esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sediment Porewater Toxicity Testing with the Amphipod Hyatella azteca  
_Hyatella azteca_ is an epibenthic and phytal amphiopod which plays a vital role in ecosystem resilience due to its detritus-feeding and potential high abundance in nature. It is widely distributed in the freshwaters of North America, South America, and the Caribbean. Algae, epiphytes, and sediment particles are the main food sources of this amphiopod. It is about 3-8 mm long (males are larger than females) and considered to be sensitive to aquatic pollutants [38]. It is known to tolerate slight salinity changes, but not to survive pH conditions of less than 6.0.  
Centrifuged porewater (50 mL) was obtained and kept at 4 \({}^{\circ}\)C until analysis. Water parameters such as pH, conductivity, temperature, and dissolved oxygen were measured before and after this process at room temperature. Specimens of _H. azteca_, aged between 7 and 10 days, were selected to ensure that the test organisms were similar. _H. azteca_ in reconstituted water (96 mg/L NaHCO\({}_{3}\), 30 mg/L MgSO\({}_{4}\), 4 mg/L KCl, 50 mg/L CaSO\({}_{4}\), and 50 mg/L CaCl\({}_{2}\) were dissolved in deionized water and aerated vigorously for at least 24 h) was used as controls. Operating conditions included a photoperiod of 16 L:8 D, temperature 23 \(\pm\) 1 \({}^{\circ}\)C, and 20 mL reconstituted water/sample volume. Samples were tested in duplicates of 10 test organisms per beaker and the controls were reconstituted waters following a standard method. Ten individuals were placed in a beaker for 48 h exposure, and their survival rates were compared. Differential survival with samples was then compared to the control (80%) to estimate the level of toxicological risk.  
In this study, after adjusting the results to the control group, samples with a relative survival rate of <75% were defined as being toxic to organisms following US EPA suggestions [39; 40]. The relative survival rate was obtained from the ratio of the observed survival rate of the sample organisms compared to the expected survival rate of the control group (Equation (1)).  
\[\text{Relative survival rate}=\frac{\text{Observed survival rate}}{\text{Expected survival rate}}\ \times\ 100 \tag{1}\]  
Figure 1: Sampling sites of riverine sediments in southern Taiwan.  
It was suggested that if the adjusted survival rate of organisms was less than 75%, this would cause adverse effects on aquatic organisms and human health. If the survival rate was between 75% and 90% for aquatic organisms, human health could be harmed, and if aquatic organisms showed a survival rate >90%, human health was certainly affected [40].
# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
## 2 Underwater Degraded Image Restoration Methods
### Experimental Environment  
The experimental environment is shown in Figure 7, and the main components include a polarization camera, a polarized light source, a linear polarizer, a glass water tank, and a target. The camera uses a color polarization camera TRIO50S-QC equipped with a Sony IMX250 MYR image sensor, which is a four-channel split-focus planar polarization camera that can simultaneously acquire polarized images at four polarization angles: 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), the maximum pixel size of each image is 2448 \(\times\) 2048. This four-channel split-focus plane polarization camera solves the problem of poor recovery effect caused by the time delay and irregular movement of scattering particles. In the experiment, the target was irradiated using an active light source (Rawray 150 W LED lamp) with the addition of a USP-50C0.4-38 linear polarizer, and the target was placed in a transparent, high-transparency acrylic tank (with a transmittance of 96%). And the inside of the tank was covered with a black coating to avoid interference from ambient light outside the lab. And in order to simulate the turbidity of natural water, milk was added to the water, and the turbidity was changed by quantitatively adding an amount of skimmed milk (1200:5), which was used to simulate the turbid underwater environment for underwater experiments, as shown in Table 1. And finally, the optimized experimental images were obtained by the method proposed in this paper.  
Figure 6: Flowchart for joint estimation of target optical polarization.  
Figure 7: Experimental environment diagram.
