esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 1 Introduction
### Research Gap and Our Contribution  
Although the benefits associated with the integration of the XR services with the DT systems are promising, the relevant research has not yet provided a comprehensive guideline for industrial XR application developers. Therefore, more effort is needed to provide an XR application development framework for the DT-based machine. In particular, one of the main concerns is that the framework should guarantee wide generality and high efficiency.  
This paper is based on the master thesis of the first author [44]. The thesis provided a VR application development framework targeting the ROS-based machine. An interactive Virtual environment was established, integrating with the communication protocol between ROS and Unity. However, there were several limitations of the previous work:  
* The control unit of the simulated model was set up by the ROS MoveIt package, which was not compliant with the PLC-based machine.
* The WebSocket protocol was used to connect the simulated model to the applications in Unity in the local network, which was not consistent with the practical industrial scenario.
* The whole framework was established under the simulated and virtual environment, lacking an interface with the physical machine.  
The main contributions in this paper, handling the limitations of previous research on the VR application framework and existing XR development framework solutions, are:  
* Providing an XR application technological framework for the DT-based machine (see Figure 1).
* Presenting software integration process for XR applications.
* Developing several XR applications based on the framework leveraging different communication middleware, including OPC UA-MQTT wrapper, OPC UA-GraphQL wrapper, and OPC UA-Unity client.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
###### Abstract  
The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis-linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included \(\sim\)2920 cm\({}^{-1}\) (C-H stretching in lipids), \(\sim\)1650 cm\({}^{-1}\) (Amide I band, corresponding to C=O stretching in proteins), and the 1300-900 cm\({}^{-1}\) range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products.  
F 2025 15 8272
# The Progress on Lung Computed Tomography Imaging Signs: A Review
## Preliminary
### The Overview Framework for Detection of CISs  
Currently, there are mainly standard multistep detection frameworks and innovative deep learning detection frameworks used to lung CT sign identification. The traditional multistep framework is built on machine learning, which essentially involves four fundamental steps: preprocessing, candidate region segmentation, feature extraction, and false positive reduction. The future stages depend on the preceding step, and the outcomes of each phase will have a considerable effect on the ultimate detection result. The advantage of this framework is that the stages are straightforward to describe and reasonably easy to implement. The disadvantages are that each phase is isolated from the others, the parameters cannot be shared and optimized simultaneously during the training process, and the traditional approaches are limited due to manual feature extraction lacking expression capacity and poor generalization ability.  
The innovative deep learning detection framework is based on deep learning. The advantage of this framework is that deep learning methods can automatically extract deep features of an image and perform end-to-end supervised training, making it easier to achieve full automation compared to traditional machine learning methods, and the overall detection performance of the model is better than traditional methods. The disadvantages are equally clear; training deep neural networks needs a considerable quantity of data, and in the medical imaging area, large-scale, well-labeled data resources are limited.  
For the reader's simple reference, this paper provides a general detection framework summarizing existing approaches for lung CT sign identification, as illustrated in Figure 2, where the content in the black rectangle box denotes the traditional method and the gray section indicates the deep learning method. CT scans may be preprocessed for further analysis following input to the detection system, and the basic preprocessing methods include eliminating noise from the original CT image for image enhancement and minimizing interference regions for lung parenchyma segmentation, and the system eventually outputs the lesion information.
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.4.2 Fdpe  
The derivative method is an effective method for processing spectral data. Due to the UV-Vis absorption spectroscopy, useful information can be obtained from the UV-Vis spectrum data by increasing the derivative order, while too high order would lead to a low signal intensity of the signal to be detected and great sensitivity to noise components [13]. A suitable derivative order is very important. The first derivative has almost no loss of the signal-to-noise ratio and can reflect the absorption spectrum change rate. By deriving Equation (1), Equation (2) can be obtained.  
\[\frac{dA}{d\lambda}=\frac{dK(\lambda)}{d\lambda}CL \tag{2}\]  
Figure 4: The first derivative spectra of COD standard solution absorption spectra, where \(\alpha\)1 is the first extreme point, \(\alpha\)2 is the second extreme point, \(\alpha\)3 is the third extreme point, \(\alpha\)4 is the fourth extreme point, \(\alpha\)5 is the fifth extreme point.  
