esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Changes in Canopy Height and Cover Using Bi-Temporal Lidar Data  
Figure 6 presents the change in CHM30 (\(\Delta\)CHM30) from 2000 to 2016/18 over the four survey areas across five height change classes: large decrease (\(<\)\(-\)5 m), moderate decrease (\(-\)1 to \(-\)5 m), no appreciable change (\(-\)1 to 1 m), moderate increase (1 to 5 m), and large increase (>5 m). The landscape exhibited moderate canopy height growth over 55.8 km\({}^{2}\) (52.0% of land area after removing permanent open water and CHM \(<\) 1 m), and large increases in height over 4.9 km\({}^{2}\) (4.6% land area) indicating that the growth of taller (>1 m) vegetation has been the dominant trend, overall. In contrast, areas showing decreases in canopy height were comparatively small with large height decreases of 0.39 km\({}^{2}\) (0.4% land area) and moderate decrease of 3.7 km\({}^{2}\) (3.4% land area).
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 3 Modified Rescaling Algorithm Development
### The NFXLMS Algorithm with Amplitude Compensation
Take the compensation matrix as:  
\[\mathbf{P}_{s,com}=diag[\frac{\max[\mathbf{p}_{s}]}{\mathbf{p}_{s}}] \tag{12}\]  
The compensated reference signal vectors are:  
\[\mathbf{r}_{s,com}(n)=\mathbf{P}_{s,com}\mathbf{r}_{s}(n),\mathbf{r}_{c,com}(n)=\mathbf{P}_{s,com }\mathbf{r}_{c}(n) \tag{13}\]  
Applying the compensated reference vectors to the NFXLMS algorithm, the two updated equations of the controller weight vectors change to the following form:  
\[\left\{ \begin{array}{l}\mathbf{w}_{s}(n+1)=\mathbf{w}_{s}(n)+2\mu e(n)[\tau_{s,com }(n)*\delta(n)]\\ \mathbf{w}_{c}(n+1)=\mathbf{w}_{c}(n)+2\mu e(n)[\tau_{c,com}(n)*\delta(n)]\end{array} \right. \tag{14}\]  
After compensating, the \(i\)th frequency component \(u_{i}(n+1)\) of the output signal \(u(n+1)\) and the \(i\)th element of the amplitude vector \(\mathbf{A}_{u}\) and phase vector \(\mathbf{\theta}_{u}\) can be calculated by the following equations, respectively:  
\[u_{i}(n+1)=\tau_{si,com}(n)w_{si}(n)+\tau_{ci,com}(n)w_{ci}(n) \tag{15}\]  
\[A_{ui}(n+1)=\sqrt{w_{si}^{2}(n)+w_{ci}^{2}(n)},\theta_{ui}(n+1)=\tan^{-1}\frac {w_{si}(n)}{w_{ci}(n)} \tag{16}\]
## 4 Experimental Results and Analysis  
### Simulation Results and Analysis  
A simulation system is created through AMEsim (2020.1) and MATLAB (R2021a) to verify the effectiveness of the designed system. The structure of this simulation system is illustrated in Figure 4.  
To ensure the optimization of the controller parameters, this paper combines empirical method and PSO algorithm for parameter tuning. First, the initial parameter ranges are determined through empirical testing, followed by optimization using MATLAB's particle swarm function. The optimization objective is set as the sum of squared tracking errors under wind disturbance conditions, with 10 particles and a maximum of 10 iterations. The initial system parameters are set as follows: the viscous damping coefficient is 1 Nm/(rev/min), and the hydraulic cylinder leakage coefficient is 5e-12 m\({}^{3}\)/s/Pa. To enhance the adaptability of both the PID and ARC controllers, wind disturbance is incorporated during parameter tuning, with the disturbance data shown in Figure 5.  
Figure 4: The simulation system.
