esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The difference in translucency of a material of uniform thickness on a black and white background is expressed as the translucency parameter (TP). Higher values for TP indicate that the material is more translucent. If the material is completely opaque, the value of this parameter is zero [16]. Ideally, the colour and translucency of composite restorations should be consistent and remain stable over time [17]. However, the colour and translucency of restorations tend to change over time [18]. The change in colour and translucency of resin composite restorations can be measured using devices such as colourimeters, digital cameras, or spectrophotometers [10]. Spectrophotometers are reported to be more sensitive than colourimeters and easier to use than digital cameras [11]. The CIE L*a*b* colour system is currently the most widely used for analysing colour and translucency changes in resin composites using spectrophotometers. In this system, \(\Delta\)E00 values are calculated from L* (white-black region), a* (red-green region), and b* (yellow-blue region) values measured at the colour coordinate [19]. The \(\Delta\)E00 value is a mathematical expression for the amount by which the L*, a*, and b* coordinates of different samples or of the same sample change over time, allowing analysis of small colour differences between dental materials. In addition, the system has some advantages such as objectivity, reproducibility, and sensitivity [20]. Recently, detox drinks have become popular because of their health benefits. Detox drinks contain fruit and vegetables. They are, therefore, consumed as a source of vitamins and energy [7,8]. The use of these drinks has increased due to the lack of time for food preparation in today's busy lifestyles [13]. Although coffee, tea, and various fruit juices have been reported to cause changes in the colour change and translucency properties of resin composites [10], there is little information on the effects of detox drinks on the colour change and translucency of resin composites. There are no studies on the effects of detox drinks on colour change and TP of both multi-colour and single-colour universal resin composites. Therefore, the aim of this study was to investigate the effects of red, orange, and green detox drinks on the surface roughness, colour change, and TP of four different universal resin composites. The null hypotheses of the study are as follows:  
* Detox drinks have no statistically significant effect on the surface roughness of universal resin composites. * Detox drinks have no statistically significant effect on the colour change (\(\Delta\)E) of universal resin composites. * Detox drinks have no statistically significant effect on the translucency parameter (TP) of universal resin composites.
## 2 Materials and Methods
### Analysis of Urinary Phthalate and Alternative Plasticizer Metabolites  
Twenty urinary phthalates and seven alternative plasticizers (four DEHTP and three DINCH) were analyzed in the urine samples as previously described [25, 26]. Standards of phthalate metabolites, including: MEHP, mono-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), mono-benzyl phthalate (MBzP), mono-cyclo-hexyl phthalate (MCHP), MEHHP, MEOHP, 5 cx-MEPP, 2 cx-MMHP, mono-n-octyl-phthalate (MnOP), mono-iso-nomy phthalate (MiNP), mono-3-carboxyl-propyl phthalate (MCPP), mono-isodecyl phthalate (MiDP), mono-ethyl phthalate (MEP), mono-methyl phthalate (MMP), mono-n-pentyl phthalate (MnPP), mono-isopropyl phthalate (MiPrP), mono-hexyl phthalate (MHxP), mono-hydroxyl-isononyl phthalate (OH-MiNP), and mono-carboxy-isodecyl phthalate (cx-MiDP); and alternative plasticizer metabolites, including: mono-2-ethylhexyl terephthalate (MEHTP), 1-mono-2-ethyl-5-carboxylpentyl-benzene-1,4-dicarboxylate (5 cx-MEHTP), 1-mono-2-ethyl-5-hydroxylhexyl-benzene-1,4-dicarboxylate (5 OH-MEHTP), 1-mono-2-ethyl-5-oxohexyl-benzene-1,4-dicarboxylate (5 OXO-MEHTP), cyclohexane-1,2-dicarboxylate-mono-4-methyl-ester (MINCH), cyclohexane-1,2-dicarboxylate-mono-7-hydroxy-4-methyl-octyl ester (OH-MINCH), and cyclohexane-1,2-dicarboxylate-mono-7-hydroxy-4-methyl-heptyl ester (cx-MINCH); as well as their respective \({}^{13}\)C\({}_{4}\) or \({}^{13}\)C\({}_{2}\)-labeled internal standards, were purchased from Cambridge Isotope Laboratory (Cambridge Isotope Laboratory, Cambridge, MA, USA) and Toronto Research Chemical (North York, ON, Canada).  