According to Equation (2), \(\frac{dK(\lambda)}{d\lambda}\) is proportional to \(C\) for a specific measurement wavelength and a specific water sample [15]. In this paper, the first derivative method was used to process the UV-Vis absorption spectrum. The first derivative spectrum of any concentration can be set as a continuous sequence \(\{x(i),i=1,2,\cdots,N\}\). In order to obtain \(\alpha_{i}(i=1,2,3,4,5)\) in the first derivative spectrum, PE is used to search the extreme points in the sequence. The calculation process is as  
(RMSEC) and root mean-squared error of validation (RMSEV). The performance indices are shown in Equations (9)-(11).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{({{\hat{y}}_{i}}-{{y_{i}}})}^{2}}}}{\sum \limits_{i=1}^{n}{{{({{y_{i}}}-\overline{y})}^{2}}}} \tag{9}\]  
\[RMSEC=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({{\hat{y}}_{i}}-{{y_{i}}})}^{2}}}} {n-A-1}} \tag{10}\]  
\[RMSEV=\sqrt{\frac{1}{m}{\sum\limits_{i=1}^{m}{{({{\hat{y}}_{i}}-{{y_{i}}})}^{ 2}}}} \tag{11}\]  
where \({{\hat{y}}_{i}}\) is the predicted value by calibration model, \(\overline{y}\) is the mean of measurements, \({{y_{i}}}\) is the measurement, \(n\) is the number of calibration samples, \(m\) is the number of validation samples, and \(A\) is the number of regression factors [19; 20].
## 4 The Proposed Detection and Restoration Framework
### Detection Method Based on Reconstruction Error
```
Data: Training dataset \(X_{\text{train}}\), Test sample \(x\), Validation dataset \(X_{\text{validation}}\) Result: Boolean indicating if \(x\) is adversarial
1 Initialize \(ae=d\circ e\)
2 whilenot convergeddo
3\(X_{\text{reconstructed}}\gets ae(X_{\text{train}})\)
4\(L=\frac{1}{|X_{\text{train}}|}\sum\|x-X_{\text{reconstructed}}\|_{2}\)
5 Update \(ae\) parameters using backpropagation
6
7 end while
8
9 Compute reconstruction errors for \(X_{\text{validation}}\) using \(ae\)
10\(t_{\text{re}}\leftarrow\) threshold based on error distribution of \(X_{\text{validation}}\)
11\(E(x)\leftarrow\|x-ae(x)\|_{p}\)
12 if\(E(x)>t_{\text{re}}\)then
13 return True // Sample \(x\) is adversarial
14
15 end if
16
17 end while
```  
**Algorithm 1**Reconstruction error-based adversarial detection.
## 1 Introduction  
Secret sharing is a cryptographic protocol in which a dealer splits a secret into several parts and distributes them among various players. The secret can be recovered only when a sufficient number of players (authorized subsets) cooperate to share their parts of the secret. The classical secret sharing scheme was first introduced independently by [PERSON] [1] and [PERSON] [2] in 1979, followed by plenty of variations [3]. However, all existing classical secret sharing schemes are not perfectly secure from eavesdropping attacks [4].  
As the combination of classical secret sharing and quantum mechanics, quantum secret sharing (QSS) is more secure due to the excellent properties of quantum theory and has become one of the most attractive research topics in the quantum cryptography. In 1999, [PERSON] et al. [5] firstly proposed a protocol of QSS using a three-photon Greenberger-Horne-Zeilinger (GHZ) state. Afterwards, this protocol was generalized into an arbitrary number of parties based on multi-particle entanglement states [6], and later to multi-particle \(d\)-dimensional entanglement states [7]. From then on, much theoretical [8, 9, 10, 11, 12, 13, 14, 15] and experimental [16, 17, 18, 19] attention has focused on QSS using multi-particle entangled states. However, it is a tremendous challenge to prepare a multiparty entanglement state with high fidelity and efficiency, which makes particle entanglement-based QSS unscalable. To circumvent the problems, differential phase shift QSS scheme using coherent light [20], similar to those used in quantum key distribution (QKD) [21, 22, 23, 24, 25, 26], has been proposed and implemented.  
Nevertheless, the linear rate-distance limitation constricts the key rate and transmission distance of QSS [27, 28]. Recently, to exceed the linear bound and further enhance the practical performance of QSS, a differential phase shift quantum secret sharing (DPSQSS) protocol [29] using a twin field (TF) [30] has been proposed. Unfortunately, this protocol suffers from low key rate and short transmission distance over channels with different transmitances, which constrains its application in a practical network setting.  
Here, we propose an asymmetric differential phase shift quantum secret sharing protocol using twin field [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] ideas and give the security proof of this protocol against individual attacks. The key point of our method is that [PERSON] and [PERSON] can adjust their source intensities independently to effectively compensate for channel asymmetry. The numerical results show that our protocol is robust against finite-key effects [45; 46; 47; 48] and can theoretically provide a two orders of magnitude higher key rate than the original protocol with the length difference between [PERSON]'s channel and [PERSON]'s fixed at 14 km. Furthermore, our protocol still obtains a high key rate when the difference in length is fixed at 50,100 km, whereas no keys are obtained with the same difference in the original protocol. Therefore, our work represents a further step along the progress of practical QSS.
