counted to measure the accuracy. The authors of [32] applied different-machine learning models to extract user reviews from Saudi institutions to improve the prediction that can help make accurate decisions for providing services. As presented in [33], the authors collected Arabic tweets that can explain and express the depression statements in the tweets. The novelty of the proposed model is that the authors applied many symptoms to classify the depression classes and then detect whether the applied sentiment contains depression terms or not. As shown in [34], the authors collected different Arabic tweets about the COVID-19 pandemic to detect the user reactions and feelings based on their polarity. In addition, the proposed model tried to predict the spread of the pandemic based on user reviews. Table 1 summarizes the learning method used and the demerits of the existing methods applied in tweet or text classification systems. The major disadvantages of the existing methods are that neither the conventional methods nor the models, such as recurrent networks or convolutional networks, can provide more significance to keywords that are significant in text categorization. Contextual awareness between words plays a key role in a tweet or text classification. Another significant disadvantage of the existing methods is that they are tested on small Arabic text corpora, and accuracy is also impacted due to outliers in data.
# Environmental Risk Source Analysis and Classification of Zones: Subway Construction
## 3 Risk Source Analysis of Subway Construction
### Cluster Building Distribution  
Cluster buildings refer to a group of buildings formed by several adjacent buildings in a city that are closely connected in spatial organization. Cluster buildings primarily refer to buildings arranged in a group. The impact of cluster buildings on subway construction is mainly as follows: the additional stress on the base has a deeper influence on depth and a slower attenuation speed, and it causes larger settlements during the subway construction process [50; 51; 52]. We quantified the distribution of cluster buildings based on building density and calculated the ratio of the total base area of the building to the occupied area within a certain range [53].  
\[\textit{Building Density}=\textit{Total Base Area}/\textit{Land Area} \tag{1}\]  
To avoid deviations in the results of environmental risk source analysis caused by the combined influence of geological conditions, super high-rise building distribution, and road network distribution, this study used the control variable method and selected four continuous subsidence areas (a, b, c, d in Figure 6e). These four subsidence areas are located in the overlapping area of Beijing Road Main Road and Metro Line 1 in Urumqi. Except for different building densities, there is no significant difference in the subway construction environment and construction methods in the four areas (Table 1). As shown in the green part of Table 1, with the increase in regional building density, the maximum surface displacement during subway construction in the region also increases, and the increasing trend is positively correlated with the building density. Therefore, building density is considered the fourth risk source of subway construction environments.  
Figure 5: (**a**) Location of monitoring points under varying road network densities. (**b**) Shows the settlement rate duration curve for the measuring points under varying road network densities during the monitoring period. The black dotted box below the graph indicates the maximum settlement rate values for Points A, B, C, and D.
## 5 Simulation Verification
### Simulations of Different Fault Resistances  
Using phase-A-to-ground faults as an example, and considering the maximum fault resistance of 220 kV transmission lines (\(\approx\)100 \(\Omega\)) along with documented instances of 150 \(\Omega\) high-resistance faults in historical 220 kV line faults [28], the phase-A-to-ground faults are applied at F\({}_{1}\), F\({}_{3}\), and F\({}_{5}\) with fault resistances of 50 \(\Omega\), 100 \(\Omega\), and 300 \(\Omega\), respectively.  
As shown in Table 1, under different fault resistances, the proposed protection can accurately identify the specific fault phase of the internal fault; for the external fault and the healthy phase of the internal fault, the protection does not act. Consequently, the protection performance remains unaffected by fault resistance.
## 2 Materials and Methods
### Statistical Analysis  
Prior to data analysis, the assumptions of normality, multicollinearity, homoscedasticity, and independence of residuals were evaluated to ensure the validity of the subsequent statistical models. When the assumption of normality was violated, [PERSON]'s rank correlation analysis was employed to examine the relationships between Se content and other variables. A two-way analysis of variance (ANOVA) was conducted to evaluate the effects of parent material, land use, and their interaction on total Se content. To validate the inverse distance weighting (IDW) interpolation method, leave-one-out cross-validation was performed, with ordinary kriging used as a benchmark for comparison. The root mean square error (RMSE) and mean absolute error (MAE) were calculated to assess the predictive accuracy of the interpolation methods. The results showed that IDW performed comparably to kriging, supporting its applicability in this study.  
