esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The flexural pile foundation is used in integral abutment jointless bridges (IAJBs) in practical engineering to effectively dissipate the horizontal reciprocating deformation induced by the ambient temperature or earthquake loadings. Various types of flexural piles including the H-shaped steel pile (HP), prestressed concrete pile (PC), prestressed high-strength concrete pile (PHC) as well as the reinforcement concrete pile (RC) have been implemented in IAJBs. However, there is a lack of comprehensive studies on the flexural deformation and seismic performances of these piles. In order to investigate and compare their mechanical behaviors and seismic performances, a low-cycle pseudo-static test on several different types of piles was carried out. The test results indicated that the plastic hinge location of piles moved to a deeper pile depth with the increase of reinforcement ratio, buried pile depth and prestressing level, which led to better pile-soil interaction. The crack resistance of a concrete pile was improved as the reinforcement ratio and prestressing level increased. Moreover, the rectangular pile had a better soil-pile interaction and energy dissipation capacity than the circular pile. The inflection point of the pile deformation shifted deeper as reinforcement ratio, buried pile depth and prestressing level increased, which improved the effective length and horizontal deformation capacity of piles. The H-shaped steel pile showed a better elastic-plastic deformation capacity, ductility and energy dissipation capacity as compared to the concrete pile. Moreover, the pile having a higher bearing ratio sustained larger lateral loads whereas the surrounding soil was subjected to higher loads. Finally, new seismic design criteria of three-stage seismic fortification and five damage level for the concrete piles of IAJBs were proposed.  
Keywords:integral abutment jointless bridges; pile foundation; seismic performance; soil-pile interaction; seismic design criteria +
Footnote †: journal: Applied Sciences
## 4 Analysis and Discussion
### Finite Element Simulation Results  
#### 4.2.1 Comparison of Simulation Results with Monitoring Results  
The comparative analysis of the simulation results with the monitoring data is a reliable means to verify the validity of the finite element model. The simulated horizontal displacement of the diaphragm wall was compared with the monitoring results to validate the established finite element model. Figure 4 shows the comparison between the simulation results and the monitoring results. Most of simulation results and monitoring simulations had similar dislocation patterns and displacement amplitudes, with good agreement. The results for monitoring points ZQT41 and 43 did not match well. The construction loads of the relocation of the Yuantai Building changed the dislocation pattern of the diaphragm wall at these two monitoring points. However, the numerical simulation did not fully describe this construction process, which may be the reason for the error at this point. Table 4 extracts the maximum deformation values of the monitoring data and numerical simulations for each monitoring location and provides the error analysis expressed in percentage. It can be observed that most of the monitoring locations have errors within 25%. In comparison, pit 3A10-1 and pit 3A8 better simulated the deformation of the pit with most of the computational errors within 15%. Collectively, the finite element models developed for the main points involved in the monitoring can effectively simulate the process of pit excavation.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 2 Background
### Attack Surface
#### 2.3.3 Attack Surface on the Protocol Interface Layer  
The attack surface on the protocol interface layer represents communication and application programming interface (API). It involves the device directly controlled by the user side, the device indirectly controlled through the cloud, and the above two types of communication process information protection issues. Security on the protocol level is not involved. For example, the abuse of IoT communication protocols and the AR-DDoS [37] attack is performed by the IoT communication protocols Constrained Application Protocol (CoAP) [38], SSDP [39], and SNMP. Its target is not the flaws of IoT devices. However, it is also an important research direction of IoT security. Attack surface on the protocol interface layer mainly includes the following three aspects: the unsafe interface of remote management, leakage of sensitive information transmission, and weak authentication.  
1. **Unsafe interface of remote management.** For portable management, IoT devices use remote management interfaces such as HTTP services, which bring multiple vulnerabilities such as SQL injection, Cross-site Scripting (XSS), and remote execution vulnerability.
