esa-sceva/satcom-chunk-collection huggingface dataset:
# Liquid Crystal-Embedded Hollow Core Fiber Temperature Sensor in Fiber Ring Laser
## 2 Working Principle and Experimental Setup
The transmission spectra of the samples were measured with amplified spontaneous emission and a spectrum analyzer (OSA, AQ7360B, Yokogawa, Tokyo, Japan). The structure diagram of fiber optic ring temperature sensor is shown in Figure 5. The 980 nm laser diode is used as the pump source and is connected to the 1.6 m erbium-doped fiber (EDF) via a 980/1550 nm wavelength division multiplexer (WDM). The function of the isolator is to suppress un-reflected light and prevent space hole burning. The light is then transmitted to an 10 mm long HCF filled with an E7 liquid crystal, which serves as both a wavelength selection filter and a temperature sensing head. After that, the output spectrum is connected to a 10:90 coupler, in which 10% of the light is extracted to the spectrometer (OSA) and the other portion is fed back to the laser cavity. Figure 4: Schematic diagram of the pre-experimental setup. SLS: supercontinuum light source. \[I=I_{1}+I_{2}+2\sqrt{I_{1}I_{2}}COS\Delta\varphi \tag{1}\]0 Schematic diagram for filling a liquid crystal into an HCF.
## 2 F0 Detection and Refinement
### Adaptive Kalman Filtering  
To begin with, the Kalman filter in its common form can be mathematically described as a simple linear model  
\[x_{t}=A_{t}x_{t-1}+w_{t-1},\ w_{t}\sim N(0,\ Q_{t}) \tag{1}\]  
\[y_{t}=B_{t}x_{t}+v_{t},\ v_{t}\sim N(0,\ R_{t}) \tag{2}\]  
Here \(t\) is a time index, \(x_{t}\) is an unobserved (hidden) state variable, \(A_{t}\) is the state transition model to update the previous state, \(w_{t}\) (state noise with zero mean) and \(v_{t}\) (measurement noise with zero mean) are independent Gaussian random variables with covariance matrices \(Q_{t}\) and \(R_{t}\), respectively, \(y_{t}\) is the measurement derived from the observation state \(x_{t}\), \(B_{t}\) is the measurement model which maps the underlying state to the observation. Alternatively, the Kalman filter operates by propagating the mean and covariance of the state through time. Recently, this method has been used for obtaining smoothed vocal tract parameters [33], and in speech synthesis systems [34; 35].  
It is known from the literature that the Kalman filter is one of the best state estimation methods in several different senses when the noise of both \(w_{t}\), \(v_{t}\) are Gaussian, and both covariances \(Q_{t}\), \(R_{t}\) are expected to be known. However, this can be very difficult in practice. If the noise statistics (estimates of the state and measurement noise covariance) are not as expected, the Kalman filter will be unstable or give state estimates that are not close to the true state [36]. One promising approach to overcoming this problem is the use of adaptive mechanisms in a Kalman filter. In particular, signal quality indices (SQIs) have been proposed by [37], and recently used in [38], which give confidence in the measurements of each source. When the SQI is low, the measurement should not be trusted; this can be achieved by increasing the noise covariance. [PERSON] et al. [14] proposed an approach to consider both the state noise and the measurement noise covariance, which are adaptively determined based on the SQI (but in [37; 38], the state noise was fixed a priori). Therefore, to improve the contF0 estimation method, we used the algorithm reported in [14] based on SQI in order to compute the confidence in both state noise and measurement noise covariance. Thereby, their covariance matrices \(Q_{t}\) and \(R_{t}\) are updated appropriately at each time step until convergence. Detailed steps of this algorithm are summarized simply in Figure 1. In this formulation, the aim of the adaptive Kalman filter is to use the measurements \(y_{t}\) to update the current state \(\overline{x_{t}}=x_{t-1}\) to the new estimated state \(x_{t}\) when \(Q_{t}\) and \(R_{t}\) are given at each time step.  
Figure 2a shows the performance of this adaptive methodology, in which the resulting contour is not influenced by the dips at frame 26, 74, and frame 295 occurring in the baseline. However, in such cases, this approach may over-fit to the speech dataset due to the number of manually specified parameters required for tuning. Thus, this technique should be used carefully.
