esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
###### Abstract
Outdoor far-field experiments demonstrated that IRS deployment significantly improved signal coverage in urban blind spots, with a signal gain increase of 8-12 dB compared to traditional metal reflectors. In [28], a 2-bit RIS prototype was investigated, focusing on fast-response 3D beam tracking and vortex beam generation to optimize dynamic wireless communications. Indoor experiments showed that 3D-focused beams could dynamically track moving targets, while outdoor experiments confirmed that the RIS possesses far-field multi-target beamforming capabilities. However, this system requires multiple feedback loops, the interaction between the UE and RIS remote servers, and a time synchronization mechanism between systems to complete the tracking process, leading to high tracking latency [29; 30]. Existing prototype systems typically use remote servers to adjust RIS reflection modes. The UE must send feedback data, wait for remote computation, and then update the RIS state, relying on a time synchronization mechanism between the two systems to complete the tracking process. Consequently, the system response time is prolonged, making it unsuitable for low-latency RIS tracking applications. To address this issue, this paper proposes two novel RIS beam tracking prototype:  
1. The Edge-Integrated RIS Control Mechanism (EIR-CM). This approach integrates the UE baseband signal processing and the RIS control system into the same edge server. It migrates the RIS feedback computation logic to a local computing unit, eliminating dependence on remote servers for codebook management. Within the Local Area Network (LAN), the UE directly controls the RIS reflection mode via TCP, bypassing remote server interactions. By adopting an integrated RIS control architecture, real-time CSI computation and RIS feedback optimization are performed on the local host, reducing beam adjustment latency by over 50% compared to traditional remote interaction schemes. This approach is suitable for low-latency, high-speed dynamic RIS tracking scenarios, particularly in applications such as smart buildings, industrial automation, and intelligent transportation, serving as a low-latency RIS control solution for 6G networks. 2. The Visually Steered RIS Control Mechanism (VSR-CM). This approach employs the YOLOv7 object detection algorithm [31] + depth camera (Intel RealSense D435i) for user detection, enabling real-time UE position recognition. Unlike traditional methods that traverse the entire codebook, this scheme directly maps the detected user position to the optimal RIS codebook via object detection, significantly accelerating beam adjustment. As a result, no feedback from the UE to the RIS control system is required, and RIS control is performed purely based on vision perception, reducing feedback communication overhead. This approach is particularly suitable for high-speed mobile users (such as drones, autonomous driving, and V2X vehicular networks) and enables millisecond-level dynamic RIS beam adjustments, improving tracking stability.
## 3 Method
### Satellite Data
#### 3.1.1 HJ-1 CCD Data  
The HJ satellite constellation consists of two satellites (HJ-1-A and HJ-1-B) launched on 6 September 2008. Both the HJ-1-A and the HJ-1-B satellites are equipped with two CCD cameras. With the four CCD cameras (Table 2), the HJ satellite constellation can image the entire Earth at two-day intervals, with a spatial resolution of 30 m.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Evaluation Metrics
#### 4.2.3 Stability  
For continuous waterline detection based on video, we often need to inspect the impact of environmental variations on a sequence of estimated image-maps when facing the same visual scenario. Thereby, a related metric called _stability_ is defined to quantify the stability of an evaluated approach under changeable environments. Specifically, the stability involves measuring the stability over four different metrics (_precision_, _recall_, _FP_, and _irrelevance_, _respectively_) on multiple estimated image-maps, when a waterline detection approach is evaluated for a specific scenario against different environmental noises. Formally, _stability_ over a metric \(p\) is defined as follows:  
\[stability(p)=\frac{\text{mean}(p)-medium(p)}{\sigma(p)} \tag{7}\]  
where \(p\) denotes the metric _precision_, _recall_, _FP_ or irrelevance, \(mean(p)\) denotes the mean of a specific metric p over all assessed samples (i.e., estimated image-maps for the same scenario), \(medium(p)\) and \(\sigma(p)\) refer to the medium and standard deviation of those samples relevant to \(p\), respectively.  
In essence, _stability_ characterizes four distribution conditions on their corresponding metrics by sampling diverse estimated image-maps that represent respective results from those facing the same visual scenario with varied environmental noises. Given a metric \(p\), if \(stability(p)\) tends to be zero, then the results about the specified metric over all samples are more convergent to normal distribution, which means that evaluated approach has more stability on this metric against environmental variations. On the contrary, the results with respect to the metric are prone to be fragile for environmental noises.
