esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 2 Background and Related Work
### Network Slicing  
The concept of network slicing involves the creation of configurable and independent virtual networks within the same physical network infrastructure. Each network slice can be tailored with dedicated resources, meeting the specific needs of the service it aims to provide. Virtualization plays a pivotal role in enabling NS, allowing for the dynamic and efficient allocation of physical infrastructure resources and ensuring individualized traffic isolation within each network slice [7]. In this sense, within the realm of 5G, NS entails the amalgamation of necessary physical resources and virtual network functions (VNFs) to deliver a distinct service, isolated from others while sharing the same physical infrastructures. A network slice encompasses both control and user plane network functions within the CN, as well as the RAN, and is defined within the domain of a PLMN. Network slice identification is achieved through single-network slice selection assistance information (S-NSSAI), comprising a slice/service type (SST) and a slice differentiator (SD) [8].
## 4 Experiment
### Simulated Signal Experiment
#### 4.2.2 Sparsity Recovery Performance Analysis  
For bearing parts of mechanical equipment operating outdoors, some fault features are buried in the noise, which disturbs the signal reconstruction process of CS. The more non-zero values in the reconstructed signal overlap with the original signal, the more noise tolerance the algorithm has. The ratio of non-zero elements in the signal can be expressed by the signal sparsity level \(L_{S}\) in Equation (34).  
\[\begin{array}{l}\hat{S}=\operatorname*{argmin}S\\ S\\ \text{s.t.}\ \frac{\|x_{S}-x\|_{2}}{\|x\|_{2}}\leq 0.2,Ls=\frac{\hat{S}}{N} \end{array} \tag{34}\]  
where \(x_{S}\) is the best S-sparse approximation obtained by reserving \(S\) non-zero elements in the original signal \(x\) and setting the remaining elements to 0. The parameters of Equation (33) are adjusted for generating simulated signals with different sparsity levels, and then the generated signals are used as experimental objects to explore the relationship between the signal sparsity level and the algorithm reconstruction accuracy, which in turn examines the ability of the ADMM-1 DNet implementation to adapt to different reconstruction tasks.  
Figure 6: Relationship of the number of network unfolding layers, algorithm reconstitution accuracy versus convergence time.  
Figure 7 shows the relationship between \(L_{S}\) and \(T_{\text{test}}\), and it can be observed that as the signal \(L_{S}\) increases, the \(T_{\text{test}}\) of the reconstructed signal of the three algorithms increases. It is noted that the reconstruction \(T_{\text{test}}\) of ADMM-1 DNet at signal sparse levels of 0.2 and 0.3 is much lower than that of the other two algorithms, and the results remain relatively stable. This demonstrates that the ADMM-1 DNet implementation scheme still has good robustness when processing signals with different sparse levels and can adapt to the task requirements of signal reconstruction with different sparse levels.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 1 Introduction
### Hybrid ROV/AUV  
Hybrid underwater vehicles such as EVAs are sophisticated systems designed to combine the capabilities of Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) [39]. These vehicles are engineered to operate both autonomously and under real-time human control, providing the flexibility to adapt to various underwater missions and environments. The HROV/AUV designs can operate tethered with some autonomous functions or untethered with some range restrictions [39]. The term hybrid can also mean that the specific vehicle can be amphibious, which means it can operate both on aerial and water missions. In this paper, we are not going to focus on hybrid underwater vehicles (HUVs) because they use one of the two main categories, which we explained how they operate in Sections 2.1 and 2.2.
## 2 Basic Theory
### Sparse Dictionary Learning
#### 2.2.3 Denoising Algorithm Based on AO-VMD and K-SVD Flowchart  
As shown in Figure 2, the integral process of the combined PD signal denoising method proposed in this paper is as follows:  
Step 1: Input the original PD signal.  
Step 2: Introduce noise into the original PD signal to simulate a realistic noisy environment.  
Step 3: Use the AO method to find optimal VMD settings. Apply VMD to break down the noisy signal into modes and then reconstruct it.  