After enzymatic hydrolysis, samples were pretreated using the Strata X SPE Cartridge (30 mg/cc, Phenomenex, Torrance, CA, USA), following a method by [PERSON] et al. [27] with a minor modification. Ultrahigh performance liquid chromatography mass spectrometry (UHPLC-MS/MS, UHPLC, Nexera X2, Shimadzu Corporation, Kyoto, Japan and API 4500, AB SCIEX, ON, Canada) was used for measurement.  
The limits of detection (LOD) ranged from 0.01 ng/mL (2 cx-MMHP) to 0.8 ng/mL (MiDP) as shown in Table S1. For each instrumental run, reagent blanks and QC samples were included for quality control. The method accuracies were between 80% and 120%, and precisions were lower than 10% relative standard deviation (RSD) for high and low concentrations of spiked samples (\(n=7\)).
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 2 Materials and Methods
### Wood Treatment, Laaching, and Fungal Test  
The NMF sols were diluted in ethanol to obtain the final concentration for impregnating into wood specimens. The durability of treated wood specimens against _R. placenta_ and _T. versicolor_ was assessed by fungal decay test performed in accordance with the European standard EN 113 [26]. Scots pine (_Pinus sylvestris_) sapwood and beech (_Fagus sylvatica_) specimens were used to test for efficacy against _R. placenta_ and _T. versicolor_, respectively. The dimensions of the wood specimens were 15 mm \(\times\) 25 mm \(\times\) 50 mm (radial \(\times\) tangential \(\times\) longitudinal). Specimens were oven-dried at 103 \(\pm\) 2 \({}^{\circ}\)C for 18 h and then evacuated at 0.4 \(\pm\) 0.2 kPa for 1 h. After evacuation, the treatment solution was introduced into the vessel and the specimens were soaked in it for 2 h at ambient pressure and room temperature. Following soaking, the specimens were placed in a conditioning room (20 \(\pm\) 2 \({}^{\circ}\)C and 65 \(\pm\) 5% RH) for 4 weeks. After conditioning, the treated specimens were leached according to the accelerated ageing test set by the European standard EN 84 [27]. Specimens were submerged in deionized water and evacuated at 4 kPa for 20 min. The water was changed nine times, during a period of 14 days.  
After leaching, the specimens were conditioned for 4 weeks and then autoclaved at 121 \({}^{\circ}\)C for 30 min. The wood specimens were tested against _Rhodonia placenta_ (FPRL 280) and _Trametes versicolor_ (CTB 863 A). Nano metal fluoride treated wood was tested against _R. placenta_ and _T. versicolor_ according to EN 113 (1996) over 16 weeks. The treated specimens were leached before the fungal test. Untreated wood specimens were used to show the virulence of the fungi during the test period, these are called virulence specimens. Four replicates of each treatment were tested in Kolle flasks. One NMF treated specimen and an untreated specimen were introduced into each Kolle flask and then exposed to the selected fungi growing on malt-agar medium for 16 weeks. Additionally, treated specimens were put only on malt-agar medium in a Kolle flask, to determine the mass loss not due to fungal decay at the end of the test, this is known as the correction value. The mass loss was calculated as the difference between the initial and final dry mass of the treated specimen at the end of the decay test.
