esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 2 UWA OTFS System Model
### OMP Channel Estimation Algorithm  
Regarding channel sparsity in the DD domain, the OTFS channel estimation problem can be formulated as a reconstruction problem involving sparse signals. In the signal reconstruction problem, each column of the perception matrix represents an atomic signal. The matching pursuit (MP) algorithm employs the linear operations with these atomic vectors to progressively approximate the observed signal. In the MP algorithm, the selected atoms are not always orthogonal to the residuals, which results in a decrease in convergence speed. The OMP algorithm addresses this issue by ensuring the selected atoms and the residual are always orthogonal, and the inner product of the selected atom and the residual in the next cycle is 0. OMP avoids the problem of repeated atom selection, thus guaranteeing the efficiency of the iteration process.  
Algorithm 1 depicts the OMP-based OTFS channel estimation algorithm. In OMP-based channel estimation, the atoms are set as the input signal \(x\), each selected atom is put in the matrix \(D\) as a reconstructed set, the observation vector is the received signal \(y\), and the residual is \(r\). By the compressed sensing technique, the channel matrix \(H\) with sparse characteristics is obtained by OMP algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Wireless Radio** & \multirow{2}{*}{**UWA Communication**} \\  & **Communication** & \\ \hline Propagation speed \(c\) & \(3\times 10^{8}\) m/s & 1500 m/s \\ Typical carrier frequency \(f_{c}\) & Several GHz & Several kHz \\ Subcarrier spacing \(\Delta f\) & Several KHz & Several Hz \\ CFO \(f_{D}\), when \(v_{t}=1\) m/s & Several Hz & Several Hz \\ Normalized CFO \(\theta\), when \(v_{t}=1\) m/s & \(\approx\)1\(\times 10^{-3}\) & \(\approx\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of terrestrial radio and UWA communications.  
```
1:Input: The observation vector \(y\); perception signal \(x\); Signal sparsity \(P\);
2:Output: Channel Matrix \(H\);
3: Initialization: the residuals \(r_{0}=y\), the index set \(A\in\emptyset\) and the reconstructed vector set \(D\in\emptyset\);
4:for each \(t\)do
5: Calculate the inner product of \(x\) and \(r\);
6: Find the optimal reconstruction vector in \(x\) with a maximum inner product and put it in the set \(D\). Put the index of the optimal vector into \(A\).
7: Reconstruct the t-th estimated channel matrix vector \(H=(D_{t}^{T}D_{t})^{-1}D_{t}^{T}y\) by LS algorithm;
8: Update the residual \(r_{t}=y-D_{t}H\);
9: If \(t>P\), stop. Else, continue.
10:endfor
11:Obtain the final channel estimation result \(H\).
```  
**Algorithm 1** OMP-Based OTFS Channel Estimation
## 7 Conclusions  
This paper proposed a new multi-channel packet scheduling and forwarding scheme to fully exploit the advantage of RR-IPT. In the tree topology based dual channel network, we mainly compared two schemes; Dual-IPT applying RR-IPT to both channels and Mono-IPT where RR-IPT is applied to the specified single channel to accommodate only downlink traffic. Computer simulation revealed that Mono-IPT exhibited higher system throughout performance with lower delay, while Dual-IPT achieved completely reduced packet loss rate. The resultant solution is to switch these schemes depending on offered traffic load: observing packet buffer filling level on the core node. Hybrid RR-IPT based multi-channel packet forwarding can realize efficient MWBN ensuring both communication quality and the high capacity.  
The authors give their sincere thanks to the editors and the anonymous reviewers for their constructive comments of the manuscript.  
[PERSON] designed the system model, the proposed scheme, conducted computer simulations and wrote the paper; [PERSON] proposed the original scheme and supervised the work.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Packet loss rate for each channel.  
Figure 10: Average packet delay for each channel.
## 3 Proposed Method
### Weighted Bidirectional Feature Pyramid Network (BiFPN)
#### 3.4.1 BiFPN Network Structure  
The bidirectional feature pyramid network (BiFPN) is a multilevel feature pyramid network designed to address the issues of feature propagation and information flow in object detection. Building upon the traditional feature pyramid network (FPN), BiFPN introduces mechanisms such as bidirectional feature fusion and feature selection to enhance the efficiency and accuracy of feature extraction. The network structure is weighted and bidirectionally connected, encompassing both top-down and bottom-up paths. By establishing bidirectional channels for cross-scale connections, features from the feature extraction network are directly fused with corresponding-sized features from the bottom-up path, preserving shallower semantic information without sacrificing too much deep semantic information. Figure 7 compares the network structures of FPN, PANet, and BiFPN.
