esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Selection of the Most Suitable VIIRS RST-FLOOD Indicator
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**ALICE** & \multicolumn{3}{c}{**All Scene**} & \multicolumn{3}{c}{**Basento River**} \\ \cline{2-7}
**Index** & **\# Anomalous Pixels** & **Mean** & **Max/Min** & **\# Anomalous Pixels** & **Mean** & **Max/Min** \\ \hline ALICE\({}_{\text{SWIR}}\) & 220 & \(-\)2.42 & \(-\)28.16 & 71 & \(-\)3.98 & \(-\)7.48 \\ ALICE\({}_{\text{RED-SWIR}}\) & 604 & 4.70 & 17.15 & 230 & 6.28 & 17.15 \\ ALICE\({}_{\text{RED-SWIR}}\) & 679 & 8.92 & 61.83 & 247 & 3.06 & 61.83 \\ ALICE\({}_{\text{NDSI}}\) & 638 & 5.78 & 23.74 & 256 & 3.01 & 23.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the ALICE indices all over the scene and for anomalous pixels along Basento River. Figure 9: (**a**) Flooded areas detected within the ROI by ALICE\({}_{\text{NDSI}}\) on 04/12/2013 at 12:10 GMT at increasing levels of the confidence from red to yellow; (**b**) magnification of the area within the white box shown in Figure 1.  
Once the most suitable ALICE index has been defined, it has been applied also on the next four days (i.e., up to 8 December 2013), when the event should have been already ended, according to previous works (e.g., [31; 37]). The map including all the anomalous pixels detected by the ALICE\({}_{\text{NDSI}}\geq 3\) during the five days is reported in Figure 10, clearly showing as the probably effects of the event were still evident up to 8 December at least. In the following days, the ROI was again cloudy up to 10 December 2013, when the flood effects were almost disappeared. The spatial extent of the detected anomalies during the five-day period was about 72 km\({}^{2}\) on December 4 th, 41 km\({}^{2}\) on 5 th, 32 km\({}^{2}\) on 6 th, 38 km\({}^{2}\) on 7 th and 30 km\({}^{2}\) on 8 th December 2013, respectively. A clear decreasing trend in the anomalous area identification is notable (as expected in the case of floods), except for 7 th December, when the large presence of clouds and relatively shadows may have produced a few false positives (see green pixels in Figure 10). Moreover, the clusters of anomalous pixels in correspondence of the main affected rivers as well as their persistence in the spatiotemporal domain, suggest their possible direct correlation with the flooded area that will be assessed in the next session.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work
### Animal Monitoring Platforms
Furthermore, these data can be stored and processed through advanced and powerful processing mechanisms, as Machine Learning (ML) algorithms, allowing the extraction of additional knowledge, not directly available on raw's sensor data. A few practical examples are the study of activity patterns of Griffon Voltures [62], the classification of goats activity [63], the classification of cattle activity [64; 65; 66] and the classification of sheep behaviour [67; 68; 69]. Despite a few adaptations, necessary to accommodate the specificities of the application scenarios (i.e., the animal breed whose behaviour is to be studied), all the works rely on a similar approach, namely on gathering field data and transmitting it to computational platforms with advanced processing capabilities. The existing solutions differ mainly on the mechanisms they use to gather the data (through WSNs, external memories, cellular technologies) and on the algorithms they use to process the data (Machine Learning classifiers, Artificial Neural Networks). SheepIIT requires that the behaviour detection must be carried out in Real-Time, and thus must be deployed at the box placed on the animals, therefore resulting in low-cost and low-power hardware, impairing the use of the advanced data processing mechanisms carried out by the aforementioned works. The inherent advantages offered by animal monitoring activities to livestock productivity has led to the emergence of several commercial solutions. Some relevant examples are CowScout [70]and Cowlar [71], that aim at monitoring and quantifying the activity of cows in order to improve the lactation and insemination processes. However, these commercial solutions are scarce in details and it is only possible to state that they allow detecting animal activity, probably relying on accelerometers' data. In summary, there are several solutions that tackle some individual (or group of) issues that SheepIT aims to solve. Table 1 summarizes the most relevant solutions surveyed. As observed, none of the presented solutions is capable of detecting and conditioning sheep's posture while grazing. An additional comparative study of some of the presented solutions is given by [PERSON] et al. [72].
# Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings
###### Abstract  
Hyaluronic acid (HA) and chitosan (CH) are biopolymers that are widely used in many biomedical applications and for cosmetic purposes. Their chemical properties are fundamental to them working as drug delivery systems and improving their synergistic effects. In this work, two different protocols were used to obtain zwitterionic liposomes coated with either hyaluronic acid or chitosan. Specifically, the methodologies used to perform vesicle preparation were chosen by taking into account the specific chemical properties of these two polysaccharides. In the case of chitosan, liposomes were first synthesized and then coated, whereas the coating of hyaluronic acid was achieved through lipidic film hydration in an HA aqueous solution. The size and the zeta-potential of the polysaccharide-coated liposomes were determined by dynamic light scattering (DLS). This approach allowed coated liposomes to be obtained with hydrodynamic diameters of 264.4 \(\pm\) 12.5 and 450.3 \(\pm\) 16.7 nm for HA- and CH-coated liposomes, respectively. The chemical characterization of the coated liposomal systems was obtained by surface infrared (ATR-FTIR) and nuclear magnetic resonance (NMR) spectroscopies. In particular, the presence of polysaccharides was confirmed by the bands assigned to amides and saccharides being in the 1500-1700 cm\({}^{-1}\) and 800-1100 cm\({}^{-1}\) regions, respectively. This approach allowed confirmation of the efficiency of the coating processes, evidencing the presence of HA or CH at the liposomal surface. These data were also supported by time-of-flight secondary ion mass spectrometry (ToF-SIMS), which provided specific assessments of surface (3-5 nm deep) composition and structure of the polysaccharide-coated liposomes. In this work, the synthesis and the physical chemistry characterization of coated liposomes with HA or CH represent an important step in improving the pharmacological properties of drug delivery systems.  
drug delivery system; liposome coating; hyaluronic acid; chitosan; polymeric materials; nanomaterials +
Footnote †: journal: Physics Letters B
## 6 Experiments and Discussion
### Field Experiments
#### 6.3.1 General Evaluations  
The main navigation errors of longitude, latitude, east and north velocities of all the considered schemes are shown in Figure 12. From Figure 12 we can find that: (1) in most cases, the accuracy of SCT5 is just marginally better than that of STC4 due to utilizing a redundant visible satellite. However, this may be counterproductive when the redundant measurements include large errors; (2) the performance of ATC is better than both STC4 and STC5 in most cases. However, when abnormal measurements occur, ATC suffers from large positioning errors; (3) MATC has an improvement over ATC owing to the RMNCE based satellite selection; (4) RMNCE-TC, which benefits from robust measurement noise estimation and the adaptive satellite selection, provides the best performance of all the considered schemes; (5) CNE-TC has an improvement over STCs and ATC, even performs better than RMNCE-TC at some epochs when the observability is good. But it still suffers from the large measurement errors when the observability quality becomes bad.  
The RMSE was also employed to evaluate the global performances of the different schemes. Table 8 lists the RMSE results in detail. From the comparison, we note that MATC and CNE-TC have a similar navigation accuracy. RMNCE-TC provides the smallest navigation error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Satellite Selection** & **Filter Technique** \\ \hline STC4 & 4 satellites, DGOP based & Standard UKF \\ STC5 & 5 satellites, DGOP based & Standard UKF \\ ATC & 5 satellites, DGOP based & RAE-UKF \\ MATC & 5 satellites, RMNCE based & RAE-UKF \\ RMNCE-TC & 5 satellites, RMNCE based & RMNCE-UKF \\ CNE-TC & Variable selected satellite number & R is weighted by CNR and \\  & based on CNR and elevation & satellite elevation \\ \hline \hline \end{tabular}
\end{table}
Table 7: Different schemes during the test.  
Figure 11: Reference trajectory of the field experiment (the blue arrows indicate the final part).  
Figure 12: Navigation differences with respect to the reference trajectory using STC4, STC5, ATC, MATC RMNCE-TC and CNE-TC: (**a**) longitude differences (m); (**b**) latitude differences (m); (**c**) east velocity differences (m/s); (**d**) north velocity differences (m/s).
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Link Efficiency Oriented Network Model  
The physical and logical topology of WBANs network model with eight BSNs and one BNC is exhibited in Figure 2. These BSNs generate heterogeneous types of data and send it to the BNC located on the body waist. The BNC simply receives data from the BSNs and does not generate any data at its own. Each sensor node determines its neighboring nodes according to its communication range. The logical topology produced from Figure 2a is shown in Figure 2b. In the logical topology, the nodes denote the sensor nodes, whereas edges indicate the wireless connections between these sensor nodes. The wireless connections are shown according to the communication range of the sensor nodes.  
Figure 2: Network topology (**a**) Physical Topology (**b**) Logical topology.  
ELR-W is developed with the following assumptions:  
1. All BSNs are fixed on a human body as exhibited in Figure 2a and no node is implanted.
2. Each node possesses the same energy, processing power, and interfaces.
3. All BSNs have fixed and limited transmission power.
4. Human body movement is not considered in this research.  
Considering the shorter distance does not always lead to lower path loss values. Due to the multipath oriented interference effect, shorter distance might lead to higher path loss values. Here it is highlighted that to incorporate the similar situations. The link quality estimation is considered as another parameter for helping in selecting the next-hop in case of shorter distance and dense environments.