# Bulk Solids Stacking Strategy of a Rectangular Ship Cabin
## 1 Introduction
[PERSON] and [PERSON] [10] studied the stacking problem of outbound containers at deep-sea container terminals. They proposed a stacking strategy that allows different types of containers to share the same stack. [PERSON] et al. [11] proposed a decision support system to improve the efficiency of container stacking operations. Dwell times were predicted for each container using analytics techniques. This prediction was used as an input for a mathematical programming model that minimized container rehandles heuristically. [PERSON] et al. [12] proposed an innovative container stacking method to overcome the potential problem of insufficient container yard space. The established system improves the efficiency of handling containers and improves the profit rate of the port. [PERSON] and [PERSON] [13] proposed a mixed 0/1 linear programming model and heuristic method to define storage rules to minimize the space use of the exit yard. [PERSON] [14] studied the filtering of lidar data in the loader environment. They proposed a high-order signal model which specified the optimal linear filter. [PERSON] et al. [15] combined a decision theory with stochastic optimization technology to optimize cargo loading and ship scheduling decisions in tidal ports. They designed a shipping decision model to calculate the cargo loading and dispatching decisions by taking into account the time series predicted by the sea level points of these ports. [PERSON] and [PERSON] [16] proposed a multi-criteria comprehensive decision method based on the extension of interval type-2 fuzzy sets for the selection of loader type in maritime transportation. This method combined the analytic hierarchy process and the similarity ranking method of ideal solutions under the IT2 FSs environment and overcame the uncertainty of expert judgment and expression in the decision-making process. To sum up, the stacking research mainly focuses on the scheduling of bulk solids stacking equipment, the intelligent monitoring of stacking site, the detection and feedback of stacking effect, and the rational utilization of yard space. However, there is little research on the strategy planning of flat stacking of bulk solids. The distribution of bulk solids in stacking process still requires manual intervention and judgment. In this study, the stacking route and the stacking locations were designed considering the flat and balanced stacking. Then, the stockpile shape of bulk solids was extracted to establish a three-dimensional model. After that, a stacking strategy was proposed by adapting the golden section algorithm with a self-influence factor to optimize the modeling efficiency, from which the location and volume of each bulk solids stockpile can be obtained. Finally, the stacking strategy was used to stack five different materials. The performance was verified by comparing and analyzing the stacking flatness.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses
### Performance Analyses of Different Algorithms  
To analyze the performance of the proposed algorithm, we compare it with some resource allocation algorithms in multi-rate and multi-user scenarios, including AFR, MSPSNR [55] and Average. AFR allocates the network bandwidth as fairly as possible to each user under the condition of satisfying the minimum and not exceeding the maximum video rate demand. Under the condition of meeting the minimum rate needs, MSPSNR gives bandwidth priority to the videos with slow or medium motion or a smooth scene, in order to improve the PSNR. The average allocates bandwidth equally without exceeding the maximum user requirement, although sometimes it cannot guarantee the minimum requirement.  
Figure 5 shows the rate allocation results of different methods under different bandwidth conditions. Figure 4(a) is the results of AFR. If the allocated bit-rate is within the rate range of each video, it will be equally divided. Otherwise, it will be allocated according to its maximum or minimum value. Figure 4(b) shows the results of MSPSNR. It first satisfies the minimum rate requirement of all videos. Then, if there is any remaining bandwidth, it is allocated to the video with the minimum \(R_{i}^{\max}\) until its rate reaches this \(R_{i}^{\max}\). This process continues until all bandwidth is allocated or all maximum rate requirements are satisfied. Figure 4(c) shows the result of the Average algorithm, which calculates the average bandwidth that could be allocated to each video. If the average bandwidth exceeds the maximum rate \(R_{i}^{\max}\) of the video, it will be transmitted at \(R_{i}^{\max}\). Otherwise, the average bandwidth value will be allocated to it. The implementation of this method is the simplest. Unfortunately, for some high-speed videos, the minimum requirements cannot always be met, and then the video will not be transmitted correctly. Figure 4(d) shows the allocation results of the proposed algorithm in this work. In order to maximize utility, each UAV iteratively updates the rate according to its own video characteristics and rate ranges. When the allocation algorithm converges, each user could be allocated reasonable bandwidth.  
Figure 6 shows the total utility of different algorithms at different channel bandwidth. In the algorithm of this paper, a utility function of users' QoE is designed and the problem is solved in order to maximize the utility. Thus, the total utility effects are better than other methods under different bandwidth conditions. The algorithm of Average has the worst performance because it does not consider the characteristics of videos. AFR takes the different needs of videos into account and stays fair. Its utility performance is much better than Average. Compared with AFR, the total utility of MSPSNR is properly improved.  
Figure 4: Number of iterations versus flow rate and total utility.  
Figure 5: Allocated rates using different methods.  
Figure 6: Total utility using different methods.
