esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
### Ablation Experiment
#### 6.3.3 Comparison among Different Feature Fusion Methods  
We conducted experimental evaluations on the Feature Fusion Pyramidal Module (FFPM). As the encoder progressively reduces the resolution during the encoding process, certain grasp-related features may be neglected and gradually lost. This can lead to the failure to recover crucial information during decoder up-sampling, resulting in suboptimal performance. Common approaches to address this issue include feature addition and feature concatenation, where low-level features are integrated into the decoder through skip connections to compensate for relevant features disregarded during the encoding process. While feature addition and concatenation are effective, we argue that compared with the proposed FFPM, our FFPM can more efficiently preserve grasp-related features. The mechanisms of feature addition and concatenation are illustrated in Figure 12.  
Figure 11: Grasping detection results with different attention mechanisms. The first to fourth columns depict grasping detection using our Res-EMA module; Res-CoA module; Res-SE module, and residual blocks, respectively.  
Figure 12: Different feature fusion methods.  
We conducted training for 50 epochs on the Jacquard dataset, evaluating the model on the test set after each epoch. The highest accuracy achieved during evaluation is depicted in Figure 13. We compared three methods, including the FFPM, feature addition, and feature and concatenation, with the FFPM demonstrating superior performance, achieving an accuracy of 0.965. In contrast, feature addition and feature concatenation exhibited mediocre performance, indicating that the FFPM excels in preserving grasp-related features.  
In Figure 14, we visualize the grasp detection results using different feature fusion methods. It is evident from the figure that the addition of the FFPM results in more precise grasp configurations. Specifically, the grasp positions generated by the FFPM closely align with the object's center of mass, facilitating more effective grasping. The key effectiveness of the FFPM lies in its role as a bridge between the encoder and decoder, effectively preserving grasp-related features and mitigating the loss of relevant grasp features caused by the deepening of the encoding process.  
Figure 14: Grasping detection results with different feature fusion methods. The first to third columns depict grasping detection using our FFPM, feature addition, and feature concatenation, respectively.  
Figure 13: Experimental results of different feature fusion methods.
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 4 Experiments
### Experimental Setup  
#### 4.1.1 Datasets and Preprocessing  
* MNIST is a large dataset of handwritten images in grayscale format, which is commonly used to train various image-processing systems. This dataset is widely used for training and testing in the field of machine learning. There are 60,000 training images and 10,000 testing images with \(28\times 28\) pixels. Half of the training set and half of the test set come from the National Institute of Standards and Technology (NIST) training dataset, while the other halves of the training set and the test set comes from the NIST test dataset.
* CIFAR The CIFAR has two datasets, CIFAR-10 and CIFAR100. CIFAR-10 has 60,000 colored natural images of 10 classes with \(32\times 32\) pixels, of which the training set contains 50,000 images and the test set contains 10,000 images. The dataset is divided into five training batches and one testing batch, each with 10,000 images. The test batch contains exactly 1000 randomly selected images from each category. Similar to CIFAR-10, CIFAR-100 has 100 classes, and each class contains 600 images (500 training images and 100 testing images).
* Processing In this paper, we preprocess the MNIST and CIFAR datasets separately before training the DNN models. For MNIST, We normalize pixels in \([0,1]\) at first, and then subtract their mean. For CIFAR, in addition to normalization, we randomly flip and crop the datasets to expand them as well.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### System Budgets  
The margin philosophy applied is as follows. Regarding the mass and power budget, a margin of 5% is applied for existing units, 10% for existing units requiring minor modifications, and 20% for new developments or existing units requiring major modifications. On top of that, a 20% margin is applied on system level. A 50% margin is used for the \(\Delta\)V, while a margin of 5 dB is applied for communication link budgets.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 1 Background and Motivation  
The rapid growth of electricity demand across national power grids has significantly outpaced forecasts in recent years, creating unprecedented challenges for grid operations. As illustrated by regional grid statistics, the Eastern China region--comprising Shanghai, Anhui, Zhejiang, and Jiangsu provinces--reached nearly 400 million kilowatts in peak load during summer 2023. Projections for 2024 indicate an additional 80 million kilowatt increase,representing a 5.8% growth rate. This accelerating demand, potentially exacerbated by extreme weather events, creates substantial pressure on power supply systems.  
