esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon-water-energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from \(-50\%\) to \(30\%\) because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1-2 \(\mathrm{gC/m^{2}/day}\) with uncertainties of approximately \(-30\%\) to \(20\%\) because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with \(10\%\) less bias in DHS and more than \(70\%\) less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.  
vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles 2018103390/rs10091329990
## 4 Theoretical Analysis of the Condition When DRL-Based Algorithm Does Not Converge
\begin{array}{l}\frac{d\tau}{dt}=\bar{J}^{T}(r)Q^{T}(\alpha P-I)\bar{J}(r) \\ =\alpha\bar{J}^{T}(r)Q^{T}\bar{J}(r)\\ =\frac{\alpha}{2}\bar{J}^{T}(r)(Q^{T}P+P^{T}Q)\bar{J}(r),\end{array}  \tag{15}\]  
where the first equation comes from that \(\bar{J}^{T}(r)Q^{T}\bar{J}(r)=0\), because the for large enough \(n\), the \(rank(Q^{T})<n\). For example [38], \(F=\left[ \begin{array}{ccc}1&1/2&3/2\\ 3/2&1&1/2\\ 1/2&3/2&1\end{array} \right]\), then the \(rank(F^{T})=2\). For any \(r\), note the \(Q\) matrix is average direction under influence constant probability of random actions. Because \(Q\) guides \(\bar{J}(r)\) to the direction of misconvergence, \(\frac{Q\cdot P}{\|Q\|\|P\|}=\theta>0\). Therefore, the \(Q^{T}P+P^{T}Q\) is positive definite. With the setting of random actions, there exists a positive constant \(c\) that:  
\[\frac{d\tau}{dt}\geq c\left\|\bar{J}(r)\right\|. \tag{16}\]  
when \(\sigma\) is positive and sufficient small, the inequality remains true. The combination of this inequality and the fact that:  
\[ \begin{array}{l}\frac{d}{d\tau}\left\|\bar{J}(r)\right\|^{2}=\bar{J}^{T}(r) (Q+Q^{T})\bar{J}(r)+2\sigma\left\|\bar{J}(r)\right\|^{2}\\ \geq 2\sigma\left\|\bar{J}(r)\right\|^{2}.\end{array}  \tag{17}\]  
It can be seen that \(\left\|\bar{J}(r)\right\|\) does not converge. The process of approximate approaching (DRL) will never reach the target if the requirements are met. **Remark 1**: _According to Theorem 2 we may draw the conclusion that if the jammer's jamming strategy is random, the DRL-based anti-jamming method will fail to converge. However, in the perspective of jammer, random jamming may receive the low jamming efficiency since it is non-targeted. Reminding that in the \(Q\)-learning algorithm the process of \"exploration\" is random, therefore in the simulation part we investigate how the randomness of RL-based jamming impacts the performance of DRL-based anti-jamming algorithm._
## 6 Experimental Work  
### Implementation Details  
To validate the performance of the CS-SR algorithm, we compare CS-SR with three known algorithms from the literature: A.H.P., ENS-SS [39], and B.W.M. The experiments were carried out on a Windows 10 P.C. with a 3.30 GHz Intel i5 processor and 4 G.B. of RAM. There are two experiments conducted for all four algorithms: In the first experiment, the number of candidate services is increased from [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 103; 101; 104; 106; 108; 109; 110; 111; 111; 112; 113; 114; 115; 1160] with an increment of 10, and execution time is calculated for bi-objective [m = 2]. In the second experiment, the number of candidate services is [101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 1160] with an increment of 10, and execution time for multi-objective [m = 5]. The data set used to evaluate the execution time calculated in (milliseconds) is taken from Github. The proposed algorithm CS-SR will find the optimal services even if the number of services or number of objectives is increased.  
The performance analysis of the proposed CS-SR approach is performed for two parameters  
1. Execution Time: Execution time is the time the user request for the service and the execution (optimal service) the user gets from the system.
2. Energy Computation:--The total amount of energy or power consumed for finding services through execution time.
## 3 Results and Discussion
### Physical and Mechanical Prediction Model  
To describe the relationship between the F-T action and the rock properties, [PERSON] et al. [37] developed a decay model to characterize the decay rate of different sandstones, incorporating the decay constant and half-life, as shown in Equation (4):  
\[-(dI/dN)=\lambda I \tag{4}\]  
where the minus sign indicates a decrease in the rock integrity; \(I\) denotes the rock integrity; \(N\) denotes the number of F-T cycles; and \(\lambda\) denotes the decay constant.  
Figure 12: F-T damage mechanism.  
Figure 13: Variation of the elastic modulus (**a**) and damage (**b**) with F-T cycles.  