## 3 Dynamic Model of Incremental Step Shift of DIHPS
### HA Dynamic Model
Simulation tests confirmed the accuracy of the design assumptions, the functionality of the dynamic model, and the achievability of the specified incremental step shift of the DIHPS. Figure 8: Hydraulic resistance circuits in the stop position of the DIHPS: (**a**) hydraulic resistance circuit, (**b**) equivalent hydraulic resistance circuit, and (**c**) equivalent hydraulic resistance. Based on the simulation results, it is possible to estimate the position error of the HA piston. The estimated step error \(e_{s}\) of the piston from \(s_{i}\) for a set position \(s_{is}\) relative to step \(s\) is expressed as follows:  
\[e_{s}=\frac{|s_{i}-s_{is}|}{s}100\% \tag{43}\]  
The precision of the step position of the HA depends on the mass load because, already, for \(m=50\) kg, the \(e_{s}\approx 1\%\). Typically, the positioning accuracy of an HA is related to the stroke length. The step error \(e_{S}\) of the piston estimated to the stroke length \(S\) is as follows:  
\[e_{S}=\frac{|s_{i}-s_{is}|}{S}100\% \tag{44}\]  
then you obtain \(e_{S}\approx 0.2\%\) for \(m=50\) kg. A quantitative method was developed in [34] to evaluate different hydraulic positioning structures based on specific influence factors. Figure 9: Dynamic response parameters of the DIHIPS during incremental step positioning: (**a**) step shift of the actuator piston, (**b**) speed of the actuator piston, and (**c**,**d**) pressure in the actuator chambers. Dynamic characteristics show disturbances in the displacement and velocity of the actuator piston, as well as pressure peaks in the actuator chambers, which are the result of hydraulic shock when the BV is closed and opened. Hydraulic shock occurs when the hydraulic fluid flow suddenly stops or increases rapidly. Hydraulic shock is the result of the change in kinetic energy into potential energy (positive hydraulic shock) when the valve is closed or the change in potential energy into kinetic energy (negative hydraulic shock) when the valve is opened.
## 3 The CLICP Construction Scheme
### CLICP Function Design
#### 3.2.3 Carrier One-Stop Service Management  
The one-stop service management function module is provided by the CLICP. This function module provides many convenient services for carriers, consists of four categories: life, finance, transaction, and freight car. The life convenience service is aimed at the truck driver under the carrier, mainly providing good-quality meal ordering, reliable accommodations, and shopping directly through the CLICP client. The platform can also direct the driver to a nearby convenience store, supermarket, or other commercial area, greatly facilitating the driver's ability to live normally while transporting goods. The platform also provides financial services, including freight insurance and loans, to assist carriers in resolving possible funding shortfalls and damage to goods. In the trading module, the platform provides second-hand car trading and new car group purchase services for carriers, and provides concessions to carriers who want to buy freight car vehicles. Freight car convenience services mainly provide a variety of services for freight vehicles, including pushing nearby parking locations (including parking fees), gas station locations, and cargo transport route GPS navigation, as well as rapid rescue and maintenance in the event of vehicle damage.  
One-stop service management is a convenient value-added service provided by CLICPs for carriers, in addition to the establishment of a social capacity pool, which greatly facilitates the freight transportation of carriers and simplifies the long and difficult process of freight transport.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
###### Abstract  
Coded aperture snapshot spectral imaging (CASSI) is a new imaging mode that captures the spectral characteristics of materials in real scenes. It encodes three-dimensional spatial-spectral data into two-dimensional snapshot measurements, and then recovers the original hyperspectral image (HSI) through a reconstruction algorithm. Hyperspectral data have multi-scale coupling correlations in both spatial and spectral dimensions. Designing a network architecture that effectively represents this coupling correlation is crucial for enhancing reconstruction quality. Although the convolutional neural network (CNN) can effectively represent local details, it cannot capture long-range correlation well. The Transformer excels at representing long-range correlation within the local window, but there are also issues of over-smoothing and loss of details. In order to cope with these problems, this paper proposes a dual-branch CNN-Transformer complementary module (DualCT). Its CNN branch mainly focuses on learning the spatial details of hyperspectral images, and the Transformer branch captures the global correlation between spectral bands. These two branches are linked through bidirectional interactions to promote the effective fusion of spatial-spectral features of the two branches. By utilizing characteristics of CASSI imaging, the residual mask attention is also designed and encapsulated in the DualCT module to refine the fused features. Furthermore, by using the DualCT module as a basic component, a multi-scale encoding and decoding model is designed to capture multi-scale spatial-spectral features of hyperspectral images and achieve end-to-end reconstruction. Experiments show that the proposed network can effectively improve reconstruction quality, and ablation experiments also verify the effectiveness of our network design.  