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 3 Resource Allocation Problem
### Reinforcement Learning Model
#### 3.3.3 Transition of State  
In the previous discussion, the arrival rate of each task is subject to Poisson distribution with parameter \(\lambda\), and the data size obeys general distribution: \(d\sim\mathcal{N}(\mu,\sigma^{2})\). Knowing that the Poisson stream is additive so that Poisson distribution can also fit the arrival of tasks assigned by many other devices.  
We assume the arrival rate of device \(I_{j}\) as \(\lambda_{j}\) so the total arrival rate for a service node is \(\lambda=\sum_{j=1}^{n}\lambda_{j}\). When the task arrives, the state \(\mathbf{S}\) is updated. The interval between the new state and old state is \(\Delta t\), and it is subject to a negative exponential distribution with parameter \(\lambda\) because the arrival rate obeys Poisson distribution. The cumulative distribution function is: \(F(\Delta t)=1-e^{-\lambda\Delta t}\).  
Then a function that integrates the system state with time is proposed. Firstly, to simplify the transition of state, we assume that all the tasks arrives at the same time, the current system state at time \(t\) is:  
\[\mathbf{S}(t)=\{d_{1}(t),M_{1}(t);\dots;d_{n}(t),M_{n}(t);b_{1}(t),b_{2}(t), \dots,b_{m}(t);D\} \tag{21}\]  
After a time slot \(\Delta t\), another group of tasks arrive, the system state changes to:  
\[\begin{split}\mathbf{S}(t+\Delta t)=\{d_{1}(t+\Delta t),M_{1}(t+ \Delta t);\dots;d_{n}(t+\Delta t),M_{n}(t+\Delta t);\\ b_{1}(t+\Delta t),\dots,b_{m}(t+\Delta t);D\}\\ b_{i}(t+\Delta t)=\begin{cases}b_{i}+\sum_{j=1}^{n}c_{ji}M_{j}-f_{i} \Delta t,&b_{i}+\sum_{j=1}^{n}c_{ji}M_{j}-f_{i}\Delta t>0\\ 0,&b_{i}+\sum_{j=1}^{n}c_{ji}M_{j}-f_{i}\Delta t\leq 0\end{cases}\end{split} \tag{22}\]  
In this way, system states will change through the arrival and action of the task. It is noted that the system state still belongs to Discrete Markov Model though it depends on both \(t\) and \(\Delta t\).
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 2 Materials and Methods
### User Study
#### 2.5.3 Variables  
All output signals from both Faros 360 and E4 were recorded and processed. Comparison between the two devices could be made with HRV and temperature signals. Since only Faros provides raw ECG signal, the evaluation of other ECG features than the ones already extracted by the devices (HRV) is not feasible. The difference with HRV is only in signal capturing methods which could provide significantly different results. Faros measures ECG signal to obtain R-R intervals (HRV) while E4 uses photoplethysmography (PPG) to obtain BVP and IBI. Two temperature readings could also provide significantly different results since measures take part at different body parts with different thermal conductance. In addition to the comparison, GSR signal from E4 was also analyzed to provide the full picture of E4's usability.  
Independent variable in our tests was each phase's driving difficulty that could take one of three values: 1 = baseline establishment, 2 = easy driving, 3 = demanding driving.  
Dependent variables for both Faros and E4 were:  
* Mean and median HRV,
* HRV SDNN--standard deviation of the R-R intervals (also known as normal-to-normal or N-N intervals, Figure 3), which reflects cyclic components during the measurement,
* HRV SDANN--standard deviation of the average R-R intervals over a shorter period (10 s), it reflects changes due to longer cycles,
* HRV SDNN index--mean of the standard deviations, calculated over a shorter period (10 s), it reflects changes due to shorter cycles,
* HRV RMSSD--root mean square of successive differences, which reflects parasympathetic nerve system activity and is not affected by respiration process,
* HRV pNN50--the number of successive differences, greater than 50 ms, derived by the number of total N-N differences, should be highly correlated to RMSSD,
* Temperature mean and standard deviation.  
