esa-sceva/satcom-chunk-collection huggingface dataset:
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 1 Introduction
Because of their LEO orbit, these antennas must scan over very wide angles simultaneously with multiple beams, thus making the phased arrays the only competitive solution [8]. Array antennas are developed as a key architecture for wireless communication systems, with Multiple Input Multiple Output (MIMO) antenna arrays included in the standards for cellular and wireless local area networks. In next-generation High Throughput Satellite (HTS) communications, these active antenna arrays will play a fundamental role [6]. Other advantages of this technology are the increase in total power due to the combined effect of the signal from the individual antennas and the improvement in reception sensitivity due to the higher signal-to-noise ratio resulting from increasing the number of individual antennas. Figure 1: Satellite communications system. This paper describes a novel workflow based in PathWave System Design (System-Vue) for modelling and design phased array antennas for LEO satellite communications gateways. The objective of this work is to study gateway ground station phased arrays for four constellations of satellites: SpaceX, Telesat, OneWeb and Amazon. After a review of the technical characteristics of these systems, two rectangular patch antennas are designed, one for transmission and one for reception, and then the number and spatial configuration of the radiating elements required to provide the service are defined. This paper is structured as follows: Section 2 reviews the basic theory to understand phased array antennas. Section 3 gives an overview of the four systems: SpaceX, Telesat, OneWeb and Amazon. Section 4 shows how patch antennas are designed and the results of electromagnetic simulation for the gateway using Keysight PathWave Advanced Design Software (ADS) Momentum. Section 5 discusses the modelling of the phased array using SystemVue. Finally, Section 6 presents the conclusions, summarising the results.
## 4 Proposed Algorithm
### Normalization and Time-Synchronization  
Even if the measured SDPPG signals originate from one source (heart), the received signals may have different amplitudes and traveling times owing to the different paths taken, as shown in Figure 2. The differences in traveling time causes phase differences between received signals, as in Figure 8a. Such amplitude attenuation and time deviation induce errors in detection and decision during signal processing. To adjust the amplitudes, the signals are scaled to have the same amplitude as the strongest signal. To compensate for time differences between signals, cross-correlation between channel signals is performed to obtain the time difference, as shown in Figure 9. The figure has a maximum value at 0.23 s, which means that the two signals had a 0.23-s time difference due to the travelling paths. Based on time difference computation, the algorithm aligns the times of the two signals, as shown in Figure 8b.  
Figure 8: Time-compensation: (**a**) originally measured signals; (**b**) time-aligned two signals.
## 4 Numerical Simulations  
In the simulation experiments, the main objective was to evaluate the performance of the LADRC-TD algorithm for the heading and depth control of the AUH and to compare it with the LADRC and PID algorithms. The simulation conditions for all three algorithms were kept consistent to ensure the fairness of the experiments.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR  
SONAR, in general, is a shipborne RS technique that has revolutionized oceanography and bathymetry. Based on the technique of receiving and recording pulses, SONAR instruments can be categorized into single-beam SONAR, multi-beam SONAR, synthetic aperture SONAR, and side scan SONAR [340]. The most frequently used SONAR systems for seafloor mapping are multi-beam SONAR and side-scan SONAR. Multi-beam instruments, also known as MultiBeam EchoSounders (MBES), mounted on the ship's hull determine the seafloor topography by transmitting sound signals and receiving the echo from the seafloor to measure the round-trip time taken during the transmission period [341]. Unlike multi-beam SONAR, which measures the timing of echoes, side scan SONAR measures the strength of echoes. Therefore, side-scan SONAR produces black-and-white photographs of the seafloor, instead of topography data [342]. As such, Multi-beam SONAR is usually used for bathymetric mapping of the seafloor for mapping both shallow and deep waters and also updating nautical charts [331].  
