esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Comparison with State-of-the-Art Frame-Wise Methods  
In contrast to video-level supervision, fully supervised segmentation is a much more researched topic, and there are more diverse benchmarks available for comparison. As expected, there was an obvious gap between the best-performing video-level methods and the state-of-the-art frame-wise method (4. ASRF):  
\[\Delta_{Frame}(4-9)=(\overline{\Delta_{F.Acc}}=8.05\pm 1.90,\overline{\Delta_{ mAP}}=9.53\pm 4.44,\overline{\Delta_{Edit}}=8.23\pm 2.32)\]  
Although 11 out of the 12 current best results obtained in different dataset/measurement combinations were provided by fully supervised approaches (highlighted in bold font in Table 4), they rely on expensive frame-by-frame manual annotations and task-specific hand-engineered pre-/post-processing techniques, while our proposal is purely automatic end-to-end without human intervention, both during the training phase and after deployment. Note that on the second dataset (YouTube Instructions), our proposed 9. I3D + Transformer + R-CTC even outperformed the best frame-wise result by a noticeable margin in terms of order-level evaluation (\(+12.7\%\) by re-normalized _Edit_ measurement). The performance improvement was statistically significant, indicating the possibility that the explicit alignment learning in a purely data-driven fashion may be more effective than fully supervised methods if end-to-end ordering information is well-learned.
## 2 Methodology  
### Spatial Division Method  
In this study, the research area was spatially divided using the geohash algorithm, which grids and encodes the target area into strings, creating a hierarchical grid map through successive quartering (2 \(\times\) 2) of the space. Figure 2 illustrates this spatial division method. The first image shows the area divided into four sections, each assigned a base-4 identifier. Each of these sections was subsequently divided further, with a new base-4 identifier assigned in sequence, following the initial IDs. This method repeatedly divides an area into four parts. For this study, the geohash algorithm was applied to divide the area spanning 126\({}^{\circ}\) to 130\({}^{\circ}\) longitude and 34\({}^{\circ}\) to 38\({}^{\circ}\) latitude in Korea.
## 3 Solving Method
### Testing Process  
The output of the encoder-decoder network is the probability distribution for the task action. During the training process, the task is selected according to the probability distribution. In the testing process, the action with the maximum probability is selected. Algorithm 4 describes the testing process.  
```
Input: Task profit \(P_{a}\), trained encoder-decoder network, heuristic rule MRC, initialize \(S_{t\gets 0}\), \(done\leftarrow\)False, total profits\(\leftarrow\)-0 Output: Total profits, observation plan
1:while not \(done\)do
2: Input \(S_{t}\) and sequence information into encoder-decoder network
3: Select the task with the maximum probability
4: Select a satellite based on MRC
5: Execute \(a\) at the earliest feasible time windows
6: update \(done\) and state \(S_{t}\gets S_{t+1}\)
7: total profits = total profits + \(P_{a}\)
```  
**Algorithm 4** Testing process  
The performance of the proposed method is validated through numerical experiments in Section 4.
## 3 Results  
### Forest Degradation Maps in the Three-North Forest Shelterbelt  
#### 3.1.1 Trends of the Indicators from 2000 to 2010  
Trends of the indicators from 2000 to 2010 in the Three-North Forest Shelterbelt were shown in Figure 3. Obviously, NDVI, EVI, RVI, LAI, FPAR and NPP showed an increasing trend in most areas of the Three-North Forest Shelterbelt, which also demonstrated that great achievements have been achieved since the implementation of the program, and the forests were in good condition in most areas. The decreasing trend mostly occurred in the middle areas of Inner Mongolia, northwestern areas in Xinjiang, some areas in Gansu, Ningxia and Shaanxi. It was worth noting that some difference existed among spatial distribution of the trends of indicators. Specifically, in some western areas in Xinjiang, NDVI, EVI and RVI showed an increasing trend from 2000 to 2010, while the LAI, FPAR and NPP showed an increasing trend. The same situation occurred in eastern and southeastern areas in Liaoning and Jilin, the NDVI, EVI, RVI and LAI were increasing while FPAR and NPP were decreasing.  
