esa-sceva/satcom-chunk-collection huggingface dataset:
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## 3 Results
### Mucoadhesion Studies  
To investigate the interaction between the formulations and mucin, a mucoadhesion index (MI), the PDI, and the zeta potentials of mixtures between the nanocapsules and mucin were determined for different mucin weight ratios f (Figure 3). For LNCSVT-LMWchit in the range of \(f\) values up to 0.3, particle sizes and MI values increased to a maximum of 474 \(\pm\) 6 nm and 2.9 \(\pm\) 0.1 (_f_ = 0.3), respectively. Similarly, LNCSVT-HMWchit particle size and MI increased in the f range from 0 to 0.55, reaching a maximum of 557 \(\pm\) 30 nm, 3.0 \(\pm\) 0.3 (_f_ = 0.55), respectively (Figure 3A). Despite the increase of the fraction by weight of mucin above f values of 0.3, for LNCSVT-LMWchit, particle size and MI decreased to 318 \(\pm\) 5 nm and 1.9 \(\pm\) 0.1 for \(f\) values of 0.85. It has been hypothesized here that while at values of f of around 0.3, nanocapsules are able to form large agglomerates with mucin chains entangled and linked with more nanoparticles, above this value, the mucin is in such a large excess that almost all single particles are enrobed with mucins, leading to a decrease in the overall mean particle size. In fact, the MI values for f = 0.55 and 0.85 did not change significantly. On the other hand, for LNCSVT-HMWchit this decrease in the MI occurred only for \(f\) values above 0.55, with particle size and MI decreasing to 321 \(\pm\) 7 nm, 1.7 \(\pm\) 0.1 (_f_ = 0.85). Therefore, the MI maximum was observed at lower f values (0.3) for LNCSVT-LMWchit, and higher f values (0.55) for LNCSVT-HMWchit, indicating a higher capacity to interact with larger quantities of mucin for nanoparticles coated with high MW chitosan, macroscopically translating to more efficient mucoadhesion.  
The PDI before nanocapsule interaction with mucin was 0.12 \(\pm\) 0.01 for LNCSVT-LMWchit and \(0.11\pm 0.01\) for LNCSVT-HMWchit. After mixing the nanocapsules with mucin, PDI progressively  
Figure 3: (**A**) Mucoadhesive index (MI) values, (**B**) PDI and (**C**) zeta potentials measured for various mixtures of mucin and nanocapsules. Values of the two formulations were obtained before (_f_ = 0) and after incubation with different mucin weight ratios \(f\).  
increased for both formulations, up to 1 (_f_ = 0.85), indicating the formation of agglomerates and the switch to highly polydispersed particle size distributions (Figure 3B). The zeta potentials of the formulations that were initially positive due to the chitosan coating immediately after interaction with even the smallest amount of mucin (_f_ = 0.04), became negative for both formulations (\(-\)4.8 \(\pm\) 0.1 mV for LNC\({}_{\text{SVT-LMWchit}}\) and \(-\)4.8 \(\pm\) 0.7 mV for LNC\({}_{\text{SVT-HMWchit}}\)) and remained roughly constant for both formulations, even with increasing f values (Figure 3C).
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods
#### 3.2.1 Variable Selection Methods  
The methods belonging to the class of variable selection have the implicit assumption that, although many predictors are measured, some are expected to be irrelevant or too noisy. Thus, various strategies are employed to select the important predictors and remove the noisy or irrelevant ones. In this class, forward stepwise regression (FSR) was selected as a representative method. FSR sequentially builds a model by including and excluding predictors based on the \(p\)-values of the partial-F test. The process starts by including the predictor with the lowest \(p\)-value (more significant contribution to explain the Y-variability). Then, the importance of all other variables is assessed (given that one predictor is already included), and the one with the smallest \(p\)-value is added to the model, as long as the \(p\)-value is below a threshold (\(p_{in}\)). After this inclusion step, the predictors are assessed, and the one with the highest \(p\)-value is excluded from the model (provided that the \(p\)-value is above a threshold (\(p_{out}\))). This iterative process continues until no predictor can be added or removed from the model. The regression coefficients are then obtained by multiple linear regression, considering only the variables that were selected in the iterative process.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 7 Experiment
### Performance of Data Insertion  
First, we study the performance of data insertion. Figure 4a presents the insertion performance of different schemes when the dataset size changes from 40 GB to 200 GB. A natural observation is that the insert latency of the three schemes increases linearly with the increase of the dataset size. For each scheme, the performance of THBase is between CTDM and SPDM, and CTDM takes the least time. The main reason is that compared to CTDM, THBase needs to update L-indexes by coprocessor while inserting the trajectory data, and the frequency of index update (segment-based) is higher than CTDM (MO-based). Otherwise, SPDM may require multiple serialization and deserialization processes to complete the data insert of a data cell, resulting in being the most time-consuming.  
