esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 3 System Model
### Device Energy Consumption Evaluation  
Mobile devices have limited battery power for the vital operation of data packet transmission. Once the battery of the device is exhausted, the network will be dead. Therefore, battery resources of relay devices play a vital role in the establishment of a reliable and optimal D2D communication over IoT-based 5G networks. The energy consumption of relay devices is evaluated by exploiting the energy consumption model of wireless communication [35; 36]. The battery of relay devices is mainly consumed during the transmitting and receiving process for data packets transmitted in the network. Transmission distance of signal (\(\lambda\)) from the transmitter (\(Tx\)) to the receiver (\(Rx\)) device is evaluated using the free space model (\(\lambda^{2}\) power loss) and multipath fading model (\(\lambda^{4}\) power loss). Consequently, a free space model is applied when the \(\lambda\) value is smaller than the cut-off value (\(d_{0}\)). At same time, the multipath fading model is applied when the \(\lambda\) value is higher than \(d_{0}\). Meanwhile, additional device energy is consumed in circuit operation time, such as transmission circuits and power amplification losses. Thus, when there is \(l\) number of data packet information in transmission in the wireless network, the energy consumed by the device is computed as bellows:  
\[E_{Tx}(l,\lambda)=\begin{cases}lllE_{Tx-elec}+l\varepsilon_{f_{s}}\lambda^{2}& \lambda<d_{0}\\ lE_{Tx-elec}+l\varepsilon_{amp}\lambda^{4}&\lambda>d_{0}\end{cases} \tag{1}\]  
\[E_{Rx}(k)=lE_{Rx-elec}, \tag{2}\]  
\(E_{Tx-elec}\) and \(E_{Rx-elec}\) are referred to as the per-bit energy of the devices consumed by the transmitter and receiver circuits. Moreover, \(\varepsilon_{f_{s}}\) and \(\varepsilon_{amp}\) are defined as the power amplification components of the free space and multipath radio models. In addition, the cut-off distance value \(d_{0}\) is calculated as \(d_{0}=\sqrt{\varepsilon_{f_{s}}/\varepsilon_{amp}}\). The Equations (1) and (2) mentioned above evaluate the energy consumption of devices when \(l\) number of data packet information is in transmission in the wireless network. However, the MBLCR approach also considers the maximum battery (\(RB_{\max}\)) and residual battery (\(RB\)) of the relay devices for the selection of a robust and reliable route. The \(RB_{\max}\) and \(RB\) values are evaluated by utilizing the linear battery model [37]. Therefore, the energy consumption of relay device \(c\) from the source to the destination is computed as bellows:  
\[DR_{(a,b)}^{c}=\frac{RB_{c}(l)}{RB_{\max}(l)\times\left\{E_{Tx}^{c}(l,\lambda) +E_{Rx}^{c}(l)\right\}} \tag{3}\]
## 3 Results
### Flux Tower Scale Simulation
#### 3.1.3 Simulation of Latent and Sensible Heat Fluxes  
It can be seen from Figure 7 that the monthly average diurnal variations of latent heat flux and sensible heat flux were both single peak curves, reaching the maximum at noon and lowest at night. Compared with sensible heat flux, the seasonal variation of latent heat flux was more significant, and it was higher in summer vegetation growing season than in non-growing season. The sensible heat flux was higher from February to April and from August to December, and was in a low peak state during the vigorous vegetation period. The monthly average peak of latent heat flux reached the maximum in June, which was 199.98 W/m\({}^{2}\), and the monthly average peak of sensible heat flux reached the maximum in March, which was 247.31 W/m\({}^{2}\).
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 6 Conclusions  
A stripe correction algorithm based on wavelet analysis and gradient equalization was proposed. Its advantage is that a Gaussian operator is introduced to perform a one-dimensional matrix convolution on the cumulative histogram of the vertical component and achieve gradient equalization in the horizontal direction. This ensures that no image details are lost in the denoising process. Compared with several algorithms with a better stripe effect, the experimental results show that the correction results of the proposed algorithm have better visual effects, and that the SSIM and PSNR indicators obtained by this algorithm are the best. It is shown that the proposed method can remove stripe noise and simultaneously preserve edge details.  