It should be noted that there is another classification of SONAR systems: active and passive. Although both of these instruments employ sound echoes for bathymetric mapping active SONAR is emitting pulses of sounds and listens for echoes. However, passive SONAR is principally listening for the sound made by other targets (e.g., whales and other vessels) [343].  
The accuracy of SONAR systems is affected by boat pitch, roll, and yaw. Moreover, SONAR techniques are usually more expensive and time-consuming than airborne and spaceborne bathymetric systems, making them inappropriate for many applications [344]. Several studies have attempted to reduce these errors. For instance, [PERSON] et al. (2020) [345] integrated depth measurements from a low-cost single beam sonar with a dual-frequency differential high-precision GNSS data for shallow water bathymetric mapping. Given this technique, they attempted to eliminate the need for applying the method based on tidal corrections, which always introduce several uncertainties. They also eliminated the influences of boat pitch, roll, and yaw on depth measurements.
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 2 Data Observation and Analysis  
The wireless communication data analyzed in this paper were from Telecom Italia, which is the traffic statistics sent or received by users in specific areas of Milan [18]. The dataset consisted of a time series of traffic from 1 November 2013 to 1 st January 2014, with an interval of 10 min, and included three parts: short message service (SMS), call service (Call), and Internet access (Internet). The entire urban area was divided into cells of size \(H\times W\). \(H\) and \(W\) represent the number of rows and columns of cells. In this dataset, \(H=W=100\), indicating that the area of Milan is composed of a grid overlay of 10,000 cells with a size of about 235 \(\times\) 235 square meters, and the value of the cell represents the statistical value of the traffic in and out of this area. Traffic data were recorded from 00:00 11/01/2013 to 23:59 01/01/2014. We merged data at ten minute intervals into hour intervals and divided each dataset into 1488 (62 days \(\times\) 24 h) fragments. In the datasets, SMS and Call contained two dimensions of traffic, namely receiving and sending. The Internet only recorded the traffic that was accessed. In order to compare the spatiotemporal characteristics of the traffic of the three services clearly, we combined the traffic of the receiving and sending dimensions into one.  
Thus, the entire dataset could be represented as data of \([c,t,H,W]\) dimensions \(F_{c,t}\) where \(c\) represents the type of cellular traffic in the dataset, \(c\in\{\)SMS, Call, Internet\(\}\). \(t\) represents the time interval of the flow, and \(t=1\) h in this work. \(H\) and \(W\) are as described above.  
\[\mathbf{F}_{c,t}=\left[\begin{array}{cccc}f_{c,t}^{(1,1)}&f_{c,t}^{(1,2)}&\dots &f_{c,t}^{(1,W)}\\ f_{c,t}^{(2,1)}&f_{c,t}^{(2,2)}&\dots&f_{c,t}^{(2,H)}\\ \vdots&\vdots&\ddots&\vdots\\ f_{c,t}^{(H,1)}&f_{c,t}^{(H,2)}&\dots&f_{c,t}^{(H,W)}\\ \end{array}\right] \tag{1}\]  
where \(f_{c,t}^{(H,W)}\) represents the traffic statistics of the cell (\(H\), \(W\)) of the traffic data of type \(c\) at time \(t\).
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 5 Performance Evaluation
### Storage Overheads  
In the proposed protocol, the SHD stores \(\{\mathbb{Z}_{\text{S}}\), \(\text{ID}_{\text{TA}}\}\) in its memory. Using the values in [40], \(\mathbb{Z}_{\text{S}}\) = 256 bits and \(\text{ID}_{\text{TA}}\) = 128 bits. Therefore, the cumulative storage overhead in the proposed protocol is 384 bits. Table 12 presents the storage overheads of the other related schemes.  
As shown in Figure 7, the protocol in [56] requires the largest storage space of 960 bits. This is followed by the schemes in [39; 43; 47] with 640 bits, 576 bits, and 512 bits, respectively.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Scheme** & **Total Costs (Bits)** \\ \hline [PERSON] et al. [39] & 512 \\ \hline [PERSON] et al. [40] & 384 \\ \hline [PERSON] et al. [43] & 640 \\ \hline [PERSON] et al. [47] & 576 \\ \hline [PERSON] et al. [56] & 960 \\ \hline Proposed & 384 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Storage overheads.  