Mean and standard deviation of recorded GSR signals were also analyzed as dependent variables from E4.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 1 Introduction
The learning with an emerging new class seems relevant to zero-shot learning, which is a hot topic in image classification and aims to classify those visual classes which did not appear the in the training data set. The zero-shot learning is assumed to work with side information, i.e., external knowledge, such as the class definitions, descriptions, or properties, that can help to associate the seen and unseen classes, and, hence, it can be treated as a kind of transfer learning. In contrast, learning with an emerging new class is a general machine learning setting that does not assume such external knowledge. In this work, we propose an efficient open-set signal recognition algorithm named open-set signal recognition based on the transformer and Wasserstein distance (OSSG\({}_{TW}\)). It contains three key sub-modules including _signal representation_, which is a sub-module based on ViT structure, _the set distance metric_ sub-module based on Wasserstein distance, and _the class space compression_ sub-module based on reciprocal point separation and central loss. In this algorithm, the representing features of signals are established based on the transformer-based neural networks, i.e., ViT, in order to extract the global information about the time series-related data. The employed reciprocal point is used to model the potential unknown space without the corresponding samples, and the distance metric between different class spaces is mathematically modeled in terms of the Wasserstein distance instead of the classical Euclidean distance. The main contributions of this work are as follows:  
1. An efficient open-set signal recognition algorithm is proposed, which employs a reciprocal point and central loss to establish the signal classification procedure, especially for unknown categories;
2. Transformer-based structure and Wasserstein distance, which are creatively used to extract features and measure category distances, respectively;3. Numerical experiments on different open-set signal recognition tasks are performed, which show that the proposed algorithm can significantly improve the recognition efficiency on both known and unknown categories. The following is the roadmap of this paper. Section 2 summarizes the major related works on the open-set learning and signal recognition methods. Section 3 presents the preliminary knowledge for our work, followed by the proposed open-set signal recognition algorithm in Section 4. Comprehensive experiments on real signals are presented in Section 5.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 4 Experimental Setup
### Dataset Description
#### 4.1.2 Data Normalization  
To facilitate the optimization process and enhance the performance of the algorithm, the entire dataset was normalized within the range of 0 to 1. This scaling process was conducted to ensure that all features contributed equally to the model, preventing any single feature from dominating due to its scale.
# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
## 5 Results
### Proposed Method Compared with TIFF  
TIFF (Tagged Image File Format) is an image storage format. The term \"Tagged\" in \"TIFF\" refers to the complex file structure of this format. TIFF allows the flexible use of compression methods to maintain image integrity and clarity. Lossless compression methods that can be used include LZW, Deflate, LZMA, and Packbits [32; 33]. The dictionary-based table lookup encoding in TIFF, such as LZW, completely avoids frequency statistics, providing a very high compression speed. However, dictionary-based lossless image compression has limitations, as it cannot effectively remove spatial redundancy in images. The Deflate method can achieve higher compression efficiency by adjusting the dictionary size. However, using an excessively large dictionary increases memory and time consumption, which may degrade the compression speed.  
In the experiment, we test the performance of LZW, Deflate, LZMA, and Packbits. The results are shown in Figure 9 and Table 9.  
The Deflate method combines LZ77 and Huffman coding. Under the condition of maximum compression efficiency, its speed is 8 times faster than JPEG 2000. However, it experiences a significant loss in compression ratio, approximately 46%. In contrast, the proposed method outperforms Deflate in both speed and efficiency.  
In TIFF, the compression efficiency of the LZW method is not adjustable. Although it achieves a significant speed improvement, approximately 31 times faster than JPEG 2000, the compression ratio loss is substantial, reaching 63%. The proposed method, compared to LZW, is better at achieving high-speed image compression while maintaining high compression efficiency.  
The LZMA method achieves a high compression ratio, but this comes at the expense of compression speed, with speeds comparable to JPEG2000 at its highest compression efficiency, yet resulting in a 17% loss in compression ratio. The proposed method outperforms LZMA in both speed and efficiency.  