Future work will focus on the spectral analysis of stripe noise; developing an adaptive stripe noise frequency detector is an interesting topic. Furthermore, additional prior knowledge of infrared images will be considered for stripe nonuniformity correction.  
Figure 16: Filtered result for Fence (**a**) Clean; (**b**) Noisy; (**c**) Corrected.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence/Method** & **Resolution** & **TV** & **CNN** & **MHE** & **Ours** \\ \hline Ceramic/Cameraman & \(512\times 512\) & 0.045 & 1.462 & 0.031 & 0.247 \\ Suitcase & \(320\times 220\) & 0.029 & 1.028 & 0.021 & 0.012 \\ Leaves & \(640\times 440\) & 0.062 & 1.634 & 0.051 & 0.039 \\ People & \(640\times 480\) & 0.065 & 1.642 & 0.059 & 0.042 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computing time (s) of different methods for simulated and raw infrared images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Noise** & **Corrected** \\ \hline PSNR & 27.56 & 30.21 \\ SSIM & 0.872 & 0.963 \\ \hline \hline \end{tabular}
\end{table}
Table 9: PSNR (dB) and SSIM results for Fence.  
**Author Contributions:** All of the authors contributed to each facet of analysis and discussion and participated equally in the work of the paper.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction System for Prostate Cancer Recurrence Using Machine Learning
## 1 Introduction  
According to a 2017 Korean National Cancer Center survey, prostate cancer is the fourth most common cancer affecting South Korean males, with an incidence rate of 29 per 100,000 people in the country's population [1].  
Biochemical recurrence (BCR) of prostate cancer occurs in approximately 25% of patients within five years after radical prostatectomy [2]. BCR is accepted as evidence of cancer recurrence if measurable serum prostate-specific antigen (PSA) levels are confirmed after radical prostatectomy, or if PSA levels increase after radiation treatments [3; 4; 5]. BCR is also accepted as an indicator of postoperative progress and outcomes [6]. If we could predict prostate cancer BCR by analyzing data on the progress and recurrence patterns of prostate cancers, it would help clinicians to make better decisions with regard to future treatment options. In addition, depending on the predicted BCR probability, the incidence of recurrence could be reduced by providing patients with physical examinations or related medication.  
Most medical institutions use computer-based systems to accurately and efficiently manage medical data [7]. In particular, the clinical decision support system (CDSS) is a valuable tool that helps healthcare providers to make decisions and solve complex problems [8; 9]. There are few prostate cancer recurrence prediction systems currently in clinical use, so providing this function, through a facility's CDSS, will facilitate postoperative follow up and aid in the clinical management of patients with prostate cancer [10].  
The Memorial Sloan Kettering Cancer Center (MSKCC) in the United States provides a function that can be used to predict the probability of 2-, 5-, 7-, 10-, and 15-year BCR-free survival after prostate cancer surgery. It applies statistical techniques such as linear regression, logistical regression, and survival progress models to provide a web-based cancer recurrence prediction system [11]. However, the incidence of prostate cancer varies with country and race [12; 13; 14; 15], which means that the predictive system that was designed for MSKCC patients may not be as accurate at predicting prostate cancer recurrence in South Korea.  
Therefore, in this study, we have proposed a web-based CDSS that can predict the BCR of Korean patients with prostate cancer to support radical prostatectomy postoperative care. Our study uses data from Korean patients with prostate cancer to predict the recurrence of prostate cancer in this population. We tested several statistical and machine learning techniques that could be used to predict prostate cancer recurrence and then adopted the model that exhibited the best prediction accuracy for use in our prostate cancer recurrence prediction system.  