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments  
To evaluate the effectiveness and robustness of the proposed Seg-Eigen-CAM method, we conducted extensive experiments on semantic segmentation models. This section presents comprehensive quantitative and qualitative analyses to assess the interpretability and stability of our approach. We begin by describing the experimental setup and evaluation protocols, followed by systematic ablation studies and quantitative comparative analyses with existing methods. In addition, we provide visual evaluations of local interpretability, qualitative assessments in complex scenarios, and detailed case studies to demonstrate the practical effectiveness of our method.
## 2 Materials and Methods  
### Materials  
Chloroform (CHCl\({}_{3}\), 99.8%) was obtained from EMD Millipore, and acetone, petroleum ether, and methanol were technical grade. Two commercial PHBV samples (PHBV-8%HV and PHBV-12%HV (Tianan Biologic Materials C. Ltd., Ningbo, China)) were used as reference materials. PHBV rich biomass samples were produced in a pilot-scale bioreactor over eight production periods (operational day 15, 32, 39, 46, 61, 71, 78, and 84) consisting of (i) sequencing batch dairy manure fermenter (760 L) to produce volatile fatty acids (VFA), (ii) an enrichment reactor (680 L) which was fed VFAs to produce an MMC culture that can produce PHBV via a least-famine regime and (iii) PHBV production reactor (450 L) which was fed VFAs to produce and accumulate PHBV [6]. The 3 HV molar ratio and the total amount of PHBV in microbial biomass are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Operational Day**} & \multicolumn{2}{c}{**Lyophilized Biomass**} & \multicolumn{2}{c}{**PHBV Yield (\%)**} & \multicolumn{2}{c}{**Purified PHBV**} \\ \cline{2-7}  & **PHBV (\%)** & \(f_{\text{3 HV}}\)****a** & **Crude****b** & **Pure****c** & **Purity (\%)** & \(f_{\text{3 HV}}\)****a** & **PHBV (\%)** \\ \cline{2-7}  & **GC-MS** & **GC-MS** & & & & **GC-MS** & **1H-NMR [6]** \\ \hline
15 & \(11.6\pm 0.1\) & \(0.23\pm 0.01\) & 20.6 & 8.9 & \(80.6\pm 3.0\) & \(0.17\pm 0.01\) & \(0.16\pm 0.02\) \\
32 & \(24.5\pm 0.4\) & \(0.27\pm 0.00\) & 31.2 & 14.6 & \(81.7\pm 2.1\) & \(0.19\pm 0.01\) & \(0.18\pm 0.00\) \\
39 & \(35.6\pm 1.9\) & \(0.32\pm 0.00\) & 31.8 & 18.6 & \(88.5\pm 4.7\) & \(0.21\pm 0.01\) & \(0.19\pm 0.00\) \\
46 & \(32.7\pm 0.8\) & \(0.26\pm 0.00\) & 27.7 & 17.5 & \(91.2\pm 0.6\) & \(0.25\pm 0.00\) & \(0.23\pm 0.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: PHBV and 3 HV content in biomass, crude and purified PHBV yields and 3 HV content in purified PHBV.
###### Abstract  
The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.  
Keywords:radio resource management; vehicle platooning; Age of Information; Multi-Access Edge Computing; Multi-Agent Reinforcement Learning +
Footnote †: journal: _Sensors_
# Research on Large Divergence Angle Laser Ranging System +
## 3 Design of Large-Divergence Transmitter and Wide-Field-of-View (WFOV) Receiver Systems
### Diverging Lens
Substitute in the formula to obtain \(\omega_{2}\) = \(\sqrt{\frac{1.535\times 10^{-3}\times 0.713}{\pi}}\approx\) 0.0186 mm. According to the relationship between laser divergence angle and beam waist radius, \(\theta\) = \(\frac{\lambda}{\pi\omega}\), it can be obtained that  
\[\theta_{2}=\frac{1.535\times 10^{-3}}{\pi\times 00186}\approx 0.0264\;\text{rad} =26.4\;\text{mrad};\]  
To sum up, the divergence angle of the laser beam through the biconcave lens is about 26.4 mrad. The Diverging Lens Ray Diagram is shown in Figure 4.  
Figure 4: Diverging Lens Ray Diagram [8]. The real ray tracing analysis of the design results of the optical system can be obtained as shown in Figure 5:  
New lens from CVMACRO:cvnewlens.seq  
Position 1, Wavelength = 1535.0 mm  
X Y 2 TANX TANY LENGTH ANG INC ANG REF  
DBJ 0.00000 -0.611E+11 0.00000 0.00000 0.00611  
1 0.00000 0.11391 0.00000 0.00000 0.00611  
STO 0.00000 0.17500 -0.00061 0.00000 0.00641 9.99957 0.05107 0.03402  
3 0.00000 0.18782 0.00071 0.00000 0.01338 2.00136 0.79749 1.19708  
4 0.00000 0.18781 0.00000 0.00000 0.01338 -0.00071 0.76663 0.76663  
5 0.00000 0.18781 0.00000 0.00000 0.01319 0.00000 0.76663 0.76663  
IMG 0.00000 0.31974 0.00000 0.00000 0.01319 10.00087 0.75587  
The simulation results show that the incident laser divergence angle is 0.35 \(\times\) 17.45 \(\times\) 2 = 12.2 mrad, and the outgoing laser divergence angle is 0.755 \(\times\) 17.45 \(\times\) 2 = 26.35 mrad, which is almost the same as the calculation results.