## 3 Optimization System
### Surrogate Optimization Criterion  
The concept of the proposed substitute criterion is based on the analysis of the main mechanism of molten bath mixing, which is natural convection. It is based on the decrease in fluid density as the temperature increases. The warmer, less dense fluid is displaced upwards by the cooler, more dense fluid. The idea is to lower the location of the Joule heat generation center to the maximum. This allows the heated liquid to flow the longest possible distance to the surface of the bath, which allows for an increase in its velocity. The first variant of the criterion analyzed in the base was based only on this quantity:  
\[C_{1}=\frac{\sum_{i=1}^{N}q_{|H,i}z_{i}V_{i}}{\sum_{i=1}^{N}q_{|H,i}V_{i}} \tag{12}\]  
where \(N\) is the number of cells in the liquid bath area, \(q_{|H,i}\) is the Joule volume power density in the \(i\)-th cell, \(z_{i}\) is the vertical coordinate of the \(i\)-th cell's centroid, and \(V_{i}\) is the volume of this cell. As can be seen, the calculation of this surrogate criterion requires only data from the electromagnetic model.  
Qualitative analysis of the phenomena associated with natural convection seemed to indicate that it is beneficial to place the Joule heat generation center as close as possible to the vertical axis passing through the center of the bath for the intensification of the bath mixing. Therefore, the research also included such a modification of the optimization goal. This can be achieved by including this second goal in the second version \(C_{2}\) of the criterion that takes into account the distance \(D\) of the Joule heat generation center from the mentioned central axis:  
\[D=\sqrt{\left(\frac{\sum_{i=1}^{N}q_{|H,i}x_{i}V_{i}}{\sum_{i=1}^{N}q_{|H,i}V_ {i}}\right)^{2}+\left(\frac{\sum_{i=1}^{N}q_{|H,i}y_{i}V_{i}}{\sum_{i=1}^{N}q _{|H,i}V_{i}}\right)^{2}}, \tag{13}\]  
\[C_{2}=C_{1}+aD \tag{14}\]  
where \(x_{i},y_{i}\) are horizontal coordinates of the \(i\)-th cell. The \(a\) coefficient in the criterion decides how strongly the system tends to place the heat generation center in the bath axis. Since this is a secondary goal, a value of \(a\) equal to \(0.5\) was assumed in the study. Of course, assuming zero \(a\) means reducing criterion \(C_{2}\) to criterion \(C_{1}\). During the research, an attempt was also made to use the opposite of this criterion, where the \(a\) coefficient had a negative value (\(-0.5\)). This means that the optimization algorithm strives to move the Joule heat generation center as far from the bath axis as possible.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 7 The Threshold Determination
### Velocity-Based Thresholds  
Velocity-based thresholds consider the rate of slope movement or rate of change in the rock mass. These values are mainly expressed in mm/day or mm/week. Because of the complicated geological conditions and complex mechanisms involved in a landslide, velocity-based thresholds are often determined based on the experiences of a rock slope engineering specialist [15].
## 2 Methods
### Neural Network Model Architecture
#### 2.2.1 The Encoder  
A deep neural network extracts features by stacking several convolutional layers and pooling layers. The features can be enriched with the deepening of the network layer. However, two problems have been found in experiments: one is the vanishing and the exploding gradient, which can be solved by using a batch normalization layer [45]. The other is the degradation problem. Adding more layers will not reduce the training error but instead make it larger. ResNet proposed a residual learning framework to address the degradation problem so that a very deep network can be built. There are two branches in ResNet, as shown in Figure 6a; the first branch extracts features through two or three convolutional layers, and the second branch is called identity mapping and retains the original input. A shortcut connection module performs an element-wise summation between the two branches. When the size of the input and output are the same, the formulation \(\mathcal{F}(x)+x\) can be directly executed (the solid line shortcut in Figure 6a). When the size is different, a \(1\times 1\) convolution with stride 2 is added to reduce the input size (the dotted line shortcut in Figure 6a). The weight of the first branch can be close to zero if identity mappings are optimal. In this way, the network of layer \(L+1\) contains more image information than layer \(L\), which ensures a deeper network will not be inferior than its shallower counterpart. DenseNet is an improvement of ResNet. It is also based on the shortcut connections. The difference is that it adopts a more intensive connection. Figure 6b shows its internal structure; each layer is connected to all of the subsequent layers so that the features of each scale can be reused to maximize the information flow between the layers. Another difference is that it concatenates the multiple feature maps instead of adding all of the elements. Figure 5: Structure of the