This study aimed to develop a machine learning model that could use the Korean Prostate Cancer Registry (KPCR) database to predict the three- and five-year BCR in Korean patients who previously had prostate cancer and had undergone radical prostatectomies.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 3 Results and Discussion
### Validation with Experimental Data
#### 3.2.4 Effect of Gasification Temperature and Make-Up of Fresh Limestone on M Module  
The \(M\) module from Equation (37) relates the gas concentrations of hydrogen, carbon dioxide, and carbon monoxide and has been considered as an important parameter which dictates the application of syngas [50]. For validity of an ideal gas, it can be written with volume fractions.  
\[M=\frac{y_{H_{2}}-y_{CO_{2}}}{y_{CO_{2}}+y_{CO}} \tag{37}\]  
For instance, a \(M\) module of two is required for full stoichiometric conversion into dimethyl ether [50] and, for methane synthesis, a value of three can be derived from methanation reactions. Higher values are mostly interesting for hydrogen production. Figure 10 shows the influence of the gasification temperature on the \(M\) module. Additionally, three different simulation results, with a limestone make-up of 0.2 kg/h, 6.6 kg/h, and 15 kg/h, are compared with experimental data. It can be seen that a higher make-up flow enables higher \(M\) modules under the same gasification temperature.  
For a low gasification temperature, the experimental data can be reached with a make-up flow of 0.2 kg/h. At a gasification temperature above 650 \({}^{\circ}\)C, the simulation model with a make-up flow of 6.6 kg/h precisely describes the experimental data. This parametric study reveals that the make-up flow is an important factor when optimizing the gasification process for a certain application. When considered from an economic point of view, operation strategies with low make-up rates are preferable.
# Effects of Replacing Corn with Hydroponically
## 3 Results  
### Nutritional Compositions of HSB  
The chemical composition of the hydroponically sprouted barley and CON diet on a DM basis are shown in Table 1. The feed with corn replaced by 30% HSB had higher crude protein (20.25), and lower crude fiber (12.16), ash (6.11), ADF (14.73), and NDF (32.39) than the control (CON) diet on a DM basis. Together, corn replaced by 15% HSB showed similar chemical compositions to the CON diet. However,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **CON** & **HSB (10\%)** & **HSB (30\%)** \\ \hline
**Ingredient** & & & \\
**compositions (\%DM)** & & & \\ Corn & 15.0 & 13.5 & 10.5 \\ Cotton seed & 3.0 & 3.0 & 3.0 \\ Corn gluten feed & 15.2 & 27.5 & 27.6 \\ Cotton seed hull pellet & 19.2 & 11.1 & 5.0 \\ Molasses & 3.0 & 3.0 & 3.0 \\ Soybean meal & 14.8 & 10.6 & 7.6 \\ Brewer’s spent grain & 10.0 & 10 & 10 \\ Alfafila hay & 8.0 & 8.0 & 8.0 \\ Klein grass hay & 3.0 & 3.0 & 3.0 \\ Timothy hay & 3.0 & 3.0 & 3.0 \\ Oat hay & 3.0 & 3.0 & 3.0 \\ Limestone & 2.0 & 2.0 & 2.0 \\ Salt & 0.3 & 0.3 & 0.3 \\ Vitamins \& Minerals mix & 0.5 & 0.5 & 0.5 \\ HSB & & 1.5 & 4.5 \\ Total & 100 & 100 & 100 \\ \hline \hline \multicolumn{4}{c}{**Chemical compositions**} \\
**(\%DM)** & & & \\ DM (\% as feed) & 79.11 \({}^{\text{a}}\) & 70.01 \({}^{\text{b}}\) & 70.62 \({}^{\text{b}}\) \\ Moisture & 20.89 \({}^{\text{b}}\) & 20.99 \({}^{\text{b}}\) & 29.38 \({}^{\text{a}}\) \\ Crude protein & 19.14 \({}^{\text{ab}}\) & 18.27 \({}^{\text{b}}\) & 20.25 \({}^{\text{a}}\) \\ Crude fat & 5.23 \({}^{\text{ns}}\) & 5.82 \({}^{\text{ns}}\) & 5.33 \({}^{\text{ns}}\) \\ Crude fiber & 14.85 \({}^{\text{a}}\) & 15.62 \({}^{\text{a}}\) & 12.16 \({}^{\text{b}}\) \\ Ash & 8.34 \({}^{\text{a}}\) & 7.65 \({}^{\text{a}}\) & 6.11 \({}^{\text{b}}\) \\ ADF & 19.34 \({}^{\text{a}}\) & 19.36 \({}^{\text{a}}\) & 14.73 \({}^{\text{b}}\) \\ NDF & 37.53 \({}^{\text{a}}\) & 39.56 \({}^{\text{a}}\) & 32.39 \({}^{\text{b}}\) \\ Energy (Mcal/kg) & 3423.00 \({}^{\text{a}}\) & 3404.33 \({}^{\text{a}}\) & 3122.67 \({}^{\text{b}}\) \\ Lignin & 3.17 \({}^{\text{ns}}\) & 3.05 \({}^{\text{ns}}\) & 2.18 \({}^{\text{ns}}\) \\ B-carotene (\(\mu\)g/100 g) & 366.14 \({}^{\text{ns}}\) & 397.26 \({}^{\text{ns}}\) & 400.83 \({}^{\text{ns}}\) \\ \hline \hline \multicolumn{4}{c}{**Feed cost (Korean won/kg DM)**} \\ Corn & 271 & - & \\ HSB & - & 583 & \\ \hline \hline \end{tabular} Values are expressed as the mean \(\pm\) SEM of the 3 replicates in each group. \({}^{\text{a,b}}\) and \({}^{\text{ns}}\) denote comparisons made between columns (\(p<0.05\)).  
\end{table}
Table 1: Feed ingredients and chemical composition of the control (CON) and hydroponically sprouted barley (HSB) fed to growing Holstein heifers for 11 months.  
no significant difference was noted for crude fat, lignin, and \(\beta\)- caroten content between the HSB (15% and 30% replacement) and CON diets on a DM basis.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### LAI Data (In Situ LAI and Upscaling, MODIS LAI, EPIC LAI)
#### 2.2.2 MODIS Lai
Thus, the Adaptive Savitzky-Golay (ASG) filter was used to smooth LAI time series for the APB. This filter is a weighted moving average based on simplified least-squares fit convolution. The filter optimizes the values of the length of the temporal window and the order of the polynomial to get the best match between observations and reconstructed values. This technique tends to preserve higher LAI values. ASG takes advantage of cloud flag ancillary data where rogue LAI data points are replaced by linearly interpolated values using adjacent points. ASG is computationally simple and can be used to reconstruct high-quality LAI time-series by setting only two parameters: the half-width of the smoothing window and the degree of the smoothing polynomial [43]. We used TIMESAT package [44] to fit a Savitzky-Golay smooth function to the data. Pixels with low data quality flags were assigned with zero weight in the data smoothing process, thus they had no impact in the smoothing function.
## 5 Analysis and Discussion
### Building Deformation Analysis Considering Thermal Expansion and Cold Contraction  
From the results in Figure 12a, it can be seen that the right side of the top of the Dianchi International Exhibition Center itself is deforming. The Dianchi International Exhibition Center was completed and put into use in 2015. This paper monitored data from February 2018 to May 2021. This shows that the Dianchi International Exhibition Center began to deform slowly after it was put into use, and the deformation rate was approximately \(-5\,\mathrm{mm/a}\). Field inspection and data analysis, as shown in Figure 17a,b, indicates that the deformation of the top right side of the Dianchi International Exhibition Center is caused by the structure of the building itself. The top of the building is a steel structure, which is subject to seasonal changes. Figure 17c shows the cumulative displacement variation at the top of the Dianchi International Exhibition Center. The upper two curves are the cumulative displacements of the coherent points located at the left side of the top of the Dianchi International Exhibition Center, and the lower two curves are the cumulative displacements of the coherent points located at the right side.  
Figure 16: On-site photograph of tilting self-built houses in an urban village.  
Figure 17: Photos of the top of **(a,b)** the Dianchi International Exhibition Center and **(c)** cumulative vertical displacements.  
