esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Results and Analysis
#### 4.4.2 Results on MSVD Dataset
TDConvED [48] introduces a TDConvED that constructs encoder and decoder by fully use convolutions. 10. GRU-EVE [49] mainly applied Short Fourier Transform into CNN features which could enrich the visual features with temporal dynamics. The performance of our model with baseline and existing models above are compared on the MSVD dataset. As Table 4 shows, SeqVLAD is a baseline in our experiments, the baseline with single semantic reconstructor (SR) and the baseline with single channel soft attention (CSA) both show a great improvement in METEOR, BLEU-4, and CIDEr. Moreover, our final model (CSA-SR) achieves a strong 83.4 value of CIDEr, which is 3.86% higher than the closest competitor RecNet [24]. The score of METEOR has only 1.71% higher than GRU-EVE [49]. This might be because GRU-EVE has applied object information and the object information might be effective in improving performance. For other metrics, the score of our method remains competitive with other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **METEOR** & **BLEU-4** & **CIDEr** & **ROUGE-L** \\ \hline SVLAD & 33.8 & 45.9 & 77.5 & 68.9 \\ \(\text{SVLAD}_{CSA}\) & 35.1 & 49.9 & 83.7 & 71.5 \\ \(\text{SVLAD}_{SLSTM}\) & 34.6 & 48.9 & 79.7 & 69.8 \\ \(\text{SVLAD}_{SR}\) & 34.8 & 51.3 & 80.4 & 70.5 \\ \(\text{SVLAD}_{CSA-SLSTM}\) & 35.2 & 49.8 & 80.1 & 71.8 \\ \(\text{SVLAD}_{CSA-SR}\) & **35.6** & **52.2** & 83.4 & **72.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different architecture variation on MSVD dataset. Figure 5 shows some examples of generated captions in MSVD datasets. We divided them into three categories: correct captions, relevant but incorrect captions, and irrelevant captions. Generally, the correct captions are very close to the ground-truth. For instance, our model generates \"a man is pouring some ingredients into a bowl\" in the third video, which can describe in detail the corresponding video content. There are mainly two kinds of mistake in relevant but incorrect captions. For example, in the first relevant video, \"a man is jumping on a horse\" has an incorrect verb \"jumping on\" which should be replaced by \"riding\". This mistake also appears in the second relevant video: \"cutting\" in \"a man is cutting a bread\" should be replaced by \"buttering\". \"a man is riding a horse\" in the third relevant video contains the wrong noun \"horse\" which should be replaced by \"motorcycle\". These mistakes might be due to the lack of training samples. Although our proposed model has achieved satisfactory results, the existence of irrelevant captions shows that our model does not always generate what we need. Our method still has a lot of room for improvement, in the sentence generation part, the quality of sentences may be improved by other learning strategies such as sequence-level reinforcement learning.
## 6 Results and Discussion
### Parametric Study
#### 6.3.2 Effect of the Working Fluids in ORC
Using R152a as the working fluid, a case study was performed by changing the turbine inlet pressure and superheated temperature to determine the optimal working parameters of the system. Figure 11 shows the influence of turbine inlet pressure on the power generation of the turbine, energy efficiency, and exergy efficiency of the system. The pressure was changed from 4500 to 15,700 kPa, and the energy and exergy efficiencies first increased and stabilized at 13,500 kPa. The power output of the turbine first increased and then decreased when the pressure exceeded 10,900 kPa. This may be the effect of decreasing the enthalpy of the inlet stream turbine when the pressure increased. Figure 12 shows the influence of the superheated temperature on the ORC system. The superheated temperature was changed from 110 to 305 \({}^{\circ}\)C as the inlet temperature of the turbine. The energy and exergy efficiencies increased from 15.9% to 22.72% when the temperature increased from 110 to 223 \({}^{\circ}\)C. After this temperature, the increase in the LMTD will result in smaller areas of the heat exchanger and inefficient heat exchange. Figure 11: Influence of the turbine inlet pressure on the R152a ORC. Figure 12: Influence of the superheated temperature on the R152a ORC.
