esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The circuit-partitioning algorithm proposed in this study demonstrates significant advantages in circuit-partitioning tasks, particularly excelling in minimizing cut edges. Compared to the widely used partitioning tools METIS and KL algorithms, the proposed algorithm shows substantial improvements in minimizing cut edges across multiple circuit instances. Specifically, in terms of the minimum cut edges, the proposed algorithm generally performs better than METIS in the cases of k = 4 and k = 8, with an average of 0.91 times the cut edges of METIS. In the case of k = 16, the proposed algorithm significantly outperforms KL in cut edges, with the average cut edges being 0.86 times that of KL. Additionally, the minimum connection numbers of the proposed algorithm are 0.65 times the minimum of METIS, with the highest being 0.98 times. In terms of average connection numbers, the proposed algorithm performs better than METIS and KL, with an average of 0.92 times that of METIS and 0.84 times that of KL. Despite the significant optimization performance, the proposed algorithm also shows a certain level of competitiveness in execution time, especially when handling medium- and small-scale circuits.  
In conclusion, the tabu search algorithm shows considerable potential in the field of circuit partitioning, particularly in optimizing cut edges and connection numbers. It is suitable for optimizing medium- and small-scale circuits. Future research should focus on improving the algorithm's structure and strategies to enhance its efficiency and stability, thereby improving its applicability and reliability in large-scale circuit-partitioning tasks.  
Conceptualization, H.H. and C.L.; methodology, L.Y. and H.H.; software, H.H.; validation, L.Y. and H.H.; formal analysis, L.Y.; investigation, L.Y. and C.L.; resources, L.Y.; data curation, H.H.; writing--original draft preparation, L.Y. and H.H.; writing--review and editing, L.Y., H.H. and C.L.; visualization, H.H.; supervision, L.Y. and C.L.; project administration, L.Y.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Provincial Natural Science Foundation of Hunan: 2021 JJ30877.  
Not applicable.  
The data can be obtained from [[https://github.com/kityAB2](https://github.com/kityAB2)]([https://github.com/kityAB2](https://github.com/kityAB2)) /CircuitPartitioningData, accessed on 5 February 2025.  
The authors declare no conflicts of interest.
## 1 Introduction
### Related Works/Existing DS System Methodologies  
After conducting a literature review on the scientific field of the present study concerning methodologies and techniques in DS systems, an overview of the most prominent studies is carried out. The key characteristics of various DSSs and their areas of application were identified and are presented in this state-of-the-art sub-section. For instance, the authors of [29] describe a method using model-based systems engineering to develop an unmanned aerial system digital twin, focusing on route optimization in military contexts with multiple attribute utility theory to improve decision making and reduce human error. Similarly, Ref. [30] presents a system for selecting surface units for search and rescue operations at sea using an Automatic Identification System (AIS) and multi-criteria decision analysis. Extending these developments, Ref. [14] proposes a DDS for Medical Evacuation (MEDEVAC) in military operations and emergencies, aimed at enhancing the efficiency of medical personnel in triaging and evacuating casualties from a region. Furthermore, Ref. [31] presents the F-35 system, designed for pilots to execute advanced tactical missions. The DSS of this aircraft equips pilots with enhanced situational awareness and decision aids, facilitating critical and timely decision making. Additionally, Ref. [32] proposes a modular control solution integrated with the DSS to effectively plan, execute, and monitor intricate missions involving multiple drones. Complementing these approaches, Ref. [33] assesses the efficacy of neural networks in an Electrocardiogram (ECG) DSS for categorizing heartbeats as normal or abnormal, thus supporting medical professionals to make an assessment of the patient's state and helping them proceed with the appropriate therapy. In a similar vein, the authors of [34] present a web-based DSS to efficiently predict, plan, and respond to fire and flood events. This system utilizes Earth observation data and real-time weather information. Moreover, the authors of [35] present a DSS design to improve police patrolling efficiency by integrating predictive policing capabilities with patrol districting models. The system aims