2. **Leakage of sensitive information transmission.** The IoT communication protocol will use weak encryption algorithms or even no encryption, which will lead to the leakage of sensitive information. For example _Passwords in the Air_[40], the WiFi password is transmitted in plain text when the IoT device is connected to the network.
3. **Weak authentication.** Due to security requirements, the management of IoT devices requires authentication binding. However, a new attack surface has emerged. Attackers can bypass authentication, duplicate bind, and obtain other user's information. The _Phantom Device Attack_[41] found four specific attack methods on this attack surface.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 4 Time-Varying Characteristics of DCB and OSB on BDS Satellites
### Stability of DCB and OSB Obtained from the CAS
Both C7Z and C8X are mixed component signals. These all indicate that OSBs with the same signal channel or signal component (i.e., I, P, X) have consistent monthly stability. The monthly STDs of OSBs more intuitively reflect the stability of a signal's code biases than those of DCBs.
###### Abstract  
Spar-type floating offshore wind turbines commonly vibrate excessively when under the coupling impact of wind and wave. The wind turbine vibration can be controlled by developing its mooring system. Thus, this study proposes a novel mooring system for the spar-type floating offshore wind turbine. The proposed mooring system has six mooring lines, which are divided into three groups, with two mooring lines in the same group being connected to the same fairlead. Subsequently, the effects of the included angle between the two mooring lines on the mooring-system's performance are investigated. Then, these six mooring lines are connected to six independent fairleads for comparison. FAST is utilized to calculate wind turbine dynamic response. Wind turbine surge, pitch, and yaw movements are presented and analyzed in time and frequency domains to quantitatively evaluate the performances of the proposed mooring systems. Compared with the mooring system with six fairleads, the mooring system with three fairleads performed better. When the included angle was 40\({}^{\circ}\), surge, pitch, and yaw movement amplitudes of the wind turbine reduced by 39.51%, 6.8%, and 12.34%, respectively, when under regular waves; they reduced by 56.08%, 25.00%, and 47.5%, respectively, when under irregular waves. Thus, the mooring system with three fairleads and 40\({}^{\circ}\) included angle is recommended.  
OC3-Hywind spar; mooring system; vibration control; wind turbine +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Decision Tree-Related Works  
The study by [21] evaluated various machine learning algorithms (MLAs), including Decision Trees for detecting MitM attacks. The study focused on leveraging AI to improve threat detection capabilities and preemptive countermeasures. The study identifies the vulnerability of traditional ICT architectures to MitM attacks, which compromise information confidentiality. The authors proposed a scheme that uses AI, demonstrating the good performance of the Random Forest (RF) algorithm (an ensemble of Decision Trees), which achieved high accuracy in detecting MitM attacks. The study primarily focused on the effectiveness of AI algorithms in detecting MitM attacks but did not extensively address the integration of these algorithms into real-world systems or their performance in diverse network environments.  
While this study effectively demonstrates the potential of AI, including Decision Trees, for MitM attack detection, it is not lightweight and is not a real-time detection scheme. Our study's emphasis on lightweight detection using CSA combined with Decision Trees addresses these gaps by ensuring low latency and adaptability to various data types.  
This study by [22] developed an anomaly-based Intrusion Detection System using a deep learning model (PCC-CNN) and evaluated its performance on various datasets. The study addressed the challenge of detecting network attacks in IoT, which are increasingly targeted by cybercriminals. They proposed a deep learning model (PCC-CNN) to improve the detection accuracy of network attacks and compared its performance with state-of-the-art machine learning approaches. The study focused on deep learning models and did not explore the potential of combining different machine learning techniques, such as Decision Trees with optimization algorithms such as CSA. While this study highlights the effectiveness of deep learning models for intrusion detection, it does not consider the benefits of lightweight and adaptive schemes. Our study's approach of integrating CSA and Decision Trees offers a more comprehensive solution that optimizes efficiency, accuracy, and adaptability.
