esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 1 Introduction
In [50; 51], the implementations of soft decision decoding of a Golay code fully based on channel observation values were presented, which can achieve a performance close to ML performance. However, the complexity is much higher compared with some decoders using hard decision results. Therefore, in this paper, we choose the Golay code and several BCH codes as examples to evaluate the proposed decoding algorithm and decoder architecture. Inspired by the permutation decoding algorithms [52; 53; 54], a new soft decision decoding algorithm, which utilizes a cyclic information set, is proposed in this paper. The proposed method is based on the cyclic property of cyclic block codes to generate the information set, which is suitable for hardware implementation, thereby the MRB is not needed. Furthermore, an efficient hardware implementation architecture is also designed to reduce the complexity of generating candidate codewords and selecting the most possible member. The decoders for several cyclic codes are implemented on the hardware platform and the performance of the proposed algorithm is verified. Experimental results demonstrate that the proposed decoding algorithm shows the error correction performance close to that of the ML decoding. It can achieve a good trade-off between complexity and performance, which renders this new decoding algorithm to be quite practical. The key parameters of the decoding algorithm are also optimized based on the hardware platform. The proposed soft decision decoding algorithm can make full use of channel information and provide a significant performance improvement compared with the algebraic decoding. In summary, our contributions lie in two points. First, the proposed algorithm employs the cyclic property of cyclic block codes, enabling the utilization of a uniform systematic generator matrix in re-encoding, which can eliminate the complex transformation to obtain the new generator matrices. Then, the hardware implementation is based on the systolic array architecture, which has low complexity and short critical paths. It offers the ability to arbitrarily set the number of information sequences and candidate codewords, supporting a flexible range of applications. The rest of this paper is organized as follows. Section 2 introduces the calculation method of the metric used in the proposed soft decision decoding of cyclic codes. The proposed soft decision decoding algorithm based on the cyclic information set is illustrated in Section 3. Section 4 presents the efficient hardware implementation architecture of the proposed soft decision decoding algorithm. The verification and parameter optimization of the algorithm based on the hardware platform are presented in Section 5. Finally, conclusions are drawn in Section 6.
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 3 Experimental Validation
### Orbital Maneuver Period Detection
#### 3.2.2 Results of Orbital Maneuver Period Detection for IGSO and MEO Satellites  
The differences in maneuver period detection between GEO and IGSO/MEO satellites are the station selected and the value of the empirical threshold. MEO satellites cannot be monitored all the time by a single station, and the strategy of selecting stations was adopted from [18]. In order to limit the length of the paper and reduce duplication of text, the results of the IGSO and MEO satellite orbit maneuver period detection and validation are given.  
In Figure 11, the start time factor series shows a sustained decreasing trend with values greater than 0 until 10:19:00. The start time of orbit maneuvering of C07 determined by the start time factor on 16 October 2017 is 10:19:00. The end time factor series shows a sustained growth trend with values greater than 0 until the end at 10:34:30. The end time of the orbital maneuver of C07 detected by the end time factor on 16 October 2017 is 10:34:30.  
In Figures 11 and 12, the period of orbit maneuvering for C07 can be determined by the start time and end time factors.  
In Figure 13, the start time factor series shows a sustained decreasing trend with values greater than 0 until 16:29:00. The start time of orbit maneuvering of G11 determined by the start time factor on 1 March 2018 is 16:29:00. The end time factor series shows a sustained growth trend with values  
Figure 11: Detection results of XMIS station for C07 on 16 October 2017; (**a**) start time factor of XMIS station for C07 on 16 October 2017; (**b**) end time factor of XMIS station for C07 on 16 October 2017.  
Figure 12: Time series of orbital semimajor axis of C07 in 2017: (**b**) detailed information of the red box (days 288–290) in (**a**).  
greater than 0 until the end at 17:00:30. The end time of the orbital maneuver of G11 detected by the end time factor on 1 March 2018 is 17:00:30.  
In Figures 13 and 14, the period of orbit maneuvering for G11 can be determined by the start time and end time factors.  
In summary, the orbit maneuvering periods of IGSO and MEO satellites can be detected by the proposed method in this study.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Clinical Trial Matching  
Clinical trials recruit at-risk individuals to test experimental drugs, but limited sample sizes and the heterogeneity of diseases hinder precise assessments of disease severity and progression. To address this challenge, multimodal data from large cohort and population studies offer promising solutions [88]. Researchers can leverage technologies like ML and DL to automatically identify patterns in vast datasets. For example, ML algorithms can analyze patient data to predict eligibility for trials with high accuracy. Deep learning models might process imaging data and genomic information to identify patients who fit complex criteria for specific studies [89]. Additionally, NLP enables the understanding and correlation of evidence in spoken or written language [90]. For instance, NLP can analyze clinical notes and trial descriptions to ensure that patient information aligns with trial requirements. This capability is exemplified by systems that automate eligibility surveillance, linking trial descriptions with clinical data in electronic health records (EHRs) to identify eligible patients accurately [91].  
