esa-sceva/satcom-chunk-collection huggingface dataset:
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 4 Influencing Factors and Control Methods of Anti-Slide Pile Deformation  
### Factors Influencing Anti-Slide Pile Deformation  
The behavior of the anti-slide piles to increase landslide stability is affected by many factors. The first layer rock under the sliding surface is affected by the overlying layer conditions of the landslide [5]. The thickness of the first weak rock is one of the main factors influencing pile head deformation. The modulus of subgrade reaction of the first weak bedrock also affects the deformation of anti-slide piles. Therefore, these factors should be analyzed for controlling the deformation of anti-slide piles set in multilayer strata. The calculation model of anti-slide piles is established in Figure 5a. This calculation model simplifies the thrust of the landslide into a triangular load acting on the pile above the sliding surface, and the pile behind the sliding surface set into rock layers of different strengths.  
The basic parameters of the pile in the calculation were the same as those of the A-type pile in the Zhenzilin landslide. The length of the A-type pile was approximately 23.5 m, with the cross-sectional (from A to A\({}^{\prime}\)) width (b) of 2 m and the cross-sectional height (a) of 3 m. The driving force of the Zhenzilin landslide (p) was 1550 kN/m in the cross-section from A to A\({}^{\prime}\). The thicknesses of the second hard rock (\(\Delta\)y\({}_{2}\)) was 7.5 m. The modulus of subgrade reaction of the second hard rock (k\({}_{2}\)) was \(1.7\times 10^{5}\) kPa/m. The thickness of the first weak rock (\(\Delta\)y\({}_{1}\)) were varied as 0.5, 1, 1.5, 2, and 2.5 m, and the corresponding embedded lengths of a pile (h) were 8, 8.5, 9, 9.5, and 10 m; the corresponding modulus of subgrade reaction of the upper weak bedrock (k\({}_{1}\)) were \(0.7\times 10^{5}\), \(1.0\times 10^{5}\), and \(1.3\times 10^{5}\) kPa/m. All factors influencing the parameters are listed in Table 2.
## 3 Results and Discussion
### Evaluation with COCO API

Table 5: **Average precision** (AP) metrics of all test sets for all networks according to COCO metric API. Figure 9: Graphic representation of COCO evaluation metrics. Although the SSD network provided the worst performance for the test sets, it is much effective in the localization of the objects when compared with the other networks. Moreover, the Yolo-v3 network provided better detection of the small objects with 0.5 IOU. Additionally, Faster R-CNN can detect the small objects of the DOTA test set with 6% AP, while the other networks cannot, and for the small objects of the large-scale image set, it has a similar performance with the Yolo-v3 (Figure 10).
## Opportunities  
1. **Growing Demand:** global market for sustainable acoustic materials projected to grow at 6.5% CAGR (2023-2030).
2. **Technological Advancements:** machine learning and 3D printing reduce development time by up to 50% and achieve material efficiencies of over 90%.
3. **New Markets:** Applications in automotive and aerospace (e.g., lightweight composites with densities of 300-550 kg/m\({}^{3}\)). Utilization in developing countries, where agricultural residues account for over 30% of waste.  
1. **Material Variability:** Inconsistent quality due to heterogeneous fibers (e.g., hemp shive density ranges from 200-600 kg/m\({}^{3}\)). Performance depends on fiber type, density, and processing.
2. **Durability Concerns:** Natural fibers degrade under moisture and UV exposure (e.g., hemp-lime compressive strength < 1 MPa). Additional treatments (e.g., mineralization) may reduce sustainability.
3. **Limited Standardization:** lack of standardized processes hinders scalability (e.g., wood-cement compressive strength varies from 1.07-1.59 MPa).
4. **Cost and Processing:** High initial costs (e.g., 3D-printed panels cost 20-30% more than traditional ones). Synthetic binders (e.g., epoxy) increase environmental impact (up to 5 kg CO\({}_{2}\) per kg).
