esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### VOCs Analysis
Previous studies have proved that aldehydes such as nonanal and decanal were characteristic aroma substances of chicken [55]. Nonanal was mainly formed by the oxidation of linoleic acid [56]. The fat content of BJY was relatively higher, so the variety and relative content of the aldehydes detected were also higher than LH and AA, contributing to a richer flavor of the chicken. In addition, saturated linear aldehydes with high molecular weight may produce pungent odors. The content of Pentadecanal and Hexadecanal in AA was relatively high. [PERSON] et al. have found that Tetradecanal might be one of the sources of the unpleasant earthly smell in fish [24]. \begin{table}
\begin{tabular}{c c c c} \hline
**VOCs** & **BJY** & **LH** & **AA** \\ \hline
**Alcohols** & & & & \\
1-Dodecanol & 2.74 \(\pm\) 0.00 & 4.18 \(\pm\) 0.00 & — \\
1-Octen-3-ol & 9.46 \(\pm\) 0.04 & 14.62 \(\pm\) 0.35 & — \\
2-(2-Ethoxyethoxy)-Ethanol & 13.76 \(\pm\) 2.33 & 6.93 \(\pm\) 0.02 & — \\
2-(Hexadecyloxy)-Ethanol & 2.74 \(\pm\) 0.00 & — & — \\
2-methyl-1-Decanol & — & 2.30 \(\pm\) 0.07 & — \\
2-Methyl-1-Hexadecanol & 2.67 \(\pm\) 0.12 & — & — \\
5-Methyl-2-(1-methylethyl)-1-Hexanol & — & 4.28 \(\pm\) 0.07 & — \\ Octahydro-ta(2H)-Naphthalene-methanol & — & — & 0.41 \(\pm\) 0.01 \\
**Phenols** & & & & \\
2,4-Bis(1,1-dimethylethyl)-enol & 8.92 \(\pm\) 0.08 & 13.36 \(\pm\) 0.03 & 0.06 \(\pm\) 0.01 \\
**Aldehydes** & & & & \\ Dodecanal & 2.28 \(\pm\) 0.01 & 1.38 \(\pm\) 0.06 & — \\ Hexadecanal & 0.17 \(\pm\) 0.01 & 2.31 \(\pm\) 0.05 & 2.45 \(\pm\) 0.10 \\ Nonanal & — & 1.84 \(\pm\) 0.01 & — \\ Pentadecanal & 0.12 \(\pm\) 0.00 & — & 2.92 \(\pm\) 0.01 \\ Tridecanal & 7.64 \(\pm\) 0.10 & — & — \\ Tetradecanal & 2.04 \(\pm\) 0.01 & — & — \\
**Esters** & & & \\
[1,1\({}^{\prime}\)-Bicyclopropyl]-2-octanoic acid, 2\({}^{\prime}\)-hexyl-, & 2.21 \(\pm\) 0.01 & — & — \\ methyl ester & & & \\
2-Hexyldecyl propionate & — & — & 3.55 \(\pm\) 0.00 \\
2-Thiopheneacetic acid, oct-3-en-2-yl ester & — & — & 1.59 \(\pm\) 0.00 \\
9-Hexadecenoic acid, 9-hexadecenyl ester & 5.37 \(\pm\) 0.01 & 3.11 \(\pm\) 0.01 & — \\ Docosanoic acid nonyl ester & 5.59 \(\pm\) 0.00 & — & — \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 5 Discussion and Conclusions
### Influence of Operating Conditions  
With respect to a certain operating speed, the internal leakage of the pump increased as the outlet pressure increased, and this is believed to be the reason why the flow ripple increased (the internal leakage leads to an increase in the flow ripple as discussed above). With respect to a certain outlet pressure, the mean outlet flowrate increased as the operating speed increased, and this is believed to be the reason why the flow ripple decreased (the denominator in Equation (25) became greater while the numerator remained roughly the same). Referring to Figure 18, the tandem pump led to a more than 45% decrease in the flow ripple than the single pump with respect to the depicted operating conditions (500-3000 rpm, 0-30 MPa). Hence, it can be seen that a tandem crescent pump with proper design parameters (\(\gamma=13.85^{\circ}\) and \(\zeta=0.5\)) can result in a significant decrease in the outlet flow ripple (more than 45%) than a single pump with the same displacement across a wide range of operating conditions (500-3000 rpm, 0-30 MPa). **Acknowledgments:** The authors are grateful to the anonymous reviewers and the editor for their constructive comments. The authors would also like to thank [PERSON] from Zhejiang University for the improvement of