Figure 4b,c show the data and index footprint of different schemes. In the aspect of storing data, CTDM has the lowest storage cost due to the MO-based data model, while THBase which has the segment-based data model consumes the larger footprint. SPDM needs to insert data into one data cell multiple times, resulting in multiple versions, so SPDM takes up the most cost. Also, in the aspect of index, the G-IT index of CTDM takes up the least cost due to its MO-based data granularity and relatively simple index entry structure. The footprint of index in THBase is larger than that in CTDM, but it only requires about 2.0% of actual storage, so the index of THBase can be completely stored in memory.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 4 Simulation and Evaluation
### Network Connectivity  
Considering the higher resource demand for nodes to use the asymmetric encryption algorithm, in this scheme, the cluster head establishes a session key with the MS by utilizing the improved ECDH algorithm, and the cluster head builds a session key with the member nodes based on the binary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PSO Parameters** & **Parameter Values** & **GA Parameters** & **Parameter Values** \\ \hline The number of particles S & 20 & The number of populations \(n\) & 100 \\ \hline Learning factors \(c_{1},c_{2}\) & 2 & Cross probability \(P_{c}\) & 0.8 \\ \hline Inertia weight \(w\) & Decrease from 0.9 to & & \\  & 0.4 linearly & Mutation probability \(P_{m}\) & 0.1 \\ \hline Evaluation factor \(a_{1}\) & 0.3 & Evaluation factor \(a\) & 0.4 \\ \hline Evaluation factor \(a_{2}\) & 0.4 & Stops \(N\) & 6 \\ \hline The number of cluster & 5 & Cross width \(W\) & 3 \\ head K & & & \\ \hline The size of the message data & 4000 bit & Acceleration index to & 2 \\  & & eliminate fitness \(m\) & \\ \hline Initial energy of node \(E_{0}\) & 0.1 J & The size of the message data & 4000 bit \\ \hline Communication radius \(d_{\max}\) & 30 m & Initial energy of node \(E_{0}\) & 0.1 J \\ \hline  & & Communication radius \(d_{\max}\) & 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameter Settings.  
We can conclude from above, with the same number of keys stored in the nodes, the proposed scheme has higher keys connectivity efficiency compared with the E-G scheme, \(q\)-composite scheme, and the AP scheme.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method
### Evaluation Metrics  
Considering that DB post-processing detection frames are diverse and not horizontal, the text detection evaluation method of ICDAR Challenges4 was referred to in this paper's experiments. A simple calculation of Intersection over Union (IoU) was used to evaluate whether the prediction was correct.  
There are three main evaluation metrics for the commonly used scene text detection algorithms, which are Precision, Recall, and F1-score. Precision is the ratio of the number of correctly predicted text boxes to the total number of predicted text boxes. Recall is the ratio of the number of correctly predicted text boxes to the total number of labeled text boxes. F1-Score is the summed average of precision and recall. The specific calculation formula is shown in Equations (22)-(24), respectively:  
\[Precision=\frac{TP}{TP+FP}, \tag{22}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{23}\]  
\[F1-score=\frac{Precision\times Recall}{Precision+Recall}\times 2.0, \tag{24}\]  
where \(TP\) is the number of correctly predicted text boxes; \(FP\) is the number of incorrectly predicted text boxes; \(FN\) is the number of positive samples not predicted; \(TP+FP\) is the number of all predicted text boxes; \(TP+FP\) is the number of all labeled text boxes.
## 2 Background and Related Works
### Social Robots and the Telepresence Scenario
#### 2.4.1 Telepresence Robot Commercial Applications  
Telepresence robots can be seen in different use cases [46]. In [47], the robot impersonates the user at work. In [46], the Telepresence robot system helps people with special needs; for example,seniors and people with disabilities can use the Telepresence robot to contact friends and family, or to work from remote locations. In [48], the authors propose the Telepresence robot for home care assistance [49], while, in [50], the system is used for English tutoring.  