Step 4: Start with an initial learning dictionary. Refine this dictionary using the K-SVD algorithm for a better representation of the signal.  
Step 5: Use the OMP algorithm with the refined dictionary for further signal decomposition and reconstruction after VMD processing.  
Step 6: Output the Reconstructed PD Signal.  
Figure 2: Flowchart of PD noise elimination.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Connection to GENERIC and Rayleigh Dissipation  
The modified equations of motion (revisited in detail in Equations (1) and (2)),  
\[\dot{\mathbf{p}}=-\frac{\partial H}{\partial\mathbf{q}}+T\frac{\partial \overline{S}}{\partial\mathbf{q}},\quad\dot{\mathbf{q}}=\frac{\partial H}{ \partial\mathbf{p}}-T\frac{\partial\overline{S}}{\partial\mathbf{p}}, \tag{7}\]  
are structurally aligned with formalisms for non-equilibrium thermodynamics, such as the GENERIC framework [32; 33]. The entropy gradient term \(T\
abla\overline{S}\) explicitly introduces irreversibility while retaining symplectic structure in conservative subsystems. Here, we assume \(T\) (temperature) to be constant, which simplifies the mathematical and physical interpretation of the system's dynamics. This assumption is valid for a wide range of problems, particularly those involving steady-state or near-equilibrium conditions. However, it is worth noting that allowing \(T\) to vary with time and space would introduce additional complexity, leading to a broader class of problems with rich dynamical behavior. These equations extend Hamiltonian mechanics by incorporating entropy gradients (\(\
abla_{q}\overline{S}\) and \(\
abla_{p}\overline{S}\)) as driving forces, as proposed in [19; 31]. Specifically, [31] introduces the concept of ergentropic dynamics, where entropy gradients play a central role in particle dynamics, while [19] reformulates classical mechanics and electrodynamics to account for entropy-driven processes.  
* **GENERIC Formalism**: The General Equation for Non-Equilibrium Reversible-Irreversible Coupling (GENERIC) [33] separates dynamics into reversible (Hamiltonian) and irreversible (dissipative) components:\[\frac{dA}{dt}=\{A,H\}+[A,S],\]  
where \(\{,\}\) denotes Poisson brackets and \([,,]\) dissipative brackets. Our entropy gradient term \(T\
abla\overline{S}\) mirrors the dissipative bracket \([A,S]\), coupling reversible mechanics to irreversible entropy production.
* **Rayleigh Dissipation**: The Rayleigh dissipation function \(\mathcal{R}=\frac{1}{2}\zeta q^{2}\) models friction as a quadratic form of velocities. In contrast, our framework generalizes dissipation through entropy gradients \(T\
abla\overline{S}\), which act as configurational friction forces. For small deviations from equilibrium, \(\overline{S}\propto q^{2}\), recovering [PERSON]'s formalism [28]. This synthesis extends Hamiltonian mechanics to non-equilibrium regimes while preserving geometric structure.
## 4 Experimental Setup and Results Analysis  
### Experimental Environment  
For this experiment, MATLAB 2020 was used to generate spectrograms. Python 3.7 was used with the OpenCV and Scikit-learn libraries for feature extraction and clustering algorithms. This implementation enabled automatic recognition of electromagnetic pulse sequence signals.