## 4 Wavelet Based Unsupervised Learning Algorithm
### Filtering the CGM Data  
After partitioning the CGM data into days as described in Section 2, we applied wavelet filters to the data. We give a brief description of wavelet filtering here. For a complete description, see [11] or any introductory text on wavelets.  
Using a pair of filters called the low pass filter, \(H\), and high pass filter \(G\), wavelet filters act via averaging and differencing of a signal, \(\mathbf{s}\). (In this paper, the signal is a day's worth of CGM data for a patient.) The results of the low pass filter, known as the _approximating coefficients_, are obtained by forming a dot product of the entries in \(H\) with entries in \(\mathbf{s}\) so that the \(k\)th approximating coefficient is given by  
\[\left(H\mathbf{s}\right)_{k}=\sum_{j\in Z}h_{j-2k}\mathbf{s}_{j} \tag{1}\]  
The high pass filter results, called the _detail coefficients_, are calculated similarly,  
\[\left(G\mathbf{s}\right)_{k}=\sum_{j\in Z}g_{j-2k}\mathbf{s}_{j} \tag{2}\]  
A property of wavelets ensures that only finitely many of the coefficients \(h_{i}\) and \(g_{i}\) will be nonzero in the above equations. Applying the low and high pass filters multiple times to the approximating coefficients allows the signal to be viewed at different levels of detail or resolution. Specifically, we applied the Daubechies-4, or \(D_{4}\), wavelet filters three times to yield three different levels, or sets of data describing a patient's daily CGM data. The filters determined by the \(D_{4}\) wavelet are  
\[H =[h_{0},h_{1},h_{2},h_{3}]=[0.48296,0.83652,0.22414,-0.12941]\] \[G =[g_{-2},g_{-1},g_{0},g_{1}]=[-0.12941,0.22414,0.83652,-0.48296]\]The \(D_{4}\) has the interesting property that when applied to a linear signal, the detail coefficients are 0. Thus, it is able to detect sections of a signal that have a local linear trend; these trends are indicated by 0's in the high pass results. This provides a possible mathematical justification for the correspondence between the wavelet-based analysis and the PLA index.
# Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems
## The Multivariate Auxiliary Model Coupled Identification Algorithm  
### The Auxiliary Model Based Recursive Least Squares Algorithm  
According to the identification model in (5), define a cost function:  
\[J_{1}(\mathbf{\vartheta}):=\sum_{j=1}^{t}\|\mathbf{y}(j)-\mathbf{\Phi}(j)\mathbf{\vartheta}\| ^{2}.\]  
Based on the auxiliary model identification idea and on the derivation of the RLS algorithm [27; 28], we use the output \(\mathbf{x}_{\text{a}}(t)\) as the unknown inner vector \(\mathbf{x}(t)\) and replace the unknown information matrix \(\mathbf{\Phi}(t)\) with its estimate \(\mathbf{\hat{\Phi}}(t)\), and obtain the following auxiliary model based recursive least squares (AM-RLS) algorithm:  
\[\mathbf{\hat{\vartheta}}(t) =\mathbf{\hat{\vartheta}}(t-1)+\mathbf{L}(t)[\mathbf{y}(t)-\mathbf{\hat{\Phi}}(t )\mathbf{\hat{\vartheta}}(t-1)], \tag{6}\] \[\mathbf{L}(t) =\mathbf{P}(t-1)\mathbf{\hat{\Phi}}^{\dagger}(t)[\mathbf{I}_{m}+\mathbf{\hat{ \[PERSON]}}(t)\mathbf{P}(t-1)\mathbf{\hat{\[PERSON]}}^{\dagger}(t)]^{-1},\] (7) \[\mathbf{P}(t) =\mathbf{P}(t-1)-\mathbf{L}(t)\mathbf{\hat{\[PERSON]}}(t)\mathbf{P}(t-1),\] (8) \[\mathbf{\hat{\[PERSON]}}(t) =[\mathbf{\hat{\[PERSON]}}_{x}(t),\mathbf{\[PERSON]}_{s}(t)],\] (9) \[\mathbf{\hat{\[PERSON]}}_{x}(t) =[-\mathbf{x}_{\text{a}}(t-1),-\mathbf{x}_{\text{a}}(t-2),\cdots,-\mathbf{x}_ {\text{a}}(t-n_{a})],\] (10) \[\mathbf{x}_{\text{a}}(t) =\mathbf{\hat{\Phi}}(t)\mathbf{\hat{\vartheta}}(t). \tag{11}\]  
The steps of computing the parameter estimation vector \(\mathbf{\hat{\vartheta}}(t)\) by the AM-RLS algorithm are listed in the following:  
1. Set the initial values: \(t=1\), \(\mathbf{\hat{\vartheta}}(0)=\mathbf{1}_{n_{0}}/p_{0}\), \(\mathbf{P}(0)=p_{0}\mathbf{I}_{n_{0}}\), \(\mathbf{x}_{\text{a}}(t-j)=\mathbf{1}_{m}/p_{0}\), \(j=1\), \(2\), \(\cdots\), \(n_{a}\), \(p_{0}=10^{6}\). Set the data length \(L\).
