esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot's continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved Canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the Canny algorithm is improved through three stages, filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the results showed that the proposed algorithm, when faced with complex environments with multiple interferences, has a correct detection rate higher than 98% and a positioning error mean of 0.9 mm. It exhibits excellent interference resistance and meets the visual positioning accuracy requirements for robot nut disassembly and assembly operations in actual working environments.  
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Footnote †: journal: Applied sciences  
Article  
Visual Localization Method for Fastener-Nut Disassembly and Assembly Robot Based on Improved Canny and HOG-SED  
Astrubtion (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 4 Methodology
### Parameter Estimation  
The parameter estimation was conducted for the datasets provided by [PERSON] et al. (2021) [14] and [PERSON] et al. (2020) [15], encompassing various experimental conditions and metals. The parameters slated for estimation are outlined in Equation (23). They were selected due to their direct association with several key assumptions, including axial dispersion, Langmuir-prescribed conditions, and the LDF approach. Furthermore, no equation exists to compute their values that is not empirical.  
\[\mathbf{P}=[k_{L},k_{s},D_{xx}] \tag{23}\]  
For the estimation, it is assumed that the measurement noise follows a Gaussian distribution with a zero mean and is both additive and independent of the parameter vector \(\mathbf{P}\). Given that \(\mathbf{W}\) is the covariance matrix, which becomes a diagonal matrix when considering a constant standard deviation in the measurements, the likelihood function can then be expressed by Equation (24). For all estimations, the standard deviation taken for measurement errors was considered constant at 5% relative to the largest experimental measurements.  
\[\pi(\mathbf{Y}|\mathbf{P})=(2\pi)^{-\frac{l}{2}}\Big{|}\mathbf{W}\Big{|}_{-\frac{1}{2}} \exp\biggl{\{}-\frac{1}{2}[\mathbf{Y}-\mathbf{Y}(\mathbf{P})]^{T}\mathbf{W}^{-1}[\mathbf{Y}-\mathbf{Y} (\mathbf{P})]\biggr{\}} \tag{24}\]  
It was also proposed that the prior probability distribution of the parameters, as shown in Equation (25), follows a Gaussian distribution, with a mean \(\mu\) equal to 95% of the parameters' reference value (that is, the initial value) and a covariance matrix \(\mathbf{V}\). For all estimations, the standard deviation associated with the parameters is held constant, representing 5% of the reference value.  
\[\pi(\mathbf{P})=(2\pi)^{-\frac{N}{2}}\left|\mathbf{V}\right|_{-\frac{1}{2}}\exp \biggl{\{}-\frac{1}{2}[\mathbf{P}-\mathbf{\mu}]^{T}\mathbf{V}^{-1}[\mathbf{P} -\mathbf{\mu}]\biggr{\}} \tag{25}\]  
To generate a candidate \(\mathbf{P}^{*}\) from the current state \(\mathbf{P}^{i-1}\), the transition kernel used in the present work has the form shown in Equation (26):  
\[\mathbf{P}^{*}=\mathbf{P}^{(i)}(1+\mathbf{w}_{\mathrm{s}}^{z}) \tag{26}\]  
where \(\xi\) is a variable N(0,1) and \(\mathbf{w}\) is the random walk.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 3 Position Control Strategy of PAM
### Feedforward-Incremental PID-Feedback Composite Control  
Since inverse compensation control is an open-loop control, its performance can be affected by model uncertainties, leading to decreased accuracy. Additionally, incremental PID-feedback control tends to perform poorly when handling complex nonlinear hysteresis systems. Therefore, this paper adopts a composite control strategy that combines feedforward control based on the QUPI inverse model with incremental PID-feedback control, defined as Control Method 1. In this method, feedforward control is first used to predict and adjust the motion of the PAM, followed by real-time correction and optimization through feedback control [21].  
Feedforward control is mainly based on a predefined action model, allowing for a rapid response and advance adjustment of the PAM's movement. Feedback control continuously monitors the difference between actual and target motion, dynamically adjusting the control input to ensure the accuracy and smoothness of the motion. At the same time, feedforward control pre-calculates the control signal, directly compensating for system nonlinearity significantly reducing dynamic errors, especially during the initial response phase, where it demonstrates fast and precise control. Figure 7 shows the block diagram of the control system, where \(Z_{d}\) represents the target displacement of the PAM, and \(Z\) represents the actual displacement of the PAM. IPID represents the incremental PID.  
