esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Lenses as Drug Delivery System for Glaucoma: A Review
## 3 Contact Lens Drug Delivery in Glaucoma
### Functional Monomers  
Cyclodext(CDs) were known since the 80 s as a promising drug delivery system due to the formation of an inclusion complex with a variety of drug molecules in solution and in the solid state [142]. The result of the application of these molecules to ophthalmology treatments is an increase in the capture and bioavailability of the drug, increasing the tolerability and reducing its cytotoxicity [154; 155; 156; 157; 158].  
The chemical combination of dextrins with hydrogels of contact lenses can offer multiple alternatives for using them as a delivery device. This combination was first used as a vehicle of acetazolamide, showing a sustained release of the drug of up to 24 days. Furthermore, this release can be controlled by varying the beta-CD concentrations during copolymerization [157].  
Similarly, different types of dextrins (alpha, beta, and gamma-dextrins), have been tried in order to improve those results. The use of poly-dextrins in contact lenses has been shown to facilitate the loading of high concentrations of ethoxzolamide (another carbonic anhydrase inhibitor), while ensuring a sustained release for several weeks [144].  
These results are very promising, especially considering that, until now, both drugs have only been administered orally, which limits therapeutic action, and these results could lead to a drastic decrease in the therapeutic dose, and therefore a decrease in unwanted effects.
## 5 The Model's Implementation
### The Case Study  
The model is based on the Container Terminal Halluin II at the Port of Lille, which functions as a multi-modal hub integrated into the transportation network of Lille's metropolitan region. The Container Terminal Halluin II is regarded as a scale model for the MASS model, representing the logistical difficulties and operational dynamics of a multi-modal hub. The following are the primary presumptions included in the case study:  
* Ships are filled with different-size, predetermined-capacity containers as they arrive;
* The distribution of the containers is determined by the predetermined end destinations of the trucks operating within the terminal;
* To ensure effective use of space and avoid overloading, containers are assigned to vehicles based only on the available capacity of the trucks and the destinations.  
The Container Terminal Halluin II is connected to a network of roads, railroads, and rivers in the Hauts-de-France region. The average daily flow in this terminal is around 452 TEU (Twenty-Foot Equivalent Units), and it has an annual throughput of 164.787 TEU [36].
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 7 Experimental Results
In the next step of the experiment, the impact of publishing content in the private network was tested. Each time a node adds new content, IPFS does not transmit the content itself to the other nodes. Instead, the network usage is caused by the update of the DHT. Every DHT update starts with several quick transactions between the origin node and each one of its connected peers. Immediately, this exchange is followed by additional transactions started by each one of the peers of the origin node, creating a controlled chain reaction. The effect of this chain reaction is shown in Figure 7. Most of the exchanged frames were very small, with an average packet size of 120 bytes.
## 6 Numerical Results
### Instances Generation  
The study has been conducted on different instances. It allowed giving, at each rescheduling step, an optimal solution. Table 6 presents the used parameters.  
The data that have been chosen can be adapted for real cases, either in a hospital or in an industrial environment. The unit of time (\(ut\)) is equivalent to a time duration which may correspond to a real situation. In our case, it is assumed that \(\Delta t\) = 1 \(ut\) and 1 \(ut\) corresponds to 10 min, i.e., every 10 min, an event may occur (arrival of a new order or arrival of a new urgent patient). Most important in our case, is to define the other problem data (\(p_{j}\) and \(r_{j}\)) with the same unit of time, in order to be closer to a fair situation. The simulation has been conducted over an 8 h time horizon (480 min) representing a hospital or a factory opening time. Hence, \(T\) = 8 h = 480 min = 48 \(ut\). The weight values can represent the 5 customers priority or the five emergency levels. The processing times can represent the durations of chirurgical operations or product manufacturing times. It is assumed that it follow a uniform distribution between 1 and 4, obtaining operations of 1 \(ut\) (10 min) to 4 \(ut\) (40 min). The values of the variable \(\theta(t)\) are randomly generated using the Bernoulli distribution, giving, at each time \(t\), a value one with probability \(p_{\theta}\) and zero with probability \(1-p_{\theta}\). By definition, \(p_{\theta}\in[0,1]\). It represents the appearance frequency of a job at each step. We  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(w_{j}\) & \(\sim\)\(U\) (1, 5) \\ \(p_{int}\) & \(\sim\)\(U\) (1, 4) (\(ut\)) \\ \(T\) & 48 (\(ut\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter values.  
