esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance of Each Time Series Model
#### 4.1.2 Performance of LSTM Model  
To improve the model accuracy, precipitation series were added for multivariate prediction in addition to the error series. The training dataset uses the simulated BTOP values from 1982 to 1984, and the 1985 data is the testing dataset. The input data are first normalized to make each feature variable contribute equally to the results, speed up the model iteration and improve the model accuracy. After many rounds of debugging, the model training parameters are: _leaning_rate_ = 0.001, _units_ = 20, _batch_size_ = 5, _epochs_ = 100. Due to the small amount of training data and the number of training sessions, overfitting occurs in the parameter debugging, so the regularization method is introduced. Figure 7 shows the loss of the training process. We can see that the _MAE_ and loss decrease significantly in both the training and testing sets, and level off after 80 iterations, indicating that the LSTM model fits well.  
Figure 6: Hydrographs from Donglin Station in 1985 with observed, simulated, and ARIMA correction runoff.  
Figure 7: Loss function process of LSTM model.  
Figure 8 is the runoff simulation diagram obtained by using the LSTM model to correct the BTOP model. It can be seen that the corrected green curve trend of the LSTM is more consistent with the observed flow, and the error is reduced in most of the low-flow seasons. However, runoff has reduced slightly at the same maximum point rather than increased, mainly because the LSTM during practice is the trend of the error sequence in learning, without considering the error that comes from different scenarios. That is, there is no difference when handling the high-flow and low-flow season of the error sequence. As a result, the correction effect is not as good as the Prophet model [58; 59]. In fact, different forecast scenarios and their corresponding forecast error distributions are important information for predicting the magnitude of forecast errors in future periods [60; 61].
# Investigation of Static and Kinematic Surveying Performance of Handheld GNSS Receiver +
## 1 Introduction
In the study conducted by [PERSON] et al. [17], the static measurement performance of the Garmin GPSMAP 66 sr unit was investigated, and they found that the accuracy could be achieved at the cm level with the relative method and at the meter-to-decimeter level with the PPP technique. In this study, the usability and performance of a handheld GNSS receiver, which costs a few hundred dollars (~500-700 USD), was investigated in geodetic measurements.
## 2 Research Methods for Reservoir Fluid PVT High-Pressure Physical Property Analysis
### Prediction and Analysis of Formation Crude Oil Viscosity
#### 2.3.5 Dataset Collection and Processing  
In the analysis of the high-pressure physical properties of reservoir fluid PVT based on the graph convolutional network model, the collection and processing of the dataset are crucial steps. The data used in this study were sourced from five typical oil and gas reservoirs in the Bohai Bay Basin, Ordos Basin, and the western South China Sea Basin in China. These oil and gas reservoirs have different geological characteristics and fluid properties, covering various types such as conventional oil reservoirs, condensate gas reservoirs, and heavy oil reservoirs. Geologically, there are three sandstone reservoirs, one carbonate reservoir, and one shale oil reservoir. The reservoir depths range from 1500 m to 4200 m, covering shallow to deep reservoirs. The reservoir temperature ranges from 75 \({}^{\circ}\)C to 165 \({}^{\circ}\)C, and the pressure ranges from 15 MPa to 85 MPa. The dataset used in this paper covered the PVT high-pressure physical property parameters of multiple oil and gas reservoirs including key indicators such as bubble point pressure, volume factor, crude oil viscosity, dissolved gas-oil ratio, compressibility, crude oil density, and saturation pressure.  
To ensure the accuracy and reliability of the data, the following collection steps were taken:  
1. Data source selection: Selected representative oil and gas reservoirs as data sources to ensure that the data covered different geological conditions and fluid properties.
2. Experimental measurement: Obtained various high-pressure physical property parameters through PVT experiments and ensured that the experimental conditions met the industry standards to improve the accuracy and comparability of the data.
3. Data integration: Integrated the data of different oil and gas reservoirs to form a unified data format for convenient subsequent processing and analysis.
4. Data cleaning: Removed outliers and missing values to ensure the integrity and consistency of the dataset.
5. Data normalization: Since the dimensions and numerical ranges of each parameter were different, we normalized the data to eliminate the influence of dimensions and improve the convergence speed and prediction accuracy of the model.
6. Dataset division: Divided the dataset into a training set, a validation set, and a test set, which were used for model training, hyperparameter tuning, and performance evaluation, respectively.  
Table 2 shows the description of dataset processing.  
Through the above steps, a high-quality dataset was constructed, providing a solid foundation for the subsequent training and analysis of the graph convolutional network model.