In the displacement tracking control process of the PAM actuator, the error function is:  
\[e(k)=l^{\mathcal{S}}(k)-l^{\mathcal{S}}(k) \tag{13}\]  
In the equation, \(l^{\mathcal{S}}(k)\) represents the target deformation of the PAM and \(l^{\mathcal{S}}(k)\) represents the actual deformation of the PAM. Therefore, the control law for PAM pressure is:  
\[p(k)=IQUPI(p(k-1),l^{\mathcal{S}}(k))+u(k) \tag{14}\]  
\[u(k)=K_{\mathrm{P}}(e(k)-e(k-1))+K_{\mathrm{i}}e(k)+K_{\mathrm{d}}(e(k)-2e(k-1 )+e(k-2)) \tag{15}\]  
In the equation, \(p(k)\) represents the pressure of the PAM, \(IQUPI(p,l)\) is the output pressure value of the QUPI inverse model, and \(u(k)\) is the pressure compensation value from the incremental PID feedback. \(K_{\mathrm{P}}\), \(K_{\mathrm{i}}\), and \(K_{\mathrm{d}}\) are the proportional, integral, and derivative gains of the incremental PID, respectively.  
Due to the fact that accumulation is not required in the control algorithm, which is only related to the last \(k\) samples (\(k=3\) in this paper), the impact of misoperation is relatively small, and it is easier to obtain better control results through weighted processing. During the parameter adjusting process, it was found that as long as the parameters of the PID controller were not small or too large, there would be no oscillation. Therefore, the nonlinear characteristics of the system have been improved.  
Figure 7: Block diagram of feedforward-incremental PID-feedback composite control system.
## 1 Introduction
A deep learning approach has also been applied to explore the complex relationship between anthropometric measurements and HRTFs [21; 22], where anthropometric measurements including detailed measurements of the head, shoulders, and ears were used as the input features for a deep neural network (DNN). However, the performance of this approach was limited because ear-related measurements were difficult to obtain in real life. Instead of directly measuring anthropometric pinna measurements, we proposed a feature extraction method for ear images using an auto-encoder based on a convolutional neural network (CNN) [23] where the bottleneck features were extracted to represent personalized anthropometric data. In this paper, our previous approach is extended to estimate a personalized HRTF using ear images instead of ear-related anthropometric measurements. The proposed neural network for the  
Figure 1: Example of an application for the reproduction of binaural sound from monaural sound using (**a**) a pre-defined head-related transfer function (HRTF) for all listeners, and (**b**) personalized HRTF based on the proposed method. personalized HRTF estimation is composed of three sub-networks, the first of which is a feed-forward DNN that uses anthropometric measurements as input features to represent information on the relationship between anthropometric measurements and HRTFs [22]. The second sub-network tries to represent the personalized anthropometric measurements from ear images and is designed using a CNN, which enables us to obtain bottleneck features as proposed in Reference [23]. Lastly, the two different internal features obtained from the anthropometric measurements and ear images are combined using another DNN to estimate personalized HRTFs. The performance of the proposed method is evaluated objectively and subjectively. As objective measures, the root-mean-square error (RMSE) and log-spectral distortion (LSD) between the reference and estimated HRTF are calculated. In addition, the distance perceived by listeners after applying the estimated HRTF to a sound source is used as a subjective measure. Next, the performance of the proposed method is compared with that of HRTF estimation methods using the average [6] and estimated HRTF by a DNN trained with anthropometric measurements [22]. The remainder of this paper is organized as follows: Section 2 briefly reviews deep learning-based personalized HRTF estimation using whole anthropometric measurements. Section 3 proposes a personalized HRTF estimation method using a neural network that combines a DNN and a CNN in parallel applied to anthropometric measurements and ear images, respectively. Section 4 evaluates the performance of the proposed method and compares it with those of HRTF estimation methods using the average HRTF and the estimated HRTF by the DNN trained with anthropometric measurements. Finally, Section 5 concludes this paper.
# Displays Based on Dynamic Phase-Only Holography
## 2 Fourier and Fresnel Holography  
The first display holograms were off-axis Fresnel holograms, which captured interference between an object wave and a plane wave reference beam. Since the hologram is the viewing window, there was an interest in using holographic recording media of a large size (see Figure 1) [7].  
The 0 order beam in Figure 1 has the same size as the illumination beam. Therefore, a high resolution holographic recording was important so that the angular separation of the orders was sufficient. Unfortunately, SLMs are not available with the same size and resolution as the photographic plates used in Reference [1].  