Figure 3: Trends of the indicators from 2000 to 2010 in the Three-North Forest Shelterbelt. (**a**) Normalized Difference Vegetation Index (NDVI) trend; (**b**) Enhanced Vegetation Index (EVI) trend; (**c**) Ratio Vegetation Index (RVI) trend; (**d**) Leaf Area Index (LAI) trend; (**e**) Fraction of Photosynthetically Active Radiation (FPAR) trend; (**f**) Net Primary Production (NPP) trend.  
The reasons for the different tends mainly that the sensitive levels of these indicators to the changes varied. Specifically, NDVI and RVI were chlorophyll sensitive, and were well related to the vegetation canopy background and fraction of vegetation coverage [38]; EVI was more responsive to canopy structural variations, including canopy type, plant physiognomy, and canopy architecture in high biomass regions through a de-coupling of the canopy background signal and a reduction in atmosphere influences [39]; LAI product was related to NDVI, and could be influenced by the forest types and observation angle [40]; FPAR was an indicator of both the vegetation structure and the material and energy change process, was a response to the changes in atmosphere and soil and was well related to NDVI [41]; The changes in NPP were well related to the changes of temperature and precipitation, and could reflect the productivity [42]. Besides, the mixed pixels in a MODIS pixel and the heterogeneity of the land surface could also induce some different characteristics occurred among the kinds of indicators.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 1 Introduction  
With the rapid development of online retail and e-commerce, there has been a surge in the demand for express delivery, imposing significant pressure on delivery enterprises to seize the market. Therefore, it is urgent to integrate duplicated outlets and improve the efficiency of single-point coordination and distribution. At the same time, the Civil Aviation Administration has promulgated \"Civil Unmanned Aeronautics Development Roadmap V1.0\" [1], which proposes that in 2025, urban short-distance, low-speed, lightweight, and small-scale terminal logistics and distribution scenarios will gradually mature and urban medium- and long-distance logistics and distribution will be gradually applied. The regularized operation of large distribution enterprises' drone (unmanned aerial) logistics and distribution has already been realized, and the pilot distribution in various regions continues to advance, which will gradually realize the networked and scaled operation of drone distribution, thus causing a major change in the traditional express delivery system, network structure, and transportation mode. Based on the integration needs of scattered outlets, combined with drone distribution means, an intensive, multi-mode common distribution network integrating drone distribution will be built, which has a greater social and application value for improving the quality and efficiency of end distribution. Among them, the key to the rational operation of this cooperative co-distribution network is thesiting of the co-distribution center, and the rationality of the siting layout scheme is crucial to the network cost and operational efficiency, but there are few research results on the siting of co-distribution centers integrating drones. So, in order to solve the aforementioned problems and provide a theoretical basis for the field of drone co-distribution research, this paper researches the siting of the urban end co-distribution center with air-ground cooperation. This paper also researches the air-land cooperative urban terminal co-distribution center-siting method and proposes a co-distribution center-siting point solution method based on regional demand points. Section 2 introduces the current research status of UAV and ground distribution. Section 3 introduces the model of UAV and ground co-distribution. Section 4 introduces the model solution method based on the GWO algorithm. In Section 5, the effectiveness and computational efficiency of the algorithm are verified through experiments. Section 6 is the conclusion of this article.
# Creating Honeypots to Prevent Online Child Exploitation
## 3 Methodology
### Obfuscation  
Honeypots are only successful if attackers believe that they are authentic. If a honeypot does not appear and respond in an expected manner, it is then obvious that they are false, and interactions with them will cease. Within this study, the honeypot websites are constructed alongside a benign fitness website. The CEM-like websites are presented to users within ad sections within the fitness website, alongside other ads to benign products and services. This tiered approach to the honeypots aims to hide the intentions of the social media ads, and to ensure that the responses to the warning messages when they appear are as authentic as possible to gain a true measure of their effectiveness.  
Modern websites are constructed using a sizable infrastructure comprised of services operating on multiple servers around the world. Likewise, the websites we visit are not stored on the same device but are hosted in different locations. While it would be possible to construct the platform used within this study on one server, it would be trivial for a user to notice, and it would then appear strange and obviously false.  
