esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Although rather simple, the method proposed has many advantages and applications. It provides a quick and reliable method to evaluate the quality of a measurement in real time, even when the shape of the expected signal is unknown. In fact, in the approximation of white noise, and of an unstructured pump-probe signal, it provides a first coarse estimate of the signal shape, and thus of the delay range that needs to be scanned. In post-analysis it provides an unbiased criterion to include each scan in an averaged measurement.  
This work was supported by the X-ray Free Electron Laser Priority Strategy Program of the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT); by JSPS and CNR under the Japan - Italy Research Cooperative Program; by the Grant-in-Aid for the Global COE Program 'the Next Generation of Physics, Spun from Universality and Emergence' from the MEXT; by the Grants-in-Aid (No. 20310055 and No. 21244062) from the Japan Society for the Promotion of Science (JSPS); by the JSPS KAKENHI Grant Number JP 16J02270; by the Swedish Research Council and the Swedish Foundation for Strategic Research; by the ERC Starting Research Grant UDYNI No. 307964; by the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641789 \"MEDEA\" (Molecular Electron Dynamics investigated by IntensE Fields and Attosecond Pulses); by the DOE-SC-BES under Award No. DE-SC0012376; and by the Italian Ministry of Education, Universities and Research (MIUR) (PRIN 2012 - NOXSS)  
K.U. conceived and designed the experiment; A.D., B.S., C.C., [PERSON], [PERSON], [PERSON], [PERSON], F.C., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], K.N., K.U., M.A., M.C.C., M.D., M.R., N.B., O.P., P.C., P.F., P.J., P.F., [PERSON], T.N., T.T., [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], Ts.T. analyzed the data; [PERSON], [PERSON], [PERSON], P.P. contributed analysis tools; [PERSON] and [PERSON] drafted the paper, which was completed in consultation with all authors.  
Figure 4: Schematic representation of the generation of a delay scan \(\{S_{i}\}\), as a function of pump-probe delay \(\{d_{i}\}\) from single-shot data \(a_{j}\). The green (red) dots symbolize shots that pass (fail) the filter conditions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EUV & Extreme Ultra Violet \\ FEL & Free Electron Laser \\ FERMI & Free Electron laser Radiation for Multidisciplinary Investigations \\  & (this acronym identifies the Free Electron Laser facility in Trieste, Italy) \\ FWHM & Full Width at Half Maximum \\ I0 & FEL energy per pulse \\ LINAC & Linear Particle Accelerator \\ \(SNR\) & Signal-to-noise Ratio \\ TOF & Time-of-flight \\ UV & Ultra Violet \\ \end{tabular}
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### General Staffing/Nurse Staffing
#### 4.1.1 Constraints  
Constraints for general hospital staff scheduling are very straightforward. Examples of the constraints are listed below:  
* Supply and Demand Constraints
* A schedule must be created every two weeks [19; 20].
* No specialties need to be covered on specific days [15].
* Workload/Availability Constraints
* Nurses must have 10 shifts [19].
* Pathologists must be assigned no more days than their FTE specifies [15].
* Pathologists must be scheduled to one of their subspecialties [15].
* Medical staff may take vacations at any time [3].
* Staff must not exceed seven calls within a 28 day planning period [3].
* Distribution Constraints
* Nurses cannot be schedules split weekends [19].
* Nurses must be allowed one weekend off [19].
* Pathologists cannot be scheduled two subspecialties in a day [15].
* Pathologists should be scheduled different subspecialties in adjacent weeks [15].
* Calls should be evenly spaced [3].
* Ergonomic Constraints
* A nurse cannot work more than six days in a row [19].
* All working periods must be a minimum of three days consecutively [19].
* A nurse may have an unlimited number of consecutive days off [19].
* Only certain subsets of subspecialties are allowable at the same time [15].
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### HRU-Net
#### 2.3.1 Multi-Feature Module Construction
This approach enhanced the network's ability to handle complex road scenarios in high-resolution remote sensing images, leading to more accurate road extraction and segmentation results.
## 3 CNN and Bi-LSTM Model with MODWT and ANFN Layers
### CNN and Bi-LSTM Model  
The CNN is a technique for extracting features of image or time-series input data through a convolutional filter. As shown in Equation (5), where X is the input data, W is the filter matrix, and b is the bias value, the window filter of the set size moves to perform a convolution, and a feature map is output with channels as the number of filters. The output feature map emphasizes nonlinearity through normalization and activation functions.  
\[\text{Feature map}=\left(\text{X}*\text{W}\right)_{\text{ij}}=\sum_{M=1}^{M} \sum_{N=1}^{N}X_{i+m-1,j+n-1}\text{W}_{m,n}+b \tag{5}\]  
We designed a hybrid convolutional autoencoder and a Bi-LSTM network to reconstruct the ECG signal. The first one-dimensional (1D) convolutional layer filtered the signal. Then, the convolutional autoencoder removed most of the high-frequency noise and captured the high-level patterns of the entire signal. Furthermore, the transposed 1D convolution layer was used to upsample 1D feature maps in the final stage of the CNN. The convolution operation downsampled the input by applying a sliding convolution filter to the input. By flattening the input and output, the convolution operation was computed by the convolution matrix and the bias vector that could be derived from the layer weights and biases. Similarly, the transposed convolution operation upsampled the input by applying a sliding convolution filter to the input.  
