esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Region-Based Collision Avoidance Beaconless Geographic Routing  
### System and Network Model  
In the proposed protocol, the greedy forwarding area is divided into two sub-areas: hidden-less area and the hidden area. The hidden-less area is similar to the restricted forwarding area of the previous protocols by including only mutually-communicable nodes. However, unlike the previous protocols, the position of the hidden-less area can be moved within the greedy forwarding area by a learning mechanism. In order to switch the hidden-less area with simple calculation, we choose a 60-radian degree area for the hidden-less area, which is a radial region that includes a 30-radian degree area around the line connecting the sender and the destination on both sides. Except for the hidden-less area, we call the rest of the area the hidden area. Note that all nodes in the greedy forwarding area have been given data packets from the previous sender at the same time, but their relaying or answering times have to be different from each other in order to prevent collisions. Therefore, we exploit the modified waiting function, which is based on both the distance from the destination (closer node from the destination having a shorter timer) and the priority value related to its geographic position in the greedy forwarding area (higher prioritized node having a shorter timer).  
Since beaconless geographic routing protocols mostly rely on the geographic greedy forwarding strategy, we assume that every sensor node in the network could know its own geographic information by GPS devices or using other localization algorithms [21], like the other beaconless geographic routing protocols. We also assume that a data source node could get the location of the destination by using the sink location service protocol [22, 23] or its global flooding.
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 2 Traffic Sign Detection
### Traffic Sign Segmentation Based on the HSV Color Space  
Color is an important feature of traffic sign, and traffic sign can be quickly located by color segmentation. Compared with RGB color space and HSI color space, the HSV color space has a faster detection speed, less affected by illumination, and has a preferable segmentation advantage. Figure 1 shows the HSV color space converted from the RGB color space. It represents the points in the RGB color space by an inverted cone, where H is the hue, S is the saturation and V is the value.  
H indicates the color change of the image. The position of the spectral color is represented by the angle, and different color values correspond to different angles. Red, green and blue are \(120^{\circ}\) apart, that is, \(0^{\circ},120^{\circ}\) and \(240^{\circ}\), respectively. S denotes the proportion of the current color purity to the maximum purity with the maximum value of 1 and the minimum value of 0. V represents the brightness change of the image. The maximum value is 1 in white and the minimum value is 0 in black. In the HSV color space, given that V is a fixed value set and H and S are highly unrelated, the HSV color space has good illumination adaptability when the illumination conditions change, and its computational complexity is small, which are conducive to the color space threshold segmentation.  
The conversion of an RGB to an HSV image is shown in Figure 2.  
Color space threshold segmentation is required after conversion to the HSV color space. Figure 3 shows the color space threshold segmentation step diagram.  
Common traffic signs mainly include red, yellow and blue colors. In order to meet the target requirements of real-time color segmentation, it is necessary to determine the corresponding threshold range. Through multiple test experiments, the three-channel threshold segmentation ranges of three colors are obtained on the premise of ensuring good segmentation effects, as shown in Table 1.  
Figure 1: The HSV color space.  
Figure 3: The color space threshold segmentation step diagram.  
Figure 2: Converting the RGB image to the HSV image.  
In the process of threshold segmentation, the pixels within the set threshold range are set to white, otherwise they are set to black, and the image is completely binarized. Since the traffic sign in the original picture is red, the obtained threshold coarse segmentation image only displays red. Figure 4 presents the threshold rough segmentation image.
## 3 System Model and Framework
### Energy Expenditure Constraints
In these equations, energy consumed in transmitting data by the BS to the nodes is excluded because the data received by the nodes are the signaling data [1] and it consumes much less energy.