The PackBits method is a simple variant of Run-Length Encoding (RLE). The RLE pattern is as follows: repeated symbol + count of repetitions. While the PackBits method provides high compression speed, in images with few repeating patterns, the overhead of recording the repetition count causes the compressed files to be twice the size of the original, resulting in no effective compression.  
It is worth noting that the method in this paper was tested only in single-threaded, single-core mode, without any SIMD (Single Instruction, Multiple Data) optimizations. Based on the above experiments, we conclude that, compared to dictionary-based methods, the proposed method ensures high compression efficiency while achieving fast compression for line-scan panoramic infrared images.  
The detailed experimental data are shown in Tables 12 and 13 in Appendix A.
## 3 Result and Discussion
### Compressive Strength  
The results of the compressive strength conducted by this research are shown in Figure 5A-F, in this test project, each sample number is taken as the basis of the study of the number of 3 samples. The compressive strength of the geopolymer increased with a decrease in L/S. When the L/S decreased from 0.5 to 0.3, the compressive strength of a geopolymer increased by 24.1 MPa-27.8 MPa (3 days), 50 MPa-53.6 MPa (7 days), 55.3 MPa-57.4 MPa (28 days) and 56.2 MPa-57.9 MPa (56 days). Because a rise in L/S increased the volume of the alkali activator, the loss of excessive water within the specimen after the polymerization produced pores within its internal structure, which reduced its compressive strength or other engineering properties. An increase in the BJPA-substitution ratio decreased the compressive strength: changing the BJPA-substitution ratio under distinct L/Ss decreased the compressive strength by 14.9-23.2% (3 days), 5.3-46.4% (7 days), 7.7-19.0% (28 days), and 8.5-21.5% (56 days). However, BJPA could reduce the instability of the GGBFS geopolymer specimen, effectively decreasing the formation of internal drying shrinkage cracks.  
Figure 5A shows a pure GGBFS geopolymer. Its compressive strength was least affected by L/S when it was 3 days and 7 days old, and the value and changing trend of its compressive strength were roughly similar. When the geopolymer was 28 days and 56 days old, its compressive strength showed a slight increase at L/Ss of 0.3 and 0.4, compared to that of an L/S of 0.5. As shown in Figure 5B, when the BJPA-substitution ratio was 20%, its compressive strength showed a clear reduction compared to that of the pure GGBFS geopolymer; according to Figure 5C,D, when the BJPA-substitution ratio was increased from 40% to 60%, its compressive strength displayed a significant reduction. Figure 5F illustrates that, when the BJPA-substitution ratio reached 100%, its compressive strength was less than 1/4 that of the pure GGBFS geopolymer. Although the BJPA could undergo polymerization with the alkali activator, it was unable to be fully involved in the polymerization process. Which means that the gain in strength only came from the reaction of GGBFS with the activator, BJPA mostly plays the role of inert material. Table 3 shows that BJPA had a higher calcium oxide content, with the C-S-H colloid serving as the source of strength for the pure BJPA geopolymer, thus reducing its compressive strength when the BJPA-substitution ratio increased.  
Figure 4: The slump of the BJPA–GGBFS composite geopolymer.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Performance Evaluation
#### 4.2.4 Mac/Phy Overhead  
Medium access control and physical (MAC/PHY) layer overhead are essential factors that influence the vehicular network's performance. Generally, the broadcast channel is overwhelmed by the vehicle's authentication requests that may cause overload RSUs. Therefore, it affects the total average of throughput in the system and increases the vehicle's delay authentication. MAC/PHY overhead is calculated based on the below equation.  