## 1 Introduction
Physical rule-based algorithms derive cloud and cloud shadow masks by establishing thresholds based on satellite bands or band ratios (e.g., [54; 55; 56]. For example, [PERSON] et al. [55] developed the QA60 bitmask approach that is the default method applied in GEE for processing Sentinel-2 imagery. QA60, the ESA-provided cloud mask layer, uses Sentinel-2 blue (B2) and short-wave infrared (B10, B11, and B12) bands, applying B11 and B12 to reduce confusion between snow and cloud and B10 to support separation of cirrus and dense cloud pixels [55]. Multi-temporal-based algorithms commonly utilize cloud-free images or pixels as a baseline and compare each pixel with this reference. Pixels showing significant variations in reflectance compared to the reference are then identified as potential cloud or cloud shadow pixels (e.g., [57]). Machine learning-based algorithms, including both machine learning and deep learning methods, are utilized to address cloud mitigation as a classification problem, distinguishing clouds and cloud shadows (e.g., [53; 58]). The Cloud Score+ method presented by [PERSON] et al. [58] employs a weakly supervised deep learning approach to assign a score to each pixel in images based on its usability, and to weight observations based on their overall quality. This study aims to characterize the relationship between in situ data and satellite-derived inputs to build models that can estimate Chl-a and phycocyanin concentrations at a 10-20 m scale in small- to medium-sized waterbodies over a large geographic area. The intent of this research is to provide a mechanism for identifying lakes with a high likelihood of algal blooms, targeting them for field assessment. The objectives were as follows:  
1. Evaluate the suitability of linear regression to relate Chl-a and phycocyanin concentration to Sentinel-2 data;
2. Evaluate different remote sensing-based indices to refine model fit;
3. Consider the influence of temporal separation of in situ and remote sensing data on model accuracy;
4. Assess the utility of model application for estimating Chl-a and phycocyanin concentration;Consider the influence of cloud mitigation approaches on model performance.
# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 3 Methods  
### Measures  
To enable an adaptive and shortened fall risk assessment, we considered a large battery of balance tests. The following standard measures were used throughout the study:  
1. The Berg Balance Scale (BBS) is a 14-item measure that assesses static and dynamic balance. Each item is scored from 0 (unable to perform) to 4 (normal performance). A score under 36 indicates a high fall risk. The BBS is widely used in clinical practice, especially during rehabilitation. Recently, due to ceiling effects, the use of the BBS as a single measure of fall risk is being questioned [23].
2. The Mini-Balance Evaluation Systems Test (MBT) includes 14 items, each scored from 0 (severe balance impairment) to 2 (no balance impairment). A score of 20 points or below indicates a high risk of fall [23].
3. The timed up and go test (TUG) assesses mobility, balance, and fall risk. The test measures the time (in seconds) required for a subject to rise from a sitting position, walk 3 m, turn, walk back, and sit down. A score of 12 s or more indicates a risk of fall [24].
4. The Four-Stage Balance Test (4 SBT) assesses static balance in four standing positions that are progressively challenging. The inability to maintain the pose without support for at least 10 s indicates an increased risk of fall [24]. We only considered the 4 th stage--standing on one foot--as the first 3 tests are at ceiling given the relatively strong subjects that were recruited.
5. The Four-Meter Walk Test (4 MW) measures walking speed (in meter/second) across 4 m. Speeds lower than 1.0 m/s are considered a strong predictor of falls [25].
6. The sit-to-stand test (STS) assesses the functional strength of the lower limbs, as well as fall risk, by assessing the number of sit-to-stand repeats in 30 s. A count lower than expected by age and sex indicates a high risk of fall [26].  
The 32 individual task scores, as assessed by physiotherapists, were used as features for training and testing the ML models. The following physiotherapist scores served as labels (ground truth) for the training and testing:  
* The Berg Balance Scale (BBS) final score.
* The Mini-Balance Evaluation Systems Test (MBT) final score.
* The timed up and go (TUG).  
* The Four-Stage Balance Test (4 SBT).
* The Four-Meter Walk Test (4 MW).
* The sit-to-stand test (STS).
# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 3 Materials and Methods
### Camera Calibration  
The elevation setting angles of the PAPI lights fall in the range of \(+/-30^{\prime}\) of the nominal approach angle, which typically equals \(3^{\circ}\). The UAV mission started d = 300 m from the lights, which gives a change in observation height ranging from \(h1\) to \(h2\):  
\[\begin{split} h1&=d\cdot tg(2.5^{\circ})=300\cdot 0.044=13.1\left[m\right]\\ h2&=d\cdot tg(3.5^{\circ})=300\cdot 0.061=18.3\left[m \right]\end{split}\]  
Lights with a transition zone not greater than 3 min of arc in depth are functioning correctly; this indicates that the resolution of observations should be better in order to follow the transition course. A resolution of 1 min of arc means registering images every 8 cm of the UAV mission in ascent or descent. Video can be registered with a frame rate of 24 to 120 fps, resulting in maximal permissible vertical speeds of 1.9 to 9.6 m/s. Commercially available UAVs are capable of ascent speeds up to 8 m/s. The lower the ascent speed, the higher the resolution of registering observation angles. The time duration of the UAV mission, determined by the battery capacity, limits the workable resolution.  