## 3 End-to-End BER Calculation for the Proposed SDF Scheme
 \tag{46}\]  
This model can be regarded as the same as the MIMO system model structure in which r > t. Therefore, the approximate average BER expression for this model can be obtained using Equation (47). After obtaining \(\Pr(e/\emptyset)\), \(\Pr(\emptyset)\) and \(\Pr(e/\emptyset)\), the end-to-end BER calculation at the destination is obtained, as given below, using Equation (43):  
\[\Pr(e)=\frac{1}{2 SNR_{s,d}}\times\frac{1}{2 SNR_{s,r}}+\binom{2L-1}{L}\left( \frac{1}{2 SNR_{Concatenated}}\right)^{L} \tag{47}\]
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 5 Discussion
### Predictive Trend Comparison
By correcting the model by the real-time monitoring data of the sensors, the trend of various pollutant concentrations can be accurately reflected, and the problem of low sensor accuracy is solved from the algorithm level.
## 3 Direct Defences  
Direct defences may prevent the insects from feeding (trichomes, thorns, thick leaves) or produce toxic chemicals that include primary and secondary metabolites that destroy or impede the development of the insect [65; 67; 74]. The presence of secondary metabolites has been demonstrated to discourage feeding by herbivores in tomato [75] and in tobacco [76]. Medicago trancatula plants' secondarymetabolites deter aphids from settling and feeding on the plants within six hours of release [77]. In cassava, both the grasshopper (_Zonocerus variegatus_) and burrowing bug (_Cytromenius bergi_), appear to be deterred from feeding by cyanogen in leaves and roots [78; 79]. Furthermore, artificial infestation in a wild species of cassava, Jatropha gossypifolia [80], with cassava mealybug (_Phenacoccus herren_) ended in 100% mealybug mortality within 48 h, due to the release of the cyanogen in the leaves.  
One of the most significant elements of insect and pathogen defence in plants is a directly induced defence response [81]. These defences are insect/pathogen-specific and they have the advantage of reducing the metabolic cost to the host plant [66; 72]. The cross-talk between the signal pathways results in the release of volatiles that further activate the expression of defence-related genes. To date, activation of plant defence signals by pierce-sucking, herbivore feeding insects has been reported to be similar to those in response to pathogen infection [82; 83; 84; 85; 86; 87]. Examples of these studies include the pathogen _Pseudomonas syringae_ pv. tomato infection in _Arabidopsis thaliana_[85]; green peach aphid (_Myzus persicae_) feeding on _Arabidopsis thaliana_[88; 89]; Russian wheat aphid (_Dinarphis noxia_) feeding on wheat and barley [86; 89]; potato aphid (_Macrosiphum euphoriae_) feeding on tomato [90]; silverleaf whitefly (_B. tabaci_ type B) feeding on _A. thaliana_[91]; greenbug (_Schizaphis graminum_) feeding on sorghum [92]; the grapevine mealybug (_Planococcus ficus_) feeding on grapevines [93]; cotton mealybug, (_Phenacoccus solenopsis_) feeding on cotton [94], and the mealybug (_Phenacoccus manibot_) feeding on cassava [18]. The production of signal molecules such as the phytohormones JA, SA and ET is induced upon biotic stress (Figure 1), and abscisic acid production is induced upon encounter with abiotic stress [95]. These hormones have also been associated with plant-insect/pathogen interactions, which are triggered upon attack by herbivorous insects and pathogens [6; 16; 18; 83; 85; 96; 97; 98; 99].
## 4 Experiment and Result Analysis
### Ablation Experiment Results and Analysis
The multi-granularity interactive fusion mechanism can enhance the interaction between character-level and word-level information, realize the complementarity between character-level and word-level information, strengthen the weight of important information, and can filter out noise at the same time, resulting in the output of more effective joint character-word feature information and improving model performance. #### 4.4.2 Multi-Head Self-Attention Pooling Ablation Experiment  
To verify the effect of multi-head self-attention pooling on the model's performance, three sets of ablation experiments were conducted in this paper. Except for the change in the multi-head self-attention pooling layer, all model parameters remained unchanged. The results of the multi-head self-attention pooling ablation experiment are shown in Table 7.  