All statistical analyses were performed using IBM SPSS Statistics 27.0 (IBM Corp., Armonk, NY, USA) under an academic license. Spatial mapping of Se concentrations and topographic features was conducted using ArcGIS 10.8 (Esri Inc., Redlands, CA, USA), while statistical graphs were generated with OriginPro 2021 (OriginLab Corp., Northampton, MA, USA). All software used in this study was licensed to China University of Geosciences (Wuhan, China).
## 1 Introduction
### Our Contributions  
The paper's contributions are summarized as follows:  
* We formulate a concrete joint user association, traffic routing, and VNF placement optimization problem targeting at the **overall** E2E network performance optimization, with minimal assumptions, that accounts for both communication and computation resources in **all** segments of a mobile network (i.e., AN, edge, and core) and explicitly account for the AN segment, typically ignored in the literature.
* Due to the NP-hardness of the resulting problem, we also propose a heuristic algorithm, evaluate its performance via simulations and demonstrate its superior performance compared with other State-of-the-Art (SoA) algorithms. The proposed solutions can be applied for internode network optimization, in conjunction with approaches targeting intranode optimization for maximum performance.
* Expanding upon our previous work in [29], we also describe the proposed orchestration solution, which, integrated with the proposed algorithm, enables the automated and optimized provisioning and configuration of heterogeneous computational and network resources across **all** network segments, targeting the orchestration of virtualized services according to the expected performance requirements and the specified SFC.  
Our formulation and algorithm can also be employed by a mobile network operator as an offline tool, during the network planning stage, to provide quantitative answers on the power expenditure and computational resources (both of them major components of Operational Expenditure (OPEX) and Capital Expenditure (CAPEX)) required to support a given set of services.  
The rest of the paper is structured as follows: Section 2 describes the system model and problem formulation, while the proposed heuristic and the employed Orchestration Platform are presented in Sections 3 and 4, respectively. Our performance evaluation methodology and comparison between the optimal solution and other SoA algorithms is described in Section 5 with the actual results being presented in Section 6. Section 7 concludes the paper. Notation-wise, sets are denoted with calligraphic symbols \(\mathcal{V},\mathcal{F}\) etc. and \(\overset{\triangle}{=}\) denotes equality by definition.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 2 Methods and Mathematical Models
### Feasibility and Benefits  
We believe that it is feasible and meaningful for several satellites to share the same PRN code to save the storage resources of the receiver. As shown in Figure 1, 11 satellites are evenly distributed in a polar-orbiting orbit. With the traditional method, 11 PRN codes are required for 11 satellites to broadcast navigation signals. If 3-4 satellites share one PRN code, 3 PRN codes are enough. In the case of Figure 1, two satellites with the same PRN code cannot be seen at all locations on Earth at the same time. If this new technology is applied, the storage resources of the receiver will be three-elevenths the size of the previous one.  
We also believe that using several satellites to share the same PRN code is useful to shorten the satellite acquisition time of the receiver. The basic idea of acquisition is to despread the navigation signal and determine the carrier frequency, code phase, and PRN code that match the signal. If the correct PRN code with the correct phase is multiplied on the navigation signal, the signal will become a continuous waveform signal. Figure 2 shows the process of navigation signal acquisition. Navigation signal acquisition consists of a three-dimensional search to synchronize a receiver-generated reference signal with the received navigation signal.  
Figure 1: Satellite PRN code assignment diagram of a constellation composed of 11 satellites. Three colors represent three PRN codes, which are enough to realize CDMA for 11 satellites. (**a**) Three-dimensional view of the PRN code assignment; (**b**) Two-dimensional view of the PRN code assignment. The points are satellite positions. The circles are the range in which the satellite PRN codes can be received on the ground.  