###### Abstract
The proposed pressure sensor system is thin and flexible and aims to utilize a Velostat film, which can be fabricated as a pressure sensor through the attachment of electrodes. The application of a Velostat film has three advantages. First, the Velostat film is a resistance-change-based film that reduces electrical resistance when external pressure is applied. Thus, it is strong against the noise caused by body impedance changes compared with capacitive-based sensors. Second, it is possible to fabricate such a sensor in consideration of individual pressure ranges so that pressure non-detection and saturation problems do not occur because the measurement curve pattern and saturation range at the time of pressure application differ. Finally, since it is easy to fabricate in various shapes and wide sizes, there are no large fabrication costs due to a need for specific arrangements for measurement at an amputated area with low muscle mass. Based on these characteristics, the Velostat-film-based pressure sensor is expected to be able to solve the problems of the non-detection of pressure changes and saturation at a low cost and in a short time frame. However, studies on specific guidelines for the fabrication of pressure sensors using Velostat films are lacking. This study aims to present fabrication guidelines that can be customized according to the user based on Velostat film sensors. Sensors fabricated through the guidelines can be applied to the positions of the upper and lower rectus femoris and biceps femoris in order to detect pressure changes caused by lower limb movements in the forward and backward and upper and lower directions of the sagittal plane. A sensor fabricated through this process will address the limitations of the existing socket internal pressure sensor system. In other words, it is possible to apply a sensor that is simple to fabricate and has excellent detection performance due to less noise against the skin reaction when applied inside the socket. In addition, by cutting the outside of the sensor in a V shape, it is possible to obtain a result that can be applied inside a wide and curved socket rather than a general small plane. In Section 3, the possibility of obtaining an improved sensor, as proposed in this study, is verified through comparative experiments with the existing capacitive pressure sensor. The proposed pressure-sensor-based intention detection system can be applied individually by prosthetic users through simple tasks, and universal applications for commercialization will be possible.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 1 Introduction
The anisotropic magnetoresistance technology fixed on the drill string measures the current change on the drill string and extracts the information carried by it, so as to realize the communication. By using this communication mode, the antenna structure can be greatly simplified, and the purpose of wireless communication has been realized. At the same time, the electrode position can be changed at will, which has strong flexibility. The simplified structure model of the down transmission system of information while drilling based on anisotropic magnetoresistance detection is shown in Figure 1, and the structure of the bit, drill string, and drill collar are simplified as the drill string of Figure 1. In the picture, the drill string, earth, and electrode form a loop. The transmitter sends out a signal, which is transmitted to the bottom of the well through the drill string and received by the receiver. Figure 1: Structure model of downlink communication system based on anisotropic magnetoresistance detection. The following assumptions are made on the premise that the electrode at the end of the transmitter is replaced by the drill string:  
1. Drill string belongs to the metal conductor, its conduction current is much greater than the displacement current, the displacement current can be ignored
2. The conductivity of electrode and drill string is much higher than that of rock soil and drilling fluid. The surface is approximately equal potential, which conforms to the boundary conditions of the electrostatic field
3. The medium in the rock, and soil layer remains unchanged
4. The magnetoresistance sensor is close to the drill string
5. Because the drill string is usually several thousand meters long, the drill string is approximately regarded as a thin line
6. The carrier signal with the communication frequency not higher than 30 kHz can be regarded as the direct current (DC) power supply.
## 4 Remote Sensing Techniques  
Remote sensing of wind is based on the Doppler technique, where the wind velocity component in the direction of the measurement signal is derived from the Doppler shift between the transmitted and the backscattered signal. The same technique is applied in Doppler lidar (light detection and ranging), Doppler sodar (sonic detection and ranging) and Doppler radar (radio detection and ranging). The known travel (half) time provides the distance range for the measurement and the obtained velocities represent the average velocity of the particles/air parcels from which the signal is backscattered. The backscattering depends on the wavelength used. For radar, backscattering takes place mainly from hydrometeors, but also from insects and birds [74]. Sodar echos on the other hand return from inhomogeneities in air density caused by turbulence and from vertical gradients in temperature and wind (e.g., [75]). lidar backscattering takes place from aerosols and other small particles in the air [76]. In order to measure wind gusts, only lida can provide high enough temporal and spatial resolution. Therefore, in this section, we will focus on Doppler lidar measurements. However, Doppler radar can potentially provide valuable information on convective gust fronts, and therefore, they are briefly discussed at the end of the section.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 3 Fluid Field Calculation
### Convergence Assessment  
The assessment of convergence is crucial for ensuring the accuracy and reliability of computational results. It serves as an evaluation method for validating the correctness of various factors, including mesh quality, physical model selection, and boundary condition configuration.  