## 2 Materials and Methods  
### Preparation of Ni/Al RMFs  
Ni/Al bilayers were deposited directly on polyimide (PI) substrate using a modified Varian 3118 electron beam evaporator with a deposition controller (Leybold XTC/2). Before deposition, the PI substrates were rinsed sequentially in an ultrasound bath by ethanol and deionized water. Then Ni/Al RMFs were prepared by electron beam physical vapor deposition. During deposition, the pressure inside the chamber was maintained lower than 5 \(\times\) 10\({}^{-4}\) Pa. The deposition controller was used to control the deposition rate and thickness of the films. The atomic ratio of the Ni/Al film was 1:1. For each bilayer, the thickness of Ni was about 40 nm while Al was about 60 nm. A Ti layer of 30 nmwas added as the bottom layer and intermittently to help the film adhere to the PI substrate and prevent delamination during deposition.
# Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
## 4 Simulations
### Impact of the Regularization for the Exploration Criterion  
The empirical modification of the cost function in (19) makes the computation of the estimator's covariance matrix feasible. In the following, we study the impact of such a regularization on the used exploration criterion.  
We consider now a simple one-dimensional scenario, with positions \(\mathcal{X}\subset\mathbb{R}\) and two features; thus, \(N=2\). The features are modeled by a one-dimensional Gaussian with a fixed width \(\sigma\) and a feature position \(\mu\) as:  
\[\varphi(x,\mu)=\exp\left(-\frac{(x-\mu)^{2}}{2\sigma^{2}}\right).\]  
Further, we define two measurement locations \(x[1]\) and \(x[2]\) for this scenario, and let \(x[1]=-4\) and \(x[2]=4\). The two features are now defined as \(\varphi_{1}(\mathbf{X}_{M},x[1])\) and \(\varphi_{2}(\mathbf{X}_{M},x[2])\), and thus, \(\mathbf{\
abla}=[\varphi_{1}(\mathbf{X}_{M},x[1]),\varphi_{2}(\mathbf{X}_{M},x[2])]\). The weights \(\widehat{\mathbf{w}}\) of this scenario are then estimated by solving a centralized LASSO problem (2). Now, we will vary the value of the penalty parameter \(\delta\) in the range \([0.001,\ldots,500]\). The parameters \(\widehat{\delta}_{i}=2\delta/|\widehat{w}_{i}|\) are computed and inserted in the exploration objective \(\log\det\widetilde{C}^{-1}\) (cf. (28)). The corresponding results of this evaluation are plotted in Figure 2.  
As we see, whenever \(\delta\to 0\), no regularization is used, and the LASSO objective function reduces to the standard least squares problem. In this case, the exploration criterion becomes highly peaked at existing measurement locations; the algorithm then selects measurement points away from the existing measurement location, as expected. When \(\delta\to\infty\), the corresponding weight estimates naturally converge towards zero. In this case, the exploration criterion does not promote any new measurement at all: the value of the exploration objective \(\log\det\widetilde{C}^{-1}\) is minimized at existing measurements \(x[1]\) and \(x[2]\). As a result, over-regularization will force the agents to stay at current locations rather than explore the whole process.  
Clearly, for a fixed \(\delta\), this effect depends on the estimated weights and on the used kernels. Therefore, to select the regularization parameter, we follow the strategy proposed in [27] Section  5.9, where the penalty parameter is adaptively chosen as \(\delta=\lambda^{-2}\sqrt{2\log(M)}\). The corresponding value of the exploration criterion is shown in Figure 2 as a thick black line. Such a choice of \(\delta\) has been found to be a good choice for achieving a stable regularization, while at the same time allowing agents to explore the field.  
Figure 2: Variation of the regularization parameter \(\delta\) and its influence on the exploration criterion. The black curve shows the case for \(\delta=\lambda^{-2}\sqrt{2\log(M)}\), where \(M=2\).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.5 Explainable AI [217; 247]  
Enhance the interpretability of AI decisions for ICO by developing transparent models that can be audited and trusted by network operators.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### Dynamic Assembly Pose Tracking
#### 3.2.4 The Method to Remove Correspondence Points and Lines under Occlusion Conditions  
In practical applications, sometimes assembly parts are occluded by each other or by hands. When occlusion occurs, if we continue to calculate the correspondence lines, points, and key points at the occlusion, we will not only waste computing resources but also deteriorate the pose results. Moreover, it is also possible that the object or camera moves too fast. This will cause interference with the local sampling data, resulting in motion blur of RGB data or an increase in depth information error near the contour. At this time, the out points increase, and we should eliminate the out points and reduce the confidence of the corresponding modality.  
