esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 6 Challenges, Opportunities, and Research Directions
### UAV Energy Consumption Challenges  
One of the most significant barriers to implementing FL in UAV communication systems is the limited energy available to UAVs. Many studies overlook this critical issue, often assuming that UAVs have sufficient energy for operations. However, in practice, UAVs must carefully manage their remaining energy to ensure a safe return to base [154]. Thus, accounting for UAV energy constraints is essential for the efficient deployment of FL.  
Although some studies have attempted to address this issue by using advanced technologies like SWIPT to recharge UAVs, most fail to provide a comprehensive energy consumption model [125; 126]. These studies often neglect important practical factors such as payload weight, flight speed, weather conditions, and temperature fluctuations, all of which can significantly impact energy usage [155]. To effectively deploy FL with UAVs, itis crucial to develop more realistic and practical energy models that take these factors into account. Addressing this gap presents a promising research direction for optimizing UAV performance and ensuring efficient energy management in FL systems.  
We have explored various aspects of energy-efficient FL in wireless networks; however, there remain significant opportunities for further research. One promising avenue involves the application of advanced machine learning and reinforcement learning techniques to address complex optimization challenges in FL. For example, as highlighted in [156], machine learning-based approaches such as linear regression and DNNs have shown effectiveness in predictive modeling. Similarly, Ref. [157] demonstrated the potential of hybrid frameworks that integrate metaheuristics with Q-learning to solve urban traffic light scheduling problems. These methodologies could be adapted to develop efficient resource allocation strategies and improve network orchestration in FL systems, particularly in large-scale and heterogeneous network environments.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Evaluation Analysis and Measures
#### 2.4.3 Testing for Atrophy Differences between Alzheimer's Disease and Normal Control Subjects  
The lack of manual segmentation limits assessing the accuracy of atrophy quantification methods [27]. The evaluation typically consists of testing for differences in atrophy rates between a pathological and a control group [22]. The evaluation process is as follows. We run FSL-SIENA on baseline and follow-up scans from OASIS and ADNI using FSL-SIENA with and without intensity standardisation. Next, we conducted analyses of covariance to determine a statistically significant difference between Alzheimer's disease and normal control subjects on detected atrophy rates. We controlled for age, biological sex, time between visits and normalised baseline brain volume (total brain volume divided by intracranial volume). Then, we estimated the marginal mean brain volume change at one year of inclusion using the resulting linear models. Finally, we estimated effect size using the [PERSON]'s \(d\) formula:  
\[\text{Cohen's d}=\frac{\mu_{AD}-\mu_{NC}}{\sqrt{(\sigma_{AD}^{2}+\sigma_{NC}^{2}) /2}}, \tag{7}\]  
where \(\mu_{AD}\) and \(\mu_{NC}\) are the mean atrophy rates for the Alzheimer's disease and the normal control groups, and \(\sigma_{AD}\) and \(\sigma_{NC}\) are their standard deviations. Given that the sample under consideration is fixed, an increase in effect size after standardisation could imply that either inter-group differences or intra-group homogeneity has increased. In practice, this situation would translate into smaller sample sizes to show statistical differences between Alzheimer's disease and control groups.
## 3 Wind Tunnel Test of Section Model
### VIV Responses of Aerodynamic Measures of Deflector Plate
#### 3.3.2 Lower Chord Deflector Plate  
The lower chord deflector plate was added to the original section and was symmetrically arranged on the outside along the lower chord of the truss. It was changed from the shape of the lower side of the original sidewalk, and its form was a right-angled deflector. The position and shape of the lower chord deflector plate are shown in Figure 13.  
After the lower chord deflector plate was added, the relationship between the vertical and torsional VIV amplitudes and the wind speed under attack angles of +3\({}^{\circ}\) and +5\({}^{\circ}\) was determined (Figure 14).  