Simultaneously, China's transition toward carbon neutrality has accelerated the integration of renewable energy sources, fundamentally altering the traditional power structure. By the end of 2025, the Eastern China power grid expects installed renewable capacity to reach 204.18 million kilowatts--a 94.8 million kilowatt increase from 2022, representing an annual growth rate of 23.4%. This dramatic shift toward cleaner energy introduces significant variability and uncertainty into grid operations.  
In this evolving landscape, regional grid-level dispatching centers face three significant forecasting challenges that motivate our research:  
First, grid-level forecasting typically relies on the direct accumulation of load forecasting data from provincial and municipal centers. However, significant regional disparities exist in economic development levels, industrial structures, and consumer behaviors, resulting in fundamentally different load patterns [1]. For instance, service-oriented Shanghai exhibits higher load levels during holidays and weekends, while manufacturing-intensive Jiangsu indicates more uniform electricity demand. Weather impacts also vary substantially across regions--coastal provinces experience different meteorological influences than inland areas. These differences make simple aggregation methods inadequate for accurate grid-level forecasting.  
Second, heterogeneous data from diverse provincial sources create integration complexities that challenge existing systems. Each province employs different forecasting methodologies with varying algorithms, data formats, and update frequencies [2].  
Third, provincial data reporting exhibits substantial time delays and quality variations. Current grid-level dispatch centers integrate provincial forecasts using simple aggregation methods that fail to account for these temporal inconsistencies [3]. This approach provides limited flexibility for adapting to the dynamic nature of modern power systems, particularly as renewable energy penetration increases [4].  
In this challenging environment, accurate load forecasting remains essential for regional electricity market operations, safety verification, and demand response planning [5; 6; 7]. While individual provinces may achieve reasonable forecast accuracy within their boundaries, the integration of these forecasts at the grid level presents a distinct challenge that has received insufficient attention in existing research.
## 2 System Description and Current Controller  
### System Model of Grid-Connected Inverter  
Figure 1 shows a configuration of an LCL-filtered grid-connected inverter connected to the weak grid, in which \(V_{DC}\) denotes the DC-link voltage; \(R_{1}\), \(R_{2}\), \(L_{1}\), and \(L_{2}\) are the filter resistances and filter inductances, respectively; \(C_{f}\) is the filter capacitance, and \(L_{g}\) is the grid inductance due to weak grid. When the grid impedance does not exist in Figure 1, inverter system can be expressed mathematically in the synchronous reference frame (SRF) as [15; 16]:  
\[\dot{\mathbf{x}}(t)=\mathbf{A}\mathbf{x}(t)+\mathbf{B}\mathbf{u}(t)+\mathbf{ De}(t) \tag{1}\]  
\[\mathbf{y}(t)=\mathbf{C}\mathbf{x}(t) \tag{2}\]  
where \(\mathbf{x}=\left[i_{1}^{q}i_{2}^{q}i_{1}^{q}i_{1}^{q}v_{c}^{q}v_{c}^{d}\right] ^{T}\) is the system state vector, \(\mathbf{u}=\left[v_{i}^{q}v_{i}^{q}\right]^{T}\) is the input vector, and \(\mathbf{e}=\left[e^{d}\,e^{d}\right]^{T}\) in the grid voltage vector. In this equation, the superscript \"\(q\)\" and \"\(d\)\" denote the \(q\)-axis and \(d\)-axis variables, respectively, \(i_{1}\) is the inverter-side current, \(i_{2}\) is the grid-side current, \(v_{c}\) is the capacitor voltage, \(v_{i}\) is the inverter output voltage, and \(\omega\) is the grid angular frequency. The system matrices \(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{C}\), and \(\mathbf{D}\) are expressed as:  
\[\mathbf{A}=\left[\begin{array}{cccc}-k_{2}/l_{2}&-\omega&0&0&1/l_{2}&0\\ \omega&-k_{2}/l_{2}&0&0&0&1/l_{2}\\ 0&0&-k_{1}/l_{1}&-\omega&-1/l_{1}&0\\ 0&0&w&-k_{1}/l_{1}&0&-1/l_{1}\\ -1/c_{f}&0&i/c_{f}&0&0&-\omega\\ 0&-1/c_{f}&0&i/c_{f}&w&0\\ \end{array}\right] \tag{3}\]  
The grid impedance is defined as j\(\omega L_{g}\). To follow the term in the conventional works, the term \"the grid impedance\" is used in this study. On the other hand, to simply represent the quantity, the grid impedance variation is denoted by \(L_{g}\) variation.  