Ablation experiment 1: The multi-head self-attention pooling layer is replaced with the max-pooling layer in the model. Ablation experiment 2: The multi-head self-attention pooling layer is replaced with the average-pooling layer in the model. Ablation experiment 3: The multi-head self-attention pooling layer is replaced with the self-attentive pooling layer in the model, i.e., all pooling operations are performed in only one subspace instead of in multiple subspaces. The accuracy and F1 score of the DCCMM model are improved by 1.08% and 1.21%, respectively, compared to the model in ablation experiment 1 on the weibo_senti_100k dataset. The accuracy and F1 score are improved by 1.19% and 1.37% compared with the model in ablation experiment 1 on the SMP2020-EWECT dataset. This shows that max-pooling only filters the obvious features and loses some key information, verifying the effectiveness of the multi-head self-attention pooling mechanism proposed in this paper. The accuracy and F1 values of the DCCMM model are improved by 1.01% and 1.04%, respectively, compared to the model in ablation experiment 2 on the weibo_senti_100k dataset. The accuracy and F1 values are improved by 1.36% and 1.72%, respectively, compared to the model in ablation experiment 2 on the SMP2020-EWECT dataset. Mean-pooling extracts the mean features, but the mean features are not necessarily important. It still cannot perform the targeted extraction of key region features, while multi-head self-attention pooling can enhance the weight of important features and achieve adaptive extraction, which is beneficial to the model's grasp of the sentiment tendency of microblog speech. The accuracy and F1 score of the DCCMM model are improved by 0.93% and 0.96% compared with the model in ablation experiment 3 on the weibo_senti_100k dataset. The accuracy and F1 score on the SMP2020-EWECT dataset are improved by 1.04% and 0.96% compared with the model in ablation experiment 3. This verifies that, compared to single subspace self-attention pooling, multi-head self-attention pooling can extract important features in the text from different perspectives, thus improving the sentiment analysis capability of the model.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 6 Discussion
### Performance Analysis
The inclusion of dynamic ownership update features in [6,15] and our approach is a distinct advantage. These features guarantee data confidentiality during ownership transitions and prevent departing clients from accessing data still stored in the cloud storage. * Server-aided capabilities, a feature supported by [6, 7, 8, 11, 13, 14] and our approach, refer to actions taken to obtain message-independent encryption keys through a key server. Our protocol generates MLE keys through independent key servers.
# Investigating the Reliability and Dynamic Response of Fully 3D-Printed Thermistors
## 2 Materials and Methods
total of ten specimens, five for each design, were fabricated for this evaluation. The layout of the adhesion test specimens is illustrated in Figure 3.  
Adhesion levels of such structures, as an indicator of mechanical reliability, were evaluated using test method B of ASTM D3359-17: standard test methods for rating adhesion by tape test [40]. For the peel test, a pressure-sensitive tape (3M 8981) with a peel strength of 7.10 N/cm was utilized. The adhesion grade of the PEDOT:PSS layer was determined by visually inspecting the pattern of film detachment on both the PC substrate and the Ag film after the test. The detachment percentage was estimated by comparing the observed detached amount of the material to reference patterns described in the ASTM D3359-17 standard classification table. Adhesion was then rated on a scale from 0B to 5B, where 5B indicates the highest adhesion and 0B represents the lowest adhesion. The ratings correspond to detachment percentages of 0%, <5%, 5-15%, 15-35%, 35-65%, and >65%, respectively. All tests were performed at room temperature and 50% relative humidity to prevent the influence of extreme environmental conditions on the adhesion of the tape or conductive films. Morphological analysis of multi-material thermistors was conducted using the scanning electron microscopy (SEM) method. Imaging was conducted on a JSM-7800F Schottky field emission scanning electron microscope (JEOL, Tokyo, Japan). Thermistors were cross sectioned into 1 mm \(\times\) 1 mm samples from their back face using a surgical scalpel. The specimens were coated with a thin layer of gold (80%) and palladium (20%) blend in an argon environment for 90 s using a rotary-pumped coater (Q150R S, Quorum, East Sussex, UK). SEM imaging was conducted at an accelerating voltage of 5 kV to capture high-resolution magnified images.