Figure 5: Disruption caused by job G arrival.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(Job\) & \(C\) & \(A\) & F & \(E\) & \(D\) & \(B\) & \(G\) \\ \hline \(p_{j}\) & 2 & 1 & 1 & 4 & 3 & 2 & **1** \\ \(r_{j}\) & 0 & 1 & 2 & 2 & 0 & 1 & **3** \\ \(w_{j}\) & 4 & 5 & 5 & 3 & 2 & 1 & **1** \\ \(C_{j}\) & 2 & 3 & 4 & 8 & 11 & 13 & **14** \\ \(w_{j}Wj\) & 0 & 5 & 5 & 6 & 16 & 10 & **10** \\ \(w_{j}(C_{j}\)-Co_{j})\) & 0 & 0 & 0 & 3 & 2 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal solution after disruption caused by job G arrival.  
start by testing a small appearance frequency, \(p_{\theta}=0.2\), since under 0.2 there is not enough disruptions. We have tested then with a medium appearance frequency, \(p_{\theta}=0.5\). Then, we have varied \(p_{\theta}\) up to 0.7 until the MILP becomes unable to solve the problem in a reasonable time. Thus, four main studies are established. At first, the impact of the efficiency-stability coefficient \(\alpha\) on the schedules. Secondly, the impact of the weight increasing effect on the system. Then, a comparative analysis of the proposed method with other heuristics and dispatching rules. Finally, the computing time of the MILP resolution on different instances for exploring its limitations according to jobs number.
## 2 Methods
### Statistical Analysis  
The performance of the models was represented using the most common evaluation measures, such as accuracy, precision, recall, specificity and F1 score. These metrics were defined as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\% \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{5}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{6}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\times 100\% \tag{7}\]  
\[\text{F1 score}=\frac{\left(\text{precision}\times\text{recall}\right)}{\left( \text{precision}+\text{recall}\right)}\times 100\% \tag{8}\]  
where TP is the true positive rate, TN is the true negative rate, FN is false negatives and FP is false positives. Accuracy represents the ratio of correct predictions over total predictions. Recall measures the proportion of true positive predictions among all actual positive instances. Specificity calculates the proportion of true negative predictions among all actual negative instances. Precision quantifies the proportion of true positive predictions among all positive predictions. Finally, the F1 score combines precision and recall into a single metric. The error represents the standard deviation of the accuracy for different gesture classes. Both metrics and error information are expressed as percentages.  
To quantify the effectiveness of the proposed approach, we conducted statistical analysis via the \(t\)-test and the Wilcoxon rank sum test. The significance level was set at \(p<0.05\). In Section 3.1, we statistically analyze the various metrics of different feature extraction modules. In Section 3.2, we compare the performance differences of different training schemes for statistical tests. In Section 3.3, we only compare the accuracy numerically due to the small number of amputees.
## 4 Conclusions  
A multi-wavelength aethalometer was used to study mass equivalent black carbon (eBC) characteristics at a suburban site in A Coruna during a two-year period. The eBC mass concentrations presented concentrations typical of other suburban sites, and lower than other urban sites in Europe. They show seasonal variations, significantly higher in winter and autumn than in spring and summer. This behavior is the result of meteorological conditions during the studied period but can be also linked to the seasonal variations in emissions, especially higher in winter due to biomass burning. AAE shows a clear seasonal cycle, with the largest values during winter and the lowest during summer. The use of the aethalometer in areas where biomass burning could be an important source of eBC is very appealing, as these instruments are easy to operate and are often already installed in order to measure black carbon concentrations. Nt shows clear diurnal cycles, similar to eBC concentrations. The interpretation of measurements regarding eBC and Nt in this paper was made through the SCALA\({}^{\copyright\otimes}\) software, an atmospheric aerosol data management software that was successfully extended to carry out this kind of study. The biomass burning source was identified (\(\%\)eBCbb = 34, two-year data average), but it is not clear whether this source corresponds to open-air biomass burning of agricultural waste or biomass burning from domestic heating or both. Both practices are common in this area. Further studies are needed in order to assess the effects of these pollutants. Additionally, future pollution management strategies should take into account that biomass burning can be an important aerosol source during winter and spring seasons, since its human health effects may be different from those caused by diesel particles.  