In the region modality, our method eliminates correspondence lines if the difference between the theoretical depth values of sampling points on the corresponding lines and the actual depth values obtained from the depth camera exceeds 3 cm. This indicates the occurrence of occlusion in that area. In order to reduce the error deletion, the actual depth value at the point is taken as the minimum depth value of 5 \(\times\) 5 pixels around the sampling point in the RGBD image.  
Figure 4: The flow chart of extracting correspondence key points.  
In the depth modality, our method will eliminate the sampling points whose absolute value for the deviation between the actual depth value of each pixel in the mask and the depth value of the CAD model is greater than 3 cm. At this point, we believe that an occlusion has occurred there, or the sampling point belongs to the background. Therefore, it should not be calculated.  
In the texture modality, out method similarly eliminates points whose expected depth is significantly smaller than the corresponding measured depth when local occlusion occurs, and the threshold is set to 5 cm.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 2 Characteristics of the 5G System
However, the 700 MHz band alone would not be able to provide broadband internet access to mobile users (eMBB), as it does not allow the use of MIMO, which would increase the capacity of each cell site. However, it can be used together with the bands listed below, which have large spectral resources. This manner of operation improves signal transmission quality from the user to the base station (i.e., \"upstream\") [7]. The 3.6 GHz band does allow the use of mMIMO (massive MIMO), while, at the same time, it constitutes a compromise between propagation and capacity in terms of spectral resources, especially when combined with the 700 MHz band, which would improve upward connectivity. This band would be used to build a coverage layer for eMBB services in several of the largest cities, including communication routes between these locations. This band can also be used to introduce services that requires reliable transmission and particularly low latency (URLLC) in applications requiring the transmission of particularly large amounts of data, such as high-resolution images for medical or navigation purposes [7]. The 28 GHz band is limited in its use, in particular, because of the requirements applied to transmission between the user and the base station (the \"upstream\" link). It can be used, for example, for broadband access points and picocell applications (cMTC/URLLC). This band, due to its large capacity and the possibility of allocating large spectrum resources, may also be considered for the provision of internet access via a fixed wireless access service [1; 7].
## 5 Conclusions  
In this work, we reported the design of an electro-optical trap for ions, suitable for experiments with atom-ion quantum mixtures. The trap assembly, composed of a linear Paul trap, an electro-optical trap, and two ovens, resulted from an adaptive design process strongly based on numerical simulations for determining the shape of the electrical potentials and the heat distribution on the electrodes. This trap was specifically designed for a quantum mixture of barium ions and lithium atoms. Nevertheless, the apparatus could be easily adapted to other atom-ion mixtures with minor changes of the optical setup. The position and shape of the Paul trap's endcaps ensured a wide optical access to the ion trapping region, facilitating the optical transport of ultracold atoms, which would be produced in an optical trap in a separate vacuum chamber [36]. The production of quantum gases and trapped ions would be synchronized by managing the overall experimental sequence with a single control apparatus [37]. The micromotion-free dynamics of the ions in the electro-optical trap would allow the ions to reach ultracold temperatures by sympathetic cooling with a quantum gas, eventually leading to atom-ion collisions in the s-wave scattering regime.  
Conceptualization, E.P., L.D., and C.S.; data curation, E.P. and L.D.; formal analysis, E.P., L.D., and C.S.; funding acquisition, C.S.; investigation, E.P., L.D., and C.S.; methodology, E.P., L.D., and C.S.; software, E.P.; supervision, C.S.; writing, original draft, E.P.; writing, review and editing, L.D. and C.S. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Average counts of photons emitted by neutral barium atoms decaying from the 5d6p \({}^{3}\)D\({}_{1}\) level are plotted as a function of time. The neutrals are emitted from one of the two ovens by applying a train of 20 current pulses 400 ms long with a duty cycle of 75%, whereas the amplitude is linearly decreased from 100 A to 70 A to keep the temperature from exponentially rising. Time \(t=0\) corresponds to the moment at which the current pulses’ train starts.  
**Funding:** This work has been supported by the ERC Starting Grant PlusOne (Grant Agreement No. 639242), the project EMPIR 17 FUN07 (CC4C), the SIR-MIUR grant ULTRACOLDPLUS (Grant No. RBSI14 GNS2), and the FARE-MIUR grant UltraCrystals (Grant No. R165 JHRWR3). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.  
**Acknowledgments:** We thank [PERSON] and [PERSON] for experimental assistance and [PERSON] for continuous support.  
**Conflicts of Interest:** The authors declare no conflict of interest.