A discretized model of the continuous-time inverter system in Equations (1) and (2) is obtained by using the zero-order hold (ZOH) method with the sampling time \(T_{s}\) of 10 kHz as follows:  
\[\mathbf{x}(k+1)=\mathbf{A}_{d}\mathbf{x}(k)+\mathbf{B}_{d}\mathbf{u}(k)+ \mathbf{D}_{d}\mathbf{e}(k) \tag{4}\]  
\[\mathbf{y}(k)=\mathbf{C}_{d}\mathbf{x}(k) \tag{5}\]  
where \(\mathbf{A}_{d}=e^{\Delta T_{s}}\), \(\mathbf{B}_{d}=(\int_{0}^{T_{s}}e^{\Delta T_{d}}dt)\mathbf{B}\), \(\mathbf{C}_{d}=\mathbf{C}\), \(\mathbf{D}_{d}=(\int_{0}^{T_{s}}e^{\Delta T_{dIn case that a lower sampling frequency is selected, higher-order discretization methods can be implemented as presented in [30]. Furthermore, the controllability and observability of system in Equations (4) and (5) are studied in the continuous- and discrete-time domain in [31; 32], respectively.  
For the stability assessment and performance comparison of two typical current controllers in an LCL-filtered grid-connected inverter connected to distorted weak grid, the direct grid current control with the capacitance current active damping [13], and integral-resonant state feedback control are employed in the following subsections [15].
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 7 Summary and Conclusions  
The key findings of this research study can be summarized as follows:  
1. Compressive strength in excess of 40 MPa and 30 MPa can be obtained at 7 days using Loy Yang brown coal geopolymer mortar (50 mm cube) and concrete (100 mm cube) specimens, following curing at 120 \({}^{\circ}\)C for 12 h and 24 h, respectively.
2. The 50 mm brown coal geopolymer mortar and concrete cubes displayed an almost identical temperature profile with an optimal curing period of 12 h. However, the profiles of 100 mm, 200 mm and 300 mm concrete cubes differed significantly, indicating that curing at 120 \({}^{\circ}\)C for 12 h is not effective for larger concrete specimens as the internal temperature is not sufficient to promote the geopolymerization.
3. The temperature profile of brown coal fly ash geopolymer under heat curing can be divided into three stages which correlate well with the proposed reaction mechanism for low calcium class F fly ash geopolymers. However, the time interval of each stage is dependent on the size of the geopolymer specimen.
4. At the optimum curing time, the dissolution of the fly ash leads to the production of a geopolymer matrix with a more uniform composition and structure. However, the continued heat curing beyond this resulted in micro-cracking in the gel matrix and a reduction in compressive strength.
5. The specimen size and curing time must be taken into consideration in the mix design stage of brown coal fly ash based geopolymer concrete in order to achieve the optimum compressive strength. Longer curing periods for larger brown coal geopolymer specimens could result in severe micro-cracking on the surface as the temperature profiles indicate that geopolymerisation in the surface concrete is completed and has reached the point where the evaporation of the residual water is likely to occur.  
[PERSON] and [PERSON] conceived of the presented idea and conducted all experiments. [PERSON] and [PERSON] verified the experimental and analytical methods, and provided the findings of this work. All authors discussed the results and contributed to the final manuscript.  
This project was funded by ARC-ITRH (Australian Research Council-Industrial Transformation Research Hub) research grant (IH150100006) allocated for nanoscience-based construction material manufacturing.  
The authors wish to acknowledge management in Loy Yang power station for the supply of brown coal fly ash. The X-ray facility, Microscopy & Microanalysis facility and scientific & technical assistance provided by RMIT University is further acknowledged.  
The authors declare no conflict of interest.
# Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
## 4 Conclusions  
Feasibility studies of CPA-EEHG were conducted both analytically and numerically with the achievable parameters of the seed lasers and electron beams at existing FEL facilities. By adopting the proposed CPA-EEHG scheme, coherent high-power ultrafast EUV FEL pulses can be generated directly from the commercial seed lasers. A compressor adopting the OPM geometry was designed at a central wavelength of 13.5 nm. Some practical issues were discussed, including the spatial chirp induced by the compressor and the tunability of the scheme. The generation of two-color FEL pulses based on the CPA-EEHG was also investigated. The simulation results demonstrated the possibility of generating femtosecond two-color pulses with a finely controllable relative time delay. The proposed method for generating ultrafast coherent FEL pulses with high peak power is expected to extend the ultrafast nonlinear spectroscopy technique to the extreme ultraviolet and soft X-ray regime.  
Conceptualization, X.W. and L.Z.; methodology, L.Z. and X.W.; software, L.Z.; validation, L.Z., H.Y. and X.W.; formal analysis, L.Z.; investigation, L.Z.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z., X.W., Y.L., H.Y. and W.Z.; visualization, L.Z. and Y.L.; supervision, W.Z.; project administration, X.Y.; funding acquisition, X.W., W.Z. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Shenzhen Science and Technology Program (Grant No. RCBS20210609104332002), the Scientific Instrument Developing Project of Chinese Academy of Sciences (Grant No. GJJSTD20190002), and the National Natural Science Foundation of China (Grant No. 22288201).  
Not applicable.  
The data that support the results of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for useful suggestions and comments on the simulations.  
The authors declare that there are no conflict of interest regarding the publication of this article.
## 3 Hydrological Applications
### Indirect estimation of Hydrological parameters
#### 3.2.1 Land Cover Mapping  
Land-cover for the heart River sub-basin of North Dakota was determined from July 2, 2002 Landsat ETM+ image for the study area using ISODATA classification (ERDAS, 1999) (Figure 5a). The unsupervised classifier yielded 30 spectral classes. Scattergram of the scaled Normalized Difference Vegetation Index (NDVI\({}_{\mathrm{s}}\)) versus scaled surface radiant temperature (T\({}_{\mathrm{s}}\)) were used to find instances of strong correlation between them and the land-cover data of the sub-basin. The spectral signatures of all these classes were used to determine the mean radiance for each band. Using the mean signature values, additional layers and vegetation indices were derived. From the scattergram, five United States Geological Survey Land Use and Land Cover (USGS-LULC) system level 1 land-cover classes ([PERSON] _et al._, 1976) were identified (Figure 5b) ([PERSON], 2004a).  
Figure 4: Devils Lake surface area progressions from Landsat images.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 3 Proposed Methodology  
### Problem Definition  
The quantization method for LSTM layers deployed on neural network accelerators discussed in this paper is as follows.  
1. The embedded computing platforms such as the neural network accelerator deploying the neural network models need to quantize the values into low-bit integers through quantization operations. However, most of the current embedded computing platforms with a fixed-point architecture do not directly support performing the quantization operation for the LSTM layer. The original structure of LSTM consists of 6 types of mapping as shown in Equations (1)-(6), named \(\{i_{t},f_{t},o_{t},g_{t},c_{t},h_{t}\}\), respectively. However, embedded computing platform (such as NPU) can only handle mapping cases with linear or nonlinear relationships such as \(Y=A+B\), \(Y=A\cdot B\) or \(Y=f(A)\). Therefore, we divide the LSTM layer into a set of regular layers, and its computation process is a combination of the fully connected layer (FC), the Eltwise layer (Eltwise Add, Eltwise Prod), and the nonlinear layer (Sigmoid, Tanh). That is, as shown in Equation (7), we strive to find a correspondence \(\varphi\) between the LSTM and the NPU in this paper, and split and map the LSTM into a certain type of Y supported by NPU. \[\varphi(LSTM)=\begin{pmatrix}\varphi(i_{t})\\ \varphi(f_{t})\\ \varphi(o_{t})\\ \varphi(g_{t})\\ \varphi(c_{t})\\ \varphi(h_{t})\end{pmatrix}=\begin{Bmatrix}A+B\\ A\cdot B\\ f(A)\end{B}=Y\] (7) (2) LSTM is mainly used to perform tasks in sequential data processing. However, the influence of sequential input data of LSTM has not been taken into account by quantization algorithms. Aiming at this bottleneck, the goal of the proposed quantization algorithm is to determine the quantization parameters of neural network models with the LSTM layer and deploy them on the NPU. The original numerical precision of neural network models is generally 32-bit floating-point numbers. The core computation of a 32-bit floating-point model can be expressed as \(Y=W\cdot X\). Since NPU only supports integer matrix multiplication, quantization converts the floating-point weight \(W\) and activation \(X\) of the neural network into a fixed-point integer value \(W_{q}\) and \(X_{q}\) by \(\phi(x)\), as shown in (8), where \(a\) and \(b\) are called quantization parameters.  