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 3 Cyberattack Scenarios on Autonomous Ships
### Communication Protocol Attack  
Autonomous ship systems have the potential to provide complete control to the ship in cyberattacks owing to the high network connectivity for facilitating communication with land [42; 43]. As illustrated in Figure 10, a communication protocol attack is an attack in which the communication used by an autonomous ship is targeted. Various protocols are used inside and outside the ship. Examples include the Ethernet bus (which is the most commonly used land-based network), 4G/5G wireless networks, and ModBus protocol (which is often used in control systems). CAN bus, a message-based protocol widely used in vehicles, is also used. Finally, the NMEA protocol, which is used to transmit and receive sensor data on ships, is also used.  
As the CAN bus and NMEA protocols used inside ships are not designed with consideration for security, there are vulnerabilities in the protocol itself. CAN bus and NMEA communication send and receive data without encryption or authentication, so if an attacker gains access to the network, all communication data can be stolen. CAN and NMEA protocols without authentication devices are vulnerable to man-in-the-middle attacks. Man-in-the-middle attacks make it possible for an attacker to manipulate or collect data and maliciously insert errors. Vulnerabilities in the satellite communications used by ships  
Figure 10: Communication protocol attack.  
Figure 9: Scenario of an attack on a neural network.  
have also been reported, allowing attackers to gain access to the ship's network [44]. For autonomous ground vehicles (AGVs) network security, [PERSON] et al. [45] investigated the path-tracking control issue of AGVs prone to deception attacks using a learning-based Event Triggered Mechanism (ETM) and proposed a novel control strategy against deception attacks. Compared with the research covering the network security of autonomous cars, the research on the network security of autonomous ships is still underdeveloped. The automotive industry is changing its networks to Ethernet, replacing CAN. This is automotive Ethernet, and standards such as IEEE 802.3 ch have emerged. The future of CAN buses in ships will change to marine Ethernet for ship navigation and communication, just as automotive Ethernet has been standardized in the automotive industry. Therefore, the contents of this study on CAN buses should continue to be researched in the ethernet environment.
## 4 Discussion
Further and more thorough analyses are then needed in the highlighted hotspots with other methods: in this perspective, the ground monitoring has an unsubstituted advantage over InSAR in real-time monitoring and the two techniques can be considered complimentary. The proposed integrated monitoring method provided a solution for such circumstances and proved that the InSAR regional-scale deformation mapping must be completed by regular field investigations or ground monitoring in order to avoid missing deformations that may be of serious consequence. With Sentinel satellite InSAR, the deformative scenario at a wide scale could be updated periodically, for instance every year or less, and the necessity of extensive field investigation could be largely reduced, at least for some typologies of landslides. The majority of the engineering remedial work in the TGRA started construction after the establishment of the Three Gorges Dam in 2003, and was sequentially built up later. Since the huge scale of landslides engineering remedial work was extremely costly due to the unexpectedly large number of natural hazards, the hazard engineering remedial works were implemented in a multi-stage plan to alleviate the financial pressure on the government. Along with this staged plan came a new problem regarding the changing setting of prevention work design. The remedial work design engineer could have not known the presence of such intense urban growth and environment changes; thus, the additional weight and interference may cause the remedial work invalidation and the landslide reactivation. For example, several landslides are distributed at the north bank of the second Wanzhou Yangtze River Bridge, including landslides with already implemented engineering remedial work or ancient landslides in a dormant state. However, the heavy load from the bridge and buildings could have largely fostered the landslide reactivation, which may have led to the sinking of the bridge pier. In such case, the landslide deformation may not be attributed to the invalidation of its engineering remedial work, but mainly due to the changes in the surrounding environmental conditions. Landslides reactivations resulting from land use changes (and particularly from urban growth) might become a general issue in this region, and thus monitoring programs are urgently needed [37]. The setting changed due to anthropic (e.g., urban expansion) and natural (e.g., climate change) factors, thus the long-term effectiveness of remedial measures should be constantly checked. The geohazard prevention and control plan of the TGRA was formulated based on the idea of hazard risk management. The engineering remedial works were primarily built on landslides with many anthropic elements (mainly buildings and roads) exposed at risk. This circumstance is ideal for the application of our methodology because the urban environment provides reflectors for and effective InSAR deformation analysis at the regional scale, and, in addition, it is possible to observe deformation features, such as cracks and other building damage, directly through field investigation.