2. Collect the observation data \(\{\mathbf{\Phi}_{s}(t),\mathbf{y}(t)\}\) and form the information matrix \(\mathbf{\hat{\Phi}}_{x}(t)\) by (10).
3. Form \(\mathbf{\hat{\Phi}}(t)\) by (9), and compute \(\mathbf{L}(t)\) by (7) and \(\mathbf{P}(t)\) by (8).
4. Update the parameter estimation vector \(\mathbf{\hat{\vartheta}}(t)\) by (6).
5. Compute the output \(\mathbf{x}_{\text{a}}(t)\) of the auxiliary model using (11).
6. If \(t=L\), stop the recursive computation and obtain the parameter estimates; otherwise, increase \(t\) by \(1\) and go to Step 2.  
**Remark 1**.: _For the multivariable RLS algorithm in (6)-(10), we can see from (7) that it requires computing the matrix inversion \([\mathbf{I}_{m}+\hat{\boldsymbol{\Phi}}(t)\boldsymbol{P}(t-1)\hat{\boldsymbol{ \Phi}}^{\mathsf{ T}}(t)]^{-1}\in\mathbb{R}^{m\times m}\) at each step, resulting in heavy computational load, especially for large \(m\) (the number of outputs). This is the drawback of the multivariable RLS algorithm in (6)-(10). This motivates us to study new coupled parameter identification methods._
## 3 Results and Discussion
### Performance Tests of VRLA Batteries
#### 3.2.2 Charge Acceptance  
The charge acceptance capability of VRLA batteries is an important performance metric for e-bike applications. It not only represents the recharge ability of the batteries, but also objectively reflects the service life of the batteries. The charge acceptance ratio (I\({}_{\text{ca}}\)/I\({}_{2}\)) of batteries with different additives is shown in Figure 4e. A higher charge acceptance ratio demonstrates that the batteries have better charge acceptance [32]. The batteries exhibit better charge acceptance and their current acceptance is better. At the same time, the polarization of the batteries is not easy to achieve, thereby prolonging the cycle life of the batteries. The I\({}_{\text{ca}}\)/I\({}_{2}\) value of CB1, CB2, CB3 and the control batteries are 3.8, 3.9, 4.5 and 2.9, respectively. Compared with the control batteries, the increase ranges between 30% and 55% with carbon black batteries. Furthermore, the charge acceptance ratio of batteries containing  
Figure 4: (**a**) Two-hour capacity tests at room temperature, (**b,c**) low-temperature performance tests at \(-15\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C of VRLA (48 V 12 Ah) batteries with carbon black additives with different SSAs, respectively. (**d**) Effect of carbon black on high-current discharge performance. (**e**) Effect of carbon black on charge acceptance performance.  
high-SSA carbon black is superior to that of those containing low-SSA carbon black. These results clearly show the strong effect of the SSA of the carbon additive. The CB3 additive has a large SSA and the particles are interconnected to form a three-dimensional (3D) network structure, which provides a larger active SSA for the crystallization and dissolution of lead sulfate particles, thus improving the charge acceptance of the batteries [32; 33].
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 5 Conclusions  
We propose a method based on camera calibration with a two-dimensional target to solve the problem of the alignment of the camera's optical center and the rotation center in a rotating optical measurement system composed of a camera and a rotating platform. An auxiliary plane coordinate system is introduced to adjust the external parameter matrix of the camera. The rectified external parameter matrix is used to calculate the optical centers in the unified world coordinate system. Multiple fitted circles can determine the rotation center more accurately than a single fitted circle, which will provide a higher precision in the following application, such as a panoramic mosaic. Simulations and experiments verified the effectiveness of the proposed method.  