## 3 SD-QGrid Routing Platform and Algorithms
### SD-QGrid Online Routing Decision Process
(2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] NTR: An Efficient Trajectory-based Routing Protocol for the Vehicular Delay Tolerant Networks. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, Banff, AB, Canada, 5-8 October 2017. * _[PERSON] et al. (2013)_ [PERSON]; [PERSON]; [PERSON] Driving Path Prediction Based Routing Protocol in Vehicular Ad Hoc Networks. _Int. J. Distrib. Sens. Netw._**2013**, \(9\), 837381. [CrossRef]
* [PERSON] (1959) [PERSON] A Note on Two Problems in Connection with Graphs. _Numer. Math._**1959**, \(1\), 269-271. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] Intersection-based Traffic-Aware Routing with Fuzzy Q-learning for Urban VANETs. In Proceedings of the 2021 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Chengdu, China, 18-20 June 2021. * _[PERSON] et al. (2013)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] The DTN Routing Algorithm based on Markov Meeting Time Span Prediction Model. _Int. J. Distrib. Sens. Netw._**2013**, \(9\), 736796. [CrossRef]
* _[PERSON] et al. (2007)_ [PERSON]; [PERSON]; [PERSON] Forwarding and Replication Strategies for DTN with Resource Constraints. In Proceedings of the 2007 IEEE 65 th Vehicular Technology Conference, Dublin, Ireland, 22-25 April 2007; pp. 1260-1264. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A Markov Chain Prediction Model for Routing in Delay Tolerant Networks. In _International Conference on Big Data Computing and Communications_; Springer: Cham, Switzerland, 2015; Volume 9196, pp. 479-490. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks. _IEEE Trans. Intell. Transp. Syst._**2022**, _23_, 5446-5459. [CrossRef]
* _Available online:_ [[https://ptx.transportdata.tw/PTX/](https://ptx.transportdata.tw/PTX/)]([https://ptx.transportdata.tw/PTX/](https://ptx.transportdata.tw/PTX/)) (accessed on 20 October 2022). * _[PERSON] et al. (2007)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Stable Routing Protocol to Support ITS Services in VANET Networks. _IEEE Trans. Veh. Technol._**2007**, _56_, 3337-3347. [CrossRef]
* [PERSON] and [PERSON] (2010) [PERSON]; [PERSON] The NS-3 Network Simulator. In _Modeling and Tools for Network Simulation_; Springer: Berlin/Heidelberg, Germany, 2010; pp. 15-34. * _[PERSON] et al. (2000)_ [PERSON]; [PERSON]-T. [PERSON]: Greedy Perimeter Stateless Routing for Wireless Networks. In Proceedings of the 6 th Annual International Conference on Mobile Computing and Networking, Boston, MA, USA, 1 August 2000; ACM: New York, NY, USA, 2000; pp. 243-254.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 2 Methods  
The flowchart of the study is given in Figure 2. Given partially overlapped multiple image strips (\(n\) images in the figure) and sensor models covering the area of interest, if one image partially overlapped with other images, it was chosen as the reference image. Each pixel of the other images (collateral images) was first projected to the ground using SRTM DEM and then back-projected onto the reference image space. These projections produce (\(n-1\)) projected images partially overlapped with the reference image. Next, image matching was carried out to extract tie points in the overlap area. A lot of matching outliers should exist because of radiometric and geometric differences, such that it requires detecting and remove them accurately. The discrepancy is expected to show irregular patterns along the image line because of push-broom sensor characteristics. Each line of image has a different position and attitude information. Therefore, we modeled the discrepancy with polynomials after dividing the whole image strip into multiple sub-image  
Figure 1: Sensor-level mosaic vs. ground-level mosaic.  
regions. Based on the polynomial model, outliers are detected and removed in each sub-image region. This leads to the outlier suppressed tie points set, which enables the irregular discrepancy estimation. The mosaicked image strip can be generated after compensating for the image line discrepancy. Finally, single sensor model information for the mosaic image strip is generated.
# Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review
## 2 Materials and Methods
### Risk of Bias Assessment  
The quality of the included studies was independently assessed by two authors ([PERSON] and [PERSON]). Any disagreements and uncertainties were resolved through mutual discussion. Risk of bias was evaluated using the ROBINS-I framework, tailored for non-randomized studies of interventions, which evaluates the following domains [51]:  
* D1: Bias due to confounding factors
* D2: Bias due to participant selection
* D3: Bias in intervention classification
* D4: Bias due to deviations from the planned interventions
* D5: Bias due to missing data
* D6: Bias in data measurement
* D7: Bias in selection of reported outcomes  
All included articles demonstrate a low risk of bias, with the exception of the first domain, where all studies exhibited a moderate risk value due to their retrospective design. Furthermore, only three studies adhere to the blind selection of patients as specified in D2.  
The absence of skeletal pattern data in the study groups analyzed may represent a confounding factor, potentially related to both the exposure and the outcome. Additionally, within D3 (bias in intervention classification), four articles could have provided a more detailed specification of the protocol for overbite correction (Figure 3).  
Figure 2: The Newcastle-Ottawa scale was employed to evaluate the quality assessment of the included studies. Bar colors reflect the methodological quality of the included studies: green bars indicate high-quality studies, while yellow bars correspond to studies assessed as having moderate quality [10; 20; 45; 46; 47; 48; 49].
## 2 Materials and Methods
### VOO Analytical Evaluations
#### 2.4.4 Polyphenols  
The extraction of VOO phenolic compounds was performed in accordance with [PERSON] et al. [38]. The HPLC analyses of the phenolic extracts were conducted according to [PERSON] et al. [33] with a reversed-phase column using an Agilent Technologies system Model 1100 (Agilent Technologies, Santa Clara, CA, USA), which was composed of a vacuum degasser, a quaternary pump, an autosampler, a thermostatic column compartment, a DAD, and a fluorescence detector (FLD). The C18 column used in this study was a Spherisorb ODS-1 250 mm \(\times\) 4.6 mm with a particle size of 5 \(\upmu\)m (Waters, Milford, MA, USA); the injected sample volume was 20 \(\upmu\)L. The mobile phase was composed of 0.2% acetic acid (pH 3.1) in water (solvent A)/methanol (solvent B) at a flow rate of 1 mL/min and the gradient changed as follows: 95% A/5% B for 2 min, 75% A/25% B in 8 min, 60% A/40% B in 10 min, 50% A/50% B in 16 min, and 0% A/100% B in 14 min; this composition was maintained for 10 min and then returned to the initial conditions and equilibration in 13 min; the total running time was 73 min. The oxidized form of the oleupopein aglycon (3_A_-DHPEA-OX) was detected by using the DAD set at 339 nm; all the other phenolic compounds were detected by DAD at 278 nm, the analyses and the elaboration of chromatographic data were conducted using the ChemStation software version A.10.02 (Agilent Technologies, Palo Alto, CA, USA). The quantitative evaluation of the phenols was carried out using single calibration curves for each compound, and the results are expressed as mg/kg of oil. The 3,4-DHPEA-OX was quantified using the response factor of caffe acid, because the standard compound was not commercially available.