Sensors_**2010**, _10_, 2995-3022; doi:10.3390/s100402995  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Article  
Implementation and Evaluation of the WADGPS System in the Taipei Flight Information Region  
[PERSON] * and Shih-Chieh Lu  
Institute of Civil Aviation, National Cheng Kung University, Tainan 70101, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel: +886-6-2757575 ext.63629; Fax: +886-6-238-9940.  
Received: 11 January 2010; in revised form: 9 February 2010 / Accepted: 14 March 2010 /  
Published: 30 March 2010  
This paper describes the implementation of the Wide Area Differential Global Positioning System (WADGPS) system in order to evaluate the operational performance of a satellite based aviation navigation system within Taipei Flight Information Region (FIR). The main objective of the WADGPS is to provide real time integrity information regarding the use of GPS for civil aviation applications. This paper uses the e-GPS observation stations operated by the Taiwan Ministry of Interior (MOI) as the WADGPS reference stations to collect the L1-L2 dual-frequency GPS measurements. A WADGPS master station is also implemented to process all GPS measurements sent from each reference station, and then generate the vector corrections. These vector corrections consist of the satellite ephemeris and clock errors, and a grid of ionospheric delays. The data stream also includes confidence bounds for the corrections and \"Use/Do Not Use\" messages to provide integrity. These messages are then passed to the WADGPS user through the Internet. This paper discusses the WADGPS system architecture and the system performance analysis. A five-day operation performance in Taipei Flight Information Region (FIR) is presented in this paper. The results show that the WADGPS can improve the accuracy performance of GPS positioning and fulfill the integrity performance required by Non-Precision Approach (NPA) defined by the International Civil Aviation Organization (ICAO).  
GPS; Wide Area Differential GPS (WADGPS); integrity; aircraft approach
## 3 Results and Discussion
### Measured Values  
Table 1 shows the average values with standard deviation for all input values of \(P\) equal to 100 and 150 W. The figures in Table 2 show the average values together with the standard deviations for all the input values of \(v\) equal to (3, 6, 9)\(\cdot 10^{-3}\) m\(\cdot\)s\({}^{-1}\). In Table 3 the average values together with the standard deviations for all the input values of AR from 3 to 11 are shown.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Value at _P_**} & **WKU** & **WKL** & **WKR** & **WHAZU** & **WHAZL** \\  & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** & **(-)** & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** \\ \hline
100 & \(0.81\pm 0.17\) & \(0.50\pm 0.09\) & \(0.48\pm 0.04\) & \(0.16\pm 0.02\) & \(0.18\pm 0.02\) \\ \hline
150 & \(0.91\pm 0.22\) & \(0.58\pm 0.14\) & \(0.77\pm 0.06\) & \(0.19\pm 0.02\) & \(0.26\pm 0.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of all the measured parameters vs. \(P\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Value at _v_**} & **WKU** & **WKL** & **WKR** & **WHAZU** & **WHAZL** \\  & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** & **(-)** & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** \\ \hline
3 & \(1.04\pm 0.11\) & \(0.65\pm 0.08\) & \(0.64\pm 0.17\) & \(0.15\pm 0.02\) & \(0.20\pm 0.04\) \\ \hline
6 & \(0.91\pm 0.13\) & \(0.56\pm 0.07\) & \(0.61\pm 0.16\) & \(0.18\pm 0.03\) & \(0.22\pm 0.06\) \\ \hline
9
## 3 Aim, Materials, and Methods
### Mixed Transport Scheduling Model under Complex Disasters
#### 4.4.3 On-the-Way Monitoring  
Following a disaster, China promptly deploys rescue vehicles to the affected area with utmost urgency. These vehicles are equipped with state-of-the-art rescue equipment and experienced personnel, poised to provide immediate emergency assistance to those affected. To ensure the safe and efficient operation of these vehicles, a real-time monitoring system is installed onboard. By continuously monitoring transmitted data from the equipment, timely updates on vehicle status, speed, position, and other relevant information can be obtained for effective guidance and dispatching purposes. Furthermore, this monitoring system also enables the real-time tracking of crucial parameters such as fuel levels and tire pressure to guarantee that emergency situations do not compromise the effectiveness of rescue efforts.