In order to capitalize on the limited SBWP available from the SLM, a Fourier arrangement is used for the hologram replay. A Fourier hologram is the hologram that is recorded when the plane reference beam is focused on a spot at the same distance as the object. The beams interfering at the hologram plane will then have approximately equal radii of curvature. This reduces the interference fringing between the two beams, which demands a share of the SBWP. When illuminated by a plane wave, the Fourier hologram will reconstruct the object at infinity. If the Fourier hologram is reconstructed with a plane wave and a lens is placed in the reconstructed beam, the reconstruction is placed in the focal plane of the lens (see Figure 2).  
The advantage of the Fourier arrangement is that the image size can be adjusted by varying the focal length of the physical lens. Unfortunately, increasing the image size reduces the field of view (FOV) [8] so that additional components are required to increase the field of view (see Section 5). The zero order, which is formed by all the light transmitted by the hologram that is not diffracted, is focused to a spot at the focal point of the lens. In addition, the many-to-one correspondence, which promotes the fault tolerance, is accentuated in the Fourier case compared with the Fresnel hologram.  
In order to separate the reconstructed image from the conjugate image in the Fourier arrangement, a software lens is superimposed on the hologram [9]. The nature of the software lens depends on the modulation used in the hologram. For a binary amplitude modulation, the lens is a Fresnel zone plate. For a binary phase hologram, it is a phase zone plate and, for an analogue phase hologram, it is a phase Fresnel lens. In Figure 3, the positions of the reconstructed image and the conjugate image are shown for the case of a weak diverging software lens superimposed on the Fourier hologram. The reconstructed image is separated from both the zero order spot and the conjugate image. The zero order spot can be readily removed by spatial filtering. The size of the reconstructed image is proportional to the distance of the replay field from the hologram. The Fourier arrangement was used by Qinetiq in their Active Tiling 3D display system [8].  
Figure 1: Viewing geometry for an off-axis Fresnel hologram.  
Figure 2: Fourier hologram with a physical lens.
## 4 Simulation and Experimental Results  
In this section, we design a simulation experiment and implement an experimental platform for transmitting and receiving signals based on SDR to verify the main performance advantages of the proposed signal, mainly including acquisition probability, acquisition complexity, and code-tracking accuracy. We compare the performance of the proposed signal with original OTFS, traditional GNSS, and Locata signals, i.e., OTFS(10,1023), BPSK(5), and Pulse-BPSK(5).
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 3 Results
### An Overview of AIS Spoofing Incidents in the Last Years  
The last two years of the decade have been remarkable in many ways. While the coronavirus pandemic has disrupted operations worldwide, some global trends have continued unabated. One of these is our increasing reliance on the GNSS [38,39]. In recent years, there have been several disruptive incidents that have caused a stir in the shipping industry, as shown in Table 4.  
From Table 4, it can be concluded that the original purpose of AIS spoofing to this day is to disguise illegal fishing and other illegal activities at sea, which includes ship spoofing and AIS hijacking. There have also been some examples of AtoNs spoofing (Ponce De Le-on Inlet). In recent years, we have seen GNSS spoofing as part of defense development in a civilian scenario. The AIS spoofing has been deliberately used for electronic warfare and concealment of military activities, just like the situation in the eastern Mediterranean and the Red Sea. The incident in the Black Sea led to speculation that it could be attributed to one of the tests of satellite spoofing technology by one of the space superpower states. Whether as part of their electronic warfare arsenal or simply as an anti-drone measure to protect very important persons [40]. A practical example of geopolitical and geo-economic competition is the July 2019 detention of a British oil tanker, the Stena Impero, which was seized by Iranian forces after she was tricked (spoofed) into altering its course into Iranian waters. As a result, the ship, her cargo, and crew had become more than pawns in a geopolitical war [41]. Many of the shipping companies operating in the region also instructed their ships to transit Hormuz only at high speed and during daylight. Nevertheless, we should not forget that one-third of the world's oil flows by sea, about 17 million barrels per day, through the strait, making it one of the most important oil trade routes in the world [40,41].