If the integrity of the sample prior to applying the F-T cycles is \(I_{0}\), it is \(I_{N}\) after \(N\) F-T cycles. By integrating Equation (4), the decay model can be expressed in an exponential formula, Equation (5):  
\[I_{N}=I_{0}\text{e}^{-\lambda N} \tag{5}\]  
In addition, another useful parameter, half-life (\(H_{1/2}\)), is defined as the number of cycles that are necessary to halve a physical or mechanical property and is introduced to measure the long-term durability of the rock. This durability parameter (half-life) is inversely proportional to the decay constant and can be calculated by using \(I_{0}/2\) to replace the \(I_{N}\) in Equation (6) as follows:  
\[H_{1/2}=\ln 2/\lambda\approx 0.693/\lambda \tag{6}\]  
Given that the integrity of rock involves physical and mechanical properties, the degradation characteristics of properties such as the P-wave velocity, density, strength, and deformation modulus of rocks under F-T action also fit Equation (5). The degradation characteristics are shown in Table 2. The half-life of the elastic modulus is only 27 cycles compared to other parameters such as density and strength, indicating that the deformation resistance of this sandstone to F-T damage is more pronounced. At the same time, the decay factor of the elastic modulus is 0.0254, which is much higher than the decay factor of P-wave velocity and density. Therefore, rock deformation in open pit mining in this area must be carefully monitored.  
As mentioned earlier, the deformation, failure behavior, and mechanical properties of the tested sandstone under loading were all affected by F-T action. These variations can be attributed to changes in the stress threshold for crack initiation, propagation, and failure during loading [38]. To deeply understand this process, the changes in the AE parameters were combined with stress to explore further the variations in crack germination, propagation, and failure degree that was caused by the freezing or thawing damage in the loading process for the tested sandstone.
###### Abstract  
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.  
RFID; IoT; machine learning; tag direction; outlier detection; data balancing Article
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 3 Medication Safety WSN Authentication Scheme
### Mutual Authentication Algorithm  
Following our previous study [21], the Padgen algorithm was proposed as the basis for mutual authentication, with the resulting key having the exclusive operation to generate a cover code and avoid vulnerabilities during the wireless transmission process. Wireless data transmission in a WMSN can be described as follows: if the node receives the correct password PW, then the user can access the message Msg. We assume that the Msg and PW are 32 bit message data and can be expressed as  
\[\text{Msg}=\text{a}_{0}\text{ a}_{1}\text{ a}_{2}\dots\text{ a}_{31}, \tag{1}\]  
\[\text{PW}=\text{P}_{0}\text{ P}_{1}\text{ P}_{2}\dots\text{ P}_{31}. \tag{2}\]  
The random number is assumed to be 16 bit for the Rt and Rm, expressed as follows:  
\[\text{R}_{\text{t}}=\text{d}_{\text{t}1}\text{ d}_{\text{t}2}\text{ d}_{\text{t}3}\text{ d}_{\text{t}4}\text{ (Base 16)}, \tag{3}\]  
\[\text{R}_{\text{m}}=\text{d}_{\text{m}1}\text{ d}_{\text{m}2}\text{ d}_{\text{m}3}\text{ d}_{\text{m}4}\text{ (Base 16)}. \tag{4}\]  
The PAD can be generated using Msg and PW through the XOR-PadGen calculation method. The details of generating the key are expressed as follows:  
\[\begin{split}\text{Msg-PadGen}\left(\text{R}_{\text{t}},\text{R} _{\text{m}}\right)=\text{m}_{\text{d}11}\text{ m}_{\text{d}1+16}\text{ m}_{\text{d}22}\text{ m}_{\text{d}2+16}\|\text{m}_{\text{d}11}\text{ m}_{\text{d}1+16}\text{ m}_{\text{d}22}\text{ m}_{\text{d}2+16}\\ \  
\[\text{PW}=\text{PAD}\oplus\text{CCPWx}. \tag{10}\]  
The above-discussed protocols have their advantages in terms of security; however, they have not been realized in wireless medical sensor networks for practical applications.
## 3 Solar Array Simulator
### The Dynamic Characteristics of the Solar Array  
The concept of modal effective mass was first proposed by [PERSON], [PERSON], and [PERSON] [27]. It is a parameter describing the interaction between modes and an excitation. The larger the value is, the greater the contribution of the mode to the dynamic response is [28]. The modal superposition method is a common method of calculating the dynamic response of a system. In engineering, the cumulative effective modal mass fraction--the ratio of cumulative effective modal mass to structural physical mass--is often used to judge whether the number of analyzed modes is sufficient. And it is generally considered that a cumulative effective modal mass fraction greater than 90% meets the analysis requirements [29].  