## 4 Results and Analysis
### Compare of Heat Fluxes Calculated Using Different LST Datasets
#### 4.3.2 Comparison of the DisTrad-LST and GWR-LST Heat Fluxes
The biggest difference occurred in SD on 14 September, and the DisTrad-LST are better than the GWR-LST. However, in Section 4.1, we verify that the LST of DisTrad in areas covered by swampland are higher than the observed values. The reason is that we use the footprint model to validate the heat fluxes, and the verification result is determined by the contribution source area. Even at the same station, which of the two methods is better changed over time. For example, at EC11, the GWR-LST LE are better than the DisTrad-LST LE on 19 June and 30 June, but the opposite pattern occurs on 13 September. The reason may be related to the underlying surface type of the flux contribution source area. According to the calculation principle of the footprint model [63; 64], the flux contribution source area is closely related to the wind speed and wind direction, and the wind speed and wind direction at the same station on different dates are likely to be different. To confirm this, we calculated Shannon's diversity index (SHDI) of the flux contribution source area (see Table 7). SHDI reflects the heterogeneity of a landscape, and it is sensitive to unbalanced distributions of various patches. In our paper, patches means different types of land use. SHDI also reflects the complexity and variability of each landscape patch type [67]. It was calculated using the following formula:  
\[\text{SHDI}=-\sum_{i=1}^{m}(P_{i}\times InP_{i}) \tag{11}\]  
where \(P_{i}\) is the area ratio of the \(i\) th patch type in the entire landscape, and \(m\) is the number of patch types. SHDI = 0 indicates that the entire landscape consists of only one patch type. The main patch type in our study area include maize, spring wheat, barley, other crop, buildings and uncultivated land. The values of SHDI are shown in Tables 7 and 8. Compared to the measured values, the GWR-LST LE is always better than the DisTrad-LST LE when the SHDI value is high (>0.74). In such cases, the footprint area contains three or more different types of objects. Combined with specific surface types, we find that this situation belongs to the second condition of heterogeneity. At EC11 on 30 June, EC14 on 13 September, and EC09 on 8 July, the footprint area contains buildings (the proportions of buildings were 7.34%, 2.56%, 1.52%, respectively) and three types of vegetation. The SHDI values at EC03 on 3 August and EC01 on 30 June are lower than those of the site described earlier, because the footprint area is composed of only two types of vegetation and buildings (the proportions of buildings were 4.06% and 7.87%, respectively). At EC11 on 19 June, SD on 8 July and SD on 27 July, SHDI is high, but its underlying surface did not
# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 2 Related Works
### Video Editing Techniques  
Several factors must be considered to generate seamless cross-edited transitions using facial features. [PERSON] investigated the impact of continuous editing in film and video on audience attention [16]. He described how continuous editing techniques, such as eye-level alignment and consistent screen orientation, help maintain logical consistency between scenes while keeping the audience engaged with the narrative. [PERSON] and [PERSON] further emphasized that continuity editing supports audience comprehension by reinforcing spatial and temporal coherence, helping viewers maintain a mental model of events and minimizing cognitive disruption during transitions [17]. [PERSON] et al. explored the automatic generation of subject-based transitions in slideshows and demonstrated through user evaluations that subject-focused transition techniques were preferred over simple crossfades [18]. They also highlighted that linear easing--where changes occur instantaneously--is less aesthetically pleasing and recommended using non-linear functions such as ease-in, ease-out, and ease-in-out for the blending process.  
In face-based transitions, where dozens of frames are utilized near the transition point, it is crucial to accurately identify shot boundaries to prevent a disruption in visual immersion. [PERSON] et al. proposed a high-accuracy method for detecting shot boundaries using histogram difference and the SURF algorithm [19]. However, this approach requires high computing power. [PERSON] et al. proposed a method for detecting scene changes based on a histogram correlation between consecutive frames [20]. [PERSON] presented a fast, fully convolutional network for shot boundary detection, achieving high speed but limited performance in long dissolves and fast-motion scenes [21]. [PERSON] et al. introduced a block-based \(\chi^{2}\) histogram method to detect abrupt transitions, though it requires manual threshold adjustments for optimal performance across different videos [22].  
In this paper, we apply editing effects based on the findings of the studies above, thereby ensuring smooth scene transitions. Additionally, we propose a shot boundary detection method using histogram correlation and the Intersection over Union (IoU) of bounding boxes obtained during feature extraction. This method is easy to implement while maintaining a high performance.
# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 5 Results
In fact, the values of the validation dataset that are near the extremes of the fuzzy sets of temperature and humidity, and thus have a very low membership degree, lead to outlier predictions and, consequently, to the OOK classification. In Figure 6, the comparison between the number of variables allocated in memory (a) and the number of FLOPs (b) is shown for both the FFNN with the standard approach and the proposed fuzzy-based approach. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \(n_{i}/n_{h,1}\) & **3** & **4** & **6** & **8**  
Specifically, with the proposed approach, the following reductions in the architectural complexity of the FFNN component are achieved:  
* The number of variables allocated in memory is reduced by 92.32%;
* The number of Floating Point Operations is reduced by 94.26%. These improvements facilitate the deployment of the FFNN on hardware with limited computational capabilities, particularly at the extreme edge of the network. Conversely, when NNs are deployed at the edge, the primary objective shifts toward maximizing classification accuracy and prediction performance, without the strict constraints of computational limitations. In such scenarios, the proposed fuzzy-based approach achieves superior performance, reaching higher classification accuracy with the globally optimal architecture consisting of 10 inputs and 60 hidden nodes, as highlighted in italics and marked with an asterisk in Table 6. In this configuration, the method achieves a 6% improvement in performance. However, the proposed methodology and the work conducted also have some limitations. First, the technique should be further evaluated on other types of NNs to determine whether its effectiveness extends beyond feed-forward architectures. Another limitation concerns the applicability of the approach itself: it is not a general-purpose method that guarantees improvements when applied to NNs. Instead, it is particularly well suited to scenarios involving time-series numerical data used for classification and prediction tasks. Nonetheless, an additional advantage of the proposed fuzzy-based approach lies in its flexibility. It enables the rapid adaptation of the model to different crops that require customized microclimate thresholds. In practice, the same trained FFNN can be reused by simply updating the fuzzy sets in the fuzzy layer to reflect new classification criteria. This stands in contrast to the conventional approach, which would require retraining the FFNN and redefining the final classification labels within the dataset.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 2 Data
### Calipso  
Launched in April 2006 into the A-Train satellite constellation, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) provides near-global cloud and aerosol observations [19]. The instrument utilized in this study is CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), which is a polarization-sensitive lidar operated at 532 and 1064 nm. The CALIPSO Level 2 Version 4 5 km cloud profile product (CPro) is used for ice cloud optical depth and vertical occurrence information. The CALIPSO Level 2 Version 4 5 km merged cloud and aerosol layer product (MLay) is used to include all feature types detected in a case study comparison. The CALIPSO Level 2 Version 4 5 km cloud layer product (CLay) is used to construct global cloud fractions.  
Cloud and aerosol detection uses a threshold algorithm detailed in [PERSON] et al. [43], and the corresponding extinction retrieval is described in [PERSON] and [PERSON] [44] with the latest extinction retrieval updates highlighted in [PERSON] et al. [45] CALIPSO cloud products estimate the lidar ratio using either constrained or unconstrained retrievals. Constrained retrievals utilize the transmission method when features are bounded by clear air [6]. The constrained retrievals constrain the feature's two-way transmittance, which results in a measure of the effective lidar ratio (i.e., the product of the lidar ratio and multiple scattering factor). Unconstrained retrievals are utilized when a constrained retrieval is not possible. Unconstrained retrievals for ice clouds estimate the lidar ratio based on the temperature following the Version 4 temperature-dependent parameterizations of the lidar ratio [45]. In this study, both constrained and unconstrained retrievals are considered.  
Ice cloud analysis is limited to randomly oriented ice particles. Optical depth retrievals are limited to those with an extinction quality control flag value of 0, 1 and 2 [45]. The cloud fraction analysis is limited to low, medium, and high-confidence cloud layers (i.e., CAD scores \(\geq\) 20). Data extends from 13 June 2006 to 31 August 2016, which represents the beginning of CALIPSO's dataset through to the end of the RL's dataset.
## 1 Introduction  
As a result of the growth of the urban population worldwide [1], cities are consolidating their position as one of the central structures in human organizations. This concentration of resources and services around cities offers new opportunities to be exploited. Smart cities [2, 3] are emerging as a paradigm to take advantage of these opportunities to improve their citizens' lives. Smart cities use the sensing architecture deployed in the city to provide new and disruptive city-wide services both to the citizens and the policy-makers. The large quantity of data available allows for improving the decision-making process, transforming the whole city into an intelligent environment at the service of its inhabitants. One of the target groups for these improved services is the \"young-old\" category, which comprises people aged from 60 to 69 [4] and that are starting to develop the ailments of old age. The early detection of frailty and mild cognitive impairments (MCI) is an important step to treat those problems. In order to do so, ambient assisted living (AAL) [5] solutions must transition from the homes to the cities.  
City4 Age is a H2020 research and innovation project with the aim of enabling age-friendly cities. The project aims to create an innovative framework of ICT tools and services that can be deployed by European cities in order to enhance the early detection of risk related to frailty and MCI, as well as to provide personalized interventions that can help the elderly population to improve their daily life by promoting positive behaviour changes. As part of the tools created for the framework, we have developed a series of algorithms for activity recognition and behaviour modelling. The recognized activities and the behaviour variations are then used to ascertain the frailty and MCI risks levels of the users. In the past, we have worked on creating single-user activity recognition algorithms for home environments [6, 7, 8], but in the case of City4 Age, we have created algorithms that take into account both the large-scale scenarios of the project and the variation in the behaviours of the users.  
In this paper, we present a conceptual classification of the user behaviour in intelligent environments according to the different levels of granularity used to describe it (Section 3). Using this classification, we also describe the algorithm developed to automatically model the inter-activity behaviour (Section 3). This algorithm uses long short-term memory networks (LSTMs) [9] to create a probabilistic model of user behaviour that can predict the next user action and detect unexpected behaviours. Our evaluation (Section 4) analyses different architectures for the creation of a statistical model of user behaviour.
# Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data
## 2 Methodology
### Database  
The area under consideration has nearly 20 thousand oil palms of Dura x Pisifera (DxP) type whose age is slightly more than eleven years. It is a flat area that records a consistent yearly rainfall of roughly 2700 mm. Most of the oil palms are healthy and they bear fruit periodically all year long. The camera used to acquire the hyperspectral data was a Resonon Pika L (Resonon Inc., Bozeman, MT, USA). It was carried by a DJI M600 Pro hexacopter drone, Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China. The hyperspectral data contained 300 spectral bands with wavelength from 350 nm to 1000 nm. The spectral resolution was 2 nm. The drone can haul a maximum load of 6 kg and sustain a flight duration of 15-20 min. A calibration tarpaulin was used as a reference on the ground.  
As much as possible, data recording was carried out under clear sky, albeit at different sunlight intensity depending on the flight time and cloud shading. The altitude of the UAV was maintained at approximately 80 m during data sorties, and that translated to a resolution of nearly 10 cm \(\times\) 10 cm of ground area for each pixel. The average speed of the drone was approximately 25 km/h during data acquisition. Figure 1 shows part of the area under consideration and Figure 2 shows a pair of the lowest- and highest-wavelength channels that were affected by noise and luminosity variation. Keep in mind that the intensity range of the images have been scaled to 255 for display. It seems that the channels of the short wavelength contain more noise than those of the long wavelength.  
Figure 1: An area of the oil palm plantation.
## 4 Data and Examples
### Parameter Prediction and State Analysis
#### 4.3.2 Traffic Spatiotemporal State Parameter Calculation Based on Prediction  
With the help of phase space LSTM predictions of traffic parameters, including the travel time, speed, and headway, we could predict the TP via Formula (12) with weight coefficients \(\alpha\) and \(\beta\). Based on the prediction results and using Formula (12), the travel  
Figure 9: (**a**) Training results from LSTM with phase space reconstruction for travel time parameters. (**b**) Prediction results from LSTM with phase space reconstruction for travel time parameters.  
time-related state parameters and spatial state parameters, such as the queue length, for each lane in the case road section could be calculated. Selecting a suitable weight coefficient of 0.25 allows the weighted traffic spatiotemporal state parameters to be obtained. These results are shown in Figure 10.  
Figure 10: _Cont._
## 2 Configuration Descriptions  
### Lab-Scale Configuration  
The experimental lab-scale configuration is composed of a perforated plate with 50 apertures of diameter \(D~{}=~{}6.25\) mm arranged in 10 lines of 5 rows (Figure 1b), which is placed within a wind tunnel test section (Figure 1a). Each perforation is extended by a duct that ends into a piston, which is used to generate synthetic jets. All of the pistons are connected to the same engine, meaning that the jets are synchronised. In the numerical study, the piston motion is approximated by a sinusoidal signal:  
\[Z_{pistons}(t)=Z_{mean}+K\,sin(2\pi ft), \tag{3}\]  
where \(Z_{mean}\) is the piston vertical mean position while \(K\) and \(f\) are the imposed amplitude and frequency, respectively.  
To study wall heat transfer, the perforated plate is heated. [PERSON] et al. [18] used a regression method to find out the heat transfer coefficient \(h\), based on [PERSON]'s cooling law: \(\Phi=h(T_{w}-T_{ref})\). The methodology relies on the imposition of several values of wall heat flux \(\Phi\) and the measurement of the wall temperature \(T_{w}\): \(h\) is then given by the slope of  
Figure 1: Test section geometry. (**a**) test section (solid and dashed lines) and numerical domain (solid lines only); (**b**) perforated plate dimensions. \(\times\): positions of the profiles provided in Figures 10 and 13.  
the linear relation between \(T_{w}\) and \(\Phi\) and the reference temperature \(T_{ref}\) as the temperature for null heat flux.  
Different operating points were considered during the experimental study, defined by the frequency \(f\), the amplitude \(K\) of the pistons and the grazing flow velocity \(U_{0}\). The present study focuses on a specific point defined in Table 1. The inlet velocity profile is turbulent (Figure 2), and the experimental data provide a maximum of turbulent intensity equal to 10.5% (Figure 2). Since one of the objectives is to determine the impact of upstream turbulence on heat transfer, two different kinds of simulations are performed: the first one reproduces the experimental turbulence by injecting a fluctuating velocity at the inlet, while the second kind ignores it.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response
### Search and Rescue  
After natural disasters, there may be survivors adrift in the water or groups in life-rafts. Failure to rapidly respond to disasters and rescuing survivors in the water can often lead to prolonged suffering and death. When performing SAR in this context, USVs' capabilities can be a powerful asset. A USV can cover a wide area fast over the water surface, carrying survival kits, or providing emergency communication infrastructure, while also performing coordinated operations along with other unmanned systems [185]. General-purpose USV research often mentions SAR as a motivation for autonomous USVs. However, few works truly address the problem of SAR directly.