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 4 Conclusions  
GQDs obtained from sucrose were successfully synthesized in this work. In general, the sizes of the nanoparticles obtained by AFM/TEM were between 1 and 20 nm for GQDs and between 1 and 6 nm for N-GQDs. XRD analyses showed that the DMSO treatment allowed the reduction of GQDs. The synthesized GQDs had several functional groups with oxygen, such as hydroxyl, carbonyl, epoxy, and carboxyl, among others. N-GQDs also had amine groups. The GQDs without doping presented PL in the blue region under excitation with UV light in the range of 300-400 nm. Doping GQDs with nitrogen could extend the luminescent emission range of GQDs and increase the PL intensity, mainly for the green to red region. Nitrogen doping should act to increase the density of states between the valence and conduction bands, resulting in a decrease in the bandgap of the system. The introduction of energy levels associated with the presence of nitrogen in the structure of GQDs can also give rise to a complex PL mechanism, facilitating the emission of lower-energy photons. The possibility of obtaining GQDs synthesized from sucrose with high quantum efficiency and emission in the green-red region is unprecedented. The methodologies used in this work can greatly simplify the processes of synthesis and doping of highly luminescent GQDs and encourage the use of more abundant precursors with low toxicity.  
Conceptualization, D.M.d.S.; methodology, A.P.d.M.R.; validation, M.I.A. and D.M.d.S.; formal analysis, M.I.A. and D.M.d.S.; investigation, A.P.d.M.R.; resources, D.M.d.S.; data curation, A.P.d.M.R.; writing--original draft preparation, A.P.d.M.R.; writing--review and editing, M.I.A. and D.M.d.S.; visualization, M.I.A.; supervision, M.I.A. and D.M.d.S.; project administration, D.M.d.S.; funding acquisition, M.I.A. and D.M.d.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Brazilian agencies Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Grant: 431162/2018-2 and 303468/2021-1); the National Institute of Photonics (INCT de Fotonica/CNPq, Grant: 465.763/2014); and the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES).  
Not applicable.  
We acknowledge Laboratorio de Cristalografia (Institute of Physics, University of Sao Paulo) for XRD measurements, Centro da Tecnologia e Informacao Renato Archer for FTIR analysis, and Laboratorio de Quimica Supramolecular e Nanotecnologia (Institute of Chemistry, University of Sao Paulo) for AFM analysis.  
The authors declare no conflict of interest.  
Figure 13: Normalized maximum PL intensities for (**a**) GQDs and (**b**) N-GQDs as a function of excitation wavelength. The solid lines are just guides for the eye.
# Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change +
###### Abstract  
This article presents a configurable, high-throughput pseudo-random number generator template targeting cryptographic applications. The template is parameterized using a chaotic map that generates data, an entropy builder that is used to periodically change the parameters of the map and a parameter change interval, which is the number of iterations after which the entropy builder will change the generator's parameters. The system is implemented in C++ and evaluated using the TestU01 and NIST RNG statistical tests. The same implementation is used for a stream cipher that can encrypt and decrypt PNG images. A Monte-Carlo analysis of the seed space was performed. Results show that for certain combinations of maps and entropy builders, more than 90% of initial states (seeds) tested pass all statistical randomness tests. Also, the throughput is large enough so that a 8 K color image can be encrypted in 2 s on a modern laptop CPU (exact specifications are given in the paper). The conclusion is that chaotic maps can be successfully used as a building block for cryptographic random number generators.  
chaos-based pRNGs; randomness tests; telecommunications; information systems; digital signal processing; image signal processing Article