In terms of the frequency, the frequency search range is \(f_{range}\), and the step is \(f_{step}\). In terms of the code phase, the code phase search range is \(t_{range}\), and the step is \(t_{step}\). The PRN code search range is \(1\sim N_{PRN}\). There are \(t_{range}f_{range}/t_{step}f_{step}\) search units. The dwell time \(T_{dwell}\) is the time required for the receiver to perform one signal search in each search unit, so the time used to search the whole indefinite interval is  
\[T_{tot}=\frac{t_{range}f_{range}}{t_{step}f_{step}}\cdot N_{PRN}\cdot T_{dwell} \tag{1}\]  
If a signal is detected during the search process, the search should stop. Assuming that the parameters are uniformly randomly distributed, the average acquisition time is estimated as  
\[T_{avg}=\frac{1}{2}\cdot\frac{t_{range}f_{range}}{t_{step}f_{step}}\cdot N_{ PRN}\cdot T_{dwell} \tag{2}\]  
From the above formula, it can be seen that the average acquisition time is associated with the number of PRN codes, and the more PRN codes there are in the navigation system, the greater the average time of acquisition is. Therefore, to reduce the average acquisition time of navigation receivers, we should assign as few PRN codes to the LEO constellation as possible.
## 3 Routing in Satellite Networks
### Routing Protocols in LEOs  
In satellite networks, which exhibit significantly different dynamics compared to ground networks, routing protocols designed for terrestrial systems often struggle to operate at peak efficiency. The techniques suggested for handling the distributions of routing load in satellite networks are outlined below.
## 5 Simulation Verification
### Simulations of Noise Interference  
The PV outputs are set at 1 MW, 100 MW, and 200 MW, and Gaussian white noise is added to the current samples at both terminals with SNRs of 5, 10, 20, 30, 40, and 50 dB. The calculated \(D_{\rm JS}(I_{\rm M},\,-I_{\rm N})\) values for the fault phases (AC) and the healthy phase (B) under varying PV outputs and SNRs are presented in Figure
## 3 BER Considering Leaky Feeder Transmitter
### BER for M-QAM Modulation Technique  
M-ary Quadrature Amplitude Modulation (QAM) is a widely used modulation technique that provides high transmission rates with high bandwidth efficiency and with the correct configuration,high energy efficiency. However, finding the expression of the bit error probability of M-ary QAM is not as straightforward as finding its symbol error probability. Square M-ary QAM involves the amplitude modulation of two carriers in the quadrature expressed as [17]  
\[S\left(t\right)=\sqrt{\left(\frac{2\,E}{T}\right)}\,A_{x}\,\cos\left(2\,\pi\,f_ {c}\,t\right)-\sqrt{\left(\frac{2\,E}{T}\right)}\,B_{x}\,\sin\left(2\,\pi\,f_{ c}\,t\right), \tag{28}\]  
where \(A_{x}\) and \(B_{x}\) are the signal amplitudes of the in-phase and quadrature components, respectively, \(T\) is the symbol duration, \(f_{c}\) is the carrier frequency, \(1<x<M\), \(M\) is number of constellation, and \(M=2^{k}\), where k is an even.
## 5 Algorithms and Standard Products for Inferring Crop-Biophysical Characteristics
### Green Leaf Area Index (LAI), Green Leaf Biomass and Chlorophyll Content  
The LAI, chlorophyll content and biomass are important crop biophysical characteristics used for climate modeling, estimating primary production, and forecasting crop yield. All three of these biophysical characteristics are related, and can be estimated remotely using different parameterization of chlorophyll indices (CI) [22, 23]:  
\[CI_{green} =\left(\rho_{NIR}/\rho_{green}\right)-1 \tag{9}\] \[CI_{red\;edge} =\left(\rho_{NIR}/\rho_{red\;edge}\right)-1 \tag{10}\]  
where reflectances \(\rho_{NIR}\), \(\rho_{green}\) and \(\rho_{red\;edge}\) are in spectral bands as in Equations (5) and (6).  
The algorithms for LAI and green leaf biomass estimation, summarized by [PERSON] _et al_. [22] and [PERSON] _et al_. [21] have been calibrated in maize and soybean for LAI ranging from 0 to more than 6 (Figure 8) and for leaf biomass up to 3500 kg/ha. Validation using independent data sets showed that algorithms allowed accurate estimation of LAI and green biomass. They were able to estimate green LAI with RMSE below 0.51 m\({}^{2}\)/m\({}^{2}\) (\(CI_{green}\)) and 0.37 m\({}^{2}\)/m\({}^{2}\) (\(CI_{red\;edge}\)) and biomass below 242 kg/ha [22].  