It can be seen from Figure 14 that when the wind attack angle was +3\({}^{\circ}\), the vertical and the torsional peak amplitudes of the UD working condition decreased compared with those of the OS working condition. After setting the lower chord deflector plate on the original section, the peak amplitude of vertical VIV decreased from 75.54 mm to 34.01 mm, and the vertical VIV locking range also changed from 5.24 m/s\(\sim\)6.72 m/s to 5.43 m/s\(\sim\)6.61 m/s. The peak amplitude of torsional VIV decreased from 0.181\({}^{\circ}\) to 0.068\({}^{\circ}\), and the torsional VIV major locking range also changed from 12.27 m/s\(\sim\)18.06 m/s to 13.71 m/s\(\sim\)16.01 m/s. At a +5\({}^{\circ}\) attack angle, the peak amplitude of vertical VIV decreased from 98.23 mm to 43.84 mm, and the vertical VIV locking range also changed from 5.01 m/s\(\sim\)6.69 m/s to 5.01 m/s\(\sim\)6.45 m/s after setting the lower chord deflector plate on the original section. The peak amplitude of torsional VIV decreased from 0.211\({}^{\circ}\) to 0.081\({}^{\circ}\), and the torsional VIV major locking range also changed from 13.75 m/s\(\sim\)19.49 m/s to 14.89 m/s\(\sim\)18.12 m/s. The right-angled lower chord deflector plate can well suppress vertical and torsional VIV at  
Figure 13: Working condition of lower chord deflector plate.  
+3\({}^{\circ}\) and +5\({}^{\circ}\) wind attack angles, greatly reduce the peak amplitude of VIV, and shorten the length of the VIV locking range. At wind attack angles of +3\({}^{\circ}\) and +5\({}^{\circ}\), the suppression effect on the maximum amplitude of vertical VIV reached 54.98% and 55.37%, respectively, and the suppression effect on the maximum amplitude of torsional VIV reached 62.43% and 61.61%, respectively.
## 5 Experimental Analysis
### Results
#### 5.1.1 Performance Evaluation of Proposed FS Algorithms  
This study proposes using the rough set rapid reduct and relative reduct with better HS FS to reduce the dimensions of a given dataset. The proposed algorithms investigated the peptides collected from the HIPdb database. There are six physicochemical properties of the peptides, and 126 numerical features were generated, and the extracted features are subjected to the feature selection process to extract the best features. The proposed RSIIHSQR and RSIIHSRR algorithms are evaluated with RSPSOQR and RSPSORR algorithms. Table 2 explicitly illustrates the result of the proposed FS algorithms, which selects theminimum features of the existing algorithms. The classification algorithms evaluate the reliability of the feature subsets. \begin{table}
\begin{tabular}{|l|c|c|c|} \hline \hline
**Classification** & **FS Algorithm** & **Precision** & **Recall** & **F-Measure** & **Kulczynski** & **Fowlzens-Mallows** \\ \hline \multirow{4}{*}{Naïve Bayes} & RSHSSR & 0.793 & 0.790 & 0.788 & 0.792 & 0.791 \\ \cline{2-5}  & RSHSSR & 0.796 & 0.790 & 0.786 & 0.793 & 0.793 \\ \cline{2-5}  & RSHSOR & 0.625 & 0.593 & 0.578 & 0.609 & 0.609 \\ \cline{2-5}  & RSHSQR & 0.620 & 0.593 & 0.581 & 0.607 & 0.606 \\ \hline \multirow{4}{*}{IBK} & RSHSSR & 0.964 & 0.958 & 0.952 & 0.961 & 0.961 \\ \cline{2-5}  & RSHSSR & 0.927 & 0.926 & 0.926 & 0.927 & 0.926 \\ \cline{2-5}  & RSHSORR & 0.788 & 0.786 & 0.782 & 0.787 & 0.787 \\ \cline{2-5}  & RSFSORR & 0.742 & 0.741 & 0.741 & 0.742 & 0.741 \\ \hline \multirow{4}{*}{J48} & RSHSSR & 0.865 & 0.862 & 0.863 & 0.864 & 0.863 \\ \cline{2-5}  & RSHSQR & 0.821 & 0.814 & 0.812 & 0.818 & 0.817 \\ \cline{2-5}  & RSHSORR & 0.679 & 0.677 & 0.677 & 0.678 & 0.678 \\ \cline{2-5}  & RSPSOOR & 0.783 & 0.783 & 0.783 & 0.783 & 0.783 \\ \hline \multirow{4}{*}{Random Forest} & RSHSSR & 0.823 & 0.823 & 0.823 & 0.823 & 0.823 \\ \cline{2-5}  & RSHSQR & 0.823 & 0.823 & 0.823 & 0.823 & 0.823 \\ \cline{2-5}  & RSHSORR & 0.670 & 0.670 & 0.670 & 0.670 & 0.670 \\ \cline{2-5}  & RSHSQR & 0.790 & 0.790 & 0.790 & 0.790 & 0.790 \\ \hline \multirow{4}{*}{JRip} & RSHSPR & 0.945 & 0.948 & 0.947 & 0.947 & 0.946 \\ \cline{2-5}  & RSHSQR & 0.925 & 0.929 & 0.928 & 0.927 & 0.927 \\ \cline{1-1} \cline{2-5}  & RSPSORR & 0.827 & 0.826 & 0.824 & 0.827 & 0.826 \\ \cline{1-1} \cline{2-5}  & RSPSOOR & 0.838 & 0.832 & 0.830 & 0.835 & 0.835 \\ \cline{1-1} \cline{2-5}  & RSHSSR & 0.956 & 0.942 & 0.941 & 0.948 & 0.940 \\ \cline{1-1} \cline{2-5} Rough Set Classifier (RSC) & 0.915 & 0.924 & 0.938 & 0.968 & 0.962 \\ \cline{1-1} \cline{2-5}  & RSPSORR & 0.825 & 0.936 & 0.854 & 0.917 & 0.966 \\ \cline{1-1} \cline{2-5}  & RSPSOOR & 0.883 & 0.843 & 0.811 & 0.839 & 0.835 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the proposed framework and other FS and classification algorithms.
## 4 Improvements in PRI Implementation  
As stated above, a series of factors hinder the interpretation of PRI and its capacity to detect RUE from foliar to ecosystem levels and at different temporal scales. The minimization and avoidance of these influences on PRI variation has thus become an urgent problem given its significance in the study of global carbon fixation. Improving the accuracy of PRI interpretation and particularly its application to long-term and global carbon uptake will require: (1) employing different kinds of instruments and models, from ground-based to space-borne satellite sensors; (2) choosing reference bands for PRI calculation (Table 2) and (3) combining other parameters and generating PRI models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Formulation** & **References** \\ \hline Original & PRI = (R531 \(-\) R570)/(R531 + R570) & [PERSON] et al. [18] \\  & PRI586 = (R531 \(-\) R586)/(R531 + R586) & [PERSON] et al. [148] \\ \hline  & & [PERSON] et al. [101] \\  & PRI515 = (R531 \(-\) R515)/(R531 + R515) & [PERSON] et al. [69,117] \\  & PRI512 = (R531 \(-\) R512)/(R531 + R512) & [PERSON] et al. [136,150] \\  & & [PERSON] et al. [126] \\  & & [PERSON] et al. [120] \\ \hline  & PRI = (R525 \(-\) R570)/(R525 + R570) & \\  & PRI = (R539 \(-\) R570)/(R539 + R570) & [PERSON] et al. [60,132] \\  & PRI = (R545 \(-\) R570)/(R545 + R570) & [PERSON] et al. [72] \\  & PRI = (R532 \(-\) R701)/(R532 + R570) & \\ \hline  & PRI551 = (R551 \(-\) R551)/(R531 + R551) & \\  & PRI555 = (R5531 \(-\) R555)/(R531 + R555) & [PERSON] et al. [136] \\  & PRI645 = (R5531 \(-\) R645)/(R551 + R645) & (simulated MODIS bands) \\  & PRI667 = (R531 \(-\) R667)/(R531 + R667) & \\ \hline  & & [PERSON] et al. [28] (MODIS) \\  & PRI = (Band11 \(-\) Band1)/(Band11 \(+\) Band1) & [PERSON] et al. [25] (MODIS) \\  & PRI = (Band11 \(-\) Band12)/(Band11 \(+\) Band12) & [PERSON] et al. [31] (MODIS) \\  & PRI = (Band11 \(-\) Band13)/(Band11 \(+\) Band13) & [PERSON] et al. [134] (MODIS) \\ \hline  & PRI600 = (R531 \(-\) R602)/(R531 + R602) & \\  & PRI670 = (R531 \(-\) R668)/(R531 + R668) & [PERSON] et al. [150] \\ \hline \hline \end{tabular}
\end{table}
Table 2: PRI formulations for assessing RUE or net CO\({}_{2}\) uptake.  
