esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix D Hyper-Inverse Wishart Objective Function  
Recall that we take the Cholesky decomposition of \(\Omega=\phi^{\prime}\phi\), where \(\phi\) is upper triangular. Using Equations (9) and (11), we define \(\Psi(\phi)=-\log L(\phi)-\log\pi(\phi)\). Even though \(\Psi\) is expressed as a function of the upper Cholesky factor \(\phi\), it is inherently a function of only the free elements of \(\phi\). As a result, the gradient of \(\Psi\) is defined element-wise,  
\[\frac{\partial\Psi}{\partial\phi_{ij}}=\begin{cases}-\frac{1}{\phi_{ii}}(\eta_ {i}+n)+\phi_{ii}+\sum_{m=i}^{p}\phi_{im}b_{mi}&i=j,\\ \phi_{ij}+\sum_{m=i}^{p}\phi_{im}b_{mj}&i\
eq j.\end{cases}\]  
Similarly, the Hessian can be computed element-wise,  
\[\frac{\partial^{2}\Psi}{\partial\phi_{ij}\partial\phi_{kl}}=\begin{cases}0&i \
eq k,\\ \frac{1}{\phi_{ii}^{2}}(n+\eta_{i})+b_{ii}+1&i=j=k=l,\\ b_{li}&i=j,i=k,l>j,\\ 1+b_{lj}&i\
eq j,i=k,l=j,\\ b_{lj}&i\
eq j,i=k,l>j,\end{cases}\]  
where \((i,j),(k,l)\in\mathcal{V}\), \(\eta_{i}=\delta+v_{i}-1\), and \(B=(b_{ij})_{1\leq i,j\leq p}\).
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 4 Experimental Results
### Medinoid  
We integrated ResNet-152-M, our most effective model, into the back-end of Medinoid, a publicly-available web application for glaucoma diagnosis and localization. Medinoid takes as input a fundus image (JPG, PNG, or BMP), showing the diagnosis outcome to the user as soon as the given fundus image has been analyzed by the model in the back-end. If the input image has been diagnosed as normal, then the result window will show the decision made, as well as the diagnostic confidence. If the input image has been diagnosed as glaucomatous, then the result window will additionally show the localization of the glaucomatous area. An illustrative screenshot can be found in Figure 9.  
Figure 7: Segmented highlights obtained for glaucomatous areas. (**a**) True positive localization examples. (**b**) True negative localization examples. (**c**) Abnormal localization examples.  
Figure 8: False localization examples. The first three figures, from (**a**–**c**), represent false positive examples, and the next three figures, from (**d**–**f**), represent false negative examples.  
Figure 9: **(a)** Screenshots of Medinoid, a web application developed in support of computer-aided glaucoma diagnosis. Once a user has uploaded a fundus image, Medinoid will automatically screen the fundus image for the presence of glaucoma using a back-end engine that has been built on top of our most effective predictive model. If the image is diagnosed to be glaucomatous, then Medinoid will print out the diagnostic confidence and visualize the glaucomatous area. **(b)** Diagnosis result for a glaucomatous eye image, showing the diagnostic confidence and highlighting the glaucomatous area. Grad-CAM, Gradient-weighted Class Activation Mapping.
## 4 Results and Discussion
### Numerical Results: Effects of the Failure Length  
Before analyzing numerical results in details, preliminary observations regarding the length of the simulated failures are introduced.  
It is assumed that the failure length influences the resulting pressure variation in the fluid, in both simulations and real situations. When numerical results are compared to experimental results from a failure test in a real reactor, the failure length in the model should be actually compared to the size of the reaction area where the over-pressure is produced in the real test and to the pressure profile in this area. However, the estimation of those characteristics is not simple. The most direct information that can be provided by tests in a real reactor are pressure histories in the inlet and the outlet section, which are given by sensors P1 and P2 (assuming that measurements closer to the failure are not possible). Depending on the possibilities in other kinds of measurements, some characteristics can be estimated afterwards. The exact starting point of the cladding failure can be determined by visual examination of the rod after the experiment. The initial size of the reaction area, the kinetic of the failure, and the fuel ejection might be deduced from temperature and energy deposit, depending on the measurement possibilities. However, such estimations require extensive works that are far beyond the scope of the present study. Moreover, as shown in [1,2], these characteristics significantly depend on rod properties and are difficult to predict.  