The required field of view (FOV) of the camera was determined using the arrangement of the PAPI and runway lights, which constitute the reference points for calculating the  
Figure 2: UAV light inspection mission processing tasks.  
position of the camera. Preliminary tests showed that about 15 reference points were necessary to achieve an error rate that does not significantly distort the desired accuracy of determining the elevation setting angles of the PAPI lights. Standard cameras have fixed focal or zoom lens with FOVs in the range of 4\({}^{\circ}\)-84\({}^{\circ}\). The observation of at least 15 reference points--runway lights--at height \(h1\) required lens with an FOV larger than 58\({}^{\circ}\). This gave a view of up to 10 rows of runway lights.  
The processing started with the calibration of the camera mounted on the UAV. The goal was to determine camera parameters, especially in the case of cameras with zoom lenses. The zoom setup was carried out manually by the UAV mission operator. The intrinsic and extrinsic properties of the camera were evaluated; these enabled us to rectify images for the proper mapping of space relations. Instead of \"chess board\" calibration, a set of images of lights with known positions were collected, and the camera parameters were calculated. The pinhole calibration algorithm was used, which is based on the pinhole camera model [32] and lens distortion model [33]. Sufficiently accurate results were obtained when at least 20 lights were registered on a few tens of images taken during a short calibration flight. The setup of the camera cannot be changed during the UAV mission.
# Steering the Clinical Translation of Delivery Systems for Drugs and Health Products
## 3 Poster Presentations
### Pentamidine-Loaded Lipid and Polymer Nanocarriers as Tunable Anticancer Drug Delivery Systems  
[PERSON] *, [PERSON] and [PERSON]  
Dipartimento di Scienza e Tecnologia del Farmaco, Universita degli Studi di Torino, Via P. Giuria 9, 10125 Torino, Italy  
* Correspondence: [EMAIL_ADDRESS]  
The class of diamines is widely known for the antimicrobial activity and, recently, some of them have been proposed in the treatment of different cancer types [18]. To efficiently deliver these molecules and reduce their side effects, lipid and polymer nanosystems have been proposed as useful delivery systems. Here we propose the formulation of pentamidine in liposomes and poly(lactide-\(co\)-glycolide) (PLGA) nanoparticles and the comparison of the physiochemical characteristics of the loaded nanocarriers.  
Liposomes were formulated by thin lipid film hydration method followed by extrusion; pentamidine-loaded nanoparticles were prepared by the nanoprecipitation technique. Nanosystems were characterized concerning the size, zeta potential, physical stability, morphology (CryoTEM), pentamidine loading and drug release profile. The anticancer activity was evaluated on a human ovarian cancer cell line over 72 h.  
Different formulations were compared to obtain non-toxic, biocompatible and biodegradable nanosystems for the optimal delivery of pentamidine. Results showed that the drug is efficiently loaded into liposomes with different counter ions (using transmembrane citrate- or sulfate-gradient) [19]. Concerning PLGA nanoparticles, ionic interactions between the drug and the polymer occurred and the formulation was characterized by high encapsulation efficiency [20]. The in vitro tests confirmed pentamidine anticancer activity; moreover, its release profiles depend on the drug form and the nanocarriers' structure.  
In conclusion, the nanocarriers proposed could be considered to be a platform for pentamidine delivery and they can increase the therapeutic application of the drug.  
This research was funded by Italian Ministry for University and Research (MIUR)--Universita di Torino, \"Fondi Ricerca Locale (ex-60%)\".  
The authors declare no conflict of interest.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results  
The results section contains five types of characterization of the wind speed products: (Section 3.1) Intercomparisons with Reference Wind Speeds; (Section 3.2) Triple Colocation Validation; (Section 3.3) Sampling Properties; (Section 3.4) Sensitivity to Significant Wave Height; and (Section 3.5) Sensitivity to Wind Speed Persistence.