The satellite solar array shown in Figure 4 is taken as an example to analyze the dynamic characteristics of the multi-fold solar array with a SADA connecting rod. The solar array panels are sandwich plates that are composed of aluminum honeycomb core and a carbon fiber composite surface, and the SADA connecting rod is made of a high-strength aerospace aluminum square tube.  
Figure 4: Solar array of a satellite.  
Figure 3: Force analysis of components.  
An accurate finite element model of the solar array was established; the model is mainly composed of panels, hinges, and a SADA connecting rod, as shown in Figure 5a. The panels were simulated by shell elements with the composite property. Spring elements and bar elements were respectively used to simulate the hinges and the connecting rod. And the constraint was applied to the end of the SADA connecting rod.  
Figure 5 also shows the first three modes of the solar array. The first mode of the solar array is the out-of-plane bending mode, the second mode is the in-plane bending mode, and the third mode is the torsional mode along the rotation axis.  
Furthermore, the mass matrix, stiffness matrix, and mode information of the solar array were obtained from the accurate finite element model to calculate the modal effective mass fractions of the first ten modes. As shown in Table 1, the fractions were compared to determine the contribution proportion of every mode in the dynamic response caused by the SADA. According to the table, the sum of the effective mass of the first ten modes is about 7.69 kg\(\cdot\)m\({}^{2}\), which is approximately equal to the moment of inertia of the solar array along the \(X\)-axis, indirectly indicating the accuracy of the finite element model [30]. The modal effective mass fraction of the third mode is 96.7%, and the third mode is the torsional mode, as shown in Figure 5. The analysis above indicates that the dynamic response caused by the SADA is mainly the torsional oscillation of the solar array.  
Figure 5: Finite element model and the first three modes of the solar array.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
This capability is achieved when the four rotors generate thrust equal to the gravitational force acting on the quadcopter;
* Quadcopters have a simple body structure, which allows for ease of control and modelling. As a result, users can easily customize and optimize their quadcopters for various applications, enhancing overall performance;
* Quadcopters are easy to assemble. This user-friendly assembly process not only reduces setup time but also allows users to easily upgrade or replace parts, enhancing the quadcopters' adaptability for various applications;
* Quadcopters are defined by their compact design, which not only makes them easy to transport but also enhances their versatility for use in a wide range of environments;
* Quadcopters are simple to control, allowing users to operate them with minimal training, which makes them accessible for a variety of applications;
* Quadcopters possess high maneuverability, enabling them to execute precise movements with ease. This capability arises from their rotors configuration, which allows for the independent control of each rotor's speed and thrust, facilitating navigation through tight and complex spaces;
* Quadcopters do not require runways for operation due to their Vertical Take-Off and Landing (VTOL) capabilities. This feature allows them to ascend and descend vertically, making them suitable for use in a variety of environments;
* Quadcopters are designed for easy pre-flight setup, allowing users to prepare them for flight quickly and efficiently;
* Quadcopters are lower in cost compared to other types of UAVs, making them more accessible to a wider range of users. After highlighting the features of quadcopters, the next step is to explore the various types utilized in the literature and available in the market to perform agricultural missions.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.5.2 Embedding Space Analysis  
To qualitatively assess the learned representations, we visualized the embedding space using t-Distributed Stochastic Neighbor Embedding (t-SNE). This technique reduces the high-dimensional embedding vectors (256 dimensions in our case) into a 2D space, allowing for visual inspection of the separation between different types of embeddings.  
We generated t-SNE plots for 1000 sampled triplets (Job Description, Positive Resume, Negative Resume ) from the test set for each trained model head (GRU, LSTM, Transformer). These visualizations (Figures 2-4) help understand how well the models cluster similar items (jobs and positive resumes) while separating dissimilar items (jobs and negative resumes). Blue points represent job descriptions, green points represent corresponding positive resumes, and red points represent corresponding negative resumes. Ideally, green points should cluster near their blue counterparts, while red points should be further away.
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 2 Taxonomy of Clustering Algorithms
### Partitional Clustering Algorithm  
Data is arranged into nested groups using a partitional clustering algorithm, but there is no hierarchical structure [1]. The authors in [29] claimed that applications requiring big data sets for which the creation of a dendrogram is computationally prohibitive can handle clustering problems using the partitioning method. They work by creating data clusters to recreate the natural groupings already present in the dataset. The optimization of a criterion function is used to iteratively divide the dataset of a items into a preset k number of unique subsets [57]. In [58], the author presented a new approach called k-SCC to get the best possible value for k when clustering categorical data. The squared error criteria are the most commonly used criteria functions in partitional clustering algorithms. The primary objective is to identify the segment that minimizes the square error for a given quantity of clusters. Problems in the patterns' deviations from the cluster centers are depicted by the error when the patterns are seen as a group of k-numbered spherical shape clusters. Using an information theoretic dissimilarity measure and a kernel-based technique for representation of cluster means for categorical items, the authors of [15] sought to build an unique extension of the k-means method for clustering categorical data.  