the paper. Additionally, the authors would like to acknowledge the support of the National Natural Science Foundation of China (Grant No. 51521064) and Science and Technology Innovation Team of Independent Design Projects of Zhejiang Province (Grant No. 2013 TD01). **Author Contributions:** [PERSON] and [PERSON] proposed the idea of the tandem crescent pump for the purpose of decreasing the outlet flow ripple; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] contributed to the test rig; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the Matlab code, conducted the numerical simulations and wrote the paper. **Conflicts of Interest:** The authors declare no conflict of interest. **Notation** \begin{tabular}{l l} \(A_{\text{val, rel, tri}}\) & flow area of the throttle valve, the relief groove, and the triangular groove (m\({}^{2}\)) \\ \(b\) & width of the gear (m) \\ \(b_{\text{B, L, L, L}}\) & parameters of the rectangular sealing area around the outlets (m) \\ \(C_{\text{d}}\) & discharge coefficient (-) \\ \(f\) & frequency (Hz) \\ \(l\) & length of the sealing area in the ring-gear/case interface(m) \\ \(l_{\text{r}}\) & length of the tooth tip (m) \\ \(m\) & module of the gear (m) \\ \(n\) & operating speed (rpm) \\ \(\text{O}_{\text{s,r}}\) & centers of the gear shaft and the ring gear (-) \\ \(p\) & pressure (Pa) \\ \(p_{\text{in, out}}\) & inlet or outlet pressure (Pa) \\ \(p_{\text{tran}}\) & transitional pressure (Pa) \\ \(p_{\text{trap}}\) & trapped pressure (Pa) \\ \(q_{\text{in, out}}\) & flowrate into and out of a chamber (m\({}^{3}\)/s) \\ \(q_{\text{l}}\) & lateral leakage of the lateral interface of the gears' lateral sides and the floating plates (m\({}^{3}\)/s) \\ \end{tabular}
## 4 Results and Discussion
### Results of Experiment 1
The real position of the receiver is represented with a black square, while the estimated position is presented with a blue square in Figure 15a,b. The estimated location is obtained by averaging 69 points, which are the output of the moving average filter, and is 54 cm away from the real position, as can be seen in Figure 15b. Because the estimated position from 69 points is 54 cm away from the real position (Figure 15), a sub-meter accuracy has been achieved for line-of-sight conditions. In addition to the 50% CEP with a radius of 3.3 m from the estimated position, we also calculated each of the 69 points distances from the real position and presented it in Figure 16. The average error of the individual samples, from the real position, is 3.37 m.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Invisibility Experiment  
The algorithm in this paper is based on the video and watermark characteristics to select the modulation method that has the least modification to the video, thus greatly reducing the impact of the embedding of the watermark on the video quality. In this paper, we use PSNR (peak signal to noise ratio) and SSIM (structural similarity index) as video quality evaluation indexes to embed watermarks in six videos to experiment with the invisibility of the algorithm, and the results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment Video** & **PSNR1** & **PSNR2** & **SSIM** \\ \hline Foreman & 35,6151 & 35,5184 & 0.9992 \\ News & 35,9164 & 35,7282 & 0.9974 \\ Mobile & 30,6107 & 30,5390 & 0.9991 \\ Hall & 36,0782 & 35,8611 & 0.9986 \\ Stefan & 34,6160 & 34,4690 & 0.9968 \\ Bus & 34,7719 & 34,7232 & 0.9993 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR and SSIM values of video after embedding watermark.  
Figure 5: Requantization BER value under different screening conditions.  