# Mapping Nanoparticles in Hydrogels: A Comparison of Preparation Methods for Electron Microscopy
###### Abstract  
The distribution of noble metal nanoparticles (NMNPs) in hydrogels influences their nanoplasmonic response and signals used for biosensor purposes. By controlling the particle distribution in NMNP-nanocomposite hydrogels, it is possible to obtain new nanoplasmonic features with new sensing modalities. Particle positions can be characterized by using volume-imaging methods such as the focused ion beam-scanning electron microscope (FIB-SEM) or the serial block-face scanning electron microscope (SBFSEM) techniques. The pore structures in hydrogels are contained by the water absorbed in the polymer network and may pose challenges for volume-imaging methods based on electron microscope techniques since the sample must be in a vacuum chamber. The structure of the hydrogels can be conserved by choosing appropriate preparation methods, which also depends on the composition of the hydrogel used. In this paper, we have prepared low-weight-percentage hydrogels, with and without gold nanorods (GNRs), for conventional scanning electron microscope (SEM) imaging by using critical point drying (CPD) and hexamethyldisilazane (HMDS) drying. The pore structures and the GNR positions in the hydrogel were characterized. The evaluation of the sample preparation techniques elucidate new aspects concerning the drying of hydrogels for SEM imaging. The results of identifying GNRs positioned in a hydrogel polymer network contribute to the development of mapping metal particle positions with volume imaging methods such as FIB-SEM or SBFSEM for studying nanoplasmonic properties of NMNP-nanocomposite hydrogels.  
Keywords:metal nanocomposite hydrogels; particle positions; nanoplasmonic sensor; preparation methods; SEM imaging; volume imaging +
Footnote †: journal: Applied Physics Letters
# Calibration of a Quartz Tuning Fork as a Sound Detector
## 3 Results  
### Microphone Sensitivity  
For the QTF calibration, the sound pressure at its location must be measured with a reference detector. This has been accomplished using an extremely sensitive ultrasound-measuring microphone. The sound pressure was determined from its signal and its sensitivity, i.e., the proportionality constant between output voltage and sound pressure.  
The microphone manufacturer provided a general sensitivity of the model series. However, for accurate results, the exact sensitivity of the microphone used is required. For this, the Multifunctional Sound Calibrator 42 AG (Class 1) from GRAS Acoustics was used. This device mechanically generates a reference soundwave with a pressure amplitude of 1 Pascal (\(L_{\mathrm{C}}=94\) dB SPL) at a frequency of 1 kHz. The calibrator was put on the microphone head. Figure 3 shows the microphone signal as a function of time. The measured root-mean-square (RMS) was as follows: \(V_{0}=1.0657\cdot 10^{-3}\mathrm{V}\). Accordingly, the sensitivity of \(1.0657\frac{\mathrm{mV}}{\mathrm{P}\alpha}\) can be transferred into decibel levels using the following equation [16].  
\[S_{Mic}(1\mathrm{kHz})=20\cdot\log_{10}\left(\frac{V_{0}}{2\cdot 10^{-5}\ \mathrm{Pa}\cdot 10^{ \frac{L_{\mathrm{C}}}{2\mathrm{P}}}}\frac{\mathrm{Pa}}{\mathrm{V}}\right)\, \mathrm{dB} \tag{1}\]  
This leads to \(S_{Mic}(1\ \mathrm{kHz})=-59.4679\) dB.  
The manufacturer's calibration certificate was used to adapt the 1 kHz sensitivity to the QTF resonance frequency: \(f_{R}=32,758\) Hz. Linear interpolation of the given values leads to a correction of \(-0.2950\) dB, resulting in a sensitivity of \(S_{Mic}(f_{R})=-59.7629\) dB, which corresponds to \(1.0277\frac{\mathrm{mV}}{\mathrm{P}\alpha}\).