Given the large variability in the lengths of real failures, and since the accurate estimation of the actual over-pressure area may not be possible, the comparison between simulation results and future experimental results will likely require several simulation iterations with various failure lengths. To predict the effects of the failure length on the pressure that could be measured in the device, simulations with three different failure lengths (3 mm, 15 mm, and 30 mm) were performed. Fluid pressure histories in the outlet and inlet sections (at the \"realistic\" measurement points) obtained with the different failure lengths are presented in Figure 4. For the sake of readability, pressure variations around the initial pressure value (155 bar in absolute value) are presented rather than absolute pressure values. Unless otherwise stated, it is the case for all pressure values in the present article.  
The effects of the differences in the failure length are clearly shown in Figure 4. The longer the failure is, the wider the first pressure peak on the signal. In the next part of the article, results obtained with a 30 mm are used. The choice is arbitrary, but, despite the noticeable difference in the signals, the analysis methodology and the physical interpretation is the same for any failure length.
###### Abstract  
Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA-MLC (Vertex Component Analysis-Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12 QI during 2001-2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from \(9.02\times 10^{-4}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) in shrubland subregions to \(2.34\times 10^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.  
leaf area index; remote sensing; vegetation dynamics; image classification; STL; annual and intra-annual LAI fluctuations 2023
## 2 Materials and Methods
### Datasets
#### 2.5.1 Prostate Cancer  
The dataset employed to evaluate the proposed algorithm is the publicly available Prostate Imaging and Cancer Analysis Information (PI-CAI) repository (PI-CAI database: pi-cai.grand-challenge.org, accessed on 15 December 2024), previously used in [24]. This dataset includes 1500 samples, each with 12 prostate cancer-related features. For the incomplete dataset setup, one attribute is systematically selected as missing and removed from the dataset to simulate incomplete data.  
The primary target variable within this dataset is case_csPCA, which indicates the presence of clinically significant prostate cancer. A subset of features, including patient_id, study_id, and mri_date, are excluded from this analysis due to their lack of predictive value for case_csPCA and potential privacy concerns. Additionally, histopath_type, which identifies the lesion sampling method, is omitted as it does not directly inform the cancer diagnosis.  
Key diagnostic features in PI-CAI include lesion_GS, which denotes the Gleason Score, a grading system used to evaluate prostate cancer aggressiveness by examining cell structure under a microscope. Similarly, lesion_ISUP consolidates the Gleason Score into five ISUP grades, aiding in clinical outcome prediction. The feature case_ISUP indicates the highest ISUP grade among sampled lesions, initially considered for analysis but later excluded due to a high correlation (0.87) with case_csPCA.  
Further, psa represents the prostate-specific antigen level, and psad refers to the prostate-specific antigen density, which can also be calculated if missing by dividing psa by prostate_volume (psad_computed). Consequently, the primary predictive features for the target case_csPCA include patient_age, prostate_volume, and either psa, psad, or psad_computed.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution
### Factors Affecting Minimum Cable Tension  
In order to determine the more important parameter of this minimum from the tension equation, the tension distribution of the cable robot needs to be analyzed from multiple angles. The key parameters include cable preload, material strength limit, cable length variation, stiffness coefficient, and so on. The following is a detailed analysis and description of these parameters.
## 2 Geopolymer Mortar (GPM)
### Geopolymer Mortar Mechanical Properties  
#### 2.2.1 Geopolymer Mortar Compressive Strength  
Numerous types of source materials are employed as base materials for producing geopolymers. The raw materials used and the proportioning factors impact the strength of GPM. [PERSON] et al. [45] tried to compare the compressive strength of GPM at the ages of 7 and 28 days, using two different mixed materials (PC mortar, slag, and zeolite). The findings revealed that the compressive strength of GPM increased by up to 48% in the first 7 days compared to the cement mortar after 28 days.  
[PERSON] et al. [46] studied the effect of blending silica-rich (MK) and palm oil fuel ash (POFA) on the strength of GPM. They indicated that the Weibull distribution is suitable for analysing the blended GPM. Low calcium FA, GGBFS, and POFA can be combined to manufacture GPM under the standard condition that their percentage should be suitable. [PERSON] et al. [47] investigated the early strength characteristics of a GPM made from palm oil fuel and ash metakaolin with various degrees of NaOH and Na\({}_{2}\)SiO\({}_{3}\) medium replacement. [PERSON] et al. [47] studied the compressive strengths of GPM with sisal fibre (SF), coconut fibre (CF), and glass fibre (GF).  