## 6 Simulation Results
### Robustness against Common Signal-Processing Effects
#### 6.3.2 Robustness against Signal Cropping  
The removal of one or more parts of the original signal is known as the \"signal cropping\" effect. This issue can occur when transmitting signals between different storage devices in IoMTs. Our scheme is evaluated against such attacks in the next experiment. To this end, a bio-signal named \"emg_healthy\" of electromyograms (EMG) was used. First, this signal was cropped at arbitrary indices, as shown in Figure 18. Then, our method was used to verify the copyrights of the protected signal. From the results displayed in Figure 18, it is clear that the BER values of the cropped signals remained equal to 0, indicating that our method is highly robust against cropping attacks. Furthermore, this result can be justified by the fact that our method was originally designed to extract four feature vectors from the original signal, which makes our scheme able to resist cropping attacks even if parts of the signal are removed.  
Figure 17: Example of binary sequences of size L = 512 used in the BER calculation for an ECG signal contaminated by “Gaussian” noise with k = 0.06.  
Figure 18: Original EMG signal and its cropped versions with the corresponding BER values.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology  
### Dataset Selection  
Selecting a URL dataset for this purpose involves selecting a dataset that comprises a list of web addresses, or URLs. Many scholars select publicly available datasets, including Kaggle, Mendeleyev, and Gram-bedding and some of the other researchers use DMOZ, PhishTank, and UNB datasets. There are a few things to consider while choosing a URL dataset: quality, integrity, reliability, trustworthiness, dataset size, and the diversity of the data. The dataset must be pertinent to the particular domain or topic of interest in the first place. The likelihood of receiving comprehensive and representative information increases with a larger and more diverse collection [16].
## 5 Experimental Results
### Part II--Results of Multi-Scenario and Multi-Objective Optimization
The GA was configured for 60 initial integer individuals, 30 generations, scattered crossover, 0.8 crossover fraction as well as 2 elite children. Figure 8 shows the obtained four-dimensional Pareto front from the Telos more based network. In the Figure, the node energy consumption and network packet loss rate of the glucose sensor node are compared with the corresponding performance metrics of the respiration and temperature nodes respectively. According to the problem requirements, a good tradeoff solution for both energy and reliability can be manually selected by the decision maker based on their knowledge and intuitive experience (e.g., dashed rectangle in the Figure 8). Algorithm configurations are also presented next to the tradeoff point in the Figure 8 (e.g., [_macMinBE_, _macMaxBE_, _macMaxFrameBackoffs_, _macMaxFrameRetries_] = [3, 5, 3, 1]). On the other hand, while the glucose sensor node is in both experiments, the results show that under such multi-scenario network conditions, the best tradeoff solution for two specific cases might not guarantee the best tradeoff for another two cases. Therefore, if an overall tradeoff solution is required for the whole network then a more comprehensive optimization on all possible scenarios needs to be evaluated. In addition, the performances (energy, reliability) of the highest sample rate based ECG node are also tested and compared with the glucose node for the iHop@Node based network. Two cases were  
Figure 8: Energy and packet loss Pareto front (Glucose _vs._ Respiration/Temperature). under investigation, 1 Mbps based high data rate and 250 kbps based low data rate. Figure 9 shows the 4D Pareto front between energy cost and packet loss for two data rate cases. The results in Figure 9 show that the balance of tradeoffs between energy consumption and packet loss still needs to be found on the Pareto front for both data rate cases. As compared to the commonly used 250 kb low data rate, the high data rate based network can provide both lower energy consumption and packet loss, which brings it closer to the ideal performance area. This is because the high data rate reduces the channel occupation condition, so more packets can be successfully transmitted. For energy saving, this comes from two parts. Firstly, with good channel conditions under high data rate, mechanisms like repeated channel check and retransmission in the unslotted algorithm are no longer necessary, since almost all the packets can be successfully transmitted with one attempt, and energy is saved from protocol overhead. Secondly, despite the fact that a high data rate case consumes more current during the packet and ACK frame transmission, the time spent in both transmissions are greatly reduced due to the high data rate, and ultimately a large amount of energy can be saved from this part.