Conceptualization, M.P.-I; methodology, M.P.-I; software and software development, [PERSON]-G. and S.S.-G; formal analysis, M.P.-I. and P.L.-M.; resources, S.M.-L. and D.P.-R.; writing--original draft preparation, M.P.-I. and J.A.-G.; writing--review and editing, S.S.-G. and P.L.-M.; supervision, S.M.-L. and D.P.-R.; funding acquisition, D.P.-R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish R&D Plan (PROACLIM project: CGL2014-52877-R and CRISOL project: CGL2017-85344-R) and by the Xunta de Galicia (GRC2013-047 and ED431C 2017/28).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Our thanks go to all the institutions and people that disseminate their models, satellite images and other types of environmental and meteorological information via the Internet, as well as knowledge about software development. Our thanks also go to the anonymous reviewers for their helpful comments/suggestions and constructive attitude. Finally, the authors would like to acknowledge [PERSON], for her invaluable technical support in improving the software SCALA\({}^{\copyright\otimes}\) software.  
The authors declare no conflict of interest.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 2 Principle of the Algorithm
### Importance of Clutter Normalization  
Clutter normalization is an important step in the proposed processing chain. The idea of clutter normalization is further illustrated in Figure 2.  
The black curve in Figure 2a is the average Doppler spectrum of a range-Doppler image that includes both the clutter and the targets. Efficient clutter normalization requires the estimation of the red curve to perform normalization over Doppler [21; 22]. This means that the potential targets have to be pre-detected first and cancelled before the estimation. Otherwise it will lead to a distorted power spectrum causing target self-whitening. After target pre-detection and clutter normalization the data can be considered as training data and can be used for fitting sea clutter models and computing a valid CFAR detection threshold (green line in Figure 2b).  
A frequent update of the training data is very important, especially in case of ship detection using airborne platforms. This is because atmospheric disturbances cause variations in the aircraft's Euler angles (roll, pitch and yaw) and these variations induce a change of the Doppler centroid over range and azimuth time. In the special case of a non-moving ocean surface, i.e., in case of stationary clutter, the clutter Doppler centroid is given as [23]  
\[f_{\text{clutter}}[r,t]\approx\frac{2v_{\text{P}}}{\lambda}[\cos(\theta_{\text {inc}}[r]+\theta_{\text{roll}}[t])\,\tan(\theta_{\text{pitch}}[t])+\sin(\theta_ {\text{inc}}[r]+\theta_{\text{roll}}[t])\tan(\theta_{\text{yaw}}[t])] \tag{2}\]  
where \(v_{\text{P}}\), \(\lambda\), \(\theta_{\text{inc}}\), \(\theta_{\text{roll}}\), \(\theta_{\text{pitch}}\) and \(\theta_{\text{yaw}}\) are the platform velocity, wavelength, incidence angle, roll, pitch and yaw, respectively.  
For a non-moving ocean surface, the computed clutter Doppler centroid \(f_{\text{clutter}}[r,t]\) as a function of range \(r\) and azimuth time \(t\) is shown in Figure 3 for a real circular flight of F-SAR. For computation, the Euler angles measured with the aircraft inertial measurement system were used.  
From Figure 3 it is clear that, due to the change of the clutter Doppler centroid along range and azimuth time, a regular training data update is essential for obtaining a stable CFAR detection.  
Note that in reality the ocean surface moves for itself and therefore will cause additional Doppler shifts. However, these shifts are anyhow considered by a regular training data update.  