Figure 6: Communication costs comparisons.  
Figure 7: Storage overheads comparisons.  
On the other hand, the scheme in [40] and the proposed protocol require the smallest storage space of only 384 bits. As such, they are the most ideal for deployment in memory-constrained smart home devices.
## 4 Discussion
### Advantages of Combining Active and Passive Remote Sensing  
Optical images are frequently contaminated by clouds and their shadows, while SAR images serve as a dependable data source in nearly all weather conditions [61]. Processed microwave remote sensing data exhibit a sharp response mechanism to plant structure [62]; thus, the inclusion of polarized data would enhance the accuracy of feature classification. As depicted in Figure 5, a distinct disparity exists between the VH/VV of garlic and winter wheat, with the garlic VH/VV peak occurring earlier and being of larger magnitude compared to that of winter wheat. Therefore, VH/VV, by amalgamating the characteristics of VH and VV, can streamline computations and furnish additional information to differentiate between the two, thus aiding in garlic extraction. Although the addition of SAR features enhances classification accuracy, as stated by [PERSON] et al., observed backscatter comprises a blend of ground backscatter perturbed by soil moisture and surface roughness and vegetation backscatter altered by the 3D structure of vegetation [63]. Consequently, SAR features may not be efficacious in discerning the class of garlic. Moreover, given that the resolution of the SWIR band of Sentinel-2 is 20 m, which is relatively low comparedto other visible light bands, it may impede the discrimination between garlic and winter wheat plots on the ground in most instances. Hence, the significance of SAR features in garlic extraction is underscored in this study, while the amalgamation of active and passive remote sensing techniques can enhance classification accuracy.
# Review of Smart-Home Security Using the Internet of Things
## 4 Analysis and Discussions of the IoT Setting
### Threats Faced by a Smart Home and Related Countermeasures
#### 4.2.4 Physical Layer Analysis
The double-collision function is used in identifying and determining the flow of tags, where the collection of duplicate hashes and duplicate tag identification detection takes place. [PERSON] and [PERSON] [140] focused on enhancing the security through a Quantum Key Distribution (QKD) authentication scheme. This scheme aims to provide mutual authentication between users and servers, while ensuring the security of the communication against various cyber threats, including eavesdropping. The implementation uses quantum properties to generate and distribute cryptographic keys securely, leveraging the inherent security features of quantum mechanics to prevent unauthorized access and data breaches, as well as harden the cloning of devices. Jamming is the main concern of wireless communications in the physical layer. [PERSON] et al. [176] focused on using the Support Vector Machine (SVM) method to detect cloning and jamming attacks in IoT sensor networks. The system utilizes SVM to classify nodes as either cloned or normal, based on the distance measurements from IoT devices to a base station, enhancing the security and integrity of the network. The approach includes the monitoring of a selected node based on the probability distribution of sensor nodes, which helps in accurately identifying and mitigating clone attacks, ensuring a robust network performance against malicious activities. [PERSON] et al. [177] suggested an advanced trust-based authentication scheme for RFID systems, designed to address vulnerabilities, such as jamming and cloning attacks. This solution, named TRAS, incorporates trust factors, like previous keys, transactions history, and node capabilities, into the authentication process, allowing for dynamic and secure verification of RFID tags. This method aims to improve the resilience of smart-home systems against these specific types of security threats by enhancing the reliability and transparency of the authentication process.