## 4 Applications of AI in Modern Communication Networks
### Role of Interpretability in Critical Decision-Making  
In critical communication systems, operators must rely on AI-driven insights to make decisions rapidly. While less interpretable models like deep learning are highly effective at detecting anomalies, their \"black-box\" nature can hinder trust and immediate action during crises. In contrast, interpretable models such as decision trees or rule-based systems provide actionable insights that operators can easily understand and act upon.  
For example, in a traffic management scenario where a sudden surge in network activity occurs, an interpretable model could indicate that the anomaly stems from specific user behavior or a cyberattack. This transparency allows operators to take targeted actions, such as throttling specific connections or isolating compromised systems. By contrast, a deep learning model might flag the anomaly with higher accuracy but fail to explain its reasoning, leaving operators uncertain about the appropriate response.  
The integration of explainable AI (XAI) techniques into these models can address this challenge, enabling high-performing models to provide interpretable results without compromising accuracy.
## 2 Materials and Methods
### Antioxidant Capacity  
The antioxidant capacity of the films was determined using the 2,2-diphenyl-l-pikryl-hydroxyl (DPPPH) reduction method [21]. 30 \(\mu\)L of samples diluted in water (1:10 for powder films) were mixed with 1 mL of 0.1 mM DPPH in methanol. The mixture was vortexed and allowed to stand at room temperature in the dark (40 min) before measuring absorbance at 517 nm. In the same way, 30 \(\mu\)L of samples were diluted in water (1:10 for the powder films) and 1 mL of ammonium solution of 2.2\({}^{\prime}\)-azino-bis-(3-ethylbenzothazoline-6-sulfonic acid) (ABTS+) from Sigma Aldrich\({}^{\circledR}\), the solutions were diluted in mixed methanol [22]. After six minutes of reaction in the dark, the absorbance at 734 nm was monitored using a spectrophotometer (UV Visible Thermo Scientific Genesys 10S, Dreieich, Germany). The results were expressed to the IC50 parameter, which allows measuring the DPPH and ABTS+ radical scavenging capacity of the AE to an antioxidant standard. The lower the IC\({}_{50}\) value, the greater the antioxidant power it will have in the analyzed sample.  
Coating Effect on Strawberry Preservation: Coating Application on Strawberries, Development of Strawberries Inculated with Aspergillus niger  
The coating application was applied on strawberries (_Fragaria vesca_ L.) and compared with the application without coating. The fruits were selected according to their degree of maturity and the absence of defects, washed with 1% sodium hypochlorite for 2 min, and then washed twice in sterile water. The fungus (_Aspergillus niger_) was inoculated both before (curative treatment) and after (preventive treatment) of the coating of the fruit with the film-forming dispersion. Inoculation was performed using a sterilized needle to transfer 5 mL of a 10\({}^{6}\) spore suspension/mL. The coating-forming dispersion was sprayed onto the surface of the fruit and dried under ambient conditions. The strawberry samples were stored in cabinets at 25 \({}^{\circ}\)C and 85% RH (using a supersaturated KCl solution) for two weeks when the coating was dry. Finally, visual observation was made every five days to determine the appearance of the fruit and the incidence of fungi during storage. In the same way, weight loss was controlled in uninoculated fruits (coated and uncoated). For the above, 20 strawberries were considered in each series (coated and uncoated fruits).