to optimize the allocation of police resources by predicting crime risks and efficiently distributing police officers across patrol areas. The authors of [36] discuss the development and successful implementation of an advanced smart support system for operators of remotely operated vehicles, particularly those designed for navigating long-distance routes synchronously, accurately, and safely. Lastly, the study presented in [37] focuses on the development and implementation of a DSS designed for a low-voltage grid that integrates renewable energy sources, specifically photovoltaic panels and wind turbines. This system is aimed at proposing decisions for achieving an energy balance within a pilot microgrid, thereby reducing reliance on external power networks.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Backscatter Communication  
We evaluate the communication reliability by first comparing the communication driven by the stable and accurate external crystal oscillator with the low-power internal oscillator. Afterward, we look at the error rates of the backscatter communication in different channels. The setup for the following evaluations is a 3.5 m long beam with several attached piezo transducers. The dimensions and channels used are shown in Figure 13. The transducers are disk-shaped with a diameter of 1 cm and a thickness of 2 mm. Their resonance frequency is around 200 kHz. All transducers were attached directly to the metal using epoxy resin.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet: Training and Testing  
The training and testing phases of the MIMONet detector involve generating training and testing data, processing the received signals through the customized FFNN-based MIMONet detection model, and evaluating the performance of the detector using SER.
## 1 Introduction
### Novelty and Key Contributions  
This work introduces the construction of an Early Fusion model, based on existing multimodal analyses of both audio and video data, in addition to the existing Late Fusion model. An additional comparative evaluation is then performed, which assesses a new method by comparing the performance and accuracy of both Early and Late Fusion meta-classifiers for the prognostication of episodes of aggressive behavior in patients with dementia. The main contributions of this study are as follows:  
**Potential Benefits From Previous Work:**  
* **Multimodal Aggression Prediction:** The breakthrough of a multimodal solution was presented that combines earlier-developed visual-based aggression analysis with new audio-based aggression analysis, in order to produce a system with consistent results under real-world conditions with improved noise removal;
* **Advanced Audio Processing Techniques:** In order to obtain valuable audio features from audio files, advanced audio processing methods were utilized, such as the MFCCs, frequency filtering, and speech prosody, which improved model classification accuracy;
* **Advanced Visual Processing Techniques:** In order to successfully extract video features from frame files, advanced techniques of video processing were applied, such as landmark extraction, pose analysis, artifact, and noise filtering;
* **Late Fusion Meta-Classifier:** A Late Fusion meta-classifier was employed to merge the multi-layered model predictions into one data frame of both audio and visual aggressive predictions;
* **Early Fusion Meta-Classifier:** An Early Fusion meta-classifier was developed for the purpose of merging the multi-layered model extracted features into a unified data frame consisting of both audio and visual aggressive cues;
* **Clinical Applicability and Proof of Concept:** Results demonstrate the advantages and disadvantages of each fusion model, with the Late Fusion model outperforming the Early Fusion model in most metrics, with an improved detection of early aggression behaviors, offering proof-of-concept validity for facilitation of clinical practice in the future.  
**New Insights:**
* Results of this multilayer approach verify that when predictions from audio-based aggression are combined with visual prediction, the fusion generated meta-classifiers enhance aggression prediction with the Late Fusion model, outperforming the Early Fusion model in most metrics, providing the potential for clinical application;
* Correlations of vocal frequencies and physical activity showcase their role in the model improvement.  
**Practical Implications:**
* This multimodal system could augment real-world clinical systems with a real-time alert system, enabling timely interventions;
* Due to its enhanced accuracy, this technique has the potential for integration into future clinical circumstances.