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 3 Methods  
The air traffic network situation dataset needs to be further processed to assess the overall situation of air traffic. In order to accurately determine the air traffic state, we transform the situation assessment into a binary classification. According to significantly good and poor air traffic operation status, samples are labeled. These samples are used as the training dataset to input the model to calculate the classification boundary. According to the distance from the sample to the classification boundary, the air traffic situation can be quantitatively described. Machine learning has inherent advantages in solving binary classification problems. However, a single machine-learning model has some problems, such as a low classification accuracy and unscientific classification standards. Therefore, the ensemble learning was used to build the situation assessment model. It can integrate the advantages of various machine-learning models to obtain a more accurate and scientific classification model.
# Quasi-Static Sliding Wear Analysis of 3D Rough Surface Considering Changes in the Point of Contact
## 2 Theoretical Background
### Numerical Procedure  
Figure 3 shows the flowchart of wear analysis, and the process is summarized as follows.  
Figure 3: Flowchart of numerical analysis for rough surface wear using deterministic quasi-static wear model.  
1. Generate a rough surface numerically based on the roughness parameters and ACF. For detailed methods of numerical generation of rough surfaces with Gaussian and non-Gaussian distributions, see [40, 41]. 2. Align the upper moving surface with the lower stationary surface and set the analysis domain. 2. Calculate the contact pressure of the analysis domain using CGM and CC-FT. 2. Move the upper moving surface by \(\delta_{s}\), and insert the data of the lower stationary surface outside the analysis area into the opposite side. 2. Repeat the contact analysis while moving the upper moving surface until the total amount of movement is the sliding distance \(S\). 3. After step (2), the surface profile is updated for each surface node using the calculated wear depth based on the average pressure during sliding. After steps (2) and (3) are repeated up to the set number of cycles \(N_{cycle}\), the worn profile is obtained.
## 3 Methodology
### Surface Defect Detection
When these micro defects were combined with macro defects, we obtained a comprehensive representation of the surface defects. Moreover, for the scratches on the magnet tile surface, we conducted a histogram analysis on the RGB color channels of each point to identify the peak-to-peak values of their normalized colors. The points within this peak range corresponded to the normal color of the magnet tile. We then categorized regions outside of the peak range: those with color values lower than the peak were designated as dark regions, while those with color values higher than the peak were classified as bright regions. Subsequently, [PERSON]'s thresholding method was applied to both the dark and bright regions, enabling the extraction of scratches from each region based on their respective thresholds. Lastly, to address the incomplete defect areas identified in the previous steps and reduce the possibility of missed detections, we utilized region growing, specifically employing DBSCAN clustering [86]. This density-based clustering technique allowed us to effectively complete the defect regions by controlling the density (\(minPts\)) between points and the required neighborhood size (\(\epsilon\)) for the core points.
## 4 Elementary State Components
### Translations  
In the case of translation-invariant dynamics, the irreducible subspaces of the closed dynamics are one-dimensional. The elementary pure states, \(|\mathbf{q}\rangle\), are characterized by their wave vector \(\mathbf{q}\), and the elementary mixed state components of the closed dynamics are the dyadic products \(|\mathbf{q}_{+}\rangle\langle\mathbf{q}_{-}|\). In the coordinate representation, we have \(A(\mathbf{x}_{+},\mathbf{x}_{-})=\langle\mathbf{x}_{+}|A|\mathbf{x}_{-}\rangle\), and the parametrization \(\mathbf{x}=(\mathbf{x}_{+}+\mathbf{x}_{-})/2\) and \(\mathbf{x}_{d}=\mathbf{x}_{+}-\mathbf{x}_{-}\) turns out to be useful. The elementary mixed state components of the closed dynamics are the dyadic product, \(D_{\mathbf{k}_{+},\mathbf{k}_{-}}(\mathbf{x}_{+},\mathbf{x}_{-})=e^{i\mathbf{k}_{+}\mathbf{x}_{+}-i \mathbf{k}_{-}\mathbf{x}_{-}}\). The irreducible subspace of the open dynamics, \(\mathcal{A}_{\mathbf{q}}\), consists of \(A_{\mathbf{q}}(\mathbf{x},\mathbf{x}_{d})=e^{i\mathbf{q}\pi}\chi(\mathbf{x}_{d})\) with some \(\chi(\mathbf{x}_{d})\). It is natural to use a continuous quantum number \(\mathbf{k}\) to define a translation-independent basis in \(\mathcal{A}_{\mathbf{q}}\),  