In Table 2, PSNR1 is the PSNR value of the encoded video image and the original video image, and PSNR2 is the PSNR value of the video image and the original video image after embedding the watermark and encoding. It can be seen that the PSNR value of the video after embedding the watermark decreases very little compared with that before embedding, which is no more than 0.2 dB, and the SSIM values are all above 0.99, which indicates that the algorithm in this paper has good invisibility. Moreover, [PERSON]'s experimental results show that although the video has only 90 frames, the SSIM is still above 0.99 when embedding the same size watermark as in other videos, which indicates that the algorithm from this paper has good invisibility for videos with fewer frames.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 4 Proposed Method  
Building upon the previous section, this section develops efficient power control algorithms for parallel multi-task Air-FL from both centralized and distributed perspectives. We begin with a centralized approach that leverages full global CSI to iteratively transform and solve the non-convex optimization problem through convex reformulations. This approach enables accurate characterization of the Pareto boundary via joint optimization across all cells. However, due to the high overhead and poor scalability of centralized solutions in large-scale systems, we then propose a distributed power control scheme based on IT. The distributed approach uses only local CSI and IT constraints, and achieves near-Pareto optimal performance through inter-cell coordination without requiring a global controller.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Nanosatellites  
Due to extreme sizing constraints, nanosatellites use the Ku-band or higher microwave frequencies (\(\sim\)100 GHz) that are increasingly sensitive to precipitation. The sensitivity of resonance bands (e.g., 60 GHz and 118 GHz for oxygen) to precipitation and altitude makes radar sounders that operate at these frequencies with short wavelengths more suitable for atmospheric sciences (e.g., tropical cyclones) than ground surface measurements [118]. Among those spectra, the Ka band is considered to achieve a good balance between high resolution and low attenuation and, therefore, was used in the first radar CubeSat, Radar In a CubeSat (RaInCube). Its rain radar performance of distinguishing isolated rain cells and strong rainstroms from ground echoes were in solid agreement with ground-based measurements [119]. The operation concept of the RainCube mission is summarized in Figure 21 (left).  
In the large-satellite class (\(>\)1 ton), the Surface Water and Ocean Topography (SWOT) satellite (2022) will use the Ka band for SAR interferometry for the first time. Short wavelengths at this high frequency enable precise measurement of wind-generated surface roughness, as demonstrated previously by airborne measurements taken by the National Centre for Space Studies (CNES) in France and JPL [120]. The mission goal is to provide global measurements of continental surface water storage exceeding 250 m\({}^{2}\) in area (lakes) or 100 m in width (rivers) [121]. Figure 21 (right) illustrates the mission concept of the SWOT mission and the satellite whose antenna area is smaller than previous SAR missions due to shorter wavelengths. Moreover, the utilization of the Ka band in vegetation canopy measurement was demonstrated in ground tests for deciduous and coniferous trees [122].  
Ku and Ka bands have not been exploited in spaceborne radar as much as lower-frequency spectra, but future nanosatellites could make use of these bands; demonstration of InSAR near the K band in the SWOT mission may also be done using nanosatellites in proximity formation flight or connected with a boom [123].
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## Evaluation
### Ablation Study  
To study how different components of LS6 contribute to the prediction, we conduct an ablation study. Seven variants of LS6 are considered: (1) LS6 (w/o SARIMA_v6): the model without the SARIMA for extracting the IPv6 traffic feature; (2) LS6 (w/o SARIMA_v4): the model without the SARIMA for extracting the IPv4 traffic feature; (3) LS6 (w/o LSTM_v6): the model without the LSTM for extracting the IPv6 traffic feature; (4) LS6 (w/o LSTM_v4): the model without the LSTM for extracting the IPv4 traffic feature; (5) LS6 (w/o IPv4): the model without the IPv4 traffic input; (6) LS6 (w/o SARIMA): the model without SARIMA; (7) LS6 (w/o LSTM): the model without LSTM.  
