esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Modelling of Ion Exchange Processes in Glass: Advances and Challenges
## 3 The Mixed Ion Effect
### Brief Review of Theories  
The origin of the MIE has been debated for a long time, but no theory has been universally accepted yet. Some theories focus on an interaction among neighbouring cationsin such a way that mixed pairs are assumed to be more energetically stable than pairs of cations of the same species [15; 55; 56; 57; 58; 59]. This assumption is supported by several experimental achievements. Namely, the interdiffusion coefficient presents a thermodynamic term for alkali IE [60; 61] and for Ag\({}^{+}\)/Na\({}^{+}\) exchange [62]; we will show that this term is missing if cations behave as an ideal gas. Furthermore, the surface mole fraction of cations in ion exchanged glasses from molten salts (the salt boundary condition) must be explained on the assumption that a non-ideal cation behaviour both for double-alkali exchange [40; 41] and for Ag\({}^{+}\)/Na\({}^{+}\) exchange [42; 43]. On the other hand, mixing enthalpy experiments do not have a clear interpretation. A negative mixing enthalpy (net attraction among dissimilar cations) was found [63; 64; 65], which correlates linearly with the excess of activation energy for DC conduction. However, the former is about 20 times weaker than the latter. Besides, mixed pairs of cations would be expected to be much more likely than pairs of the same species in the presence of interaction, but nuclear magnetic resonance experiments [66; 67; 68], as well as neutron and X-ray diffraction [69] show that they are rather randomly distributed. Consequently, other authors attributed the MIE to relaxation processes in the glass structure [70; 71; 72; 73]. In particular, each cation is assumed to modify its neighbourhood after a relaxation time to achieve an energetically favourable site. Therefore, in a monoalkaline glass, all the sites are of the same type. Once a foreign cation enters this glass and modifies a site, it is difficult for it to diffuse because all accessible sites are the wrong type. Even if the cation gains access to one of them, most likely, it will return before the new site relaxes. Moreover, diffusion is assumed to occur through conduction pathways, so a foreign cation can block several indigenous ones. This explanation is compatible with the ideal behaviour of cations, that is with a random distribution.  
The theory that we assumed is relevant because, depending on it, the resulting diffusion equation is slightly different, as we will show below.
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements  
Watermarking protocols can be effective only if two basic requirements are met. The first concerns the watermark embedding algorithm, which has to be both robust against non-intentional manipulations and secure against intentional attacks. Thus, the watermark extracted from a content can unambiguously identify its original buyer. The second involves the watermarking protocol, which has to ensure that only the original buyer of a content has access to it in its final, protected form.  
However, the nature of the requirements reported above has usually led researchers to focus on the security design of watermarking protocols, thus overlooking their practicality for the current web context. Hence, there is a need to revise the set of the classic requirements documented in the literature on watermarking protocols. The main aim is to mainly differentiate \"practically\" requirements from security requirements. The former, also named \"usability\" requirements, deal with the participation of buyers and sellers in the interaction schemes defined by the protocols; the latter specify the conditions that have to be ensured by the protocols in order to be considered secure. Both are usually described in terms of problems that the protocols have to solve.
###### Abstract  
The objective of this research was to explore the effects of different aeration methods on NH\({}_{3}\) and greenhouse gas (GHG) emissions and the losses of carbon and nitrogen from composting of cow manure and corn stalks in the laboratory-scale reactors. Here, we designed three treatments, including continuous aerated treatment C1 (aeration rates 0.21 L-kg\({}^{-1}\) dry matter (DM)-min\({}^{-1}\)) and intermittent aerated treatments I1 (aeration rates 0.42 L-kg\({}^{-1}\) DM-min\({}^{-1}\); aerate 10 min, stop 10 min) and I2 (aeration rates 0.84 L-kg\({}^{-1}\) DM-min\({}^{-1}\); aerate 5 min, stop 15 min). The results showed that the physicochemical parameters (temperature, pH values, and germination index) of composting products met the requirements of maturity and sanitation. Compared with continuous aerated treatment C1, the cumulative NH\({}_{3}\) emissions of I1 and I2 treatments decreased by 24.37% and 19.27%, while the cumulative CO\({}_{2}\) emissions decreased by 13.01% and 20.72%. On the contrary, the cumulative N\({}_{2}\)O emissions of I1 and I2 treatments increased by 22.22% and 43.14%. CO\({}_{2}\) emission was the principal pathway for the TOC losses, which comprised over 65% of TOC losses. C1 treatment had the highest TOC losses due to its highest cumulative CO\({}_{2}\) emissions. The TN losses of I1 and I2 treatments reduced 9.07% and 6.1% compared to C1 treatment, so the intermittent aerated modes could reduce the TN loss. Due to the potential for mitigation of gaseous emissions, I1 treatment was recommended to be used in aerobic composting of cow manure.  