snapshot compressive imaging; spatial-spectral correlation; Transformer; complementary fusion +
Footnote †: journal: Applied Sciences  
1
## 2 Methodology
### Scoping Criteria  
To address the research questions concerning the common types of attacks on blockchain technology, their impact on security and integrity, and the security measures employed to detect and mitigate such attacks, we established comprehensive scoping criteria. These criteria focus on identifying, categorizing, and evaluating blockchain vulnerabilities, attack mechanisms, and mitigation strategies. In order to limit our scope, we focused on the top three cryptocurrencies (Bitcoin, Ethereum, and Tether) based on their market capital according to Forbes [24]. In order to guarantee that this systematic evaluation covers the most recent blockchain security risks, new attack vectors, and cutting-edge mitigation techniques, publications from 2020 to 2024 were chosen. With developments in decentralized finance (DeFi), smart contracts, Layer 2 scaling solutions, and cross-chain protocols, the blockchain environment has swiftly changed, bringing with it new vulnerabilities including bridge attacks, reentrancy problems, and flash loan exploits [25]. Furthermore, modern cryptographic countermeasures and security frameworks are required due to recent advancements in quantum computing and AI-driven cyberthreats [26]. Additionally, security compliance and governance models have been impacted by global regulatory changes, such as the EU's Markets in Crypto-Assets (MiCA) framework and the U.S. SEC's heightened scrutiny [27].  
Our analysis explores key attack vectors, including 51% attacks, bribing attacks, double-spend attacks, smart contract vulnerability attacks, selfish mining attacks, and Sybil attacks [28; 29]. For example, a 51% attack allows malicious actors to gain control of the majority of a network's computational power, enabling them to manipulate transactions and even reverse previously confirmed transactions. A notable instance occurred in the Ethereum Classic blockchain in 2019, resulting in significant financial losses. Similarly, the study examines vulnerabilities within smart contracts, such as the infamous Poly Network attack in 2021, where a critical exploit led to the theft of USD 611 million.
# Vector Optical Bullets in Dielectric Media: Polarization Structures and Group-Velocity Effects
## 2 Materials and Methods
\begin{split}&\mathbf{N}_{0}(\mathbf{r},\omega)=\frac{1}{k}\left(\left\{-\frac{I _{m}^{\prime}\big{(}k_{\rho}\rho\big{)}}{\rho}(\cos\varphi+\mathrm{i}m\sin \varphi)+J_{m}\big{(}k_{\rho}\rho\big{)}\left[\frac{\mathrm{i}m}{\rho^{2}}( \sin\varphi-im\cos\varphi)+k_{z}^{2}\cos\varphi\right]\right\}\hat{\mathbf{e}}_{ \rho}\\ &+\left[J_{m}^{\prime\prime}\big{(}k_{\rho}\rho\big{)}\sin\varphi+ \frac{I_{m}^{\prime}\big{(}k_{\rho}\rho\big{)}}{\rho}\mathrm{i}m\cos\varphi-J _{m}\big{(}k_{\rho}\rho\big{)}\left(\frac{\mathrm{i}m}{\rho^{2}}\cos\varphi+k_ {z}^{2}\sin\varphi\right)\right]\hat{\mathbf{e}}_{\varphi}\\ &+\left[J_{m}^{\prime}\big{(}k_{\rho}\rho\big{)}\mathrm{i}k_{z} \cos\varphi+\frac{J_{m}\big{(}k_{\rho}\rho\big{)}}{\rho}k_{z}m\sin\varphi \right]\hat{\mathbf{e}}_{z}\end{split} \right)\mathrm{e}^{\mathrm{i}k_{z} +\mathrm{i}m\varphi},\end{split} \tag{10}\]  
where \(J_{m}^{\prime\prime}\) is the second-order derivative of the Bessel function of the \(m\)-th order. This expression describes a linearly polarized (in the \(x\) direction) Bessel vortex beam; see Reference [49] for a slightly different definition. We note here that these basis functions describe vector beams, which are optical vortices with predominantly linearly polarized components. Substitution of these four different basis functions into Equations (5) and (6) gives integral expressions for vector optical bullets inside a dielectric material. The selection of the vector \(\mathbf{\mathrm{a}}=\mathbf{e}_{z}\) gives us azimuthally and radially polarized optical bullets, and the selection of the vector \(\mathbf{\mathrm{a}}=\mathbf{e}_{x}\) gives us linearly polarized (in the \(y\) and \(x\) directions) optical bullets. In general, these expressions cannot be analytically integrated, but integration can be performed under some strict conditions. For example, in the vacuum, when \(\gamma=0\)\(\upmu\)m\({}^{-1}\) and the spectral envelope \(S(\omega)\) has a particular expression  
\[S(\omega)=\exp\Bigl{[}-\Delta t^{2}(\omega-\omega_{c})^{2}/4\Bigr{]}, \tag{11}\]  
then the integrals from Equations (5) and (6) can be analytically evaluated to azimuthally and radially polarized pulsed Bessel-X vortices [62].