# Virtual Reality in Cultural Heritage: A Setup for Balzi
###### Abstract  
This study presents the creation of a virtual reality experience for the Museo Preistorico dei Balzi Rossi e Zona Archeologica (hence Balzi Rossi Museum) commemorating the centenary of Prince [PERSON]'s archaeological work at the site. The project aims to preserve and convey the site's heritage through advanced VR technology. Photogrammetry was used for 3D reconstruction of the entire Balzi Rossi coastal cliffs, including the notable \"Caviglione\" and \"Florestano\" caves, known for their upper Paleolithic rock engravings. Two subsequent development phases produced the final public VR experience, incorporating Nanite technology for enhanced visual fidelity. This advancement resulted in a more detailed and immersive VR experience, presenting the Balzi Rossi cliffs across different historical periods, including the Wurm glaciation. Key to this phase was optimizing the VR experience for performance, focusing on stable frame rates and minimizing motion sickness, and integrating a multi-lingual interface for broader accessibility. Since November 2023, the VR setup at Balzi Rossi Museum has been an educational and interactive feature enabling visitors to virtually explore the site's history. This study aims to describe a process for optimizing and enabling the creation of VR experiences while maintaining a high polygon count within the context of small teams.  
virtual reality; 3D reconstruction; cultural heritage +
Footnote †: journal: Applied Sciences
## 2 MDM-Duobinary (MDM-DB) and MDM-Modified Duobinary (MDM-MDB) Formats
#### DB/MDB MDM-Transmitter Model  
Figure 3a illustrates the MDM-MDB transmitter setup, which comprises pseudo-random bit sequence (PRBS) generators, DB-NRZ and MDB-NRZ modulators for respective configurations, spatial continuous-wave (SCW) lasers, optical filters, and an optical mode  
Figure 2: (**a**) Generation of MDM-MDB signal; (**b**) MDM-based link SPM analysis setup.  
combiner (MC). The PRBS generator produces a pseudo-random bit sequence at 40 Gbps and 40 GHz for each mode, with a \(2^{9}-1\) bit sequence length. The spatial CW lasers emit equally spaced wavelengths in the 1549 nm to 1554 nm range, ensuring efficient wavelength allocation for the multiple modes. Two lithium niobate (LiNbO\({}_{3}\)) dual-port modulators, each with an extinction ratio of 30 dB, are employed for modulation. For the MDB-NRZ configuration, an SCW laser generates the three modes, which are driven by a data modulator to produce the modulated optical signals. These signals serve as inputs to the three input ports of the optical mode combiner, which combines the modes into a single transmission channel. To minimize signal crosstalk between adjacent modes, an optical filter is used for each mode, ensuring high signal integrity and performance. Figure 3b depicts the measured optical spectrum at the input, demonstrating distinct and well-defined wavelengths generated by the setup.
# Continuous-Time Perfect Control Algorithm--A State Feedback Approach
## 2 System Representation  
As the perfect control algorithm reveals its characteristics for multivariable plants, the system is considered here as an LTI state-space plant described in the continuous-time domain as follows:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}(t)=\mathbf{A}\mathbf{x}(t)+\mathbf{ B}\mathbf{u}(t),\ \ \ \mathbf{x}(0)=\mathbf{x}_{0}\\ \mathbf{y}(t)=\mathbf{C}\mathbf{x}(t)\end{array}\right., \tag{1}\]  
where \(\mathbf{A}\in\mathbb{R}^{n\times n}\), \(\mathbf{B}\in\mathbb{R}^{n\times n_{u}}\) and \(\mathbf{C}\in\mathbb{R}^{n_{y}\times n}\) are system input and output matrices, respectively. Moreover \(\mathbf{u}(t)\), \(\mathbf{y}(t)\), and \(\mathbf{x}(t)\) are \(n_{u}\)-input, \(n_{y}\)-output, and \(n\)-state vectors, respectively. As in all state-space-based scenarios, we additionally define the state initial condition \(\mathbf{x}_{0}\) vector equivalent to the \(\mathbf{x}(t)\) for \(t=0\).  
**Remark 1**.: _Due to the nature of the perfect control law, we rather chose to omit the systems with \(n_{u}<n_{y}\). For such plants, it is shown--e.g., in Ref. [26]--that the perfect control cannot be established._  
Before we continue of the perfect control algorithm, let us recall the concept concerning nonunique matrix inverses. These inverses will later be used in order to establish the perfect control law; thus, inverse-oriented preliminaries are given in the next section.