The same models as in Equations (9) and (10) were parameterized for accurate remote assessment of total canopy chlorophyll content in crops [23] that brings users a quantitative measure of crop greenness, as shown in Figure 9. When the red edge band was selected (720-730 nm), \(CI_{red\;edge}\) became non-species-specific and, thus, the algorithm for chlorophyll content estimation did not require re-parameterization for the diverse canopy and leaf structures seen in C3 and C4 crops such as maize and soybean [23]. Using the close relationship between crop total canopy chlorophyll content and gross primary production (GPP) [13], we developed a technique for remote estimation of GPP.  
Figure 7: Relationship between red-edge NDVI and _f_APARgreen for maize and soybean. Regression line is significant at the 0.01 level.  
In addition to these indices, useful in estimating chlorophyll content, analogous indices have been developed to estimate other pigment content including carotenoids and anthocyanins [15,24].
## 4 Computational Analysis and Discussion
### Factor of Safety
The results show that the rate of increase in the safety factor increases with the increase in burial depth, and this phenomenon is more obvious in the silo-type LRC. For the same burial depth, the silo LRC is able to provide a greater ultimate pressure, so the silo LRC is more economical in terms of burial depth. The calculation results also provide some guidance for engineers to make quick decisions in site selection and preliminary design. Based on the stress characteristics of lined rock caverns, the surrounding rock mass may experience tensile failure [25], which is not considered in this paper. Additionally, the failure of the sealing structure is closely related to the response of the surrounding rock, such as excessive deformation of the surrounding rock, which may not be related to the depth of burial. Although this paper does not consider the effects of cyclic loading, their impact objectively exists. [PERSON] [26] investigated the long-term stability of lined rock caverns and found that long-term failures occurred only within a 1-m radius from the cavern's perimeter. This suggests that the impact of cyclic loading on the stability of the overlying rock mass is limited. Neglecting the effect of cyclic loading would cause this paper to overestimate the stability of the overlying rock mass. To compensate for the aforementioned unconsidered factor, it is possible to reserve a certain margin of safety by establishing appropriate safety factor control standards. Conceptualization, G.S. ; methodology, Q.Y. and G.S. ; software, Q.Y. ; validation, Z.S., S.L. and H.L. ; formal analysis, Q.Y. ; investigation, Z.S. ; resources, Z.S. ; data curation, Q.Y. ; writing--original draft preparation, Q.Y. ; writing--review and editing, S.L. and H.L. ; visualization, Q.Y. ; supervision, G.S. ; project administration, G.S. ; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Hubei Province (Three Gorges Innovation Development Joint Fund) (Grant No. 2022 CFD031). The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s. Author [PERSON] was employed by the company China Dating Corporation Ltd. Ningxia Branch. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.1 Image Stacking  
In this stage, two images are piled to augment the number of features analyzed for each pixel and thus improve the overall performance for crop classification. This procedure is reported in [30]. More concisely, for the proposed methodology, the images are stacked in only two sequences as follows: the first one was formed by the images from October 2015 to February 2016 (a total of five images), and the second one was formed by the images from March to July 2016 (a total of six images). Each Landsat image has seven bands, consequently, sequences 1 and 2 yield images with 35 and 42 bands, respectively.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
###### Abstract  
Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. Furthermore, the design of the proposed method considers three key principles: sustainability, adaptability, and trustworthiness. The proposed data pre-processing and engineering system significantly reduces the amount of data to be processed by the CNN, helping to limit the processing load and associated energy consumption towards more sustainable Artificial Intelligence (AI) techniques. Furthermore, the data engineering process, which includes sampling, feature extraction, and transformation of data into images, is designed considering its adaptability to integrate new sensor data and to fit seamlessly into a zero-touch system, following the principles of Machine Learning Operations (MLOps). The designed CNNs allow for the investigation of AI reasoning, implementing eXplainable AI (XAI) techniques such as the correlation map analyzed in this paper. Using the ToN-IoT dataset, the results show that the proposed FL-IDS achieves performance comparable to that of its centralized counterpart. To address the specific vulnerabilities of FL, a secure and robust aggregation method is introduced, making the system resistant to poisoning attacks from up to 20% of the participating clients.  
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Footnote †: journal:  
_Article_  
**Keywords:** sustainability; 6G; trusted AI; explainable AI; cooperative smart buildings