In Fluent simulations, convergence is typically determined by monitoring the trend of residuals or customizing one of the physical parameters. Fluent's default convergence criteria state that convergence is achieved when all variable residuals fall below 1 \(\times\) 10\({}^{-3}\), except for energy residuals, which have even more stringent requirements, demanding that they be less than 1 \(\times\) 10\({}^{-6}\)[23]. Residuals refer to the sum of fluxes on each face of each element, predominantly monitoring physical quantities like mass, energy, velocity, turbulence, and more.  
In the current computational process, when the number of iterations reaches 56, the mass residual is below 1 \(\times\) 10\({}^{-3}\), thus meeting the convergence criteria.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.13 Reactive Routing Protocol for VANETs (RRPV)  
RRPV [12] was proposed based on the idea of combined metrics as direct-connection reliability, hop count, and cochannel noise. With this selection, it tries to choose an appropriate path which minimizes interference.  
When top-count \(HP\), average best cochannel noise \(BEST_{CCN}\), and average reliability-probability \(DCRP_{path}\), then the RRPV metric has to be adequately defined as a multiobjective function, as shown in Equation (14).  
\[m_{RRPV}(p)=\beta_{1}\cdot m_{HC}(p)+\beta_{2}\cdot m_{CCN}(p)+\beta_{3}\cdot m _{DCRP}(p) \tag{14}\]  
where \(p\) is a considered route, and \(m_{HC}\), \(m_{CCN}\), and \(m_{DCRP}\) are three normalized related terms that can be expressed as follows:  
\[m_{RRPV}(p)=\begin{array}{c}\beta_{1}\frac{BEST_{CCN}(p)}{CCN_{MAX}}+\beta_{2 }\frac{HC(p)}{HC_{MAX}}+\\ \beta_{3}\left(1-\frac{DCRP(p)}{DCRP_{MAX}}\right)\end{array}, \tag{15}\]  
where terms in the metric have been normalized in order to be comparable. At this moment, when node \(v_{i}\) has to select among different routes to a destination, the choice of the best path is made by minimizing the following value:  
\[m_{RRPV}(p^{*})=\min_{p}[m_{RRPV}(p)]. \tag{16}\]  
The proposal establishes a time of 60 ms as parameter to update the period of calculation of the metric to select the next hop due to high speed and topology changes. This value is optimal in order to guarantee a correct path refresh.
# An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition
## 3 Problems and Improvement of KIII Model
### Feature Extraction
#### 3.2.1 Adaptive Histogram Equalization  
Based on previous research, a comparative analysis of image quality enhancement methods, such as Adaptive Histogram Equalization, Local Contrast Enhancement, Sharpening, Saturation Enhancement, and Dynamic Range Compression, has been conducted, as summarized in Table 1. Relative to other techniques, Adaptive Histogram Equalization displays a superior local contrast adjustment capability, which justifies the selection of this adaptive method in our study to more effectively manage local details in images, thus improving the overall image quality.  
Adaptive Histogram Equalization (AHE) divides an image into small local regions and performs histogram equalization within each region. This process enhances the image's contrast while preserving its details and background information. This method takes into account the differences in brightness distribution in different regions of the image, ensuring that each local region's histogram is properly equalized, thus improving the visual quality of the image. In the case of Adaptive Histogram Equalization, the choice of key parameters can lead to different enhancement effects. Based on previous experimental research, this study sets the contrast enhancement limiting factor, clipLimit, to 1.0 and uses an \(8\times 8\) grid size, referred to as tileGridSize, to partition the image into local blocks.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Adaptive Histogram Equalization & \begin{tabular}{c} Performs histogram equalization \\ based on the local characteristics, \\ suitable for scenarios with uneven \\ illumination within the image. \\ \end{tabular} & It may introduce noise, making the image appear over-processed. \\ \hline Local Contrast Enhancement & \begin{tabular}{c} Capable of highlighting local features of an image and enhancing \\ detail. \\ \end{tabular} & It may result in the image looking overly sharp or processed, necessitating careful parameter adjustment. \\ \hline Sharpening & \
\begin{tabular}{c} Enhances the edges and details of an image. \\ \end{tabular} & It can potentially lead to noise or artifacts. \\ \hline Dynamic Range Compression &
\begin{tabular}{c} Helps in preserving more details \\ in the image, especially in high- \\ contrast scenes. \\ \end{tabular} & The image may appear darker, requiring appropriate parameter adjustments to balance brightness and detail. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of image quality enhancement methods.