It should be noted that the optical center of the initial position of the camera should be kept at an appropriate distance from the rotation center during calibration to increase the accuracy of the circle fitting. By the way, although this paper only discusses the alignment of the optical center of the camera with a one-dimensional rotating optical system, the proposed method can also be extended in principle to the problem in the alignment of the optical center with the rotating axis of a two-dimensional rotating optical system. However, the current experimental setup is only suitable for the alignment problem of one-dimensional rotating optical systems. In the following work, we will find a device suitable for two-dimensional (pitch and yaw) rotating platforms.  
Conceptualization, Y.H.; X.S. and W.C.; methodology, Y.H.; X.S. and W.C.; investigation, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, X.S. and W.C.; funding acquisition, X.S. and W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Scientific Apparatus Development Project of China (under Grant No. 2013 YQ490879). The authors declare no conflict of interest.
## 3 Research Results and Discussion
### Energy Indicators  
The lowest Brake Specific Fuel Consumption (BSFC) was achieved throughout the entire range of SOI research (0-18 CAD BTDC) with the engine running on HVO fuel with a high (43.65 MJ/kg) LHV. With SOI being 6 CAD BTDC, consumption of HVO fuel was 2.1% lower compared to diesel fuel, because HVO's LHV is 2.0% higher. The additional supply of methane increased BSFC: D40M60--6.8%, HVO40M60--3.3%, even though the LHV of D40M60 was 8.8% higher and LHV of HVO40M60 was 9.6% higher (Figure 3). The changed combustion process resulted in a higher fuel consumption. Compared to diesel fuel, additional supply of biomethane significantly increased the BSFC: D40 BM60 by 124.5% and HVO40 BM60 by 120.1%. Using biomethane, the fuel mass consumption increased, because LHV of D40 BM60 was 46.9% lower than that of diesel and LHV of HVO40 BM60 was 46.6% lower. Dual fuel BSFC declined advancing SOI to 12-15 CAD BTDC, because of a more complete gas combustion, which is evident from a declined volumetric fraction of incomplete combustion products (CO, HC, and Smoke) in exhaust gases (Figures 6, 7 and 9).  
With the engine running on diesel and HVO fuel, the values of the Brake Thermal Efficiency (BTE) are close in the entire SOI range. Compared to diesel, with the engine running on dual fuel with added methane, BTE decreased: D40M60 by 14.1% and HVO40M60 by 11.9%. The use of a biomethane additive reduced BTE: D40 BM60 by 16.5% and HVO40 BM60 by 14.3%. HVO partially offset the reduction in BTE, especially when using dual fuel with biomethane gas.
## 4 PAU-RAD Design, Development and Integration
### PAU-RAD Software Description
The communications between the control interface and the CU is bidirectional. Some of the most relevant applications consist of the possibility to change the integration time, the inter-calibration period, the current CU status, the steering angle of the synthesized beam, among others. Furthermore, the interface has a real-time display to present the information sent by the CU. The most relevant information is displayed with regard to the system status, the current radiometric acquisitions, and _RUT_s calibration information. The graphical interface helps in understanding the system's operation and detecting hardware or software errors. All the received data is collected, time stamped and stored for further processing.
## 3 Wave Propagation in Non-Linear Dispersive Media
\right\} \tag{17}\]  
\(q\): positive valued penalty exponent, \(L\): Positive valued penalty constant, \(\delta_{i}\): penalty coefficients  
**Optimization process**:  
\[\mathbf{\
u}_{k+1}=\mathbf{\
u}_{k}+\alpha_{k}\mathbf{p}_{k}\]  
\[\mathbf{p}_{k}=-\mathbf{H}_{k}\
abla\!f_{k}\]  
\[\mathbf{s}_{k}=\mathbf{\
u}_{k+1}\!\!-\!\mathbf{\
u}_{k}\]  
\[\mathbf{y}_{k}=\
abla\!f_{k+1}\!\!-\!\
abla\!f_{k}\]  
\[\mathbf{H}_{k+1}=\left(\mathbf{I}-\rho_{k}\mathbf{s}_{k}\mathbf{y}_{k}^{\text{T}}\right)\!\!H_ {k}\!\left(\mathbf{I}-\rho_{k}\mathbf{y}_{k}\mathbf{s}_{k}^{\text{T}}\right)\!+\!\rho_{k} \mathbf{s}_{k}\mathbf{s}_{k}^{\text{T}}\left(\text{BFGS}\right)\]  
\[\
abla\!f=\left[ \begin{array}{c}\frac{f(\
u_{1}+\epsilon;\
u_{2},\cdots,\
u_ {N})-f(\
u_{1},\
u_{2},\cdots,\
u_{N})}{\epsilon}\\ \frac{f(\
u_{1},\
u_{2}+\epsilon,\cdots,\
u_{N})-f(\
u_{1},\
u_{2},\cdots,\
u_ {N})}{\epsilon}\\.\\.\\.\\.\\ \frac{f(\
u_{1},\
u_{2},\cdots,\
u_{N}+\epsilon)-f(\
u_{1},\
u_{2},\cdots,\