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Results  
#### 5.4.1 First Experiment: Query Newest Data  
Figure 11 shows the average response time of query the newest data (i.e., the average response time of 400 query/round) for the proposed framework and the traditional cloud architecture. The vertical axis represents the time in milliseconds and the horizontal axis represents the round. Results show that the average response time for querying the newest data from the proposed framework is very low. It ranges from 20 ms to 80 ms. In contrast, the average response time ranges from 120 to 180 ms when using the traditional cloudarchitecture. Results indicate higher response time for the traditional cloud architecture compared with the CFC framework.  
Moreover, our proposed framework was compared with three fog computing-based frameworks [26; 27; 28] that were published in the GitHub website [29] Figure 12 shows the results of the comparison. Results show that the average response time when querying the newest data from the proposed framework is lower than the others. The proposed framework outperforms the other fog computing-based frameworks regarding querying the newest data.  
Figure 11: The response time of query the newest data (the proposed architecture vs. traditional cloud).  
Figure 12: The response time of query for the newest data.  
In cloud architecture, the time delay increases in an exponential manner and it ranges from 5 to 400 s. The reason behind this is the large size of data stored in the cloud; thus, the selection operation of the query tracker will be subjected to a heavy load. It is important to note that while performing data query, there are many other clients who are uploading their data simultaneously. This situation will affect the insertion speed as well. Results of the second experiment indicate that the query response will not include the newest data as time delay increases in an exponential manner when using the traditional cloud architecture.
## 4 Conclusions  
An experimental study of the variations of tangential and normal MMM signals around five small V-shaped notches on the surface of an ASTM-A36 steel pipe was reported. These signals were measured using system formed by a rotational mechanism, a magnetoresistive sensor (MAG3110, Austin, TX, USA, Freescale semiconductor), an Arduino mega (ATmega2560, Santiago, Chile, Arduino) and a data processor. This system does not need expensive equipment, external magnetic field sources and operators with extensive experience. The relationship between the depth and width of each defect with respect to the variations of the tangential and normal MMM signals were studied. The tangential MMM signals around each defect registered a behavior similar to U-shaped curve, which the maximum shift of this signal was related with the defect depth. On the other hand, the normal MMM signals about each defect presented a maximum peak before notch center and a minimum peak after notch center. The separation between these peaks were related to the width of each defect. The smallest defect registered the low shift (14.32 uT \(\pm\) 1.62 uT and 27.95 uT \(\pm\) 1.14 uT) of tangential and normal MMM signals in comparison with those of the other notches. The variations of the tangential MMM signals had an increment for the first three defects. However, this signal had a magnetic saturation for the fourth and fifth notch, keeping a small difference in comparison with those of third notch. The largest notch had the highest change (68.75 uT \(\pm\) 1.10 uT and 71.37 uT \(\pm\) 0.72 uT) of tangential and normal MMM signals with respect to those of the other defects. The relations between these MMM signals and the size of the V-shaped notches were studied. The MMM method could be used to quantify the size (depth and width) of the V-shaped notch.  