## 2 Experimental Details
### Characterization  
The amorphous nature of the SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\) multilayer thin films was evaluated by means of an X-ray diffractometer (X'pert Pro, Panalytical) with CuK\(\alpha\) (=1.542 A) used as a source. X-ray photoelectron spectroscopy (NEXSA, Thermo Fisher Scientific) studies were carried out to determine the chemical states of the niobium, silicon, and oxygen at the surface of the SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\) multilayer thin films. The coefficient of thermal expansion of the substrates was measured using a dilatometer (DIL402F3, NETZSCH, Selb, Germany) in the range of 0-450 \({}^{\circ}\)C with a heating rate of 5 \({}^{\circ}\)C/min. under a N\({}_{2}\) atmosphere. The surface morphology and elemental compositions of the SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\) multilayer thin films were characterized using scanning electron microscopy (SU8100, Hitachi, Tokyo, Japan). The surface roughness of the thin film was measured using an atomic force microscope ([PERSON], Billerica, MA, USA), which yielded the quantitative root mean square (RMS) value for the SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\) multilayer thin films. The transmittance spectra were recorded by a UV-Vis spectrophotometer (Cary 500 scan, Varian H) within 480-600 nm. The commercial thin films substrates were purchased from Edmund (ED) and Isuzu (IS) as a reference sample. The hardness of the thin films was characterized by a nanoinder with a Berkovich tip (SteP6 UNHT3, [PERSON], Graz, Austria). A spectroscopic ellipsometer (M2000, [PERSON], Lincoln, USA) was used to measure the refractive index (\(n\)) of the single-layer SiO\({}_{2}\) and Nb\({}_{2}\)O\({}_{5}\) thin films. Nano-indentation tests were conducted seven times and the average values were obtained. The Oliver-Pharr method was used to calculate the hardness of the thin films. The penetration depth of indentation was determined within 10% of the thin-film thickness to minimize the influence of the substrates [4].
## 4 Case Study 2
### Definition of the Variability and Aggregation  
Similarly to case study 1, the analysis of the variability of each function was carried out, evaluating when and how the output of one function affects an aspect of another function. In Figure 6, a scheme of the FRAM instantiation is represented, while the complete output is available in the Supplementary Material (the method was applied using the software FRAM Model Visualiser (FMV Pro, rel. 2.1.6 [65])).  
In more detail, the analysis of the variability of each function was carried out in collaboration with the hospital staff. From this analysis, it emerged that although Function CHE (to perform the metal detector check) represents the last checkpoint before entering the MRI room (and thus it is crucial to avoid a ferromagnetic object being introduced in the room), it presents a high level of vulnerability. Indeed, as reported by the medical staff, it is not rare that one of the following situations happens:  
* Untrained/unauthorized hospital personnel enter the MRI room for different tasks, such as picking documents and avoiding the metal detector check;
* When an emergency occurs, the patient is rapidly brought to the MRI room without passing the previous check phases.  
Unfortunately, the above situations are not uncommon, resulting in the onset of risks related to the presence of ferromagnetic objects inside the MRI room. In particular, as the hospital has an accident and emergency department (A&E), the need for an urgent MRI examination is frequent. Besides these outcomes, the vulnerability of Function CHE also emerges from Figure 6, since in the case of operators, no further controls are foreseen before Function ENT. Accordingly, if Function CHE is not carried out correctly, Function ENT also cannot be performed correctly.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 2 Method
### Watermark Extraction  
As the inverse operation of watermark embedding, watermark extraction is also conducted in the noisy latent space. Therefore, the VAE encoder, as in the embedding process, is imposed on the watermarked image followed by the inversion process, yielding the latent vector \(z_{Tw}\). Then, we apply DCT on the channel that was selected for watermark embedding. The extracted DCT coefficients are denoted as \(\mathbf{F^{\prime}_{w}}=[F^{\prime}_{0},F^{\prime}_{1},\dots,F^{\prime}_{n-1}]\). Note that the indexes of DCT coefficients in \(\mathbf{F^{\prime}_{w}}\) are the same as that of the embedding. The original watermark is recovered by calculating the correlation between the spread spectrum code and the detection matrix:  
\[m_{i}^{\prime}=\begin{cases}1,&\text{if }\langle\mathbf{F}_{\mathbf{w}}^{\prime}, \mathbf{P}_{\mathbf{i}}\rangle\geq 0,\\ 0,&\text{otherwise},\end{cases} \tag{9}\]  
where \(\langle\cdot,\cdot\rangle\) denotes the inner product operation.