u_ {N})}{\epsilon}\end{array} \right]\quad,\;\;\rho_{k}=\frac{1}{\mathbf{y}_{k}^{ \text{T}}\mathbf{s}_{k}}\]  
The convergence rate of the BFGS algorithm is super-linear, but our formulated problem is a non-linear optimization problem (non-linear programming). Therefore, the convergence is not reached immediately. Furthermore, the recursive computation of the Hessian matrix slows down the convergence rate. For these reasons, the computation of the optimum frequency values takes a great amount of time. Finite-Difference Time-Domain Solution of the Gain Factor Optimization Problem in Optical Parametric Amplification  
Equations (5a,b) and (7a,b) can be discretized using the finite-difference time-domain (FDTD) method as stated in Equations (18a,b) and (18c,d), for every iteration k of our optimization problem. Our initial goal is to discretize Equation (5a,b) in order to get \(E_{2,k}(i,j+1)\) i.e., the value of \(E_{2,k}\) at a certain spatial coordinate at the next time step. Since \(E_{2,k}\) and \(P_{2,k}\) are coupled to each other, initially we attempt to solve for \(P_{2,k}(i,j+1)\) and then substitute its value into the wave equation for \(E_{2,k}(i,j+1)\). These two equations are solved recursively for every time step and for all spatial points in a given one-dimensional solution domain. In order to increase the accuracy of our obtained solution, we should select \(\Delta t\) and \(\Delta x\) as small as we can [12; 13]. Then we may proceed on the discretization of Equation (7a,b) and substitute the previously obtained value of \(P_{2,k}(i,j)\) from Equation (5a,b), for the solution of \(E_{1,k}(i,j+1)\) in Equation (7a,b). Finally, we update the values of the optimization parameters based on the BFGS algorithm, and we repeat the entire procedure for every iteration of the optimization process until the desired gain factor is achieved (see Figure 4).
## 1 Introduction
This approach is based on short-time Fourier transform (STFT) modification and allows the CR at each point in the TF plane to be determined. This is consistent with the assumption that when the baseline and the trajectory of the object are crossed, the signal appearing in the receiver can be approximated with the linear frequency modulated waveform, and this technique is  
Figure 1: Simplified passive forward scattering radar (PFSR) geometry. \(\beta-\)bistatic angle, T\({}_{\rm x}-\)non-cooperative transmitter, R\({}_{\rm x}-\)radar receiver, TGT—target, \(L-\)baseline, \(R_{1}-\)distance from the transmitter to the target, \(R_{2}-\)distance from the target to the receiver, \(D-\)distance from the receiver to the crossing point. dedicated for such a problem. In addition, the method is computationally efficient, which reduces the calculation time. This paper is organized as follows: Section 2 presents the CR estimation theory background, including Cramer-Rao lower bound (CRLB) analysis. Section 3 depicts simulation results, and Section 4 covers real-life signal analysis provided by the GSM PFSR. Discussion and comments close the article.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 3 Our Proposed Architecture
### Architecture of Feature-Attended Federated LSTM  
As shown in Figure 1, the proposed architecture includes two parts, a feature-attended LSTM algorithm and a correlation-based federated learning scheme.  
The part of feature-attended LSTM is designed based on the time correlation among different features. In this part, the features of input data will be divided into multiple groups according to the correlation, and then the grouped features will be used to train the local model in edge servers. With this part, the model can make full use of the training data in the training process as well as balance the cost of training calculation while improving the model performance.  
Figure 1: Architecture of FAF-LSTM.  
The other part is a correlation-based federated learning scheme, which is based on the correlation of training data belonging to different detection nodes. In this part, edge servers send the local model parameters to the center server, and then the correlation-based federated learning technology we proposed is used to realize the cooperative training of each detection node and solve problems like lack of data or imbalance in the independent training process. Finally, the central server sends the aggregation model parameter to each edge server, and the aggregation model parameters based on the correlation degree could be used to optimize the local model as well as improve the adaptability of the model in the differential environment and optimize the model accuracy.