Figure 9: Comparison of the strength of the relationships between PRI and RUE across temporal and spatial scales. Each bar represents the average coefficient of determination for each source of temporal variation (daily or seasonal) and spatial scales (leaf, canopy and ecosystem). Different letters indicate significant differences (\(p<0.05\)). Error bars represent the standard errors, and the numbers of reported correlations are shown in brackets.
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 3 Results
### Performance Analysis on Multi-Node
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Processors**} & \multicolumn{3}{c}{**100-Period Folded Waveguide**} & \multicolumn{3}{c}{**16 \(\times\) 16 Phased Array Antenna**} \\ \cline{2-7}  & **Time(s)** & **Scalability** & **Efficiency (\%)** & **Time(s)** & **Scalability** & **Efficiency (\%)** \\ \hline
1 & 4,672,834 & 1 & 100.00\% & 996,042 & 1 & 100.00\% \\
2 & 2,352,144 & 1.99 & 99.3\% & 505,605 & 1.97 & 98.6\% \\
4 & 1,211,352 & 3.86 & 96.4\% & 258,712 & 3.85 & 96.3\% \\
8 & 613,031 & 7.62 & 95.3\% & 131,403 & 7.58 & 94.8\% \\
16 & 308,052 & 15.17 & 94.8\% & 66,848.5 & 14.9 & 93.4\% \\
32 & 164,602 & 28.39 & 88.7\% & 35,611.1 & 27.97 & 87.4\% \\
64 & 84,938.1 & 55.01 & 86.0\% & 187,05.0 & 53.25 & 83.2\% \\
128 & 50,349.4 & 92.81 & 72.5\% & 9717.5 & 102.5 & 80.1\% \\
256 & 23,981.7 & 194.85 & 76.1\% & 4981.7 & 199.94 & 78.1\% \\
512 & 12,190.1 & 383.33 & 74.9\% & 2573.3 & 387.07 & 75.6\% \\
1024 & 6264.5 & 745.93 & 72.8\% & 1316.3 & 756.7 & 73.9\% \\ \hline \end{tabular}
\end{table} 
Table 2: Muti-node parallel testing results table. Figure 11: **(a)** The time consumption of the 100-period folded waveguide; (b) the time consumption of the 16 \(\times\) 16 phased array antenna.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### Attacks by the Dishonest Controller  
Compared to external attacks initiated by an eavesdropper like [PERSON], internal attacks by a dishonest controller are more severe. In the MCSQD system, the controller acts as a core  
Figure 3: Schematic diagram of the entangle-measure attack.  
component responsible for coordinating the communication process among participants. If the controller behaves dishonestly, it poses a significant threat to the overall security of the communication system. The following analysis explores specific methods of attack by a dishonest controller.