One effective solution is automating clinical trial eligibility surveillance to address oversights in patient eligibility. An automated system utilizes NLP and ML/DL algorithms to detect eligible patients by linking trial descriptions to clinical data in EHRs [92]. For instance, systems like IBM Watson for Clinical Trial Matching use NLP to parse and interpret trial protocols and patient records, matching individuals to appropriate trials based on complex criteria [93]. Furthermore, Bayesian nonparametric models (BNMs) offer a flexible, nonparametric approach that allows for the utilization of infinite-dimensional parameter sets with a finite subset of limited parameters, streamlining the clustering process and reducing trial design time [73].
## 2 Experimental Setup and Materials
### Operation Algorithm  
The laser diode illuminates in the mode of rectangular pulses with a duration of 5 sec. Sets of photocurrent samples from all 8 photodiode channels are repeated at a frequency of 1 sec-1 and subjected to median filtering. Then, the median of dark current is subtracted from the median of the photocurrent during laser illumination. This photocurrent median difference is taken as the intensity value at the corresponding scattering angle. We have established a sensitive parameter--the ratio of the intensity at a certain angle of side scattering (72-108\({}^{\circ}\)) or forward scattering (0-36\({}^{\circ}\)) to the intensity at one of the backscattering angles (144\({}^{\circ}\), 162\({}^{\circ}\)), which is functionally related to the fat content. The controller calculates the decimal logarithm of this ratio and then, using the calibration curve, outputs the corresponding value of fat content.
# Data Science in the Management of Healthcare Organizations
## 4 Case Study
### Evaluating the Entropic State of the Healthcare Workers for the Best-Case Scenario
\[HOWS=\sqrt{1-ES^{2}} \tag{3}\]  
\[QoI=1-ES \tag{4}\]  
where \(ES\) stands for entropic state, which is defined as the sum of exergy and vagueness. The QoI is a multidimensional construct encompassing the economic, ecological, and social dimensions of sustainability in both qualitative (Table 2) and quantitative forms (Table 4). It helps determine the HCW's entropic conditions within the HO, illustrating how HCWs identify with and adapt to the organizational environment. This is measured based on the truth values of multi-valued logic, with a bandwidth ranging from 0 to 1 [40]. The answers of participant one to topic HOL-4 at month 0 will be used to illustrate the evaluation of HOWS and QoI. Thus, for this topic, one may have the following:  
\[HOWS_{HOL-4_{5-1}}=\sqrt{1-\left(0.05+0\right)^{2}}\approx 0.9987\]  
\[HOWS_{HOL-4_{1-5}}=\sqrt{1-\left(0.09+0\right)^{2}}\approx 0.9959\]  
\[QoI_{HOL-4_{5-1}}=1-\left(0.05+0\right)=0.95\]  
\[QoI_{HOL-4_{1-5}}=1-\left(0.09+0\right)=0.91\] \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Scale (5) to (I)**} & \multicolumn{4}{c}{**Scale (I) to (5)**} \\ \cline{2-11} \cline{2-11}  & **EX** & **VA** & **AN** & **HOWS** & **QoI** & **EX** & **VA** & **AN** & **HOWS** & **QoI** \\ \hline HOL-4\({}_{5-1}\) & 0.05 & 0 & 0.70 & 0.99 & 0.95 & HOL-4\({}_{1-5}\) & 0.09 & 0 & 0.16 & 0.99 & 0.91 \\ \hline HOWE-3\({}_{5-1}\) & 0.06 & 0 & 0.27 & 1.00 & 0.94 & HOWE-3\({}_{1-5}\) & 0.13 & 0 & 0.54 & 0.99 & 0.87 \\ \hline HOTW-3\({}_{5-1}\) & 0.33 & 0 & 0.34 & 0.94 & 0.67 & HOTW-3\({}_{1-5}\) & 0.01 & 0 & 0.32 & 1.00 & 0.99 \\ \hline Catch-all clause & 0.15 & 0 & 0.44 & 0.98 & 0.85 & Catch-all clause & 0.08 & 0 & 0.34 & 0.99 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values of exergy (EX), vagueness (VA), anergy (AN), healthcare organization work satisfaction (HOWS), and quality of information (QoI) for each topic under investigation (healthcare organization leadership (HOL-4), healthcare organization work engagement (HOWE-3), and healthcare organization teamwork (HOTW-3)) for participant one at month 0, in the best-case scenario, for both scales, where (5) to (I) ranges from the most to least positive response option, and (I) to (5) ranges from the least to most positive response option. The catch-all clause denotes an overall term that cover all individual terms. Figure 4: Evaluation of the healthcare organization work satisfaction (HOWS) based on the entropic state (ES), defined as the sum of exergy and vagueness. Similarly, using the framework outlined above, the values of exergy, vagueness, anergy, HOWS, and QoI for each participant can be calculated. For participant one, the values for each topic under investigation (i.e., HOL-4, HOWE-3, and HOTW-3) are presented in Table 4 for the BCS.