[PERSON] et al. [44], who studied the compressive strength of GPM with different NaOH dosages (6, 10, and 14 mol/dm\({}^{3}\)), found that high calcium FA GPM comprised of PC type I showed increasing PC replacement levels and NaOH concentrations as well as increasing mortar compressive strengths.  
[PERSON] [48] discovered that the strength of geopolymer mortars was affected by the use of construction and demolition waste (CDW) fine aggregates. GPM was formed by combining biomass FA waste and MK as a binder, sodium hydroxide as an activator, alkali sodium silicate solution, and CDW as fine aggregates. Except for the mortar created with particles of 1.0-2.0 mm, when the maximum strength was acquired with sand, CDW was used as aggregate. For CDW-geopolymer mortars, the values were 21 MPa (1.0-2.0 mm), 34 MPa (0.5-1.0 mm), and 40 MPa (0.5-2.0 mm). The mixed fraction had the highest strength values due to the maximum packing density. Table 2 illustrates the effect of various additives on the behaviour of geopolymer mortar.  
Figure 10: Effect of NaOH molarity on GPM setting times [43].
# Temporal Encoding to Reject Background Signals
## 4 Materials and Methods
### Real Time Link  
To demonstrate a practical system, an FPGA based synchronisation system was implemented, involving data transmission in frames consisting of a \(6\) bit clock word and \(32\) data bits. The clock word, \"\(001101\)\", allows both frame level and symbol level synchronisation of data streams. Details on choice of clock work and synchronisation methods can be found in the Supplementary Materials of Reference [21]. A block diagram of the experimental setup for real-time transmission is shown in Figure 6b. On the transmitter side, the FPGA was used to generate a data stream in frames, withthe 6 bit clock word. In contrast to the offline setup in Figure 2b, this FPGA directly supplied the falling edge trigger for the LED board, without the need for extra logic circuitry. The receiver FPGA (Xilinx Spartan-3, XEM3010, [PERSON], Portland, OR, USA) was connected to a separate PC, and clock synchronisation removed the need for a trigger from the transmitter. However, due to limitations from the FPGA boards, the achievable data rates with the real-time setup are limited to 20 kb/s. It should also be noted that the data rates quoted here include transmission of the clock word. This 18.75% overhead reduces useful data transfer to 8.42 and 16.84 kb/s for 10 and 20 kb/s links, respectively.
## 3 Related Work
### V2G Systems Optimization under Dynamic Conditions  
Due to the ever-changing nature of real-life scenarios, dynamic features are present in a variety of ways. While dynamic constraints increase the complexity of problems, taking them into account can greatly influence the solutions generated by the solving methods. For this reason, in recent years, authors have developed methods that generate solutions for optimization problems while dealing with the dynamic conditions they present. An example is the study conducted by ur [PERSON] and [PERSON] (2018), who tackled the problem of designing a control algorithm to provide frequency regulation services and control the charge and discharge periods of EVs. The system charges the EVs while allowing these vehicles to sell back the stored energy to the grid during peak hours for frequency management. In the problem, dynamic pricing is considered, and the electricity price changes every hour. To model the price uncertainties, a Markov decision process with unidentified switching probabilities is utilized. To optimize the profit obtained by the owners, a Q-learning algorithm is implemented, which executes for each EV connected to the V2G system, considering the projected departure time and state of charge. The performance of the proposed algorithm is evaluated under different pricing scenarios, demonstrating its efficiency in increasing the profits while providing better frequency regulation services compared to those offered by conventional charging schemes. [PERSON] et al. (2018) discuss an optimization challenge within a V2G system, with the aim of minimizing the overall cost of micro-grid operations. This problem includes the consideration of both conventional and renewable energy generation, along with dynamic pricing to optimize energy costs for consumers. Moreover, a load variance index and a fuzzy logic-based approach are integrated to minimize operational costs without compromising micro-grid stability. To address this problem, an improved elephant herding optimization algorithm is deployed. The method's efficacy is initially tested using test functions, obtaining better solutions compared to the latest published methods. Subsequently, the method is simulated across various scenarios, demonstrating superior performance over PSO and conventional elephant herd optimization approaches. The authors conclude that allowing the free trade of power between micro-grids and the main grid could provide more economical and stable power generation processes due to the implementation of G2V and V2G operations. Table 1 summarizes the papers reviewed in this section, including their characteristics and the solution strategies taken by the authors.  