## 4 Discussion
In the different treatments of this study, no lactones were detected in raw milk; however, two lactones, 5-octanolide and undecanolactone, were detected in 135 \({}^{\circ}\)C-treated milk, which further explained the flavor deterioration of 135 \({}^{\circ}\)C-treated milk. The correlation analysis revealed that 5-octanolide and undecanolactone showed a significant positive correlation with furosine and lactulose and a significant negative correlation with protein parameters. The detection 5-octanolide and undecanolactone also indicates a reduction in milk quality. Many studies have proved that thermal processing significantly affects the VOC composition of milk, which is related to the Maillard reaction [35]. Triglycerides account for approximately 98% of milk's fatty acids, but triglycerides are not volatile and heat processing converts them into volatile compounds [36]. Short- and medium-chain fatty acids in milk butterfat produce many aroma compounds. Aldehydes, acids, and alcohols are converted from unsaturated fatty acids, while esters are converted from free fatty acids [37]. Fat content has an impact on the VOC richness of milk. In UHT milk, the types of VOCs of low-fat milk are lower than those of high-fat milk, and acetoin, 2-nonanone, and 2-heptanone were positively linear correlated with fat content [36]. Therefore, the fat content in milk has a significant impact on milk flavor, and the control of milk flavor is primarily a matter of fat regulation. From a broad perspective, milk flavor is influenced by many things, such as the cow diet, breed, natural enzymes in milk, microorganisms and processing conditions that may alter milk flavor balance [38; 39]. [PERSON] found that different microbial starter cultures had a significant effect on the flavor of yogurt, and the content of benzaldehyde and acetoin could be significantly increased by using L. helveticus H9 strain [40]. The Sun study showed that the levels of acetaldehyde, 2,3-butanedione, acetoin, butyric acid, decanoic acid, hexanoic acid, and octanoic acid were significantly higher at 37 \({}^{\circ}\)C than at 42 \({}^{\circ}\)C, indicating that low temperatures contribute to the formation of flavor compounds [41]. High-temperature treatment of milk to inactivate harmful bacteria is an effective means used worldwide. However, it also promotes the transformation of nutrients such as fat and protein in milk and changes the flavor of milk. The OPLS-DA model screened out 17 compounds with metabolic differences due to temperature changes (Figure 6), and high-temperature treatment intensified the accumulation of furfural (baked odor), methyl ketones (fruity, fresh sweet, and spicy odor), esters (sweet fruity, fatty and creamy odor), and hydrocarbons, leading to the change in milk flavor.
# Abstractive vs. Extractive Summarization: An Experimental Review
## 3 Evaluation
### Experimental Setup and Evaluation Results
#### 3.2.2 Evaluation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **R1** & **R2** & **RL** & **RLSum** & **B1** & **B2** & **SB** \\ \hline \multirow{8}{*}{Extractive} & _TopicRank_ & **0.1828** & **0.0258** & **0.1313** & **0.1313** & **0.1246** & 0.0266 & **0.0266** \\  & _TextRank_ & 0.1738 & 0.0248 & 0.1266 & 0.1266 & 0.1196 & **0.0271** & 0.0227 \\  & _TextRank_ & & & & & & \\  & _(pyTextRank)_ & 0.1646 & 0.0219 & 0.1197 & 0.1197 & 0.1125 & 0.0227 & 0.0248 \\  & _Luhn_ & 0.1734 & 0.0246 & 0.1254 & 0.1254 & 0.1193 & 0.0265 & 0.0244 \\  & _e-LexRank_ & 0.1725 & 0.0243 & 0.1256 & 0.1256 & 0.1147 & 0.0239 & 0.0259 \\  & _LexRank_ & 0.1696 & 0.0226 & 0.1237 & 0.1237 & **0.1149** & 0.0238 & 0.0257 \\  & _PositionRank_ & 0.1618 & 0.0184 & 0.1188 & 0.1188 & 0.1121 & 0.0200 & 0.0247 \\  & _LSA_ & 0.1518 & 0.0165 & 0.1101 & 0.1101 & 0.1047 & 0.0165 & 0.0250 \\ \hline \multirow{8}{*}{Abstractive} & _PEGASUS-XSum_ & **0.4573** & **0.2405** & **0.3840** & **0.3840** & **0.3484** & **0.2374** & **0.1602** \\  & _BART-large-XSum_ & 0.4417 & 0.2194 & 0.3649 & 0.3649 & 0.3445 & 0.2250 & 0.1470 \\ \cline{1-1}  & _disllBART-XSum-12-6_ & 0.4397 & 0.2197 & 0.3665 & 0.3665 & 0.3350 & 0.2204 & 0.1449 \\ \cline{1-1}  & _mT5-multilingual_ & 0.3520 & 0.1386 & 0.2845 & 0.2845 & 0.2536 & 0.1394 & 0.0889 \\ \cline{1-1}  & _BART-large-CNN-_ & 0.2084 & 0.0435 & 0.1399 & 0.1399 & 0.1251 & 0.0429 & 0.0231 \\ \cline{1-1}  & _disllBART-CNN-12-6_ & 0.1985 & 0.0357 & 0.1307 & 0.1307 & 0.1122 & 0.0348 & 0.0199 \\ \cline{1-1}  & _PEGGASUS-_ & 0.1979 & 0.0356 & 0.1339 & 0.1339 & 0.1247 & 0.0368 & 0.0209 \\ \cline{1-1}  & _BART-large-CNN_ & 0.1972 & 0.0339 & 0.1303 & 0.1303 & 0.1167 & 0.0340 & 0.0197 \\ \cline{1-1}  & _PEGGASUS-large_ & 0.1654 & 0.0266 & 0.1146 & 0.1146 & 0.0988 & 0.0252 & 0.0190 \\ \cline{1-1}  & _PEGGASUS-multi\_news_ & 0.1578 & 0.0491 & 0.1114 & 0.1114 & 0.0812 & 0.0387 & 0.0173 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Evaluation Results on _XSum_.
# FLaMAS: Federated Learning Based on a SPADE [PERSON]
## 3 Multi-Agent Learning Based on Federated Learning
### FlaMAS  
_FLaMAS_ is a new federated learning tool for multi-agent systems based on SPADE. _FLaMAS_ brings together the benefits of deep learning and multi-agent systems to create a new distributed learning tool. The system allows the training of deep learning models from anywhere in the world, following the main features of a federated learning algorithm, being distributed (enhanced by the implementation in SPADE agents), and ensuring privacy of the data (as only the model being learned is shared and not the data used for the training). These features are enhanced by the inclusion of the learning algorithms in a MAS, such as that formed by SPADE agents, i.e., a distributed framework designed for distributed applications, with easy and rapid adaptation to failures in nodes, or new nodes entering the learning process during its execution.  
In the _FLaMAS_ system, there exist two different agent roles:  
* Client Role: its main goal is to receive the input data from the device where it is embedded, train the model and send it to the agent playing the Server role.
* Server Role: Its main function is to be in charge of receiving at each iteration the different trained models of the agents playing the Client role in the system. The agent playing the Server role takes the average of all those models and this new unified model is sent to the different agents playing the Client role.
## 6 Conclusions  
Geological data from the research area were collected, and ERT was employed to generate five ERT profiles through data inversion. By integrating drilling results with other geological information, the status of the abandoned mine's underground passages was identified. The following conclusions were drawn:  
1. The ERT profiles reveal a total of eight underground voids along the expressway route, with two in Zone-1 being identified as karst caves and six in Zone-2 being identified as abandoned mining tunnels.
2. When two low-resistivity zones are large in volume and closely spaced, they may appear as a single low-resistivity zone in the inversion results.
3. Boreholes not only validate the accuracy of the ERT results but also improve the precision of the ERT method.
4. ERT is effective in reflecting the morphological characteristics of underground voids, providing valuable technical guidance for subsequent project construction and void treatment.  
Conceptualization, M.S. and R.L.; methodology, M.S. and R.L.; software, J.O. and T.L.; validation, M.S., C.C. and R.L.; formal analysis, M.S. and J.O.; investigation, M.S.; resources, J.O.; data curation, R.L.; writing--original draft preparation, M.S.; writing--review and editing, R.L. and J.O.; visualization, C.C.; supervision, T.L.; project administration, J.O.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Fence diagram showing the location of the 2D ERT within a 3D space.  
**Funding:** This research was funded by Open Fund of Hunan Geological Disaster Monitoring, Early Warning and Emergency Rescue Engineering Technology Research Center (No. hndzgczx202401) and National Natural Science Foundation of China (grant Nos. 42374093, 42130810).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data from this research will be available upon request to the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.