The potential of the Telepresence robot system has allowed the development of a number of Telepresence robot platforms, i.e., Giraff Technology's Giraff [51], Anybots' QB 2.0 [52], VGo Communications' VGo [53], Willow Garage's Texai [54] and Double Robotics' Double2 [55], to cite a few.
## 3 Results and Discussion
### Effects of Derivatization Temperature  
The effects of temperature on the uptake efficiency of carbonyls in shown in Figures 3 and 4 for very short (10 s) and long (45 min) extractions, respectively. The fiber was doped with 17 mg/mL PFBHA at the same temperature, 40, 60 or 80 \({}^{\circ}\)C, as in the derivatization procedure. The concentration of each carbonyl compound in the standard solution was about 5 \(\mathrm{\SIUnitSymbolMicro g}\)/L for the 45 min extraction test. Ethanol was at 2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for the 10 s extraction test, while other target chemicals were at 5 \(\mathrm{\SIUnitSymbolMicro g}\)/L. The highest recovery of the majority of target compounds was obtained at 60 \({}^{\circ}\)C. The lower recovery results at 40 \({}^{\circ}\)C is likely due to lower carbonyl concentrations in the headspace (driven by Henry's law) and lower reaction rates between PFBHA coating and carbonyls in the headspace. Extraction at 60 \({}^{\circ}\)C generated the highest carbonyls recovery from headspace. The maximum temperature of 80 \({}^{\circ}\)C was also tested. However, a decrease in recovery was observed with 80 \({}^{\circ}\)C extraction. The increase in headspace carbonyl concentrations was likely offset by desorption of PFBHA and oximes from the SPME fiber. This finding is consistent with the observation by [PERSON] _et al._[26]. Thus, PFBHA doping and on-fiber derivatization were performed at 60 \({}^{\circ}\)C for the rest of this study.  
Figure 4: Effects of temperature on on-fiber derivatization efficiency. Identical derivatization time of 45 min was used for comparisons.  
Figure 3: Effects of temperature on on-fiber derivatization efficiency. Identical derivatization time of 10 s was used for comparisons.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 2 Analysis and Design of Optical System of Dim Target Simulator
### Analysis and Design of Depolarization Optical Engine
#### 2.2.1 Selection and Splicing of Spatial Light Modulator  
(1) Selection of spatial light modulator  
The field of view of the displayed star chart is 10.2 \(\times\) 10.2\({}^{\circ}\), the single star angle is 20\({}^{\prime\prime}\), and at least the display image elements should be shown in the orthogonal direction, as shown in Equation (1).  
\[\left(10.2^{\circ}\times 3600^{\prime\prime}\right)/20^{\prime\prime}=1836 \tag{1}\]  
The number of single LCOS pixels is only 1920 \(\times\) 1080, which cannot meet the minimum pixel requirement of the target simulation. Therefore, the full image plane of LCOS1 (1920 \(\times\) 1080) and partial image plane of LCOS2 (1920 \(\times\) 840) are used for stitching, and the size of the image plane after overlapping stitching is 16.128 \(\times\) 16.128 mm. The selected LCOS is shown in Figure 2, and the main technical parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Index** \\ \hline Effective field of view & 10.2 \(\times\) 10.2\({}^{\circ}\) \\ Single star angle & 20\({}^{\prime\prime}\) \\ Working caliber & \(\lesssim\)\(\phi\)18 mm \\ Operating band & 380–760 nm \\ Illumination & \(\lesssim\)10\({}^{-10}\) lx \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main technical index.  
Figure 1: Working principle diagram of dim target simulator.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Sound Speed Modeling  
One of the most important acoustical variables in seawater is sound speed. Differences in sound speed affect all the other acoustic parameters in different types of seawater at variable depths and temperatures. The sound speed is determined by the density distribution of the ocean. The sound speed in water for many activities is presumed to be 1500 m/s. Within certain conditions and ranges, this speed is accurate. Bathymetry data state that the velocity of sound in the Pacific Ocean ranges between 1450 and 1485 m/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sr. No.** & **Parameters** \\ \hline
1. & Sound Speed Modeling \\
2. & Propagation Loss Modeling \\
3. & Absorption Coefficient \({}^{\prime}\alpha^{\prime}\) \\
4. & Noise Variance Modeling \\
5. & Multipath Modeling \\
6. & Distance vs SNR \\
7. & Delay Spread \\
8. & Doppler Shift \\
9. & Channel Capacity \\
10. & Doppler Shift Estimation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Underwater channel modelling parameters.  