## 5 Analysis and Discussion
### Analysis of Land Surface Settlement in Urban Villages  
The main reason for surface subsidence in urban villages is the poor foundation of the self-built houses themselves. Excessive load leads to ground settlement, triggering house tilting, wall cracking, and even the collapse of self-built houses. For example, 29 people were killed and 28 injured due to the collapse of rural self-built houses in Xiangfen County, Shanxi Province, on 29 August 2020. On 19 June 2021, a self-built house collapsed in Luyangtown, Rucheng County, Chenzhou city, Hunan Province, killing five people and injuring seven others. On 29 April 2022, a self-built house collapsed in Changsha, Hunan Province, killing 53 people. From the monitoring results in Figure 12, it can be seen that C1, C2, C7, and C8 all have large surface settlements. The main deformation rates are approximately \(-8\) mm/a, \(-8.5\) mm/a, \(-6\) mm/a, and \(-6.6\) mm/a, respectively. After fieldwork, it was found that all four areas comprised urban villages. Figure 16 is a photo of the tilting site of the self-built house in the urban village. The red lines and arrows in the picture mark the direction of the tilted houses, and traces of tilting can be clearly seen.  
The surface settlement in these four areas is mainly caused by foundation issues common to self-built houses in urban villages. These self-built houses generally have problems such as dense construction, poor foundations, and additional floors without permission. High-intensity load on the ground with soft soil will lead to accelerated surface consolidation effect and produce significant surface settlement. In addition, the deformation caused by the quality problems of the buildings themselves will also be detected in the time-series InSAR results. Due to the insufficient resolution of Sentinel-1 imagery, it is necessary to collect high-resolution SAR data and other auxiliary data to distinguish the type and cause of deformation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Settlement Center**} & \multicolumn{4}{c}{**Interval/Year**} \\  & **2007–2008** & **2008–2010** & **2016–2017** & **2018–2021** \\ \hline \hline S\({}_{2}\) study area & \(-21.6\) & \(-20.2\) & \(-20.6\) & \(-10.81\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Surface subsidence rate in the S\({}_{2}\) study area of Kunming during the different periods (mm/a).  
Figure 15: C6 communities’ distribution map (Google Earth image).
## 3 Results
### ERA5 Temperature Assessment  
Figure 6a,b illustrate the absolute temperature deviations between the ERA5 reanalysis data and microwave radiometer (MWR)-observed temperatures across various altitudes. In Figure 6a, temperature deviations for January, February, and March are presented, with green dashed boxes highlighting notable blue outliers. These outliers are attributed to frequent sandstorms and severe pollution during winter in Inner Mongolia. The ERA5 reanalysis data, based on model simulations, do not account for the impact of aerosols and only  
Figure 5: **(a–I) Scatter plots comparing wind direction observations at various heights with ERA5 reanalysis data.**assimilate limited high-altitude atmospheric observation data [43]. In Figure 7a-i, black-circled outliers represent data points under conditions of sandstorms and heavy pollution.  
In Figure 6a, during winter, ERA5 overestimates air temperatures at approximately 500 m and above 1000 m while underestimating them below 200 m. In Figure 6b, for summer, the median of absolute deviations remains near 0 K below 1500 m, though temperature deviations increase with altitude above 2000 m. At 3000 m, the ERA5 reanalysis data overestimate temperature by an average of 7 K compared to observations. Additionally, Figure 6b displays several outliers in absolute deviations due to intense convective weather events, such as thunderstorms and hailstorms. In Figure S3, the time series of absolute deviations below 1000 m reveals a significant overestimation of temperatures by ERA5 from 12 July to 15 July during strong convective weather, as indicated by prominent red shading in the temperature series. Persistent rainy weather from 23 August to 30 August results in the underestimation of nighttime temperatures by ERA5 in the absolute deviation time series.  
Figure 6: Absolute and relative deviations of temperature at various heights. (**a**) Absolute deviations of temperature (ERA5 reanalysis data minus MWR observations) for January, February, and March; (**b**) absolute deviations for July and August; (**c**) relative deviations (ERA5 reanalysis data minus MWR observations, divided by MWR observations) for January, February, and March; (**d**) relative deviations for July and August. Red circles and blue dots indicate the distribution of deviation data, with deeper colors representing denser areas. Blue dots represent outliers, and the light green box plot shows the interquartile range (75 th percentile, median, and 25 th percentile).  