The platform within the study involved a fitness website constructed and hosted by a nationally known marketing company. This included content targeted at the demographic of interest to ensure it would be an authentic web experience upon arriving after clicking on the social media ad. Within this website was several ads provided by an advertising service hosted on a server run by the research team. This advertising service delivered a range of ads, including those for the CEM-like honeypot. These were inserted randomly within the fitness website. The warning messages were delivered from same server as the ad-service, but with a different subdomain, referring to a service that provided web filtering services. Finally, the CEM-like honeypots themselves were hosted on a third server, operated by the researchers but at a different location to the ad-service server. The overall architecture of the honeypot is shown in Figure 1.  
All of the websites, including the CEM-like honeypot, were all protected by Transport Layer Security (TLS), with valid certificates to prove authenticity, even though contact information and business registrations provided to registrars was patently false. TLS is common throughout the web due to pressure from Google to mark websites as insecure without protection [50]. Similarly, malicious websites often use the same protections to appear authentic [51].
## 3 Results
### Inhibition of Fungal Growth  
As a result of plate tests, the direct impact of hemp extracts on the growth dynamics of the tested fungal species was determined. The effect of hemp extract on the growth of the tested fungus strain varied based on the extract concentration and its duration of action. Hemp extracts exhibited the most potent fungistatic activity against _Trichoderma koningii_, _Botrytis cinerea_, and _Colletotrichum cococodes_, while the weakest effect was observed against _Fusarium granimearum_ (Figures 1 and 2, Table 3). The antifungal activity of hemp extracts intensified with higher concentrations, with the most potent effects observed for 20% hemp extracts. The highest activity of the hemp extract was recorded for the H20 concentration against _B. cinerea_. In the control (C20), the fungus displayed dynamic growth, reaching a diameter of 90 mm on day 8, whereas in the H20 combination, it reached only 37 mm. Similar trends were observed for _T. koningii_, where in the control (C20) on day 4, the fungus reached a diameter of 87.6 mm, while in the H20 combination, its diameter was only 13.3 mm. _Fusarium_ species exhibited varying growth dynamics depending on the species and the concentration of the extract in the medium. The fastest growth was recorded on day 6 for _F. culmorum_ (C20: 89.3 mm, H20: 24.2 mm) and on day 8 for _F. sporotrichoides_ (C20: 90 mm, H20: 36.5 mm). The remaining _Fusarium_ species grew more slowly, with the diameter of their colonies on the 10 th day of the experiment after the addition of the extract being lower than 40 mm (Figure 1). Figure 1: _Cont._Figure 1: Colony diameter (mm) of fungi growing on potato dextrose agar (PDA) with addition of a hemp extracts; C5, C10, C20—control with 5, 10, 20% residue of solvent; H5, H10, H20—5, 10, 20% hemp extract concentration. Figure 2: _Cont._
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
## 2 Results and Discussion
### Comparison of Essential Oil Yield from Four Feijoa Cultivars  
The optimized HD was applied to the extraction of essential oils from four feijoa cultivars (Apollo, Unique, Opal Star, and Wiki Tu). As shown in Figure 2, the Wiki Tu cultivar produced the highest volume of essential oil, relative to the other three cultivars, reaching 0.68 mL/kg fresh peel, while no significant difference was observed among the Apollo, Unique, and Opal Star cultivars. The essential oil yield of the Wiki Tu cultivar was about three-times the amount of the other selected cultivars, which suggested that cultivar difference could significantly affect the yield of feijoa essential oils. As mentioned before, [PERSON], [PERSON] and [PERSON] [27], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [28] and [PERSON] and [PERSON] [16] conducted essential oil extraction, while none of them compared the essential oils from different feijoa cultivars. Therefore, our research is the first to confirm cultivar difference affecting essential oil production. It may also be of interest to assess additional feijoa cultivars to select the most essential oil-rich cultivar for commercial development.  
Figure 1: Optimization of the extraction of feijoa essential oils by steam-distillation (**A**) and hydro-distillation (**B-1,B-2**). (**B-1,B-2**) show the optimization based on the material to water ratio and extraction time, respectively. Letters a, b, c are indicators for statistical significance (\(p<0.05\)), identical letters indicate no statistically significant difference.
###### Abstract  
This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5 Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute relative error (MARE) and determination coefficient (\(R^{2}\)) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5 Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective.  