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 2 Materials and Methods  
### Overview  
This contribution summarizes the results of two independent experimental campaigns performed by two research groups: (i) Christian Doppler Laboratory (later labeled \"CDL\"), University of Natural Resources and Life Sciences, Vienna, Austria, and (ii) Centre Scientifique et Technique du Batiment, France (later \"CSTB\"). Each group performed a set of experiments comprising aggregate and concrete characterization tests and structural tests on anchors under tensile and shear loading in accordance with the unconfined tension test setup [10] and edge breakout shear test setup [7; 10]. The slabs were cast in accordance with the uncracked concrete test member provisions [7; 10]. The slabs were not reinforced in the unconfined test regions to avoid the influence of reinforcement on the behavior of the anchor (unrestricted breakout area). The slabs contained reinforcement to allow handling and the distribution of load transmitted by the test equipment. CDL performed both concrete material characterization and structural tests at 28- and 70-day concrete age, while CSTB performed tests at 28 and 180 days. In total, the presented results cover 11 different concretes and include 78 cube compression tests, 100 anchor pull-out tests, and 87 anchor shear tests. All slabs for anchor tests and specimens for material characterization belonging to a specific concrete were cast from the same concrete batch.
## 6 Results
### Data Preparation and Training  
Before training the model, a series of data preprocessing steps were implemented to ensure the accuracy and reliability of the input data:  
* Data loading and cleanup: the dataset was initially retrieved from the database. Preliminary processing steps, such as interpolating missing values, were applied to maintain data integrity and ensure that the model is trained on a complete and coherent dataset.
* Temporal aggregation to hourly intervals: the original dataset, which was recorded at minute-level granularity, was resampled by computing hourly average values. This transformation reduces data complexity by minimizing short-term fluctuations while preserving broader temporal trends in energy consumption.
* Normalization: since LSTM networks and other neural architectures are scale-sensitive, power consumption values were normalized to a [0,1] range using the MinMaxScaler algorithm. This normalization step is crucial to enhance the stability and efficiency of the training process.
* Sequence generation: the LSTM model requires sequential input data to identify temporal dependencies. A sliding window method was applied, converting the dataset into a supervised learning problem, where a fixed number of past time steps are used to predict future values.
* Model architecture: the LSTM model was designed to capture both short-term and long-term correlations in the data. Additionally, dropout layers were incorporated into the architecture to mitigate overfitting, by randomly omitting units during training, thus enhancing the model's generalization ability.
* Training and validation: the model was trained using the preprocessed time-series sequences, with a portion of the dataset reserved for validation purposes. This validation process ensured that overfitting was monitored and prevented. The training convergence curve is illustrated in Figure 8.
* Post-training evaluation: the trained model was evaluated using real-time data to assess its predictive accuracy. The Root Mean Square Error (RMSE) obtained was 0.04, indicating a high level of accuracy in forecasting energy consumption trends.  
Figure 7: Measured energy trends.  
The graphs in Figure 9 illustrate the comparison between actual and predicted energy consumption. The low prediction error observed in the ANN model demonstrates its high accuracy in predicting energy consumption. This result highlights the model's ability to effectively capture general trends and seasonal variations in energy consumption.  
The energy consumption prediction model uses historical data to identify patterns and temporal trends in energy consumption. By analyzing periodic variations and absolute consumption levels, particularly during peak demand periods, the model is trained to  
Figure 8: Training convergence curve.  
Figure 9: Actual energy consumption trends compared to forecast.  
accurately predict future energy demand. This approach enables a data-driven strategy to optimize energy management and improve efficiency.
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2 Risk Evaluation and Treatment  
The risk evaluation process helps to determine whether the threats and vulnerabilities are acceptable or not by calculating the impact and likelihood level. Furthermore, risk treatment will help to decide how each unacceptable risk will be addressed. Figure 8 illustrates the steps to conduct risk evaluation and risk treatment.