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## Validation Guidelines and Technical Specifications
### Progress on the Development of Sampling Design and Scale Approach
#### 3.3.1 Sampling Design  
[PERSON] et al. [112] presented a geostatistical-model-based method to optimise the spatial sampling design for estimating GPP. This investigation occurred in the Babao River Basin, located upstream of the Heihe River Basin (HRB) over grassland. Sampling locations were optimised based on a stratified block kriging (StrBK) approach, taking the heterogeneity (represented by stratification and anisotropy) into account. The error variance of regional GPP estimation decreased by 10.1% compared with a sampling scheme without considering land surface heterogeneity. Over the same watershed, [PERSON] et al. [164] reported a universal co-kriging (UCK) model-based sampling design optimisation. This scheme was embodied in the design of a WSN that monitored three eco-hydroological variables (LST, precipitation, and soil moisture). The results demonstrated that the proposed sampling method could consider the relationship of target variables and environmental covariates as well as other spatial statistics. Compared with a sampling scheme without consideration of the multivariate correlation, the proposed design performed better by reducing prediction error variance. Another study occurred in the midstream of the HRB, as reported in [88], an efficient and robust LAI sampling strategy was developed based on multi-temporal prior knowledge (SMP) for long-term, fixed-position LAI observations. The proposed SMP-based sampling scheme was compared with another four methods, including random sampling, systematic sampling, sampling based on the land-cover map, and sampling based on vegetation index prior knowledge, using the PROSAIL model-based simulation analysis. The results indicated that the average RMSE of the LAI reference maps decreased from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%.
## 3 Initial Predistortion Modeling for LED Distortion Correction  
As mentioned, Figure 3 shows the OFDM-based visible light communication transceiver system, including a predistortion module. In the existing OFDM system without predistortion, the transmitter processes signals with Inverse Fast Fourier Transform (IFFT) after QAM modulation, and it expresses signals as the orthogonal frequency components. When the system processes the IFFT output signal via LED modulation and transmits LED output, its performance deteriorates due to the nonlinearity of LEDs and the generated distortion. The distortion causes signal to become noise-sensitive and damages the orthogonality between the frequency components. The result of the distortion is the substantially high error performance at the receiver. To resolve this problem, the predistortion module, as shown in Figure 3, can compensate for LED nonlinear matters and is placed in front of the Digital-Analog Converter (DAC) for data transmission.  
Since the goal is to design the predistortion module with the LED coefficient value, we describe initial predistortion modeling schemes and then coefficient approximation under the fixed value assumption for the predistortion module to compensate nonlinearity of LED luminance. Note that both models use the distortion characteristic of bias signals, and we discuss the nonfixed coefficient assumption case in the later section.  
Figure 2: A comparison among concave curve with \(\zeta=0.732\), convex curve with \(\zeta=0.268\), and linear static transfer functions.  
In the ideal condition, the primary method is to implement a predistortion by calculating the inverse function for Equation (2). We can reformulate (2) as  
\[P_{out}(t)= b_{2}I_{in}(t)^{2}+(b_{1}-2b_{2}I_{DC})I_{in}(t)+ \tag{4}\] \[b_{2}I_{DC}^{2}-b_{1}I_{DC}+b_{0}\]  
When we define \(g_{inv}(t)\) as the inverse function of Equation (4), we can derive it as  
\[g_{inv}(t)=\sqrt{\frac{1}{b_{2}}I_{in}(t)+\frac{b_{1}^{2}}{4b_{2}^{2}}-\frac{b _{0}}{b_{2}}}-\frac{b_{1}}{2b_{2}}+I_{DC} \tag{5}\]  
where \(I_{in}(t)\) is the input current to the predistortion module. Based on this inverse function, we can design the predistortion module and correct LED distortion.