## 5 Conclusions  
This study evaluated eight ML models to predict the production of gases from biomass gasification (CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\)). The hyperparameter combinations for the optimal and suboptimal conditions of each model were determined using Bayesian optimization. The XGBoost model demonstrated superior performance and robustness in predicting the levels of all four gases separately, achieving the highest R values and the lowest error metrics in both optimal and suboptimal conditions. The findings highlight the potential of ML models in effectively predicting biomass gasification outputs, facilitating better process control, enhancing energy efficiency, and reducing environmental impact. Notably, the Bayesian optimization approach significantly improved the models' accuracy and stability, particularly for XGBoost, which emerged as the most reliable model for capturing complex and nonlinear relationships. The Elastic Net and KNN models, on the other hand, not only exhibited the lowest R values under optimal conditions but also showed a significant decrease in performance under suboptimal conditions compared to the other models. These findings underscore the challenges of relying on models with limited robustness across varying parameter settings and emphasize the importance of careful model selection and parameter tuning. Future research could expand the dataset with a broader range of biomass feedstocks and integrate dynamic modeling techniques to account for temporal variations in gasification processes. Additionally, integrating ML models with real-time process control systems can further optimize biomass gasification for industrial applications.  
This research received no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data used in this manuscript are referenced in the literature section of the paper. The ML regression models developed in this study are available at [[https://github.com/pcihan/Biomass_gas_prediction](https://github.com/pcihan/Biomass_gas_prediction)]([https://github.com/pcihan/Biomass_gas_prediction](https://github.com/pcihan/Biomass_gas_prediction)).  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 3 Results and Discussion
### Comparative Results of PAH Concentrations  
For chemical analysis, naphthalene, benzo(b)fluoranthene, dibenz(a,h)anthracene, and acenaphthalene were not detected in any of the porewater samples. Among the detected PAHs, fluoranthene was the most abundant quantified PAH in the porewater at all stations, followed by pyrene. High concentrations of the B[a]A, CHRY, and FLTH at site H] were probably due to the five major industrial pollution sources (refineries, export processing zones, two industrial zones, and plastic manufacturing) found nearby, which bear a large amount of industrial wastewater and household sewage. Site DG1 was surrounded by livestock industries in the region which caused livestock wastewater pollution. The pollution sources in River YS could be related to domestic wastewater and additional wastewater discharge from the Linhai Industrial Zone and surrounding steel plant. Moreover, the mainstream of SY is crossing through two major industrial areas, coupled with discharged wastewater from the livestock industry. Pollution at river DB is due to the metal industry along the river, causing serious metallic and domestic wastewater pollution. These are caused by the major metal surface treatment industry and animal husbandry in the AGD upstream. The main pollution source impact of livestock wastewater on the WR and LK may be related to high concentrations of PAHs. The results of chemical analysis are shown in Table 4.  
The \(\Sigma\)PAHs in the porewater of the 35 stations ranged from 0.085 to 378 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and the average \(\Sigma\)PAHs of the eight streams ranged from 4.807 \(\pm\) 5.614 (AGD river) to 69.846 \(\pm\) 151.487 (WR river).The sampling station with the highest \(\Sigma\)PAHs was WR1, followed by HJ1. Studies showed that the concentration of \(\Sigma\)PAHs in the porewater ranged between 48.2 and 205.7 \(\mathrm{\SIUnitSymbolMicro g}\)/L from the Lanzhou Reach of the Yellow River (China). The concentration of \(\Sigma\)PAHs detected in the porewater of the Mersey Estuary (UK) ranged from 0.095 to 0.742 \(\mathrm{\SIUnitSymbolMicro g}\)/L [44]. The concentration range within porewater of Xiamen Harbor (China) was \(<\)1 to 3548 \(\mathrm{\SIUnitSymbolMicro g}\)/L [45]. The total PAHs detected in the Jiulong River Estuary and Western Xiamen Sea, China ranged from 158 to 949 \(\mathrm{\SIUnitSymbolMicro g}\)/L [46]. Compared to other studies, the concentrations of the 16 PAHs in the porewaters of this study are still slightly higher  
Figure 6: Effect of pollutants from sediment porewater on the heartbeat rate of zebrafish embryos. Hatching rate of zebrafish embryos exposed for 72 h at sampling stations with slightly toxic and moderately toxic porewaters. Error bars represent the standard deviation of the six zebrafish embryos. * \(p\) < 0.05 indicates significant differences between the control and exposure groups.  
than in other aquatic environments, indicating that the toxicity of the porewater is worthy of a follow-up discussion.