The initial dataset partition serves as the basis for the partitional clustering algorithm, which allocates the data items in clusters iteratively in such a way that it can minimize the square error. The initial partition can be determined by selecting at random from the pattern matrix a set of K seed points that are widely spaced from one another. The authors of [15] emphasized the need of choosing starting points from preexisting data items that are appropriately separated from each other, appropriate seed points could be discovered. The square error tends to decrease as the number of clusters rises, and thus minimizing is only possible for a given number of clusters.  
Several techniques for partition clustering employ the square error criterion function to generate K-numbered clusters that are as small and different as feasible. It is more energy efficient than alternative criteria functions [20]. Because square-error-based techniques can tend to the local optimal solution, particularly if the beginning points are not widely separated, potentially different divisions can produce various clusters as an outcome [18]. Partitional clustering is subdivided into three main categories which include fuzzy clustering, hard/crisp clustering and the last is mixture resolving.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Performance over Different Distortion Types and Levels  
In this subsection, we examine the performance of the proposed _ActMapFeat_ over different image distortion types and levels of KADID-10k [44]. Namely, KADID-10k consists of images with 25 distortion types in 5 levels. Furthermore, the distortion types can be classified into five groups: blurs, color distortions, compression, noise, brightness change, spatial distortions, and sharpness and contrast.  
The reported mean PLCC, SROCC, and KROCC values were measured over 100 random train-test splits in Table 2. From these results, it can be observed that _ActMapFeat_ is able to perform relatively uniformly over different image distortion types with the exception of some color- (color shift, color saturation 1.), brightness- (mean shift), and patch-related (non-eccentricity patch. color block) noise types. Furthermore, it performs very well on different blur (Gaussian blur, lens blur, motion blur) and compression types (JPEG, JPEG2000).  
The performance results of _ActMapFeat_ over different distortion levels of KADID-10k [44] are illustrated in Table 3. From these results, it can be observed that the proposed method performs relatively uniformly over the different distortion levels. Specifically, it achieves better results on higher distortion levels than on lower ones. Moreover, the best results can be experienced at moderate distortion levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distortion** & **PLCC** & **SROCC** & **KROCC** \\ \hline Gaussian blur & 0.987 & 0.956 & 0.828 \\ Lens blur & 0.971 & 0.923 & 0.780 \\ Motion blur & 0.976 & 0.960 & 0.827 \\ \hline Color diffusion & 0.971 & 0.906 & 0.744 \\ Color shift & 0.942 & 0.866 & 0.698 \\ Color quantization & 0.902 & 0.868 & 0.692 \\ Color saturation 1. & 0.712 & 0.654 & 0.484 \\ Color saturation 2. & 0.973 & 0.945 & 0.798 \\ \hline JPEG2000 & 0.977 & 0.941 & 0.800 \\ JPEG & 0.983 & 0.897 & 0.741 \\ \hline White noise & 0.921 & 0.919 & 0.758 \\ White noise in color component & 0.958 & 0.946 & 0.802 \\ Impulse noise & 0.875 & 0.872 & 0.694 \\ Multiplicative noise & 0.958 & 0.952 & 0.813 \\ Denoise & 0.955 & 0.941 & 0.799 \\ \hline Brighten & 0.969 & 0.951 & 0.815 \\ Darken & 0.973 & 0.919 & 0.769 \\ Mean shift & 0.778 & 0.777 & 0.586 \\ \hline Jitter & 0.981 & 0.962 & 0.834 \\ Non-eccentricity patch & 0.693 & 0.667 & 0.489 \\ Pixelate & 0.909 & 0.854 & 0.681 \\ Quantization & 0.893 & 0.881 & 0.705 \\ Color block & 0.647 & 0.539 & 0.386 \\ \hline High sharpen & 0.948 & 0.938 & 0.786 \\ Contrast change & 0.802 & 0.805 & 0.607 \\ \hline
**All** & 0.959 & 0.957 & 0.819 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean PLCC, SROCC, KROCC values of the proposed architecture for each distortion type of KADID-10k [44]. Measured over 100 random train-test splits. The distortion types found in KADID-10k can be classified into five groups: blurs, color distortions, compression, noise, brightness change, spatial distortions, and sharpness and contrast.