## 3 The Proposed AIM
Since NDZs, as shown in Table 1, are valid only when AIMs depend on the frequency trip settings, this paper presents a novel method using a correlation parameter without depending on the frequency trip settings. \[C_{p}[k]=\sum_{n=0}^{n=N}(cf[k-n]-cf[k-n-1])\times(freq[k-n]-freq[k-n-1]) \tag{7}\]  
where \(k\) is the present status and \(N\) is the number of line frequency for \(C_{p}\) calculation. As shown in Equation (7), this paper presents the correlation parameter \(C_{p}\) as an islanding detection indicator without depending on the frequency trip setting. The proposed method uses the fact that the frequency of the DG inverter voltage has a strong correlation with the current frequency deviation after islanding. As shown in Figure 5, when the grid is connected, the inverter voltage of the DG is dominated by the rigid grid voltage source and has a weak correlation with the proposed AFD frequency perturbation. Thus, \(C_{P}\) is maintained to be almost zero before \(t_{0}\). On the other hand, after islanding, \(C_{P}\) starts to increase after \(t_{0}\) and reaches \(C_{P,set}\), and islanding is prevented. Figure 5: Operational waveform of the proposed anti-islanding method. For the AFDPF method, the key design parameter of _cf_ is the positive feedback gain \(K_{p}\), which accelerates _cf_ deviation as frequency changes. \(K_{p}\) was designed and chosen by trial-and-error simulations for short frequency trip settings, 59.3 and 60.5 Hz from IEEE Std. 1547-2003. For IEEE Std. 1547-2018, the frequency trip settings, 56.5 and 62 Hz, are wider. Thus, if AFDPF method is used, \(K_{p}\) for IEEE Std. 1547-2018 should be larger than \(K_{p}\) for IEEE Std. 1547-2003, which increases the harmonic components. In this paper, \(K_{p}\) 0.02, which is designed for 59.3 and 60.5 Hz, is also used to show the feasibility for 56.5 and 62 Hz by using the proposed AIM. In summary, the proposed method does not depend on the frequency trip settings and is based on the correlation between the rate of change of the input current perturbation and the rate of change of the corresponding frequency. This means that islanding can be detected using the proposed correlation parameter with less _cf_ perturbation under IEEE Std. 1547-2018.
## 1 Introduction
studied the coherence of the SC generated by LG beams with different TC values in water [39] and found that spectral broadening is not related to the TC value; however, they did not study the SC induced by filamentation at different locations in water. Due to the different geometries (i.e., focal spot shape and size, etc.) of BG and LG beams, the filaments generated by BG and LG beams exhibit different shapes. As a result, the laser fluence, intensity as well as plasma density inside the filament generated by the two kinds of vortex beams differ much from each other, which further affects SC generation. In this paper, by analyzing the changes of the SC induced by the filamentation of BG and LG beams at different locations in a cuvette filled with water, we discuss the characteristics of the SC induced by these two vortex beams.
## 5 Discussion
### Comparison Results with State-of-the-Art Models  
To thoroughly assess the effectiveness of our ConvResBiGRU-SE model, we perform extensive comparisons with state-of-the-art methods using the UCI-HAR and WISDM datasets. These comparisons cover a range of architectural paradigms, from traditional deep learning models to modern hybrid architectures, offering a comprehensive evaluation of our model's capabilities. The evaluation includes accuracy and detailed performance metrics to ensure a fair and thorough comparison.  
We divide this analysis into two parts. First, we examine the results of the UCI-HAR dataset, a standard benchmark in the field. Then, we evaluate the performance of the WISDM dataset, which presents unique challenges due to its distinct data collection methods and activity patterns. Through these comparisons, we aim to highlight the advantages of our proposed architecture and its contributions to advancing human activity recognition.
# Hierarchical Modelling for CO\({}_{2}\) Variation Prediction for HVAC System Operation
## 6 Results and Discussion
### HMCOVP vs. FECOP vs. 1D CNN  
Table 4 summarises the performance of the three methods, our proposed methodology HMCOVP, FECOP, and 1D CNN, which are evaluated using **Thresh_MAE** and their training times. The best-performing combination configurations of hyper-parameters for the FECOP and 1D CNN are applied to the testing set. Similarly, the HMCOVP was implemented using the configurations outlined in Table 3. The HMCOVP significantly outperforms the other two methods in the prediction of the future CO2 variations in all  
Figure 5: MAE for Different Thresholds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**h&f** & **Ensemble** & **CNN Model** & **CNN FCL** & **Method** & **Thresh_MAE** \\ \hline h-5\_f\_5 & DT & Xception & [512] & local & 0.11 \\ \hline h-10\_f\_10 & DT & Resnet\_50 & [128; 64] & local & 0.6 \\ \hline h-15\_f\_15 & DT & Resnet\_152 & [256; 128] & local & 1.0 \\ \hline h-20\_f\_20 & DT & Resnet\_101 & [128; 64] & local & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best-Performing Combinations.  