The Bi-LSTM is a model that learns the bidirectional dependencies of time-series data, and extended features can be obtained because the LSTM is applied in both directions. As shown in Equations (6)-(11), where W indicates the input weights, R is the recurrent weight, and b is the bias, this model consists of an oblivion gate that determines whether to exclude cell information from the previous time period and an input and output gate. The role of the Bi-LSTM layer in this paper is to further refine the signal details.  
\[\text{Input gate I}_{\text{t}}=\sigma_{g}(W_{i}x_{t}+R_{i}H_{t-1}+b_{i}) \tag{6}\]  
\[\text{Forget gate F}_{\text{t}}=\sigma_{g}\left(W_{f}x_{t}+R_{f}H_{t-1}+b_{f}\right) \tag{7}\]  
\[\text{Cell state G}_{\text{t}}=\sigma_{c}\left(W_{g}x_{t}+R_{g}H_{t-1}+b_{g}\right) \tag{8}\]  
\[\text{Output gate O}_{\text{t}}=\sigma_{g}\left(W_{o}x_{t}+R_{o}H_{t-1}+b_{o}\right) \tag{9}\]  
\[\text{C}_{\text{t}}=F_{t}\text{:}C_{t-1}+I_{t}\text{:}G_{t} \tag{10}\]  
\[\text{H}_{\text{t}}=O_{t}\cdot\sigma_{c}(\text{C}_{t}) \tag{11}\]  
The Bi-LSTM should specify the number of hidden units. Eight units are used in this paper. The number of hidden units corresponds to the amount of information that the layer maintains between each time step. The hidden state can contain information from all previous time steps, regardless of the sequence length. If the length of the hidden units is too long, the layer may overfit the training data. The hidden state does not limit the number of time steps that the layer processes in one iteration.
# A Survey on Hardware Accelerators for Large Language Models
## 7 Quantitative Comparison
### Scalability and Flexibility  
One of the most important issues in the domain of LLM platforms involves scalability and flexibility, especially in the face of a growing model size and complexity. Each technology faces unique scalability and flexibility limitations when adapting to the demands of modern AI workloads. GPUs are versatile and widely used for LLMs; advanced interconnect technologies such as NVLink and NVSwitch allow multiple GPUs to be interconnected, facilitating easy scalability. However, their scalability is constrained by memory bandwidth and interconnect limitations. As model sizes grow into trillions of parameters, fitting models across multiple GPUs requires advanced techniques like tensor parallelism, which adds complexity to the system design.  
FPGAs offer flexibility through re-programmability, making them suitable for customizing operations and optimizing energy efficiency. However, their scalability is limited  
Figure 1: Speedup versus energy efficiency.  
by lower computational density compared to GPUs and ASICs. Additionally, the time and expertise required to program FPGAs for large-scale LLMs reduce their practicality for rapidly evolving AI research.  
ASICs, such as TPUs, provide unmatched efficiency and performance for specific tasks, making them ideal for large-scale training and inference. However, their fixed-function design lacks flexibility, making it challenging to adapt to new model architectures or unforeseen workloads. The high development costs and lead times for ASIC production further hinder their ability to keep pace with the rapidly advancing field of LLMs.  
In summary, while GPUs provide the most flexibility, they struggle with energy efficiency. FPGAs excel in adaptability for new models but face limitations in performance and ease of use. ASICs offer the best scalability for predefined tasks but lack the flexibility needed to address the ever-evolving landscape of LLM architectures.
## 6 Conclusions  
In order to shorten the convergence time of PPP, we put forward the idea of the LEO-enhanced GNSS system, which is called LeGNSS. The LEO constellation can transmit GNSS signals to enhance GNSS service. We investigated the benefits of LeGNSS compared with the current GPS, BDS, and Iridium constellation itself. The conclusions are summarized as follows:  
1. The LEO constellation can improve the availability of the current GNSS system, especially in the polar areas, since LEO are usually polar satellites. With 66 Iridium satellites, it is difficult to provide LEO-only positioning service since there are not enough satellites at medium and low latitudes.
2. LEO satellites are moving quite fast, flying overhead in minutes compared to the GNSS satellites in hours, which gives rise to improving the geometric condition of ground stations, resulting in the fast convergence time of PPP.