greenhouse gas; ammonia; aeration methods; aerobic composting; cow manure +
Footnote †: journal: Applicid
## 3 Results
### Validation Results
The method detection and quantification limits are 0.04 ng/mL and 0.111 ng/mL for \(\Delta\)9-THC-D\({}_{3}\) from urine, 0.113 ng/mL and 0.279 ng/mL for THC-OH, and 0.235 ng/mL and 0.531 ng/mL for THC-COOH, which are lower than the detection and quantification limits from serum due to the concentration factor of the urine method. The coefficients of variation of the measurement precision are 0.83% for THC-OH, 1.59% for THC-COOH and 1.22% for \(\Delta\)9-THC-D\({}_{3}\), indicating a slight impairment of the measurement by matrix components still originating from urine or substances introduced during hydrolysis. This is also supported by the slightly lower recoveries from urine, as the recoveries of \(\Delta\)9-THC-D\({}_{3}\), THC-OH and THC-COOH from saliva are also higher than from urine with a range of 89.62% and 108.62% and coefficients of variation between 1.36% and 3.97%. Overall, recoveries from saliva are the highest, indicating slight analyte losses due to previous protein precipitation or hydrolysis. However, these are not serious, stating a satisfactory performance of protein precipitation and bond breakage of THC-COOH and its glucuronide conjugate by basic hydrolysis. The coefficient of variation of the measurement precision of \(\Delta\)9-THC-D\({}_{3}\), THC-OH and THC-COOH from saliva is less than 0.78%. The methodological limits of detection and quantification are 0.037 ng/mL and 0.082 ng/mL for \(\Delta\)9-THC-D\({}_{3}\) from saliva; 0.328 ng/mL and 0.773 ng/mL for THC-OH; and 0.942 ng/mL and 2.247 ng/mL for THC-COOH, and are thus the highest limits of detection and quantification for THC-OH and THC-COOH, despite the concentration factor, indicating increased baseline noise, which may be due to the lack of an additional sample preparation method before SPE. Overall, the high recoveries demonstrate that automated extraction of \(\Delta\)9-THC-D\({}_{3}\), THC-OH and THC-COOH from the three matrices works well using a similar SPE method. The low limits of detection and quantification indicate that the use of specialized break-up and preparation techniques for the more complex matrices enables the sensitive detection of analytes. The effect of optimizing the method and pipetting parameters of the liquid handling workstation is demonstrated by the low coefficients of variation < 4%.
# Innovative Approaches to Semi-Transparent Perovskite Solar Cells
## 4 Stability of ST-PSCs
In another approach, sandwich-structured electrodes using dielectric material/metal/dielectric material (DMD) are used to increase conductivity and transmittance [30]. The fabricated inorganic layer/metal/inorganic layer, in which the bottom inorganic layer acts as a permeation barrier for the metal as well as the seed layer, inhibits metal from reacting with the halide elements in the perovskite layer. Moreover, the top inorganic layer acts as a capping layer to inhibit the metal from reacting with moisture and oxygen, thus improving the ambient and thermal stability of the devices. Furthermore, approaches have utilized AgNWs, along with an ultra-thin Au layer, and few researchers have utilized AgNWs doped with filler materials, such as C\({}_{60}\), to enhance the stability of ST-PSCs.