The region shows a cyclical variation, as seen in the figure. The Dianchi International Exhibition Center is semicircular in shape; the top of the left side faces the ascending satellite orbit and the top of the right side has its back to the ascending satellite orbit, a situation that results in opposite periodic changes. To study the causes of the cyclical variation, this paper obtained daily average temperature data from February 2018 to May 2021 in Kunming city through Kunming meteorological stations ([[https://rp5.ru/world_weather](https://rp5.ru/world_weather)]([https://rp5.ru/world_weather](https://rp5.ru/world_weather)) (accessed on 9 March 2022)). The daily average temperature data were also superimposed on the cumulative displacement of the top feature points of the Dianchi International Exhibition Center. The results show that displacement is due to the cyclic variation of the structure itself, which is caused by thermal expansion and contraction due to temperature changes.
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
###### Abstract  
High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been already employed for regression and binary classification problems. Building on these methods, we extended this framework to support multi-class classification. Indeed, applied to a multi-class classification task for breast cancer subtypes, our model achieves accuracy between 73% and 81% under various block-wise missing data scenarios. Additionally, we assess its performance on a regression problem using the exposure dataset, integrating a larger number of omics datasets. Across different missing data scenarios, our model demonstrates a strong correlation (75%) between true and predicted responses. Furthermore, we have updated the bwm R package, which previously supported binary and continuous response types, to also include multi-class response types.  
Keywords:data integration; multi-omics; block-wise missing data; R package +
Footnote †: journal: Applied sciences
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology
### Channel Attention Model
The output of the \(n\)-th channel is calculated as follows:  
\[\hat{u}_{n}=F_{scale}(u_{n},s_{n})=s_{n}u_{n}, \tag{16}\]  
where \(\hat{u}_{n}\in\hat{\Omega}=[\hat{u}_{1},\hat{u}_{2},\cdots,\hat{u}_{C}]\), \(\hat{U}\) is the final overall output, and \(s_{n}\) is the weight of the \(n\)-th channel.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 1 Introduction
This is a necessary first step towards dynamically changing the PHY layer, the object of our future research. Specifically, we augment the OpenWSN reference 6 TiSCH implementation to support the following three PHYs--O-QPSK 2.4 GHz at 250 kbps, FSK 868 MHz option 1 at 50 kbps, OFDM 868 MHz option 1 Modulation and Coding Scheme (MCS) 3 at 800 kbps. The literature indicates that these three PHYs cover the range of possibilities of IEEE802.15.4g: very high data rate with OFDM 868 MHz, very long range with FSK 868 MHz, O-QPSK 2.4 GHz being a balance between range and data rate [12; 18; 19]. We then conduct a comprehensive experimental campaign on the OpenTestbed, a 42-node testbed of OpenMote B boards [20], a platform which features the AT86 RF215 and CC2538 chips. We compare the performance of the network in terms of network formation time, battery lifetime, end-to-end latency and end-to-end reliability. The remainder of this article is organized as follows. Section 2 surveys the most relevant related work. Section 3 clearly states the problems and lists the contributions of this article. Section 4 introduces the agile extension to the OpenWSN PHY-layer that enabled this research. Section 5 introduces the testbed used for the experimental campaigns of this paper and the methodology of the experiments including the used metrics and KPIs. Section 6 demonstrates the experiment results and KPI evaluations. Finally, Section 7 provides insights and conclusions based on the results.
Remote Sens._**2014**, \(6\), 7442-7462; doi:10.3390/rs6087442  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
A New Method for Modifying Thresholds in the Classification of Tree Models for Mapping Aquatic Vegetation in Taihu Lake with Satellite Images  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;  
E-Mails: [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (H.D.); [EMAIL_ADDRESS] (W.H.);  
[EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (C.L.)
2 Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth,  
Chinese Academy of Sciences, Beijing 100094, China; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-25-8688-2168; Fax: +86-25-5771-4759  
_Received: 17 March 2014; in revised form: 24 July 2014 / Accepted: 25 July 2014 / Published: 12 August 2014_  
**Abstract:** Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available.  
**Keywords:** aquatic vegetation; wetlands; remote sensing; classification tree; spectral feature