In the Atlantic Ocean, it lies between 1450 and 1500 m/s while its range is between 1490 and 1540 m/s in the Bay of Bengal, the Indian Ocean, and the Arabian Sea [5]. Moreover, factors such as temperature, season, salinity, location, depth, and time of the day directly influence the speed of sound. The above-mentioned aspects are also interdependent and vary across the ocean. This introduced the requirement of a precise model to observe the influence of these parameters on the speed of sound.  
For the analysis and calculation of the speed of sound in water, several mathematical models have been researched and proposed. However, the [PERSON]. [PERSON] model is mostly used that calculates the speed of sound with an estimated error of almost 0.070 m/s [151]. Later, it is found that the resulting change in the speed of sound in seawater is within 0.024 and 0.017 m/s for the UNESCO and the DelGrosso equations, respectively [152]. The sound speed underwater based on the [PERSON] model can be calculated as:  
\[\begin{array}{l}\mathrm{c}=1448.96+4.591\mathrm{T}-5.304\times 0^{-2}\mathrm{T} ^{2}+2.374\times 10^{-4}\mathrm{T}^{3}+1.340(\mathrm{S}-35)+\\ 1.630\times 10^{-2}\mathrm{D}+1.675\times 10^{-7}\mathrm{D}^{2}-1.025\times 10^{-2} \mathrm{T}(\mathrm{S}-35)-7.139\times 10^{1}3\mathrm{TD}^{3}\end{array} \tag{9}\]  
In Equation (9), T denotes the temperature in degrees Celsius from 2\({}^{\circ}\) to 30\({}^{\circ}\); \(S\) specify the salinity that ranges between 25 and 40 PPM, and the sea depth \(D\) in meters from 0 to 8000 m. [PERSON]. [PERSON]'s model showed the variation in the speed of sound (under constant salinity) with an increase in temperature at variable depths depicted in Figure 14.
## 3 Results
### Buckling Analysis
#### 3.4.2 Fbln5\(-/-\) Mice  
A similar analysis of buckling is performed for the carotid artery of the Fbln5\(-/-\) mice. The geometry and the Fung law constants of the first specimen from [19] are used here. These allowed for the most accurate FEM predictions of the Fbln5\(-/-\) artery response during inflation, so they will be further validated. The buckling of the Fbln5\(-/-\) mice arteries is also checked using the Holzapfel constitutive relation with the new \"C\" set of constants, described in Section 3.3.2. This particular set of constitutive relation parameters seems to be very accurate. The response of the artery axis, defined as the relation between the pressure \(p_{int}\) and the displacement \(u_{A}\), is shown for the Fbln5\(-/-\) FEM analyses in Figure 6b, while the corresponding buckling pressures and the relative errors are collected  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Stretch**} & \multirow{3}{*}{**Experiment**} & \multicolumn{6}{c}{**FEM Predictions**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**Analysis of the**} & \multicolumn{3}{c}{**The Pressure at \(u_{A}=D_{out}/2\)**} \\ \cline{3-8}  & & **Post-Buckling Response** & & & & & \\ \cline{2-8}  & & **Fung Constants** & **Holzapfel,** & & & & \\ \cline{3-8}  & & **from [19]** & **Set \"B\"** & & & & \\ \hline \(\lambda_{z}\) & \(p_{exp}\) & \(p_{buck\_FEM}\) & \(p_{buck\_FEM}\) & \(p_{uu\_FEM}\) & \(p_{uu\_FEM}\) & **Relative** & \(p_{uu\_FEM}\) & **Relative** \\  & **[mmHg]** & **[mmHg]** & **[mmHg]** & **[mmHg]** & **Error [\%]** & **[mmHg]** & **Error [\%]** & **Error [\%]** \\ \hline
1.0 & 67 & 40 & 71 & 47 & 29.9 & 77 & 14.9 \\
1.2 & 111 & 61 & 107 & 64 & 42.3 & 118 & 6.3 \\
1.4 & 180 & 142 & 152 & 168 & 6.7 & 181 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the buckling pressures for the Eln+/+ mice determined experimentally by [19] and in FEM calculations.  