\[\phi(x)=\phi(round(ax+b))=\begin{pmatrix}\phi(W)\\ \phi(X)\end{pmatrix}=\begin{Bmatrix}W_{q}\\ X_{q}\end{Bmatrix} \tag{8}\]  
Therefore, the calculation of \(Y_{q}=W_{q}\cdot X_{q}\) can be performed on the NPU. However, it will cause \(Y_{q}\
eq Y\). Therefore, \(Y_{q}\) needs to be inversely quantized to \(Y\) by \(Y=\phi^{-1}(Y_{q})\). Then, finding the reasonable values of the quantization parameters \(a\) and \(b\) becomes the problem that needs to be solved in this paper.
## 7 Adaptive Single-Point and Multi-Point Equalization
### SISO/SIMO Approaches  
These techniques can be classified into time domain and frequency domain approaches.
###### Abstract  
The path-planning of unmanned delivery vehicles (UDVs) has garnered significant interest due to their extensive use in contactless delivery during severe epidemics and automated delivery of parcels in diverse scenarios. However, previous studies have focused on achieving the shortest path or time based on the comprehensive cost consumption in the transportation process and ignored the impact of different customers' different delivery time requirements in the actual interactive system. Hence, a path-planning model is presented to tackle the routing dilemma of UDVs in logistics. This new dilemma, called the unmanned delivery vehicle routing problem (UDVRP), considers the comprehensive transportation cost consumption of distribution vehicles and the customer satisfaction of each distribution point. Customer satisfaction is defined based on the delivery time requirements of different customers. This novel deep neural network model incorporates an attention mechanism and applies a method called point-graph joint embedding and dual decoders (PGDD) to solve the problem. The network's architecture, consisting of an encoder and two decoders, directly determines the path for unmanned delivery vehicles. In addition, the model is trained offline using a deep reinforcement-learning strategy in combination with pseudo-label learning. In this scenario, the output of one decoder serves as the label for another, overseeing its learning process to choose the most effective path. Experimental results demonstrate that PGDD reduces total costs by 8.73% on average compared to state-of-the-art algorithms in 100-node scenarios, with performance gains reaching 12.5% for larger-scale problems (400 nodes), validating its superiority in complex path-planning. Additionally, PGDD improves customer satisfaction by 15.2% and achieves a response time below 90 ms in real-world deployment tests. The experimental results demonstrate that the proposed method is superior to several state-of-the-art algorithms in solving the path-planning problem of unmanned distribution vehicles.  
unmanned delivery vehicle; path-planning; customer satisfaction; attention mechanism; pseudo-label +
Footnote †: journal: Applied sciences
# A Study on a New Independent Metering Valve for Hydraulic Boom Excavator
###### Abstract  
Nowadays, hydraulic excavators are an indispensable part of the construction industry; however, conventional hydraulic excavators consume a great deal of fossil fuel and release a large amount of pollution emissions into the environment. This causes many unwanted costs, therefore, effective solutions are required to solve the above-mentioned problems. In this paper, a new independent metering system is proposed to improve energy-saving and reduce costs of a conventional system. In detail, a directional valve is used to control movement and three electro-hydraulic poppet valves are integrated to adjust the flow rate at the inlet and outlet ports of the boom cylinder. In addition, a control strategy based on the coordination between the speed of the pump and the opening area of the spool valve is designed to improve the performance of the system. Specifically, the valves are controlled based on the strategy that the meter-in valve is opened fully to reduce throttling losses and that the meter-out valve is controlled to reduce leakage. The speed of the pump is adjusted according to the feedback position signal. To demonstrate the effectiveness of the new configuration, a real test bench of the boom system was built under laboratory conditions. From the experimental results, the new independent metering valve system not only works with a high tracking precision, but it also reduces energy consumption. Compared with a conventional independent metering system, the fuel economy of the proposed structure can achieve a reduction of approximately 6.5%.  
energy consumption; energy-saving; hydraulic boom system; independent metering valve