In future research, we will consider the influence of the magnetic field due to the stress state of the pipe material.  
Methodology, J.J.V.-S. and J.J.D.-C.; conceptualization, M.A.E.-R. and H.V.-L.; investigation, J.M.-C.; writing--review and editing, A.L.H.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Request should be directed to the corresponding author of this article.  
Figure 14: Maximum variation of normal MMM signals versus width of the V-shaped notch of the ASTM-A36 steel pipe.  
**Acknowledgments:** This research was supported by project PFCE 2019 \"DES Tecnica Veracruz\" through grant 30 MSU0940B-21.
## 2 Materials and Methods
### Uncombined Dual- and Triple-Frequency Multi-GNSS Decoupled Clock Model
#### 2.1.1 PPP Observation Equations  
To define and clarify the notation that is being used for the uncombined Decoupled Clock Model, the general PPP equations are introduced. Consider a receiver \(r\), and a satellite \(s\). The pseudorange and carrier-phase observation equations on frequency \(i\in\{1,2,...,n\}\) are defined as:  
\[\left\{\begin{array}{llll}P_{r,i}^{s}&=\rho_{r}^{s}+c(dt_{r}-dt^{s})+\gamma_{ i}I_{1}^{s}+M_{r}^{s}T_{r}+&(&b_{r,i}-b_{i}^{s})+\epsilon_{P_{i}}\\ \Phi_{r,i}^{s}&=\rho_{r}^{s}+c(dt_{r}-dt^{s})-\gamma_{i}I_{1}^{s}+M_{r}^{s}T_{r }+\lambda_{i}&(N_{i}^{s}+&B_{r,i}-B_{i}^{s})+\epsilon_{\Phi_{i}}\end{array}\right. \tag{1}\]  
\(P_{r,i}^{s}\) and \(\Phi_{r,i}^{s}\) are the pseudorange and carrier-phase measurements, respectively, \(\rho_{r}^{s}\) is the geometric range. \(c\) is the speed of light in a vacuum, and \(dt_{r}\) and \(dt^{s}\) are the receiver and satellite clocks, respectively. \(\gamma_{i}=\frac{f_{i}^{2}}{f_{i}^{2}}\) is the ratio of frequencies to be applied to the first frequency's ionospheric delay \(I_{i}^{s}\). \(M_{r}^{s}\) is the mapping function that needs to be applied to the zenith tropospheric delay \(T_{r}\) to recover the slant tropospheric delay. \(\lambda_{i}\) is the wavelength corresponding to frequency \(i\), with the integer ambiguity on that frequency being defined as \(N_{i}^{s}\), \(b_{r,i}\) and \(b_{i}^{s}\) are the receiver and satellite pseudorange hardware biases, respectively, while \(B_{r,i}\) and \(B_{i}^{s}\) are the receiver and satellite carrier-phase hardware biases, respectively. \(\epsilon_{P_{i}}\) and \(\epsilon_{\Phi_{i}}\) contain residual errors on the pseudorange and carrier-phase measurements, respectively, in the form of multipath, measurement noise, unmodelled errors, etc.
## 4 Performance Analysis
### Simulation Environment  
The proposed NC-Euler-WESPRIT DPD algorithm is experimented on a system having 16 processors with 64 GB RAM and 2.5 TB hard Disk. The code is running with Windows 10 and MATLAB R2018b version.
## 5 Conclusions and Future Enhancement  
This research experimented with various PEMFC designs with various humidity levels. Machine learning models, such as SVMR, LR, and KNN models, were tested with various humidity levels. The performance is compared by using the error rate of each model. The model which gives the lesser error is considered to be the best algorithm. The experimental analysis model yields the SVMR error rate of 0.0046, LR error rate of 0.0034, and the KNN error rate of 0.004. It is observed that the LR model gives better result. In the future, the research will extend to enhance the performance of PEMFC by using a deep learning approach while considering various parameters like oxygen level, water, humidity, and various cell designs [39].  
Conceptualization, P.S.S.; methodology, P.S.S.; validation, K.J.; formal analysis, P.S.S.; investigation, A.S.; resources, A.P.; writing--original draft preparation, P.S.S.; writing--review and editing, K.J.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.  
Figure 12: Comparison with numerical study.