As shown in Table 4, all components are important for LS6. Specifically, the LSTM for extracting the IPv6 traffic feature is the most critical part. We also find that the importance of IPv4 traffic and SARIMA is proven.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **MAPE** \\ \hline \multirow{8}{*}{DHU} & Naive-2h & 0.7664 \\  & Naive-2h & 0.2878 \\  & ARIMA & 0.7556 \\  & SARIMA & 0.2675 \\  & SVM & 0.7471 \\  & LSTM & 0.4557 \\  & Bi-LSTM & 0.3678 \\  & PLSTM & 0.7975 \\  & LS6 & **0.2410** \\ \hline \multirow{8}{*}{ECNU} & Naive-2h & 0.6062 \\  & Naive-24h & 0.3546 \\ \cline{1-1}  & ARIMA & 0.7418 \\ \cline{1-1}  & SARIMA & 0.3175 \\ \cline{1-1}  & SVM & 0.6998 \\ \cline{1-1}  & LSTM & 0.3367 \\ \cline{1-1}  & Bi-LSTM & 0.5479 \\ \cline{1-1}  & PLSTM & 0.4509 \\ \cline{1-1}  & LS6 & **0.3146** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction performance of LS6 and baseline methods on different datasets.
## 4 Experiment
### Ablation Study
#### 4.3.4 Scaling Factor in Cross-Correlation Loss  
When calculating the cross-correlation loss, the model expands the dimensions of the feature sequences. As a result, the values of elements in the correlation matrix become relatively large. To balance the cross-correlation loss in the objective function, we introduced scaling factors. Figure 7 illustrates the impact of scaling factors on the final results. Since we set different feature dimensions for unimodal features from different datasets (128 for CMU-MOSI and CMU-MOSEI, 32 for CH-SIMS), and applied different linear mapping layers for dimension expansion when calculating the cross-correlation loss for different datasets, the optimal scaling factors also vary. Specifically, we used \(5\times 10^{-5}\) for CMU-MOSI and \(1\times 10^{-3}\) for CH-SIMS.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-3 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline Direct Concat & 0.408 & 0.614 & 41.2 & 66.4 & 80.4 & 80.4 \\ Standard CCDA & **0.393** & **0.628** & **43.3** & **68.3** & **81.1** & **81.0** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Impact of correlation coefficients in fusion strategy on CH-SIMS. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-3 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline w/o corr loss & 0.708 & 0.795 & 47.4 & 54.2 & 84.9 & 84.9 \\ Standard CCDA & **0.696** & **0.807** & **48.0** & **54.8** & **85.7** & **85.6** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Impact of cross-correlation loss in the objective function on CMU-MOSI. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-3 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline w/o corr loss & 0.400 & 0.610 & 42.0 & 66.7 & 80.1 & 80.1 \\ Standard CCDA & **0.393** & **0.628** & **43.3** & **68.3** & **81.1** & **81.0** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Impact of cross-correlation loss in the objective function on CH-SIMS. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.
## 4 Discussion
### Limitations and Future Research  
One limitation of this study is its focus on the strength of EMG-COP correlations without exploring other types of correlations, such as positive or negative cross-correlations. These variations could provide deeper insights into specific muscle contributions to postural control [28, 46]. Future research could examine the relationship between lower limb muscle activity and postural stability in individuals with and without a history of injuries (e.g., ankle or knee injuries from sports), which could inform sports-related injury prevention and rehabilitation [49]. Expanding the participant range to include different age groups and activity levels would enhance the generalizability of the findings.  
Another limitation is the reliance on cross-correlation analysis, which, while useful for examining temporal relationships [16, 17], may not capture the non-linear dynamics of neuromuscular control [51]. This suggests the need for alternative methods to gain a more comprehensive understanding of postural control.  
Additionally, the current study focused exclusively on young, healthy participants, which may not fully represent neuromuscular control mechanisms in individuals with traumatic injuries. Future studies could involve individuals with musculoskeletal injuries to explore how trauma affects postural stability and muscle coordination, using healthy participants as controls. Research could also assess how myoelectric activity influences postural stability across different populations, aiding in rehabilitation and injury prevention. Finally, future work could explore the effects of varying surfaces, footwear, and environmental factors, as well as long-term studies to understand how neuromuscular control evolves with age or rehabilitation.