Figure 7: Buckling of the mouse artery: (**a**) predicted in the FEM analysis of Eln+/+ mice, for \(\lambda_{z}=1.2\), using the Holzapfel constants set “B”, at the moment when \(u_{A}=D_{out}/2\); (**b**) predicted in the FEM analysis of Eln+/+ mice, for \(\lambda_{z}=1.0\), using the Fung law constants set, at the moment when \(u_{A}=D_{out}/2\); (**c**) local buckling predicted in the FEM analysis of Eln+/+ mice, for \(\lambda_{z}=1.0\), using the Fung law constants set, deformations are scaled 4\(\times\); (**d**) three half-wave deformation pattern observed in tests carried out on CB57 BL/6 mice.  
in Table 3. The buckling modes that were observed during the analysis are similar to the ones observed for the Eln+/+ mouse model, as shown in Figure 7.
## 3 Results and Discussion
### EAD Dynamics  
Forming film efficiently is important, but realizing the controllability is even more essential. In this study, the EAD dynamics of nano-Bi\({}_{2}\)O\({}_{3}\) particles in the optimal suspension were studied in detail. Figure 2 shows the relationship of the assembly efficiency (mg/cm\({}^{2}\)) of functional superhydrophobic nano-Bi\({}_{2}\)O\({}_{3}\) films and the EAD, or assembly time (min). The assembly efficiency of the target film increased nearly linearly with the EAD time, increasing from 0 to 8 min, and the rising tendency of the assembly efficiency decreased sharply as the EAD time continued to rise. It is worth mentioning that the relative weight gain of a product was not obvious after 18 min because of the gradual decrease of the concentration of particles.  
Figure 1: The typical FESEM images with low (**a**) and high (**b**) resolution ratios of obtained functional superhydrophobic nano-Bi\({}_{2}\)O\({}_{3}\) films.  
In addition, the effect of different applied field strengths on the assembly efficiency of the product is also analyzed in Figure 3. Interestingly, when the field strength was relatively low (<15 V/mm), there also existed a similar linear correlation equation between the assembly efficiency and the field strength. The assembly efficiency increased at a much lower rate as the field strength exceeded 15 V/mm, which was caused by several reasons, including the more serious severe disturbance of suspension, the gradual increase of the resistance of electrodes, and the sedimentations of part particles [33]. These results provided a theoretical reference for a controllable assembly of nanoparticles by an optimal EAD process.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 2 Experimental Methodologies
### Experimental Setup
#### 2.2.2 Software Configuration  
The TelosB nodes in the experiments run the NesC codes. NesC is a programming language specially devised for wireless embedded devices, and it is commonly used together with the TinyOS, a light-weight and open-source operating system developed by UC Berkeley. To evaluate the link performance, we let nodes exchange messages according to a pre-configured plan. In detail, the program on each node controls the message sending with a specific period of time and records necessary information once a message is received; the message includes the message sequence, the RSSI (received signal strength indicator) and the LQI (link-quality indicator) measurements, as well as the receiving time. The RSSI and the LQI are two simple link quality metrics and can both be directly returned by the CC2420 radio chip, without needing any application-level computation.  
The experimental data are delivered to the laptop, which connects with the TelosB node via a USB port and runs a serial-port listening program of Java, thereby being able to receive all of the link information sent by the connected TelosB node. Once the data (a record) arrives, the laptop will immediately push it into a local database of MySQL. We will give more details, in later sections, about how to carry out the experiments.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 7 Related Work
### Flow Table Management Scheme  
Commercial OpenFlow switches typically support a limited number of flow entries, which result in the flow table overflow even without adversaries [12; 40]. Existing flow table management strategies include optimizing timeouts and rule replacement.  
Several studies have criticized the fixed timeout setting and proposed adaptive timeouts [15; 18; 21; 41] considering flow characteristics. Intelligent Timeout Master [15] assigns idle timeouts based on the flow characteristics and the current flow table occupation. HQ-timer [16] uses a Q-learning-based mechanism to decide the timeout value. [PERSON] et al. [21] proposed methods based on real-time traffic monitoring. An analysis of different dynamic timeout is reviewed in [41].  
For replacement algorithms, they can be implemented on the controller or in switches. We have shown that Open vSwitch [42] can delete entries that expire soonest. The performance of the _random eviction_ and _FIFO eviction_ methods is explored and compared in [17]. The authors also point out that _LRU_ is impractical to implement on the controller. Machine-learning-based methods are proposed in [43]. Flow table management considering switch performance and security issues are reviewed in [14].  
Flow rules can be compressed with wildcards [44; 45; 46; 47]. CacheFlow [44] and CRAFT [45] are designed to manage the lifetime of wildcard rules with rule dependencies. [PERSON] et al. [46] proposed a frequency-based method to replace wildcard rules. Moreover, novel caching structures [19] are designed to compress the required storage space.