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 3 Model Development
### Radial Basis Function Regression (RBFr)  
Radial basis function network (RBFr) has a unique structure, as explained in Figure 9. Equation (5) illustrates the basis function of this network [55]. For solving the issue, radial basis function regression can be used by fitting a collection of kernels for the dataset. In addition, this method attends the position of noisy samples.  
\[O_{i}=K\left(\frac{\left\|x-x_{i}\right\|}{\tau_{i}^{2}}\right) \tag{5}\]  
\(O_{i}\) stands for the output of the neuron, and \(x_{i}\) shows the center of kernel \(K\). in addition, the term \(\tau_{i}\) stands for the width of the \(i^{th}\) RBF unit.  
The model of RBFr utilizes a bath algorithm for predicting the number of developed kernels. This prediction is performed by bath algorithms. The specific function of expectation that is utilized in RBFR model is as below:  
\[F(x)=\sum_{i=1}^{z}k(\left\|x-x_{i}\right\|)\ \varphi_{i}. \tag{6}\]  
Figure 9: Typical architecture of radial basis function regression (RBFr) neural network.  
\(||x||\) stands to symbolize the Euclidean norm on \(x\). \(\{k(||x-x_{i}||)\,i=1,2,\ldots,z\}\) stands as a group of \(z\) non-linear along with constant RBFr. In addition, the term \(\varphi_{i}\) shows the coefficient of regression.
## 5 Conclusions  
WUE represents a measure of trade-offs between carbon and water fluxes at the ecosystem level. Our study enriches the scarce literature on the long-term inter-annual and seasonal variability of GPP, ET, and WUE in a plantation forest ecosystem. The absence of distinct seasonal and inter-annual patterns in WUE for a mature stand (age > 10) under normal climate conditions, confirms that carbon and water cycles are tightly coupled. Although the processes involved in CO\({}_{2}\) and H\({}_{2}\)O fluxes varied individually, they converged towards a relatively stable water use efficiency as the pine plantation matured. A stable WUE suggests that the pine ecosystem productivity can be readily predicted from ET and vice versa.  
The historic extreme drought events in 2007/2008 affected little of pine plantation WUE. Pine plantations on the coastal plain were effective in coping with low soil water availability for carbon gain under periodical drought. A different sensitivity of ET and GPP to drought should be properly considered in developing ecosystem models. Our study presents aspects of improving ecosystem models to understand better the trade-off between the carbon absorbed and water released in vegetation-climate-hydrology feedback loops.  
Conceptualization, M.A. and G.S.; methodology, M.A. and G.S.; software, M.A., P.P. and B.M.; validation, G.S., P.P. and B.M.; formal analysis, M.A. and G.S.; investigation, M.G., P.P., K.J.M. and B.M.; resources, A.N. and J.K.; data curation, M.A., P.P., K.J.M. and B.M.; writing-original draft preparation, M.A. and G.S.; writing-review and editing, G.S., J.-C.D., S.M., M.G. and J.K.; visualization, M.A. and G.S.; supervision, A.N. and J.K.; project administration, A.N., G.S., S.M. and J.K.; funding acquisition, A.N., J.K., S.M. and G.S. All authors have read and agreed to the published version of the manuscript.  
Primary funding was provided by the USDA NIFA (Multi-agency A.5 Carbon Cycle Science Program) award 2014-67003-22068. Additional funding was provided by the DOE NICCR award 08-SC-NICCR-1072, the USDA Forest Service award 13-JV-11330110-081, and the DOE LBNL award DE-AC02-05 CH11231. We are grateful to Weyerhaeuser NR Company for the long-term access to the managed lobloly pine plantations and others in-kind support.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Principles of the Method
### Filtering Diffusion Technology  
Image filtering is a digital image processing technology, which is mainly used to eliminate image noise or extract image features. The method performs convolution operation on a 2D image block and a 2D matrix, named kernel. To be specific, the additions of all the multiplications of the kernel pixels and image pixels are used as the value of the current pixel. The current pixel value corresponds to the center element of kernel, and the other elements correspond to adjacent pixels. \(R\) is the selected image block, the central pixel \(R_{x,y}\) is the pixel to be processed, and M is the kernel. Assuming the kernel size is \((2n+1)\times(2n+1)\), the calculation of image filtering can be defined as  
\[R^{\prime}_{x,y}\ =\ \sum_{i\ =\ -n}^{n}\sum_{j\ =\ -n}^{n}M_{i+n+1,j+n+1}R_{x+i,y+j}. \tag{5}\]  
Selecting a suitable kernel to filter can emphasize some features or remove unwanted parts in the image. However, we can also use a randomly generated kernel to diffuse the image for encryption. Since the slight change of the current pixel would affect the value of each subsequent pixel, it has a good diffusion effect.  