We can notice that most of the authors opt to take into account multiple objectives in their problems. Additionally, a great variety of solution approaches have been explored to address these problems, which include exact methods, evolutionary algorithms, a variety of metaheuristic procedures, and different combinations of methods from different areas.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Selection of Sensor Evaluation Indicators
#### 2.1.4 Repeatability  
Repeatability refers to the discreteness of the data obtained each time the sensor performs periodic data collection in a gas under the same measurement conditions. During the working process of the sensor, the baseline drift often leads to reduced repeatability of the measurement data. Sensors with good repeatability are of great significance to ensure the stability of experimental results. Repeatability can be measured by Equation (4)the smaller the value of \(RSD\), the smaller the deviation of each experimental data, and consequently, the better the repeatability of the sensor.  
\[RSD=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}}}{\overline{x}} \tag{4}\]  
where \(x_{i}\) is the response value of the sensor to the \(i^{th}\) category gas, \(\overline{x}\) is the average response value of all measured data, and \(N\) represents the number of experiments.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 3 Results  
### Verification of the CFD Model  
Initially, the behavior of the vibration plate of the experimental setup was tested. For the metal sheet membrane, the first mode of vibration was identified at f = 2.76 kHz. The position of the center of the plate is presented in Figure 7. The deflection of the plate is non-symmetrical due to asymmetrical voltage parameters supplied to the PZT element. The mean value of deflection is determined at x\({}_{0}\) = \(-\)5 \(\upmu\)m with the amplitude of A = 35 \(\upmu\)m.  
The results in Figure 8 show the height of levitation of the sample positioned above the membrane. The value of the levitation height through the test was equal to 140 \(\upmu\)m, with the amplitude of vibration at 20 \(\upmu\)m.  
Figure 8: The experimental results of the lifting of the sample (\(\phi\)20 mm, m = 0.373 g, f = 2.76 kHz).  
Figure 7: Deflection of the vibration plate at the first mode of vibration.  
A part of the profile presented was used as input for the fluid film calculation. The result for the single profile is presented in Figure 9. The value of the mean amplitude of the vibration is equal to 140 \(\upmu\)m, which directly corresponds to the experimental value. The value of the amplitude differs significantly. The CFD results are equal to approximately 10 \(\upmu\)m, whereas the experimental value is read to be twice thereof. The discrepancies can be attributed to the limited capacity of the model. The model is unable to describe the lateral motion of the sample, and similarly, there is a lack of capacity to describe the non-parallel motion (e.g., precession) of the sample.
## 3 Problem Formulation & Terminology
### Definitions  
Hereunder, we outline the basic terms that will be used throughout this article and provide brief explanations.  
**Node:**: Systems that can serve one or multiple connections. Note that attacks against one of these nodes may lead to leakage or corruption of data. Nodes may be part of the original configuration or virtual replicas.
**Replica:**: A virtual copy of the original system, typically a VM or a container. Notice that replicas are not honeypots. Their purpose is not to study the connections.
**Configuration:**: An instance of the system including all nodes and the connections distributed among these nodes.
**Healthy Node:**: A node whose connections are all benign. Typically, the purpose of our partition strategy is to discover such instances as fast as possible. The connections of healthy nodes are excluded from subsequent partition-shuffling iterations.
**Partition:**: The process of spawning nodes with the sole purpose of migrating a subset of active connections to these new nodes. Each node essentially provides a testing environment for its connections. If the result of the evaluation is _ongoing attack_, then this implies that at least one of these connections is responsible for the attack. In the simple variation of the problem, the defender cannot distinguish between the connections, namely, the malicious connection(s) are hidden in plain sight.
**Evaluation:**: The assessment of the health status of each replica node after the partition process.
**Splitting Factor:**: The number of new nodes generated from a single node during an epoch.
**Pollution Factor:**: The rate of benign to malicious connections across all nodes.
**Epoch:**: A cycle of _partitioning_ and _evaluating_ processes.
**Stopping Criterion:**: A condition which, when met, forces the partitioning strategy to exit. Typical stopping criteria include that a certain number of connections has been identified as benign and was salvaged, or that a certain number of replica nodes has been spawned. In the experiments we conducted we applied three stopping criteria, namely, (a) total time spent, (b) the total number of replicas spawned, and (c) the percentage of connections saved.