## 3 Proposed Method
### Anomaly Detection and Threat Identification  
Anomaly detection and threat identification involves a two-stage cascade, which is represented in Figure 3. The approach utilises several random forest classification models. The first one in the cascade is responsible for binary classification, which indicates whether a feature vector is considered normal or anomalous. The second part of the cascade is responsible for threat identification. It is triggered only if an anomaly is detected. This approach enables achievement of a modular architecture, where new threat detection models can be added at anytime, without retraining the whole system from the beginning. Moreover, it may also happen that an anomaly alert will be triggered when none of the threat identification modules can provide an unequivocal answer.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 3 Materials and Methods
### Historical Map Dataset  
The dataset comprises high-resolution scanned historical maps from some Chinese cities: Beijing, Tianjin, Shenyang, and others. We show some examples of our dataset in Figure 2. These maps, predominantly exceeding 1500 x 1500 pixels in resolution, span multiple dynasties and exhibit significant variations in cartographic techniques and artistic styles. Owing to their historical prominence as political and cultural hubs, Beijing and Tianjin possess richer archival collections, with 20 and 22 meticulously preserved maps, respectively. For comparative analysis, over four maps per city were collected from other regions.  
During the curation phase, preliminary visual inspections were conducted to assess map integrity and image fidelity, ensuring the quality of our dataset. Through collaboration with museums and archaeological institutions, the physical maps underwent professional-grade digitization using non-invasive scanning protocols. Given the fragile material composition of centuries-old maps, multi-phase scanning and precision stitching algorithms were employed to achieve higher geometric accuracy.  
For artifact suppression, a custom map segmentation pipeline was developed, combining morphological operations and deep learning-based inpainting. Prior to model training, the maps were tiled into 512 \(\times\) 512 pixel patches and annotated by cartographic experts to delineate structural elements (roads, landmarks, text). The raw scans and annotations were organized into a hierarchical geodatabase with spatiotemporal metadata (e.g., dynastic period, geographic coordinates), enabling reproducible computational analysis.  
Figure 1: Workflow of the main steps of the method.  
Figure 2: Example historical map images.
# instantiation of decorator function #profile
## 4 Conclusions  
Smart irrigation systems integrate IoT technology with smart agriculture to conserve water consumption during the irrigation of agricultural land. This research focuses on the evaluation of the adoption of a lightweight security protocol to secure communication in smart irrigation systems. The adopted encryption algorithm (Expeditious Cipher) creates a secure channel between publishers and the broker of the MQTT protocol as well as between the broker and its subscribers. MQTT was chosen as the IoT application protocol for messaging in our proposed irrigation system, as it is a bandwidth- and energy-efficient protocol. The evaluated lightweight protocol reduces resource consumption in terms of memory usage and power consumption as well as minimizes the execution time, which makes it suitable for IoT-based resource-constrained systems. There is a trade-off between the security level and the memory and energy-processing resources required. AES achieves a higher security level at the cost of more memory usage and energy consumption, which causes performance degradation in constrained devices, opposite to lightweight cipher, which achieves an adequate level of security with low memory usage and reduced energy consumption. Comparing our selected X-cipher protocol with the PRESENT cipher reveals a lower memory usage and higher throughput in favor of the X-cipher. In the future, we will investigate the integration of our proposed system with software-defined networking (SDN) technology to implement the security models at the controller to offload publishers and brokers from the additional processing required by security algorithms. Moreover, we will develop a deep-learning model to predict the irrigation time and the required amount of water required for irrigation.  
Conceptualization, C.F. and H.M.A.; methodology, C.F. and H.M.A.; software, H.M.A.; validation, C.F. and H.M.A.; formal analysis, C.F. and H.M.A.; investigation, C.F. and H.M.A.; resources, C.F. and H.M.A.; writing--original draft preparation, C.F.; writing--review and editing, C.F.; visualization, C.F. and H.M.A.; project administration, C.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol-Process** & **Throughput KBps** \\ \hline X-cipher-encryption & 164 KBps \\ X-cipher-decryption & 172 KBps \\ PRESENT-encryption & 140 KBps \\ PRESENT-decryption & 140 KBps \\ AES-encryption & 200 KBps \\ AES-decryption & 210 KBps \\ \hline \hline \end{tabular}
\end{table}
Table 4: Throughput results comparison between X-cipher and PRESENT.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol-Process** & **Memory Usage (MB)** \\ \hline X-cipher-encryption & 20.04 MB \\ X-cipher-decryption & 22.38 MB \\ PRESENT-encryption & 90 MB \\ PRESENT-decryption & 105 MB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Memory usage results in comparison between X-cipher and PRESENT.