the h&f combinations. The results prove the GAF images are better representatives of the time correlation aspects than the local features extracted by the 1D CNN to reflect these correlations. Additionally, the combination of image representations and numerical features present in the HMCOVP outperforms the rigorous feature engineering process of FECOP. While the HMCOVP incorporates some of the FECOP features, the distinctive quality of the features obtained from CNN models contributed to its superior performance. In some way, the HMCOVP combines the numeric features extracted in the FECOP and the time correlation found in the 1D CNN approach. These results prove the significance of hierarchical modelling.  
The training times highlight salient differences between the methods. The 1D CNN method training time decreases with the expansion of the time window. This trend is expected given that fewer data are available for training when this happens. On the other hand, this trend is reversed for the FECOP. This phenomenon can be attributed to the feature engineering step, which extracts lagged versions of each environmental feature. As such, more features are extracted with the expansion of the time window, contributing to an increase in training time. Lastly, the HMCOVP does not exhibit any trend with the changes in the time window. Different configurations with varying model complexities contribute to the non-uniformity of training times. Compared to the FECOP and 1D CNN, the HMCOVP takes significantly more time to train given that it requires training six DL models and one ensemble model. The training of individual models can be executed in parallel, which significantly reduces the total training time. Therefore, a tradeoff exists between training times and the accuracy of the developed models.
## 3 Results
### The Temporal Characteristic of SIUS
#### 3.1.3 Per Capita SIUS  
In Henan Province, the per capita SIUS grew from 0.56 t/cap in 2000 to 5.09 t/cap in 2023, reflecting an annual growth rate of 10%. As shown in Figure 4, the growth in per capita SIUS is positively correlated with the growth in per capita GDP. Currently, per capita SIUS in China is experiencing rapid growth. However, the per capita GDP of Henan Province is lower than China, its growth rate is slower than China.  
Compared to developed countries, the EU's per capita SIUS has gradually reached a saturation state, exceeding 10 t/cap [13]. This means that despite continued growth in per capita GDP, per capita SIUS remains stable. The USA reached saturation in 2004, approaching 11 t/cap [42], and its per capita GDP is much higher than China's. China's per capita GDP and SIUS lag behind those of developed countries. Similarly, Henan Province lags behind China as a whole. This gap indicates significant potential for future growth in per capita SIUS in Henan Province as the economy develops.
# An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem
## 4 Computational Experiments  
In this section, to evaluate the effectiveness of the proposed improved method, HAGA, four computational experiments are designed and implemented. The rectangle packing instances, which include zero-waste and non-zero-waste datasets, are used for testing. The first three benchmark instances as zero-waste are the \(J\) instances (\(J1-J2\)) proposed by [PERSON] [37], the \(C\) instances (\(C11-C73\)) proposed by [PERSON] et al. [18], and the \(N\) instances (\(N1-N13\)) proposed by [PERSON] et al. [19]. The scale and optimization difficulty of these three benchmark instances increase sequentially. The optimal solution for each instance is known beforehand, in which the total area of the given items is equivalent to the area of the sheet, resulting in a 100% filling rate. So, they all theoretically have a 100% filling rate but are not balanced dimensionally. However, considering that non-zero-waste problems often occur in real application, the non-zero-waste datasets, including 4 instances (cgcut1-cgcut3) given in Ref. [38], 13 instances (gcut1-gcut13) in Ref. [39], 12 instances (ngcut1-ngcut12) in Ref. [40], and 10 instances (beng1-beng10) in Ref. [41], are used for further testing.  
The SGA, AGA [26], AGA [29], and HAGA involved in the experiment are implemented in Python language programming and calculated on an Intel Core i5 CPU at 2.5 GHz with 8 GB memory. We tested the effects of SGA and AGA [26] for each individual fitness proposed by [PERSON], AGA [29] for the evolutionary process proposed by [PERSON], and the HAGA proposed in this paper. Then, a comparison of HAGA with some recent algorithms is provided. In order to balance the calculation accuracy and efficiency of the test algorithm, the number of iterations \(NIND\) is set to 2000, and the calculation result is the average of twenty operations.
###### Abstract  
Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space-air-ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2-3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of \(1\times 10^{-2}\) when the channel estimation error is \(-30\) dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2-3 dB to achieve a BER of \(1\times 10^{-3}\).  
U  
A _drones_  
10.390/drones8050180