# The Development of the Pipe Jacking Guidance Technology
## 8 Discussion  
The accelerating pace of new city construction necessitates the development of innovative solutions to address the challenges posed by recurrent road excavations, the intricate underground pipeline network and the frequent occurrence of pipeline accidents. In this context, pipe jacking construction has emerged as a promising approach, offering a range of advantages, including low noise and vibration, high precision, environmental sustainability, a short construction period and reduced economic costs. This research area holds significant promise for the future of underground pipeline construction, offering a promising avenue for addressing the pressing issues associated with urban infrastructure development. Pipe jacking construction is no longer only for short-distance and straight-line pipe jacking construction but also for long-distance and curved pipe jacking construction, which reduces the construction cost and improves the overall construction efficiency at the same time. However, how to realize the guiding accuracy of long-distance and curved pipe jacking construction has put forward higher requirements to the research institutions.  
Multi-source data fusion is complementary and redundant. In the field of engineering applications, a single measuring instrument cannot fully meet engineering requirements, so additional measuring instruments are often used to meet the design requirements of engineering applications. Adding different kinds of measuring instruments can fully complement the defects between different instruments, and adding the same kind of measuring instruments or the same kind of functional measuring instruments can improve the system's stability and measurement accuracy through redundancy. The future development of pipe jacking guidance systems will be characterized by a shift towards the integration of multi-source sensors. This approach will leverage the complementarity of different sensors and the redundancy of similar sensors. The implementation of these systems will adapt to the complex pipe jacking construction conditions, thereby enhancing the guidance accuracy.
## 3 Metaverse Classroom: A Layered Architectural View
### Security and Privacy Layer  
#### 3.6.1 Data Encryption and Secure Platforms  
Data encryption and secure platforms are critical to protecting sensitive student and educator information in the Metaverse classroom [196]. Encryption converts data into a secure code accessible only by authorized users, ensuring that personal and academic records remain private [197]. In a Metaverse setting, where students submit assignments, join discussions, and access resources, encryption safeguards these interactions from unauthorized access [198]. Even if a data breach occurs, encrypted information remains unreadable, reducing the risk of identity theft or data misuse. Compliance with data security standards, such as GDPR and FERPA, adds another layer of protection by enforcing strict regulations on data handling and storage [199]. These standards ensure user consent is respected and student records are kept secure, building trust among parents, students, and educators [132]. Secure platforms are essential for maintaining privacy in a Metaverse classroom [16]. Platforms like Unity and Unreal Engine offer security protocols, but for education, additional measures like secure login, encrypted data handling, and user verification are necessary [54].  
Multi-factor authentication (MFA) and single sign-on (SSO) ensure that only authorized users can access the virtual classroom [200]. In addition, regular software updates and automated threat detection further protect against vulnerabilities and suspicious activity [201]. User access control allows schools to manage who can view and edit sensitive data. For example, teachers can access student grades, while students can only view their records [197]. Furthermore, limiting third-party access also helps prevent unauthorized data exposure, ensuring that external collaborators have restricted permissions [196]. These access control measures balance security with the collaborative needs of the Metaverse classroom [16]. Finally, educating students and staff on data security practices is crucial to maintaining a safe virtual environment [17]. Even with advanced security protocols, user behavior significantly prevents breaches [202]. Schools can offer training on password management, recognizing phishing attempts, and responsible data handling [203]. Teaching students and educators about privacy settings and secure file sharing empowers them to actively protect their information. This collective understanding fosters a culture of responsibility, ensuring that all users contribute to a secure Metaverse classroom [203].