3. The convergence time of combined G/C/L can be shortened to 5 min in most of the areas of the world with 5 s data while, as far as we are concerned, it is impossible with GNSS to achieve such quick convergence even with 1 s data.  
We emphasize that the achievements of this LeGNSS results are with just 66 LEO satellites. It is very promising that the performance will be much better if more LEO satellites, especially in inclined orbits, are involved in the LeGNSS system.  
All authors contributed to the writing of this manuscript. [PERSON], [PERSON] and [PERSON] conceived the LEO enhanced GNSS system (LeGNSS). [PERSON], [PERSON] and [PERSON] processed the data and investigated the results. [PERSON], [PERSON], [PERSON] and [PERSON] validated the data. This research was funded by National Natural Science Funds of China (41622401, 41574023), Scientific and Technological Innovation Plan from Shanghai Science and Technology Committee (17511109501, 17 DZ1100802, and 17 DZ1100902), and National Key Research and Development Program of China (2016 YFB0501802). The first author is financially supported by the China Scholarship Council (CSC) for his study at the German Research Centre for Geosciences (GFZ).  
The authors declare no conflict of interest.
## 6 Conclusions  
The paper investigated the behaviour of a four-span continuous twin-I girder bridge using prestressed precast concrete deck due to concrete creep. Simplified two-span I girder models are analysed to find the effect on the prestressed compression and force transfer between concrete deck and steel girders. Major findings are summarized as follows:  
1. For the continuous twin-I girder bridge, the prestressed tendons introduce compressive stress in the concrete deck and the compressive stress under constructed condition is big and it can overcome the tensile stress induced by shrinkage and live load.
2. In the hogging regions, the prestressed stresses in the concrete deck are reduced due to the concrete creep effect and the decrease is up to 50% of the initial prestressing stress.
3. The stresses in the steel girders in the hogging regions vary big, especially for girder flange in tension and the changes are due to force transfer from compressive stress in concrete deck.
4. The stresses in the tendons have almost no change and the prestressed force transfers from concrete deck to steel girders in the hogging regions.
5. The steel girder stiffness has no effect on the prestressing stress loss in the concrete deck.
6. The concrete deck, initial prestressing stress and additional imposed load have an effect on the initial prestressing stress loss in the concrete deck due to concrete creep.
7. The prestressing stress loss in the concrete due to creep mostly is over 50% and it is transferred to steel girders to change the stress distribution of composite section in the hogging regions.  
Figure 12: Stress variation in the concrete deck in the hogging region due to different effects for different additional imposed load.  
**Funding:** This research was funded by National Key R&D Program of China, grant number 2018 YFC0809606, National Natural Science Foundation of China, grant number 51608378 and Science and Technology Commission of Shanghai Municipality (18 DZ1201203, 17 DZ1204300) and the Fundamental Research Funds for the Central Universities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Cluster Head Selection
### Assisted Schemes
#### 2.2.2.1 Balanced Cluster  
[PERSON], _et al_. in [51] proposed a new method of CH selection according to the number of nodes in the cluster and the number of CHs within the transmission range of the nodes. The main goal of the presented algorithm is to fairly distribute the CHs and balance the cluster sizes. Like other cluster based algorithms, selected CHs broadcast advertisement messages to announce their selection during the setup phase and then each cluster area is divided into several _sub-regions_ using the number of received advertisement messages (_J_). In other words, \(J\) is the number of CHs within the transmission range of a node and the algorithm advocates the idea that the nodes with the same value of \(J\) are located in the same sub-regions. During the steady-state phase, each node transmits the sensed data including \(J\) and the index of its remaining energy to its CH. The current CH selects the next-round CH towards balancing the cluster size by comparing the number of cluster members with the average size of the cluster. If the cluster size is larger than the average size of clusters, the current CH selects the subsequent CH amongst the nodes having the most remaining energy and with \(J\) value equals to one. In this way, the number of cluster members in large clusters reduces for the next round. From the perspective of the nodes with \(J\) larger than one, which means the nodes located in the sub-regions closer to the boundaries of the cluster, and the next round's CH moves away from them. Thus, they have greater chance for the subsequent round to join neighbouring clusters. This leads to a decrease in the cluster size. On the other hand, if the cluster size is smaller than the average size of clusters, the current CH selects the next CH from the same sub-region where it is located. This is because of the fact that there are neighbouring CHs having more number of cluster members and according to the previous case, the next CHs in the neighbouring clusters will be selected in a way that the nodes in the non-exclusive sub-regions of the larger cluster will join the smaller cluster for the next round. In the example shown in Figure 4, CH1 is located in the area of \(J\!=\!2\) and its cluster size is larger than other two clusters. Therefore, for the next round CH1 selects node \(A\) from area of \(J\!=\!1\) to move the cluster boundaries towards making the cluster size smaller, while CH2 and CH3 select nodes B and C from the same \(J\) areas, respectively. In this way, without imposing extra traffic or complex computations on the network, the algorithm balances the size of clusters and their traffic loads, and thus increases the network lifetime.