\[MAC/PHY_{overhead}=\frac{PHYBytes_{total}-AppBytes_{total}}{PHYBytes_{ total}} \tag{3}\]  
From the equation above, \(PHYBytes_{total}\) and \(AppBytes_{total}\) denote, respectively, the physical and data link layer traffic that represents the physical/user network layer in our proposed model architecture. On the other hand, the value of MAC/PHY overhead is in the range \([0,1]\) and is given by Equation (3). Here, the lower value indicates better performance of the system, and vice versa. Figure 8 shows the result of MAC/PHY overhead in 100 s simulation time. As shown in the figure, we can see that the overhead is relatively stable after 20 s of simulation time within the range of 0.2 to 0.3, and it even slightly decreases  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline power transmission & 20 dBm \\ \hline transmission rate & 2.048 Kbps \\ \hline data rate & 6 Mbps \\ \hline application packet size & 64-byte \\ \hline blockchain packet size & 512-byte \\ \hline packet interval & 100 ms \\ \hline antenna height & 1.5 m \\ \hline channel bandwidth & 10 MHz \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._over time. Based on these results, our proposed protocol is relatively efficient due to does not incur high overhead.
# the hexadecimal number specifies the device
## 6 Discussion
A sensor network may incorporate the capability to share firmware updates between peers instead of only relying on a central source. This way, the relaying nodes should get less overloaded, and therefore, firmware upgrade propagation should be more efficient. From the security point of view, this is no worse than a direct source as long as proper verification mechanisms are in place. However, on a diversified sensor mesh network, updates like these are not possible as each node should essentially have its own unique version of the latest firmware. The architectural possibilities for updating node firmware, therefore, are more limited than on a network free of diversification. A diversified update should be used only on a single node to keep diversification unique, which means there needs to be a repository of N > M unique variations of the firmware, where M is the number of nodes requiring updates in the network. Therefore, one should adjust or at the very least test the network's ability to handle updating during normal operation. To alleviate the bandwidth requirements, nodes can be updated in phases or one at a time. A diversified network should also require a considerably longer time to breach than an ordinary network, because the attacker first needs to find a vulnerability, be able to exploit it and then use brute force to guess the diversification secret before being able to compromise even a single node. Finally, if diversification is applied to network protocols, the diversification has to be distributed outside one device. That is, all nodes involved have to know and support the diversified protocol, and the diversification secret has to be shared. This is not very practical in big networks, but protocol diversification can still be a useful approach within smaller groups of sensor nodes and in private networks. These challenges and limitations of internal interface diversification and their possible solutions should be discussed more closely in the future work.
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
###### Abstract  
Distribution of volatile compounds in different fruit structures were analyzed in four tomato cultivars by headspace-solid-phase microextraction (SPME)-gas chromatography-mass spectrometry (GC-MS). A total of 36 volatile compounds were identified in fruit samples, which were primarily aldehydes, hydrocarbons, alcohols, ketones, furans, esters, nitrogen compounds, and sulfur and nitrogen-containing heterocyclic compounds. The volatile compositions in pericarp (PE), septa and columnella (SC), locular gel and seeds (LS), and stem end (SE) tissues showed different profiles. The PE tissue showed the highest total volatile concentration due to a high abundance of aldehydes, especially cis-3-hexenal and benzaldehyde. Meanwhile, it showed higher aromatic proportion and herbaceous series intensity than other tissues. Floral and fruity series showed higher intensity in SC and LS tissues. The concentration of alcohols in the LS was higher than that in other tissues in association with the higher abundances of 2-methyl propanol, 3-methyl butanol, and 2-methyl butanol. However, the numbers and concentrations of volatile compounds, especially cis-3-hexenal, benzaldehyde, and geranyl acetone were lower in SE than in the other tissues, indicating less tomato aromas in SE. SE tissues were also lacking in floral and fruity characteristic compounds, such as geranyl acetone, 1-nitro-pentane, and 1-nitro-2-phenylethane. \"FL 47\" contained more volatile compounds than the other three, and the contents of aldehydes, ketones and oxygen-containing heterocyclic compounds in the \"Tygress\" fruit were higher than the other cultivars.  
tomato; volatile; aroma; different tissues Article