Figure 2: Illustration of the average Doppler spectrum of a range-Doppler image containing clutter and ship targets (**a**) before and (**b**) after normalization over Doppler. The red curve in (**a**) is the average Doppler profile estimated without considering the targets. The green line in (**b**) is the detection threshold computed based on clutter plus noise level.
## 4 Using the Proposed VehDS-LA to Perform Cell Selection in 5G Networks  
In this section, the proposed VehDS-LA was used to perform the cell selection process in 5G networks. The distribution of 5G small base stations (BSs) depends on a dataset that was published by data.LAcity.org (accessed on 22 February 2022) [53]. The dataset contains information about 5G small BSs in the city of Los Angeles, which are attached to street lights. To model the network and to accomplish the cell selection process, MATLAB 2021b simulator was used because it provides a powerful platform. The simulation parameters, which are used in this work, are shown in Table 2. Path loss is modeled based on a model called urban microcell-line-of-sight (UMi-LOS) (street canyon), which is described in the 3 rd-Generation Partnership Project (3 GPP) technical report 38.901 version 16.1 [54].  
Figure 18: Using the proposed VehDS-LA in SDN-based vehicular network studies.  
Handover (HO), which is the process of transferring the connection from one BS to another [56], is performed based on the strongest value of the received signal strength indicator (RSSI). Figure 19 displays the average number of horizontal handovers, which occur between small BSs, under various vehicle speeds. Sojourn time of vehicles inside a serving small cell is shown in Figure 20. The results demonstrate that there is an inverse relationship between the sojourn time and the number of horizontal handovers. As the vehicle speed increases, the sojourn time decreases and the number of horizontal HOs will increase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Transmit power (dBm) & 30 \\ \hline Path loss model (dB) & 3 GPP UMi Model \\ \hline Carrier frequency (GHz) & 28 \\ \hline Number of 5G small BSs & 198 \\ \hline Small BS height (meters) & 10 \\ \hline Small cell radius (meters) & 600 \\ \hline RSSI threshold (dBm) & \(-90\) \\ \hline Handover delay (ms) & 50 [55] \\ \hline Simulation time (sec) & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 19: Average number of horizontal handovers under various speeds.  
If the sojourn time of a vehicle within a small cell is less than the handover delay, HO failure happens. Unnecessary handover occurs when the sum of HO latencies to move into and out of a 5G small cell is longer than the sojourn time in the small cell [31]. Figures 21 and 22 show the averages of the number of HO failures and unnecessary HOs, respectively.  
Figure 21: Average number of HO failures under various speeds.  
Figure 20: Average sojourn time under various speeds.
## 3 Results and Discussion
### Investigation of Water Matrix Effect  
The water matrix is a crucial parameter to consider when evaluating the performance of an AOP. TW and SW typically contain radical scavengers that can significantly reduce the rate of degradation (and mineralization). Inorganic anions such as Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and CO\({}_{3}\)\({}^{2-}\) react with hydroxyl radicals and form less reactive oxidative species [50; 51]. Additionally, these anions tend to adsorb on the catalyst's surface, reducing the active surface area available for the oxidation process [50; 52]. Since the hydroxyl radicals are not selective oxidants, they can also react with organic compounds other than the target contaminants. This means that the presence of additional organic compounds (especially natural organics in SW) can act as scavengers and slow down the kinetics of SDX degradation and mineralization. Therefore, to realistically assess the performance of heterogeneous photocatalysis, we tested spiked samples of TW and SW. This approach allows a comprehensive evaluation of the photocatalytic process under conditions that are very similar to real environmental conditions.  
The scavenging effects were evident in both the TW and SW matrices. As shown in Figure 3, the degradation kinetics of SDX were significantly decelerated in both real sample matrices, with the contaminant not fully degraded even after 60 min of processing. In addition, mineralization was very low at only 2% in TW and 6% in SW. It is important to note that the 6% mineralization in the SW sample refers not only to SDX, but also to the natural organic matter in the river.  