###### Abstract  
Precise identification of water bodies in agricultural watersheds is crucial for irrigation, water resource management, and flood disaster prevention. However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. Additionally, a feature-based knowledge distillation strategy is employed to further improve the representational capacity of the lightweight model. Experimental results show that LKF-DCANet achieves an Intersection over Union (IoU) of 85.95% with only 0.22 M parameters on a public dataset. When transferred to a self-constructed UAV dataset, it achieves an IoU of 96.28%, demonstrating strong generalization ability. All experiments are conducted on RGB optical imagery, confirming that LKF-DCANet offers an efficient and highly versatile solution for water body segmentation in precision agriculture.  
remote sensing; lightweight network; semantic segmentation; learnable Kalman filter; deformable convolution; attention mechanism; transfer learning +
Footnote †: journal: Applied sciences
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 6 Analysis of Simulation Results
### Iterative Analysis of GAs  
According to the parameters of the setting algorithm of the fuzzy PID control system structure, the population size and the iteration number were set to 30 and 100, respectively. The crossover probability and mutation probability were 0.9 and 0.1, respectively. \(K_{p}=100\), \(K_{i}=100\), and \(K_{d}=10\) at the initial stage.  
Figure 9 shows that it converged at the 52 nd iteration, while traditional calculations often require thousands of iterations. The traditional method of calculation mentioned here is trial and error. Trial and error is a method of setting parameters empirically. In the closed-loop control system, the adjustment was carried out in the order of \(K_{p}\), \(K_{i}\) and \(K_{d}\). While adjusting the parameters, the process was observed until the requirements were  
Figure 8: Blocks of the fuzzy self-tuning PID control algorithm.  
met. The optimal objective function value and the optimal matching parameter value of the fuzzy PID controller could be obtained at the end of the iteration. The parameter values of the fuzzy PID controller obtained by the GA were substituted into the simulation model of the servo control system. The experimental analysis of step characteristics, sinusoidal characteristics, and joint trajectory tracking was carried out.
## 2 State of the Art
### Smart--Intelligent PSS  
The definition of a Smart PSS is as follows: _\"an IT-driven value coreation business strategy involving various stakeholders as players, intelligent systems as infrastructure, smart, connected products as media and tools, and their generated e-services as key values delivered that continuously strives to meet individual customer needs in a sustainable manner.\"_[37] Stakeholders, Service and Performance Checkout System (SCPs) and e-services, and intelligent systems are key elements, according to the definition, while value co-creation with sustainable concerns is the business value proposition. Stakeholders are participants in the value co-creation process who are divided into three categories: users, service providers, and manufacturers and vendors.  
* Users: The people who demand or consume the value.
* Service providers: The companies/individuals who are responsible for designing, sharing, and/or performing maintenance of the offered services are designated as service providers.
* Manufacturers and vendors: All parties involved in the SCP manufacturing process are represented by manufacturers and vendors.  
Additionally, the authors in [37] have presented the Smart PSS solution design, which is a complex process, with three general hybrid concerns outlined, as illustrated in Figure 1. More specifically, the design and development of a Smart PSS requires the co-existence of three entities, namely the intelligent system, the stakeholders, and the IT providers. The conjunction of the above-mentioned entities in pairs, starting from the top in a clockwise fashion, leads to the \"hybrid value\" creation, which corresponds to the added value offered by PSSs and the extension of the product lifecycles, as a result of the integration of intangible services to the tangible/physical products. Moving on, the \"hybrid design\", corresponds to the creation of new services, thus new products, and by extension new a PSS as a result of the cooperation of the stakeholders with the PSS engineers/designers. The two parties have to collaborate closely in order to address existing challenges. Finally, \"hybrid intelligence\" corresponds to the utilization of data and information acquisition, processing,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Industry 4.0 Technology** & **PSS Approach** & **References** \\ \hline IIoT and smart sensors & Smarter products & [28] \\ BDA and AI & Data-driven services & [29] \\ Blockchain & Smarter service & [30; 31] \\ AR & Smarter service design and delivery, Training, Maintenance & [32] \\ Cloud Computing & Flexible computing resources for digital services & [33] \\ AM & Product customization and adaptation & [34] \\ Energy Services & Servitization, environmental impact, resource planning, Energy & [35; 36] \\ Demand Management, scheduling & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation of key Industry 4.0 and their contributions to servitization.  
and transmission through a wide variety of smart and intelligent tools, technologies, and techniques.