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 4 Discussion and Results
### Packets Delivery Ratio  
The packet delivery ratio is the percentage of packets received over packets sent to the sending nodes. The proposed approach's delivery ratio is measured according to the distance inside the network. Figure 9 shows the packet delivery ratio results from the proposed technique. With the distance increase among the vehicles, the AODV and CBDRP performance is slightly lower and provides lesser communication overhead. On longer distances, the overhead time and ratio are also lessened, which provides a shorter overhead time compared to packet delivery. The performance goes down when delivery ration goes down but, inside the proposed architecture, the performance was lower compared to the existing techniques. The BBSF performs better in comparison to the existing technique.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
###### Abstract  
In rolling bearing intelligent fault diagnosis (FD), lightweight models are constrained by issues such as noise interference and the scarcity of fault data, making it challenging to achieve real-time, high-accuracy diagnosis on resource-limited devices. To address these challenges, this study proposes a lightweight model that combines the hierarchical fine-grained decision fusion (HFDF) strategy with an improved EfficientNetV2 architecture (HFDF-EffNetV2). The model optimizes depth and width multiplicity factors to enhance parameter utilization efficiency. It uses pyramidal convolution (PyConv) combined with Fused-MBConv (Fused-MBPyConv) to obtain multi-scale time-frequency information. Additionally, an enhanced MBConv, termed BSMB-Conv-MLCA, integrates subspace blueprint separable convolution (BSConv-S) with mixed local channel attention (MLCA) extract deep-grained fault features. The HFDF strategy outputs confidence in stages and updates weights to prevent the model from falling into local overfitting when handling confusable samples. Experimental results on Case 1 and Case 2 show that HFDF-EffNetV2 achieved 100% accuracy with diagnostic times of 18.67 millisecond (ms) and 17.56 ms, respectively, and 1.85 million (M) parameters. Under noisy conditions, average accuracies reached 98.19% and 85.68%, respectively. Additionally, the model performed well with small samples, yielding accuracies of 98.69% and 97.51%. These results highlight its superior robustness to noise and lightweight performance compared with other advanced models.  
fault diagnosis; lightweight; EfficientNetV2; robustness +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Evolution of the Volatile Compounds during Dehydration
#### 3.3.1 Volatile Compounds Evolution Expressed as \(\upmu\)g/L
Undoubtedly, as the bound fraction was the main form of IPDs (Figures 2a and 6), the total content (free + bound) of IPDs was also mainly classified into Cluster 2, except for hotrienol, citronellol, 6-methyl-5-hepten-2-one, and \(\upbeta\)-damascenone. The evolution of some organoleptic key monoterpenoids and norisoprenoids is plotted in Figure 6a. Although the content of the bound forms of \(\beta\)-myrorene, linalool, and geraniol fluctuated during the late stages of grape dehydration, they were all considerably increased at T5 compared to T1. However, the increase in the free forms during this process was more limited, except for the _cis-_ or _trans-_ rose oxide, whose free state increased steadily during dehydration. Dominated by the free fractions, \(\beta\)-damascenone showed a different trend from monoterpenoids, with the free forms increasing far more sharply than the bound forms from T1 to T5, especially after T3. In general, the results of hierarchical clustering for volatiles expressed as \(\mu\)g/L in grape juice indicated that the vast majority of the free and bound fractions (Clusters 1 and 2) were finally enhanced by on-vine dehydration, except for some FADs (Cluster 3), such as nonanal, decanal, butyl propionate, and propyl octanoate. It should be noted that most of the bound forms of terpen and volatile phenols (Cluster 2) showed complex fluctuating changes during the on-vine dehydration process, indicating that these compounds were not only enhanced by the water loss-induced inspissation, but also reduced by other mechanisms. Figure 4: Evolution of some C6 compounds during on-vine grape dehydration. The red dashed line marks the threshold level: (**a**) \(\mu\)g/L; (**b**) ng/berry. T1, T2, T3, T4, and T5 represent the sampling points at different dehydration degrees. Values are the means of three replicates. The error bars represent the standard deviation.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 5 The Applications of Electrosprayed PCL Particles  
Electrospraying is a useful process for tailoring the nano- or microparticles, since the control of the formulation and operating parameters allows adjusting the particle size, the encapsulation efficiency, the core/shell ratio, the porosity of the shell as well as the morphology. Therefore, combined with their biodegradability, biocompatibility, and non-toxic properties [2; 38; 95], electrospray PCL with different sizes as well as morphologies can be used in various fields, including control release and drug delivery [155; 161] tissue engineering [162], and the food industry (Table 6).