## 2 Materials and Methods
### Environmental Covariates Used for Digital SOC Mapping  
The remote sensing data from the Sentinel-1, Sentinel-2, MOD09A1, MOD09 GA, MOD11A1, MCD12Q1, MOD15A2H, MOD16A2, MOD17A2H, MCD12Q2, and CHELSA bioclimatic variables were utilized for the modeling of the environmental covariates for prediction in 1000 m spatial resolution. CHELSA v2.1 data were manually downloaded from their official website, while Google Earth Engine was used for downloading the remaining remote sensing data. All multitemporal imagery represented a 2018 annual median, which coincided with the soil sampling time frame. Sentinel-1 radar data were used  
Figure 2: Two study areas, including mainland France and the Czech Republic.  
to estimate soil moisture and texture [19], while multispectral Sentinel-2 data were used to estimate vegetation health and its phenological properties [20]. MOD09A1 and MOD09 GA surface reflectance data were additionally used to estimate a broad scope of vegetation and soil properties based on multispectral imaging. MOD11A1 land surface temperature data were used to estimate soil moisture and thermal properties [21]. MCD12Q1 and MCD12Q2 land cover and land cover change data estimated the influence of land use and possible land use change on SOC concentrations [22], while MOD15A2H leaf area index data were used to estimate vegetation productivity and biomass [23]. Additionally, MOD16A2 gross primary productivity data were used to estimate carbon sequestration potential, and MOD17A2H net primary productivity data were used to estimate carbon cycling dynamics. CHELSA bioclimatic variables provided climate data, which were used to estimate the influence of climate on SOC concentrations and were proven to have high importance in digital soil carbon mapping [24]. Any covariates with cross-correlations quantified by a correlation coefficient higher than 0.9 were removed during preprocessing to avoid multicollinearity issues that could negatively impact model performance.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 3 Results
### Accuracy Analysis of Electron Density Profile
#### 3.1.2 Comparison with COSMIC Data

Table 2: MAE, RMSE, and its precision improvement (PI) percentage for the difference between the bottomside electron density and COSMIC data for IRI-2016 and upda-IRI-2016 in January 2015 and January 2019. Figure 5: Comparison of COSMIC electron density profiles with IRI-2016 and upda-IRI-2016 outputs driven by IG12 and IG-up, respectively, on day 155 or 2015. Figure 6: Comparison of COSMIC electron density profiles with IRI-2016 and upda-IRI-2016 outputs driven by IG12 and IG-up, respectively, on day 351 of 2019.  
first month. Compared to IRI-2016, the precision improvement of MAE and RMSE for the upda-IRI-2016 output bottomside electron density profiles amount to 21.3% and 20.8%, respectively.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Data Preprocessing  
Given the multiple factors such as instrument sensor failures, climate fluctuations, and atypical changes in customer consumption patterns, preprocessing the raw building energy consumption data becomes particularly crucial [136].  
During the preprocessing stage, two major challenges need to be addressed urgently: data missingness and data anomalies. Data missingness primarily stems from sensor malfunctions or disruptions caused by unexpected events, resulting in the omission of data samples. These missing values are often marked as \"null\" in the system or can be identified through the continuity check of timestamps. Common strategies to tackle this issue include deletion, constant imputation, and interpolation. Deletion is suitable for scenarios where the data volume is sufficient, missing values are scarce, and the variable's sensitivity to time changes is low. Constant imputation involves filling in missing values with the average of the data series or a reasonable constant based on experience [137], particularly useful when numerical fluctuations are small or missing data are concentrated. Interpolation methods, including advanced techniques such as nearest neighbor interpolation, polynomial interpolation, spline interpolation, Kriging, and Lagrange interpolation [138], predict unknown values based on known data points, often yielding higher accuracy than simple constant imputation.  
Outliers refer to observations that significantly deviate from the overall data distribution and may negatively impact model learning and prediction performance [139]. Before handling outliers, their existence must first be determined using algorithms like boxplot analysis [140], Z-score testing [16], or isolation forests [141]. Once outliers are confirmed, various methods can be employed to eliminate their adverse effects, including direct deletion, substitution with the upper or lower limits of the data series, or generating replacement data using missing value handling techniques.  
Furthermore, data normalization, as a pivotal step in preprocessing, aims to convert all data to a common scale or range, thereby mitigating the interference from irrelevant dimensions and ensuring fair comparisons among features [142]. This process not only helps reduce the disturbance caused by fluctuations in dataset values but also makes the training process more stable and efficient. Common normalization methods include MinMax normalization and Z-score normalization. MinMax normalization maps the original data to a specified interval through linear transformation [143], while Z-score normalization transforms the data into a standard normal distribution form. Both methods can effectively enhance the efficiency and accuracy of data processing and analysis.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.6 Minimize Average End-To-End Delay  
Figure 10 shows the average end-to-end delay under different objective functions. \"E2E delay\" identifies the solution that minimizes the average end-to-end delay, while \"coverage\" corresponds to the solution that maximizes coverage. \"Static\" deployment only considers the current sensor node positions and traffic. The results indicate that end-to-end delay is lowest when the objective to minimize it is explicitly invoked. However, the solutions to the end-to-end and coverage objectives are equal when the number of gateways is six because this number of gateways is sufficient to cover all sensor nodes in Layer 1. Thus, there is no difference in the two solutions concerning the average end-to-end delay.  
Figure 10: Minimization of average end-to-end delay.  
Figure 9: Impact of tracking accuracy.