strain prediction network. In the encoder part, two networks are designed: one is StrainNetR, which extends ResNet, and the other one is StrainNetD extending DenseNet. Table 3 lists the implementation details of StrainNetR and StrainNetD. StrainNetR consists of four different bottlenecks; each bottleneck includes two \(3\times 3\) convolutional layers. StrainNetD is implemented by four dense blocks, and each block comprises a \(1\times 1\) followed by a \(3\times 3\) convolution with stride \(1\). \(1\times 1\) convolution is used to reduce dimensions, making the input of \(3\times 3\) layer smaller, thus improving the calculation efficiency. The transition layer is responsible for connecting two consecutive dense blocks, which includes a batch normalization layer, a \(1\times 1\) convolutional layer, and a \(2\times 2\) pooling layer. After the transition layer, the size of the feature maps will be reduced by half.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Characterisation of Nanoparticles
#### 2.5.2 Surface Morphology  
Common techniques used to characterise surface morphology are AFM, SEM and TEM. TEM is better capable of providing composition and morphology, allowing resolution of atomic scale. SEM uses a beam of electrons to image the surface of the sample, which means the sample must be conductive prior to analysis. SEM can examine the aggregation and morphology and in used frequently than TEM despite having a poorer resolution. AFM is another important technique that can analyse the topography, particle size and distribution of nanoparticles and can be operated under various conditions such as air, liquid and vacuum.
## 2 Immune System, MASLD, and HCC
### Pathogenesis of HCC in MASLD  
The mechanisms underlying HCC development in MASLD are complex and not yet fully understood. Lipid accumulation in liver cells and the resulting lipotoxicity create a proinflammatory environment. Chronic hepatocyte regeneration and tissue remodeling, driven by sustained inflammation, contribute to alterations in DNA damage response pathways and hepatocellular genomic instability, ultimately increasing the risk of tumorigenesis [2; 11; 77].  
The diverse pathways involved in carcinogenesis also lead to heterogeneity in the types of HCC that develop.  
An emerging hypothesis highlights immune system dysregulation as a critical factor in promoting HCC in MASLD, particularly in the context of NASH. Preclinical studies have shown that CD8+ T cells may play a pivotal role in the progression of NASH to HCC.  
[PERSON] et al. [68] demonstrated in murine models that depleting CD8+ T cells reduced liver damage and HCC incidence. Similar results were observed with the co-depletion of CD8+ T cells and NK cells. Conversely, anti-PD-1 immunotherapy exacerbated liver damage, increased hepatic CD8+PD-1+ T cell populations, and had minimal impact on CD4+PD-1+ T cells.  
Transcriptome analysis of CD8+PD-1+ T cells in NASH-HCC mice revealed elevated expression of both effector and exhaustion markers, including thymocyte selection-associated high mobility group box protein (TOX). TOX, a transcription factor in the high-mobility group box family, plays a critical role in promoting and sustaining CD8+ T cell exhaustion [79; 80]. By reducing PD-1 degradation and facilitating PD-1 translocation to the T cell surface, TOX exacerbates T cell exhaustion [81].  
In summary, CD8+PD-1+ T cells appear to drive the transition from NASH to HCC by impairing tumor surveillance and promoting T cell-mediated tissue damage. Moreover, anti-PD-1 immunotherapy, rather than alleviating liver inflammation, may inadvertently support HCC development in this setting.
# Fiber Monitoring System Applied to Railway Bridge Structures in a Near-Fault Region
## 2 Measurement Theory
### Angular Displacement of Expansion Joints  
Expansion joints are located between the box girders to absorb the temperature-induced expansion and contraction of structures. The angular displacement must be taken into consideration when analyzing the geometric structure of the bridge continuously. The angular displacement can be calculated by simple trigonometry-approximating calculations in this case with changes in the small angles.  
\[\theta\approx\frac{b-a}{w}=horizontal\text{ or vertical angluar displacement} \tag{5}\]  
There are two directions of angular displacement: horizontal and vertical.  
In Figure 5, b \(-\) a equates to the horizontal or vertical displacement of the expansion joint. w is the horizontal or vertical width of the box girder.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Channel Model  
A mmWave channel with two paths and two clusters is considered here. Over the range of [0,2\(\pi\)], AoAs and AoDs were uniformly distributed. The noise is complex additive white gaussian noise (AWGN), with zero mean and \(\rm\sigma_{q}^{2}\) variance. A signal-to-noise Ratio (SNR) of 30 dB, mathematically defined as \(\rm SNR\triangleq\rm\sigma_{q}^{-2}\), was used for simulation. The frequency of the mmWave channel model used for simulation was 90 GHz.