\[A_{\mathbf{q},\mathbf{k}}(\mathbf{x},\mathbf{x}_{d})=e^{i\mathbf{q}\pi+i\mathbf{k}\mathbf{x}_{d}}=D_{\frac {q}{2}+k_{+}\frac{q}{2}-\mathbf{k}}(\mathbf{x},\mathbf{x}_{d}). \tag{6}\]  
This equation, the change of basis between the elementary closed and open components, is the analog of (5) with \((n,\ell)\leftrightarrow(\mathbf{k},\mathbf{q})\). An elementary open component can be written in the form of a Fourier integral,  
\[A_{\mathbf{q}}(\mathbf{x},\mathbf{x}_{d})=e^{i\mathbf{q}\pi}\int\frac{d^{3}k}{(2\pi)^{3}}\chi _{k}D_{\frac{q}{2}+k_{+}\frac{q}{2}-\mathbf{k}}(\mathbf{x},\mathbf{x}_{d}), \tag{7}\]  
Note that the quantum number \(\mathbf{q}\) is not related to the momentum. In fact, the momentum operator is the sum of two terms:  
\[\mathbf{p}_{\pm}=\frac{\hbar}{i}\mathbf{\
abla}_{\pm}=\frac{\hbar}{i}\bigg{(}\frac{1} {2}\mathbf{\
abla}\pm\mathbf{\
abla}_{d}\bigg{)} \tag{8}\]  
with \(\mathbf{\
abla}_{\pm}=\partial/\partial\mathbf{x}_{\pm}\). The contribution of the first term to the expectation value of a momentum-dependent observable is vanishing for normalizable states, and a simplified \(\mathbf{q}\)-independent momentum operator, \(-i\hbar\mathbf{\
abla}_{d}\), can be used in coordinate-independent observables. In the case of translation noninvariant observables, the term with \(\mathbf{\
abla}\) is needed, and it takes care of the coordinate dependence of the observables.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 2 Materials and Methods  
### Instrument Design  
The design of the 3D-printed flexible grasping instrument was created in collaboration with experienced gastroenterologists and surgeons who shared their clinical needs and current technological limitations. Based on their insights, a set of requirements for a prototype of the instrument was established. The instrument should offer physicians additional capabilities for manipulating objects and tissue in the upper and lower gastrointestinal (GI) tract, ultimately improving the outcome of various endoscopic procedures.  
In order to meet the requirements of medical professionals, the instrument needed to incorporate features from both laparoscopy and flexible endoscopy. The distal tip of the instrument had to provide a secure and long-lasting grip while still remaining gentle enough to handle pathologically important tissue without causing any damage. This is particularly important in the case of endoscopic procedures, which require precise and careful handling to avoid injuries and perforation. To achieve this, the instrument had to be designed with an adequately large grasping area and a gentle rounded distal tip.  
In addition to its grasping capabilities, the instrument must also be highly maneuverable. The distal tip should have a high range of bending, allowing steering angles greater than 180\({}^{\circ}\). This is necessary to ensure that physicians can access and manipulate target tissue from a variety of angles, as well as to navigate through the twists and turns of the GI tract. Furthermore, the instrument must also be independent of the endoscope and its vision, allowing for better traction and control during procedures.  
To ensure that the instrument is adaptable to different procedural needs, it must have an appropriate length, which can be adjusted by using a longer or shorter flexible shaft and corresponding wires. This allows the customization of the instrument based on the different procedure-specific demands, such as operating in the upper or lower GI tract.  
Additionally, the instrument should be affordable to produce and intuitive to control and use. By incorporating all these requirements, the new prototype provides physicians with a range of features for improving and performing endoscopic procedures in a safe, effective, and precise manner. Table 1 provides a detailed breakdown of the requirements for each element of the instrument.