To make the filtering invertible, we set the center pixel of the kernel as one and set other pixels as integers. To get a better diffusion effect, the lower-right corner of the kernel is set to correspond to the current pixel, namely M\({}_{2n+1,2n+1}\) = 1. The current pixel \(R_{x,y}\) is processed by the upper and left adjacent pixels. Figure 2 shows the filtering diffusion process.  
Figure 2: The filtering diffusion process.  
According to filtering diffusion theory [43; 44], the calculation of filtering diffusion can be expressed as  
\[R^{\prime}_{x,y}\ =\left(\sum_{i=-n}^{n}\sum_{j=-n}^{n}M_{i+n+1,j+n+1}R_{x+i,y+j} \right)\text{mod}F, \tag{6}\]  
\[R_{x,y}\ =\left(R^{\prime}_{x,y}-\sum_{i,j\in[-n,\ldots,n]\cap(i,j)\
eq(0,0)}M_{i +n+1,j+n+1}R_{x+i,y+j}\right)\text{mod}F. \tag{7}\]  
Before filtering the current pixel, the upper and left pixels should be processed. When doing inverse filtering, it is also required that the upper and left adjacent pixels have not been restored. Therefore, the processing order of inverse filtering should be reversed from that of the forward operation.
## 2 Materials and Methods
### Analyzing Vegetation Indices
#### 2.3.2 Mean Monthly and Annual VIs and Their Variability  
The mean monthly VI data based on the 2000 to 2019 time series was used to calculate the mean monthly VIs [50]. Equation (1) was used to calculate every pixel of 20 years of arithmetic mean \(\overline{\text{VI}}_{\text{m}}\), with n and \(\text{VI}_{\text{m}}\) represent the number of years and the monthly VIs value, respectively.  
\[\overline{\text{VI}}_{\text{m}}=\frac{1}{\text{n}}\sum_{1}^{\text{n}}(\text{ VI}_{\text{m}}) \tag{1}\]  
The mean annual calculations of the VIs were used to calculate the mean annual VI (\(\overline{\text{VI}}_{\text{a}}\)). To adequately represent vegetation growth, the VIs for the budding season (January to December) were used to calculate the annual mean of VIs and their variation. The deviation (\(\text{DVI}_{\text{I}}\)) from the years 2000 to 2019, mean annual VIs at a given spatial location for a certain year \(i\) was calculated using the following equation.  
\[\text{DVI}_{\text{i}}=\text{VI}_{\text{i}}-\overline{\text{VI}}_{\text{a}} \tag{2}\]  
Furthermore, the relative annual VI deviation (\(\text{rDVI}_{\text{i}}\)) was estimated using the following equation, which defines the deviation as a percentage of the mean annual VIs:  
\[\text{rDVI}_{\text{i}}=\frac{\text{DVI}_{\text{i}}\times 100}{\overline{\text{VI}}_{ \text{a}}} \tag{3}\]  
The annual mean of VIs variability (\(\overline{\text{VVI}}\)) was estimated with the relative annual VI deviation (\(\text{rDVI}_{\text{i}}\)), with n being the number of years using the following equation.  