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 3 Results
### Simulation Setup  
_Simulation Environment_: Our foundational simulations are conducted using randomly generated graphs. For more realistic validation, we also implement a higher-fidelity simulation environment in ROS2 Humble on Ubuntu 22.04. In these ROS2 scenarios, the mission topology graphs are predefined to model specific operational environments. It is important to note that these are not merely abstract graphs with uniform edge weights. For instance, the 'City' environment (Figure 2) models a dense urban area where travel costs between tasks are non-uniform and are estimated via trajectory planning that accounts for obstacles and no-fly zones. Similarly, the 'Maze' environment (Figure 6) introduces highly constrained navigation challenges, where direct paths are often impossible. This underlying complexity ensures our validation is performed on non-trivial, realistic problem instances.  
_Baseline algorithms_: Given that the RMOPWB (Algorithm 1) is introduced in this paper, there exists no pre-existing efficient algorithm for comparative analysis. Consequently, the RMOPWB algorithm, which is an evolution of the established RMOP algorithm, is juxtaposed against its predecessor as a benchmark. This comparative framework serves to demonstrate the effectiveness of our proposed method. The Sequential Greedy Assignment (SGA) proposed in [15] is used as our RMOP Solver. The GCB algorithm [31] is employed as a subroutine to address the OP due to its efficiency and ease of implementation.  
Figure 6: The top figure shows our maze simulation environment. The bottom figure shows the resulting task assignments for three UAVs produced by our RMOPWB algorithm. Operating on a given task topology, the GCS assigns targets whose interconnecting path costs are approximated via trajectory planning. The three colors distinguish the target assignments for each UAV.
## 2 Materials and Methods
### Signal Processing and Outcome Measures  
The main acquisition outcomes were the MVC recorded with a dynamometer, the number of inspected repetitions from each subject and the Median Frequency calculated from the EMG signal. All the parameters were computed in the PRE (before rehabilitation) and POST (after rehabilitation) therapy sessions. EMG data were sampled at 1 kHz and band-pass filtered (20-450 Hz). A double threshold algorithm [38] was used for signal segmentation and employed to detect EMG activation phases. Each epoch of contraction, having nominal time length of 10 s, was subdivided into 2 s time windows. For each subject, we computed the median frequency (MF) separately for all the 20 contractions performed in each session, in each of the 2 s time windows. We then computed the mean MF for each epoch of contraction (each one composed of 5 time windows). Lastly, we performed a linear regression analysis by interpolating the progression of the mean MF in time. Positive fatigue detection was considered when the slope \(m\) of the linear  
Figure 2: Positioning of EMG electrodes. Vastus lateralis (proximal head), vastus lateralis (distal head), vastus medialis, rectus femoris (proximal head), and rectus femoris (distal head).  
regression was negative (indicating a shift towards lower frequencies in the spectrum) and \(m<-0.5\) Hz/_epoch_. When \(m\) was below this value, we considered that muscle under the effect of fatigue--corresponding to reasonable median frequency drop already documented in the literature [32; 39] (considering 20 repetitions, this choice corresponds to a mean lowering of about 10 Hz). Consequently, every muscle for each subject was assigned with a binary value (fatigue/no fatigue). However, since the choice for \(m\) is arbitrary, we also repeated the analysis with other reasonable threshold values (\(-0.3\) Hz/_epoch_; \(-0.4\) Hz/_epoch_; \(-0.6\) Hz/_epoch_; \(-0.8\) Hz/_epoch_). The linear regression was performed for all muscles and all subjects/acquisitions.
# Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks
## 4 Illustrative Example  
In this section, the application of online monitoring is illustrated with the data collected from a cleaning process in integrated circuit (IC) carrier production. The process quality characteristic of interest is the concentration of a sodium carbonate solution. The manufacturer applied an individual and moving range chart (_I-MR_ Chart) with supplementary rules [2; 3; 4] to monitor the concentration of the solutions. The chemical concentration exhibited abnormal variations that might be due to multiple assignable causes: variation in the initial bath make-up, mistakes in dose calculations, an incorrect dosing formula, irregular manual dosing operations, or the malfunction of the automatic dosing system. Irregular demand can also induce abnormal variation in the concentration. The assignable causes in this example made the data to show periodic changes and downward trends.  