Overall, above 2000 m, ERA5 reanalysis data tend to overestimate temperatures in both winter and summer, with absolute temperature deviations decreasing with increasing altitude during winter. Below 2000 m, ERA5's overestimation of winter temperatures gradually diminishes, while absolute temperature deviations during summer mostly remain within \(\pm 5\) K.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 4 Discussion
### Differences in Ecosystem WUE between Karst and Non-Karst Landforms  
We explored the differences in ecosystem WUE between non-karst and karst landforms. As shown by the results presented in Table 1, the mean values of ecosystem WUE were higher in non-karst than karst landforms, consistent with previous studies [16; 42]. However, some studies had shown that leaf and ecosystem WUE showed almost opposite results [43]. The WUE of individual plants is reportedly higher in karst than non-karst landforms [44; 45]. In fact, these findings are not contradictory to our findings. With adaptation to the habitats of karst areas, monocots coexist with multiple species and have higher leaf phosphorus content with increased upper and lower epidermal thickness, and higher leaf photosynthetic capacity, resulting in higher WUE in non-karst areas [44; 45]. Nonetheless, this study focused on the larger regional scale of the ecosystem. Karst areas have severe rock desertification and low forest coverage, while non-karst areas have high forest coverage, so the WUE is greater in non-karst than karst areas.  
The variation rate of ecosystem WUE was much greater in karst than non-karst landforms, which is consistent with the expected results. Ecological restoration projects have restored vegetation in both karst and non-karst areas, but more ecological projects have been invested in karst landforms to address ecological problems, such as stone desertification [9]. In addition, vegetation restoration has been more effective in karst than non-karst areas [46], as demonstrated by the comparatively increased rate of GPP. Meanwhile, the rate of GPP variation is relatively slower for indigenous vegetation in non-karst landforms. Along with the return of farmland to forests and grasslands and  
Figure 6: (**a**) Regional distribution of significant interannual variations in EVI from 2003 to 2017. (**b**) Interannual variation in ecosystem WUE in regions exhibiting significant changes.  
agricultural intensification in karst, vegetation recovery has continued to improve [47] with an increased GPP variation rate. Therefore, with little change in ET, the variation rate of GPP dominated the trend of ecosystem WUE, which significantly increased the ecosystem WUE in karst landforms greater than in non-karst landforms.
# Food Waste Detection in Canteen Plates Using YOLOv11
## 2 Related Work
### Implementations in the Food Detection Context
#### 2.2.3 Food Waste Detection  
FW detection is an emerging area with great potential to reduce the amount of FW, especially in industrial and food production environments. The idea is to identify damaged or leftover food so that it can be properly disposed of or reused.  
[PERSON] et al. (2019) [26] proposed a DL approach for detecting FW and automatically sorting waste from food trays. Using the aster Region Based Convolutional Neural Networks (R-CNN), the results showed a significant improvement in the accuracy of FW detection and sorting, which can be applied in industrial processes to optimize recycling and reduce waste.  
[PERSON] et al. (2024) [28] used YOLOv8 to detect damaged food in the food industry. The research showed how the integration of AI, specifically YOLOv8, can help quickly identify damaged or unfit-to-eat food, which can optimize quality control processes and reduce waste on production lines.  
[PERSON] et al. (2023) [27] focused on FW detection using DA techniques and YOLOv4. The innovation of the study was the use of Generative Adversarial Networks (GAN) to generate FW images, which helped to improve the performance of the YOLO model to detect waste more efficiently and accurately.  
These studies highlight the growing use of advanced detection technologies, such as convolutional neural networks and YOLO, to improve FW detection, not only in industrial environments but also in waste management systems, with the aim of optimizing processes and reducing environmental impact.