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 4 Ccrlb
### CCRLB for DOA-TDOA Hybrid Localization  
Define the unknown vector \(\mathbf{\xi}=\left[\mathbf{u}^{\mathrm{T}},\left(\mathbf{\theta}^{o}\right)^{\mathrm{T}}, \left(\mathbf{s}_{t}^{o}\right)^{\mathrm{T}}\right]^{\mathrm{T}}\) and the measurement vector is \(\mathbf{z}=\mathbf{z}^{o}+\mathbf{n}\). \(\mathbf{z}^{o}=\left[\left(\mathbf{\theta}^{o}\right)^{\mathrm{T}},\left(\mathbf{\varphi}^{ o}\right)^{\mathrm{T}},\left(\mathbf{r}^{o}\right)^{\mathrm{T}}\right]^{\mathrm{T}}\). The covariance matrix of \(\mathbf{n}\) is \(\mathbf{Q}_{z}\). The FIM of hybrid localization can be given as  
\[\mathbf{FIM}=\begin{bmatrix}\mathbf{F}_{1}&\mathbf{F}_{2}&\mathbf{F}_{3}\\ \left(\mathbf{F}_{2}\right)^{\mathrm{T}}&\mathbf{F}_{4}&\mathbf{F}_{5}\\ \left(\mathbf{F}_{3}\right)^{\mathrm{T}}&\left(\mathbf{F}_{5}\right)^{\mathrm{T}}&\mathbf{ F}_{6}\end{bmatrix} \tag{47}\]  
where  
\[\left\{\begin{array}{l}\mathbf{F}_{1}=\left(\partial\mathbf{z}^{o}/\partial\mathbf{u}^{ \mathrm{T}}\right)^{\mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{z}^{o}/ \partial\mathbf{u}^{\mathrm{T}}\right)\\ \mathbf{F}_{2}=\left(\partial\mathbf{z}^{o}/\partial\mathbf{u}^{\mathrm{T}}\right)^{ \mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{z}^{o}/\partial(\mathbf{h}_{t}^{o})^{ \mathrm{T}}\right)\\ \mathbf{F}_{3}=\left(\partial\mathbf{z}^{o}/\partial(\mathbf{u}^{o})^{\mathrm{T}}\right)^{ \mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{z}^{o}/\partial(\mathbf{s}_{t}^{o})^{ \mathrm{T}}\right)\\ \mathbf{F}_{4}=\left(\partial\mathbf{z}^{o}/\partial(\mathbf{h}^{o})^{\mathrm{T}}\right)^{ \mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{z}^{o}/\partial(\mathbf{h}^{o})^{ \mathrm{T}}\right)+\mathbf{Q}_{h}^{-1}\\ \mathbf{F}_{5}=\left(\partial\mathbf{z}^{o}/\partial(\mathbf{h}^{o})^{\mathrm{T}}\right)^{ \mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{z}^{o}/\partial(\mathbf{s}_{t}^{o})^{ \mathrm{T}}\right)\\ \mathbf{F}_{6}=\left(\partial\mathbf{z}^{o}/\partial(\mathbf{s}_{t}^{o})^{\mathrm{T}}\right) ^{\mathrm{T}}\mathbf{Q}_{z}^{-1}\left(\partial\mathbf{r}^{o}/\partial(\mathbf{s}_{t}^{o})^ {\mathrm{T}}\right)+\mathbf{Q}_{s}^{-1}\end{array}\right. \tag{48}\]  
Therefore, the CRLB and CCRLB can be expressed as  
\[\mathbf{CRLB}=\mathbf{FIM}^{-1}=\begin{bmatrix}\mathbf{F}_{1}&\mathbf{F}_{2}&\mathbf{F}_{3}\\ \left(\mathbf{F}_{2}\right)^{\mathrm{T}}&\mathbf{F}_{4}&\mathbf{F}_{5}\\ \left(\mathbf{F}_{3}\right)^{\mathrm{T}}&\left(\mathbf{F}_{5}\right)^{\mathrm{T}}&\mathbf{ F}_{6}\end{bmatrix} \tag{49}\]  
\[\mathbf{CCRLB}=\mathbf{CRLB}-\mathbf{CRLB}\overline{\mathbf{\Lambda}}\left(\overline{\mathbf{ \Lambda}}^{\mathrm{T}}\mathbf{CRLB}\overline{\mathbf{\Lambda}}\right)^{-1}\overline{ \mathbf{\Lambda}}^{\mathrm{T}}\mathbf{CRLB} \tag{50}\]  
where \(\overline{\mathbf{\Lambda}}=blkdiag\left(\tilde{\mathbf{\Lambda}},\mathbf{0}_{\left(N_{1} +3N_{2}\right)\times\left(N_{1}+3N_{2}\right)}\right)\).