To implement the proposed method, the moving/sliding window size was set to 32. At each step, the window advances to include a new observation and discard an old one. The multi-label CNN model developed in experiment 1 was applied to this monitoring task. In the illustration shown below, only parts of the windows are used to display the results of online monitoring.  
Figure 10(a) shows the 50 observations collected, all of which were standardized to have zero mean and unit variance. The process began to exhibit a cyclic pattern and downward trend from the 19 th observation onward. Figure 10(b) is the first analysis window. Although the number of cyclic pattern data only accounts for 44% (14/32) of the window size, the CNN classifier can still correctly identify periodic changes. If the assignable cause for the periodic changes can be correctly diagnosed and eliminated, the cyclical variation will be no longer present. In case that the assignable causes cannot be eliminated, the downward trend will become more noticeable as more data are collected from the process. Starting from the 10 th analysis window (Figure 10(e)), the identification results of the CNN model are a cyclic pattern and downward trend. Figure 10(j) shows the complete concurrent pattern data. It is evident from this figure that the non-random patterns shown in the process data are cyclic and downward trend.
## 2 Data and Methodology
### Methodology
#### 2.3.4 Garlic Extraction  
Initially, winter crop distribution maps (WCMs) were derived using RF classification based on Sentinel-2 NDVI time series data obtained from the GEE cloud platform (see Section 2.3.1). Subsequently, the WCM was segmented based on different feature combinations to produce garlic distribution maps (GDMs) for each region.  
A total of nine distinct feature combinations were utilized to classify the WCM using RF, resulting in the generation of garlic distribution maps (GDMs) for each region. The accuracy of the classifications obtained from all feature combinations was compared to determine the optimal combination for each county and to generate the planting distribution map and area. The classification feature combinations encompass various time series data, with each combination demonstrating a certain level of similarity. Comparing the classification accuracy of different feature combinations allows for the analysis of the importance of specific information.  
In this study, we designate x-y (where x = me, mg, mn, se, sn, n, e and y = sm, s, m, swir) as a feature combination, where x represents the vegetation index time series data included in the combination, and y represents other spectral time series data and SAR time series data. We categorize vegetation index time series data, spectral band data, and microwave data as the three types of data considered in this study. Typically, spectral bands comprise the four bands of red, green, blue, and shortwave infrared (multi-bands), while the three microwave time series data of VH, VV, and VH/VV are collectively referred to as SAR. In the feature combination, x comprises two letters to denote that the vegetation index time series data of the combination are derived from the DITS, while one letter signifies the time series data of a single index. Each letter in y then represents a feature, where \"swir\" denotes the shortwave infrared band only. For example, the feature combination labeled \"me-sm\" includes time series data from the DITS of MSAVI and EVI and SAR time series data, as well as time series data from the red, green, blue, and shortwave infrared bands. In summary, the classification feature combinations may encompass time series data from MSAVI, SAVI, EVI, NDVI, GNDVI, SAR, and multi-bands. The timing data included in each feature combination is depicted in Table 1. It is noteworthy that in feature  
Figure 5: Time series of Sentinel-1 curves for garlic and winter wheat. (**a**) Time series data on the ratio of vertical–vertical (VV) and vertical–horizontal (VH) polarization in garlic and winter wheat. (**b**) Time series data of VV and VH polarization for garlic and winter wheat. The curves in the figure represent the average values derived from the samples.  
combinations containing two vegetation indices, the index positioned at a later point in the table is situated in the first half of the DITS, while the other is located in the second half.