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion
### Characterization of the Sample Which Has the Greatest Degree of Grafting Using CPTMS  
The sample which had the highest degree of grafting using CPTMS was distinguished by using FT-IR measurements (Figure 2), TGA (Figure 3), SEM (Figure 4) analysis, and elemental analysis for C and H (Table 1). The FT-IR measurements for unmodified and modified HNTs show the two sharp bands at 3680 and 3618 cm\({}^{-1}\) for HNT that represent the hydroxyl group, (O-H) stretching vibrations. The band at 1652 cm\({}^{-1}\) for unmodified HNT represents the deformation of (O-H) stretching of intercalated water, as shown in Figure 2[36]. The new band that appears at 2925 cm\({}^{-1}\) for modified HNT represents the CH\({}_{2}\) stretching vibration, and this confirms the creation of siloxane bonds. The presence of CH\({}_{2}\) groups confirms the coating of HNTs by CPTMS molecules. The TGA results of unmodified HNT shows the main mass loss at 400\(-\)500 \({}^{\circ}\)C, as is reported for the dehydration of hydroxyl groups that are located on the HNT surface [37]. The modified HNT shows an additional mass loss step between 300 and 450 \({}^{\circ}\)C, indicating the presence of the organic moieties on the HNT surface as shown in Figure 3. Finally, the SEM morphologies confirmed that the surface modification and reaction conditions did not alter the tubular shape of the halloysite nanotubes, and the halloysite tubes are coated with CPTMS with noticeable sticking together of species (Figure 4).  
Figure 2: FT-IR spectra of unmodified HNT and modified HNT sample.
###### Abstract  
(1) Autologous chondrocyte implantation (ACI) is a prominent method for treating cartilage damage, but periosteal patches can cause chondrocyte leakage. This study evaluates the potential of a decellularized membrane derived from the cell-produced extracellular matrix of 1-day-old porcine cartilage (pcECM-DM) to act as a substitute for periosteal patches. (2) The interaction between young rabbit chondrocyte cells and pcECM-DM was assessed through cytotoxicity, differentiation, cell viability, cell migration, and adhesive ability. Rabbit chondrocyte cells, cultivated until passage two, were seeded onto a 6 mm diameter membrane. Assessments included DAPI-PKH26 staining, histological staining, live/dead assay, WST-1 assay, and proteomics analysis. (3) Results: DAPI-PKH26 staining showed successful adhesion and the uniform distribution of cells on the membrane. Safranin-O and H&E staining confirmed that the membrane supports chondrocyte adhesion and extracellular matrix production with high cell density and typical chondrocyte morphology. The live/dead assay demonstrated a high proportion of viable cells at 24 and 48 h, with increased cell proliferation over time. The WST-1 assay showed a significant increase in OD450 values, confirming cell proliferation and biocompatibility. Proteomic analysis revealed the significant enrichment of genes associated with extracellular matrix organization, cell adhesion, and cartilage development. (4) Conclusions: This novel biomaterial holds the potential to enhance cartilage regeneration and offer a viable alternative to periosteal patches.  
pcECM-DM; osteoarthritis; cartilage repair
###### Abstract
The DFE branch specifically focuses on extracting local spatial features. Additionally, we propose a simple feature dynamic fusion module (DFM), introducing two learnable parameters for the dynamic fusion of spectral and spatial features. By combining these features, the spectral and spatial data required for the ultimate prediction can be captured. A CNN-based network, incorporating dilation convolution (DC), serves as the lightweight component of the detail feature extractor, capturing local spatial information. Depthwise Separable Convolutions (DSCs) [21] are introduced to reduce computational costs and parameters. The base feature extractor adopts a novel SeNet-based channel attention mechanism, utilizing the discrete cosine transform (DCT) [22; 23] instead of traditional average pooling operations. We introduce the DCT block to obtain the frequency domain representation of different frequency components of feature maps, using average frequency responses to remodel channel-wise responses through a simple multi-layer perception (MLP). Extensive experiments demonstrate that the DCTransformer achieves a state-of-the-art (SOTA) performance in the Houston, Indian Pines, Trento, and MUUFL datasets. The effectiveness of the DCTransformer has also been proved with numerous ablation studies. In summary, our main contributions are as follows:  
* We present a specialized two-branch network architecture named the DCTransformer. Within this network, spectral and spatial features are extracted flexibly and effectively by their respective branches. Owing to this advantage, the network facilitates a subsequent dynamic focus module (DFM) that adaptively learns spatial and spectral features with varying emphases to address different land cover characteristics. These characteristics may prioritize either spatial visual features or the categorical information conveyed by spectral features. * We introduce the DFE module, which effectively extracts spatial features while minimally increasing the total number of parameters. * We introduce the BFE module, which is composed of the channel attention mechanism combined with the DCT method, to effectively extract spectral features and to extract the base spectral features, including global frequent information. * We provide a DFM that fuses the spectral and spatial features self-adaptively. This paper's remainder is structured thusly. Section 2 describes the materials and methods, the 3D-CNN, the DCT, and the attention mechanism. ResNet will be introduced in the related work subsection, and the DCTransformer model detail will be elaborated. Section 3 describes the experiment results compared to other methods. Section 4 and Section 5 present the discussion and conclusions.