Keywords:fiber reinforced polymer; concrete; column; confinement; stress; strain; model +
Footnote †: journal: Applied Sciences
## 6 Results and Analysis
### Ablation Experiment  
#### 6.3.1 FFPM and Res-EMA Ablation Tests  
To assess the impact of each module on network performance, we conducted ablation studies using the Jacquard dataset, in which four sets of different tests were carried out.  
In test 1, our network did not incorporate any additional modules. In test 2, we added the Feature Fusion Pyramid Module (FFPM) without including the Residual Efficient Multi-Scale Attention (Res-EMA) module. In test 3, we included the Res-EMA module without the FFPM. Finally, in test 4, both the Res-EMA module and FFPM were integrated into the network.  
Table 3 presents the results of the model ablation experiment. Comparing test 2 with test 1, the addition of the FFPM resulted in a 1.1% increase in grasping detection, demonstrating its effectiveness in enhancing performance. Similarly, comparing test 3 with test 1, the inclusion of the Res-EMA module led to a 1.2% improvement. In test 4, where both modules were utilized simultaneously, a 2.1% increase in performance was observed compared with test 1, suggesting that the combined use of both modules yielded the best grasping detection performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Year** & **Accuracy (\%)** & **Speed (ms)** \\ \hline SAE [9] & 2015 & 73.9 & 1350 \\ GG-CNN [4] & 2020 & 73.0 & 19 \\ ROI-GD [8] & 2019 & 93.6 & 40 \\ GR-CNN [6] & 2020 & 97.7 & 20 \\ Cascaded Net [19] & 2021 & 95.2 & - \\ RSE-Net [12] & 2021 & 96.4 & 5 \\ Det\_Seg\_Refine [36] & 2021 & 98.2 & 63 \\ TF-Grasp [10] & 2022 & 98.0 & 42 \\ Efficient-Grasp [21] & 2022 & 97.8 & 6 \\ SE-Res-Unet [11] & 2022 & 98.2 & 25 \\ QQCNN [22] & 2024 & 97.7 & 17 \\ Ours & 2024 & 98.9 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results in the Cornell dataset.  
Figure 7: Grasping detection results in the Cornell dataset.
## 1 Introduction
These algorithms are the standard Optimized Link State Routing Protocol (OLSR) [9], the Directional-OLSR (D-OLSR), the Mobility and Load Aware OLSR (ML-OLSR), and the Predictive-OLSR (P-OLSR). We chose to study and simulate these routing protocols because OLSR is the most relevant routing solution specifically in a search and rescue (SAR) environment;
2. It tests the performance of the above OLSR-based routing algorithms in a SAR environment. NS-3 was used to simulate the above protocols and replicate three SAR scenarios;
3. It considers three simulation scenarios, including (1) increasing mobility, (2) increasing scalability, and (3) increasing the allowed space of UAVs. Moreover, multiple performance QoS parameters, including packet delivery ratio (PDR), latency, energy consumption, and throughput, were used to analyze the performance of the selected OLSR-based routing protocols. Figure 1 illustrates the workflow of our approach. UAVs can be used to monitor infrastructure for livestock, historic sites, or forest fires in difficult-to-access locations. Specialized sensors for geological surveys could also be installed on the UAVs. As with any other connected device, UAVs are vulnerable to attack. They could also threaten our physical, digital, and personal security [10]. Notably, suppliers use implementation or design secrecy as their primary means of system security. As a result, attackers may \"simply\" take control of a UAV using common \"hacking\" tools to prevent it from carrying out its missions or, worse still, inflict damage. Aside from UAV hijacking, the majority of UAVs capture and retain data locally. Often, data that are stored is not encrypted, and even data that are transmitted wirelessly (such as telemetry) can be readily overheard by outside parties. It is noteworthy that UAVs, as new gadgets, are equally susceptible to \"normal\" Internet attacks if they are connected to the Internet. Thus, there is a pressing need to strengthen UAV security [11]. The remainder of this paper is structured as follows: Section 2 discusses related works. Section 3 presents the differences between OLSR, D-OLSR, ML-OLSR, and P-OLSR. Section 4 elaborates on the study methodology, simulation setup, simulation scenarios, and performance measurements. Section 5 presents the simulation results and their related discussion, while Section 6 concludes the paper.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.7 Man-in-the-Middle Attack Resistance  
Suppose that \(A\) intercepts the request message \(\{PID_{i},M_{1},M_{3},M_{4}\}\) and response message \(\{M_{5},M_{7},M_{8}\}\); in order for the attack to succeed, the new information flow \(\{PID_{i}^{*},M_{1}^{*},M_{3}^{*},M_{4}^{*}\}\) and \(\{M_{5}^{*},M_{7}^{*},M_{8}^{*}\}\) need to be forged by \(A\), or the previous information flows need to be replayed. As mentioned above, the scheme can resist replay attack and impersonation attack, so \(A\) cannot authenticate through both smart meters and data concentrators simultaneously. Therefore, the scheme can resist man-in-the-middle attack.