Beyond the consumption of generated radicals by organic matter, due to their non-selective nature, the elevated ionic content of both TW and SW (Table S1) contributes to further scavenging of these radicals, hindering the overall degradation kinetics. Moreover, it is noteworthy that the pH value was higher than 7 in both TW and SW samples (>pH\({}_{\text{PZC}}\)), indicating that the unfavorable electrostatic interactions between the catalyst nanoparticles and SDX were present in these samples too. This condition further diminishes the efficiency of heterogeneous photocatalysis, as shown by the degradation rates. Regarding these rates, the kinetic parameters of the UV-A/TiO\({}_{2}\) system fit well to the first-order kinetic law in all of the tested water matrices (Table 2). These results provide a better understanding of the interference in heterogeneous photocatalysis caused by the radical scavengers present in each water matrix. The respective kinetic diagrams for each water matrix can be found in the Supplementary Materials (Figure S2).
## 3 Calorimetric Measurements
### Experimental Setup  
A picture of the experimental setup is given in Figure 3. The PCC is horizontally oriented and the plasma plume is fed to the target, a thin metal foil placed vertically. The target distance from the PCC can be modified by means of a micrometric translation stage. The IR camera looks at the metal target from the opposite side with respect to the PCC head, hence the plasma is not seen directly.  
Figure 3: Experimental setup for calorimetric measurements. (**Left**) Plasma source and camera are placed on the opposite sides of the target. (**Right**) PCC plasma plume impinging on target. The distance between plasma source and substrate can be varied by means of a micrometer linear actuator.  
Dealing with a small average power (few Watts), thin foils (from 40 to 80 \(\upmu\)m) were used, so to have a sufficiently small heat capacitance. In fact, considering the power level of the PCC, the temperature increase of thick sample would be quite small and thus affected by considerable errors. The adopted metal sheets, made of steel and brass, had dimensions of \(12.7\times 12.7\) mm\({}^{2}\) and \(12.7\times 5\) mm\({}^{2}\). To increase the thermal emissivity up to \(\epsilon=0.8\), the surface observed by the IR camera was coated with a blackening layer (Molykote). The contribution of such a layer to the heat capacitance of the sample is considered negligible.  
The IR camera (a FLIR A655 sc) has a maximum sampling frequency of \(f_{s}\) = 50 Hz and a field of view of \(15^{\circ}\times 11.25^{\circ}\) with a 640 \(\times\) 480 pixel\({}^{2}\) sensor. The thermal power \(P_{cal}\) is estimated from the average temperature increase \(\Delta T_{avg}\) of the target at \(t\) = 0.5 s after the plasma ignition as \(P_{cal}=C\Delta T_{avg}/t\), where \(C\) is the target heat capacitance. Such a short measurement interval was chosen to limit both the underestimation of \(P_{cal}\) due to the convective losses towards air and the error due to timing at \(1/f_{s}\)\(t\), that is 4%.  
Typical time traces of the target average temperature are shown in Figure 4.
# Identifying Energy-Use Behavior and Energy-Use Profiles of Hotel Guests
## 3 Background
### Theory of Planned Behavior (TPB)  
The Theory of Planned Behavior (TPB) states that the main antecedent of individuals' behavior is their intention toward the behavior [6; 27]. TPB is divided into three main categories: (1) Attitude towards a Behavior (Attitude); (2) Social Norms (Subjective and Descriptive Norms); (3) Perceived Behavioral Control (PBC) [8; 26; 27].  
Figure 1: The Integrated Energy-Use Framework.  
* Attitude illustrates occupants' overall evaluation of the behavior or the degree of individuals' performance of the behavior that is either positively or negatively valued by the occupants [28].
* Social Norms are divided into two major sub-categories: subjective and descriptive norms. First, subjective norm would apply to energy consumption when occupants believes that they are under social pressure by surrounding people/peers to engage or not engage in energy saving behaviors [8; 27]. Second, descriptive norm captures the perceptions of others' actual behaviors (e.g., the perception of companions' actual wasting or saving energy behaviors in hotel buildings) [8; 42; 43].
* Perceived Behavioral Control is a function of the occupants' perception of how easy or difficult it would be to perform the desired energy-related behavior [8; 27; 28].