# A New Understanding and Modelling of TSP and BP Indices Compared to Safety IMO Ship Requirements
## 2 Materials and Methods  
In the present paper, in order to relate the BP with TSP of the tugs, the main characteristics of more than 25 escort tugs of different towing companies were analysed, classified, and then modelled. Although this is not a number that is excessively high, mainly due to the limited number of escort tugs available in the world with their TSP published, it is a number that allows carrying out solid statistics analysis. The selected escort towing companies are located all over the world, and tugs are rendering assistance to vessels that are very different in size and characteristics. Furthermore, these tugs are employed in areas and under weather conditions very different between them, so they can be considered as representative of the escort towing fleet in the world.  
Moreover, as it is also necessary to connect the TSP of tugs with the required TSP of escorted vessels, real cases data of different tanker vessels were selected and modelled, taking into consideration that each vessel has particular steering characteristics, which are a function of the speed and the steering angle [25; 26; 31]. Finally, after analysing the models' errors, we show the relation between variables of tugs and assisted vessels.  
In order to model the multiple variables involved, Minitab version 18 was selected [34; 35]. The selected variables of tugs and units for the present research are included in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Code** & **Units** \\ \hline Type of Tug & TT & - \\ Length & L & m \\ Beam & B & m \\ Depth & De & m \\ Draft & Dr & m \\ Bollard Pull & BP & Tons \\ Tons of Steering Pull at 10 knots & TSP & Tons \\ Maximum Tug Speed & TS & Knots \\ Horsepower & BHP & HP \\ Year of Built & YB & - \\ \hline \end{tabular}
\end{table}
Table 1: Identification of tugs’ variables and used units.  
The Type of Tug variable was codified with numerical values, as it can be seen in Table 2.  
It was not necessary to codify the rest of the variables, so they were used with their own values, which were numerical values.  
Finally, in order to compare our results with widely employed guidelines, we used the graphs of TSP published by the IMO, which were traced for oil tankers and LNG carriers sailing at 10 knots and considering the guidelines \"Standards for Ship Manoeuvrability\" [36; 37]. These guidelines are recommendations only, and some authors [26] consider that in real cases, the needed TSP are higher than the theoretical values obtained in the graphs. However, other authors consider that the figures obtained from the graphs are excessive [38]. In any case, the scope of these works was not to analyse the reason for these discrepancies.  
We used 10 knots as a reference value because it is considered by the literature as the reference escort speed. However, it is known that this speed only can be reached in certain areas of escort operations and sailing several nautical miles, because most seaports have the maximum speed established at 6-7 knots, corresponding to ship-handling assistance.
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 3 Experimental Results
### Comparison Experiments  
The comparison experiments aim to verify the accuracy of the two-dimensional micro-displacement measurement system. A Renishaw XL80 laser measurement system was used for the comparison experiments, and its displacement resolution was 0.1 \(\upmu\)m. The displacement of the CMM spindle carrying the measuring system was detected by Renishaw XL80, which was compared with the results from the measuring system. The comparison results for the two directions are shown in Figures 8 and 9, in which the red straight line is the linear fitting of black squares representing the comparative data. The horizontal axis represents the readings of the XL80 laser measurement system, and the vertical axis represents the displacement coming from the proposed measured system, which is translated from the displacement of the light spot. The fitting correlation R\({}^{2}\) value is 0.9999 in Figure 8, whereas the fitting correlation R\({}^{2}\) value shown in Figure 9 is 0.9999. Obviously, the two systems indicate very good agreement in the \(x\) and \(y\) directions. Moreover, the residuals between our system and the XL80  
Figure 6: Calibration results in the \(x\) direction.  
Figure 7: Calibration results in the \(y\) direction.  
laser system are within \(\pm\) 0.3 \(\upmu\)m in the \(x\) direction and also within \(\pm\) 0.3 \(\upmu\)m in the \(y\) direction. These are shown in the following figures as red stars.
## 6 Numerical Results  
The obtained end-to-end SNR statistical results for \(P_{\gamma}(\gamma_{th})\), \(P_{BER}(\gamma)\), \(C_{\gamma}\), \(N_{\gamma}(\gamma_{th})\) and \(A_{\gamma}(\gamma_{th})\) of an FSO RIS-ACs link over F-S \(\mathcal{F}\) TI fading propagation channels under various TI fading severity conditions were numerically evaluated and presented in this Section.