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 5 Task Offloading Algorithms in Our Model
### Aggressive Wait Offloading Algorithm (AGG-1)  
The orchestrator application with the first greedy algorithm AGG-1 tries to offload tasks until all the queues on the available processors are full. New assignments to the same processor can be made before the current assigned task's data transfer is completed. If the next assigned task is downloaded before the first one, the second task will be processed after the first is completed. The rationale is to complete the previously assigned task, probably being the one closer to its deadline, earlier. The AGG-1 algorithm is described in Algorithm 1, and it is run until the simulation time is up.  
```
Wait for a system event \(ER\); if\(ER\) is \(Event.TaskComplete\)then  Mark the completed \(K_{i}\) as completed and remove it from \(Q_{j}\) where \(x_{ij}=1\);  Increase \(P_{i}\)'s available \(S_{Q_{j}}\) by \(S_{K_{i}}\); ifThere is a task in \(Q_{j}\) and \(K_{m}\), the one on the first line of the queue is already downloadedthen  Start to process \(K_{m}\);  Add \(Event.TaskComplete\) to global event registry for \(t_{process,m}\) later. elseif\(ER\) is \(Event.TxComplete\)then  Mark the transmitted \(K_{i}\) as waiting on \(Q_{j}\); if\(K_{i}\) is the one on the first line of \(Q_{j}\) where \(x_{ij}=1\)then  Start to process \(K_{i}\) and mark it as processing;  Add \(Event.TaskComplete\) to global event registry for \(t_{process,i}\) later. elseif\(ER\) is \(Event.NewProcessor\) for \(P_{i}\)then  Add \(P_{i}\) to the available processors; elseif\(ER\) is \(Event.ProcessorDeparture\) for \(P_{j}\)then  Remove \(P_{j}\) from available processors;  From the tasks assigned to \(P_{j}\), add the ones which are in the transmission phase to \(TP\); elseif\(ER\) is \(Event.TaskOwnerDeparture\)then foreach\(K_{i}\) belongs to the departed \(V_{m}\)do if\(K_{i}\) in \(TP\)then  Remove \(K_{i}\) from \(TP\) and mark \(K_{i}\) as failed; elseif\(K_{i}\) in transmission phasethen  Remove \(K_{i}\) from the assigned \(Q_{j}\) and mark \(K_{i}\) as failed;  Increase available \(S_{Q_{j}}\) by \(S_{K_{i}}\); elseif\(ER\) is \(Event.NewTask\)then  Offload to cloud with a probability depending on the current \(TP\) size; if\(It\) is not assigned to cloudthen  Add it to \(TP\); whiletruedo  Calculate \(w_{i,P_{j}}\) for each \(K_{i}\) from \(TP\) and the available \(P_{j}\) with remaining queue size \(\geq S_{K_{i}}\); ifThere is no task-processor pair used for the calculationthen  break; \(X_{ij}=\) The task-processor pair with the lowest \(w_{i,P_{j}}\);  Assign \(K_{i}\) to \(P_{j}\) and mark \(K_{i}\) as being transmitted;  Remove \(K_{i}\) from \(TP\);  Decrease available \(S_{Q_{j}}\) by \(S_{K_{i}}\);  Go back to the first step.
```  
**Algorithm 1**Aggressive-Wait Offloading Algorithm (AGG-1)  
Go back to the first step.