\[\overline{\text{VVI}}=\frac{1}{\text{n}}\sum_{1}^{\text{n}}\lvert\text{rDVI}_ {\text{i}}\rvert \tag{4}\]
# Intent Classification and Slot Filling Model for In-Vehicle Services in Korean
## Experiments
### Results and Analysis  
#### 4.4.1 Main Results  
We conduct experiments of intent classification and slot filling on the in-vehicle services dataset. To show the efficacy of multi-tasking in our model, we also include experiments on the results from single training. The single training model only trains one task with the dataset. In Table 4, ICO denotes the model that trains with intent classification only, and SFO indicates the model that is learned with slot-filling only. According to the experimental result, it is found that multitask-learning improved intent accuracy, slot accuracy, and value prediction at the same time. Especially, the score of value prediction is improved while indicating that the learning intent and slot simultaneously affect each other task performance in a positive way.  
It is also revealed that the mBERT's value prediction is better than that of other Korean-based language models. We assume that the performance difference in value prediction is due to the uniqueness of the in-vehicle service dataset. As mBERT is trained with diverse, multilingual languages, it is more robust than other Korean-based language models on slot value prediction since a substantial number of the slots' values include foreign words, including English.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Intent Acc.**} & **Slot Acc.** & **Cat Acc.** & **Non-Cat EM** & **Non-Cat F1** & **JGA** \\ \hline \multirow{3}{*}{ICO} & KoBERT & 98.50 & - & - & - & - & - \\  & KLUE-RoBERTa & 97.14 & - & - & - & - & - \\  & mBERT & 96.70 & - & - & - & - & - \\ \hline \multirow{3}{*}{SFO} & KoBERT & - & 99.46 & 51.54 & 85.51 & 94.68 & - \\  & KLUE-RoBERTa & - & 98.47 & 51.57 & 54.00 & 86.88 & - \\  & mBERT & - & 98.45 & 51.57 & 78.94 & 91.27 & - \\ \hline \multirow{3}{*}{ICO + SFO} & KoBERT & 98.90 & **99.70** & 93.71 & 86.67 & 95.53 & 86.55 \\  & KLUE-RoBERTa & **98.98** & 99.45 & 94.29 & 76.41 & 92.84 & 86.42 \\ \cline{1-1}  & mBERT & 98.38 & 99.52 & **94.51** & **89.97** & **95.83** & **90.74** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main Experimental Results. ICO denotes the model that trains with intent classification only, and SFO indicates the model that is learned with slot-filling only. Acc. is the abbreviation of accuracy.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 3 Results
### Evaluation of Combined Uncertainties  
Although we heavily altered the samples from CropScape and corrected them using multiple approaches, we acknowledged the propagation of errors into our analysis. Considering the CropScape errors at the state-level for California (8.7%) and Texas (22.1%) [70], and supposing we did not altered the samples, then the combined errors for OAs are 22.2% and 16.8% for single-band DTW30 and multi-band DTW30 for TA1, and 24.6% and 25.4% for single-band DTW45 and multi-band DTW30 for TA2.  
For TA1 in California, most of our UAs and PAs errors were lower than those from state-level CropScape (Table 8). We had significant lower PAs errors for vegetables, fallow, sugarbeets, other hay, and wheat, while we also had lower UAs errors for alfalfa, onions, sod/grass seed, vegetables, and water. Our UAs errors for sugarbeets and PAs errors for alfalfa and water were higher than those from CropScape (Table 8).  
Figure 11: DTW dissimilarity values scatter plots computed for each class for the single-band DTW45 of Texas, with R\({}^{2}\) values in the upper left of the diagonal. Classes analyzed are corn, cotton, winter wheat1, winter wheat2, alfalfa, fallow/idle cropland, grass/pasture, and double crop.  
For TA2 in Texas, we got lower UAs errors than CropScape state-level for corn, winter wheat, grass, and double crop and lower PAs errors for all classes, except for cotton from single-band approach (Table 9). We had slightly higher UAs errors for fallow and cotton from the multi-band approach and higher PAs errors for cotton from the single-band DTW approach (Table 9).