## 2 Materials and Methods
### Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Parameter** & **Data Source** & **Period** & **Spatial** & **Temporal** \\  & & & & **Resolution** & **Resolution** \\ \hline \multirow{3}{*}{Vegetation} & Land cover & European Space Agency & 1992–2020 & 300 m & Yearly \\  & NDVI & MODIS MOD13Q1 & 2000–2022 & 250 m & 16-day \\  & EVI & MODIS MOD13Q1 & 2000–2022 & 250 m & 16-day \\ \hline \multirow{3}{*}{Climate} & Temperature [41] & National Earth System & \multirow{3}{*}{190–2100} & \multirow{3}{*}{1 km} & \multirow{3}{*}{Monthly} \\  & & Science Data Center & & & \\ \cline{1-1}  & Precipitation [41] & National Earth System & \multirow{3}{*}{190–2100} & \multirow{3}{*}{1 km} & \multirow{3}{*}{Monthly} \\  & & Science Data Center & & & \\ \cline{1-1}  & Surface solar radiation [42] & National Tibetan Plateau & \multirow{3}{*}{1983–2018} & \multirow{3}{*}{0.1\({}^{\circ}\) (\(\sim\)10 km)} & \multirow{3}{*}{Monthly} \\  & & Data Center & & & \\ \cline{1-1}  & Evapotranspiration [43] & Terra Climate & 1958–2021 & \(1/24^{\circ}\) (\(\sim\)4 km) & \\ \cline{1-1}  & Potential & \multirow{3}{*}{Terra Climate} & \multirow{3}{*}{1958–2021} & \multirow{3}{*}{1/24\({}^{\circ}\) (\(\sim\)4 km)} & \multirow{3}{*}{Monthly} \\ \cline{1-1}  & evapotranspiration [43] & & & & \\ \hline \multirow{2}{*}{Boundary} & Study area boundary & National Cryosphere & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{–} \\ \cline{1-1}  & Desert Data Center & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The dataset in this study. processing can produce a smoothing effect on the data, thus it is considered to be suitable for resampling continuous data. The measured data are mainly from the work of [PERSON] et al. [44] and [PERSON] et al. [45]. They collected above-ground biomass in the wild from July to September of each year from 2001-2005. The locations included the Bayinbuluk Grassland Eco-System Research Station, the Chinese Academy of Sciences, and the temperate grasslands of northern China. In the sampling process, a large sample plot of 10 m \(\times\) 10 m was first set up, from which five small sample plots were taken to investigate the vegetation species, height, cover, longitude and latitude, etc. Next, the above-ground part of the vegetation was moved flush with the ground, removing the incidental soil and gravel, and taken back to the laboratory to be dried at 65 \({}^{\circ}\)C to a constant weight. Finally, the average value of above-ground biomass of the five small sample plots was taken as the value of the large sample plot and converted to carbon sequestration (g C/m\({}^{2}\)) by a 0.45 conversion factor. Eventually, a total of 21 measured points were collected for the validation of the NPP simulation results in our study. The study area boundary was extracted from the 1:100,000 land desertification dataset for the ARNWC, which was obtained from the National Cryosphere Desert Data Center ([[http://www.ncdc.ac.cn](http://www.ncdc.ac.cn)]([http://www.ncdc.ac.cn](http://www.ncdc.ac.cn)) (accessed on 9 January 2022)).