esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Dataset Description
#### 3.2.1 Massachusetts Road Dataset  
The Massachusetts road dataset comprises a significant collection of remote sensing images, specifically encompassing 1171 high-resolution aerial photographs from across the state of Massachusetts [39]. Each image within this dataset measures 1500 \(\times\) 1500 pixels and is rendered in an RGB tri-channel color scheme. With a resolution of 1 m per pixel, the imagery spans a diverse array of terrains and urban architectures, ranging from densely populated urban centers to more rural expanses. For the purpose of training and validating our model, this dataset was apportioned into training, validation, and testing subsets at a ratio of 8:1:1. Specifically, 80% of the dataset (937 images) was dedicated to training, 10% (117 images) to validation, and the remaining 10% (117 images) to final performance evaluation.
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 2 Methodology of the Random-Speckle Optical Detection and Processing Algorithm  
### Principle of Digital Imaging Correlation (DIC)  
The laser triangulation principle with random-speckle projection is commonly used for the depth detection of 3D tested surface. Optical elements for generating laser random-speckles include an infrared laser, micro-lens arrays, and a diffuser. Figure 2 shows the optical configuration of the 3D surface profilometry, in which a near-infrared (NIR) laser is used for random-speckle projection. An imaging sensor is employed to detect the shifted phase corresponding to the difference in optical paths formed by the detected surface depth.  
To generate random speckles of various intensities without a repeating pattern, the NIR laser was used as a light source to reduce interference from environmental light. A large grit ground glass served as a diffuser to produce high-contrast grey-level patterns, while a low dark current charge-coupled device (CCD) camera captured the correct pixel intensity.  
The DIC principle involves cross-correlating the surface depth detected using a deformed speckle image with the corresponding depth of the reference image in the stored database. Each random-speckle image is unique in its database; hence, the deformed pattern with a specific depth can be matched with its reference image. The surface depth of each image block is thus transformed into the surface depth of the object by triangulation transformation.
# Screening Wild Yeast Isolated from Cocoa Bean Fermentation Using Volatile Compounds Profile
## 2 Results and Discussion
### Identification and Quantification for Volatile Compounds (VC's)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Families Compounds**} & \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Concentration [mg/kg]**} & \multirow{2}{*}{**OTV * [mg/kg]**} \\ \cline{3-4}  & & & **Min** & **Max** \\ \hline \multirow{6}{*}{alcohols} & ethanol & 1.9755 & 974.8454 & 40 \\  & 3-methylbuthanol & 0.1687 & 65.4272 & 3 \\  & 2-pentanol & 0.0813 & 10.2901 & 4 \\  & 2-phenylethylanol & 0.0759 & 51.4509 & 14 \\  & alpha-terpinol & 0.0034 & 2.4455 & 0.33 \\  & 2,4-DI-tert-butylphenol & 0.1968 & 6.3304 & - \\ \hline \multirow{6}{*}{esters} & Ethyl acetate & 0.0141 & 102.0740 & 5 \\  & 2-penthyl acetate & 0.2473 & 14.5378 & 0.005 \\  & 3-methylbuthyl acetate & 0.0314 & 317.1846 & 0.16 \\  & methylpropil acetate & 0.0315 & 25.1614 & 1.6 \\  & ethyl benzoate & 0.0002 & 17.3732 & 0.06 \\  & di-ethyl butanolate & 0.0245 & 3.8429 & 200 \\  & ethyl octanoate & 0.0094 & 12.9963 & 0.58 \\  & 2-phenylethylly acetate & 0.0081 & 276.7966 & 0.65 \\  & ethyl decanoate & 0.0104 & 14.3622 & 0.2 \\  & ethyl 3-phenylpropanoate & 0.0547 & 12.0306 & 0.0016 \\  & ethyl 3-phenyl-2-propenoate & 0.0355 & 6.9843 & 0.0011 \\  & ethyl dodecanoate & 0.0150 & 10.4593 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds concentrations (mg/kg) produced by yeasts quantified by gas chromatography and mass spectrometer (GC–MS) and their odor threshold value. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Family** & \multicolumn{3}{c}{**Compounds**} & **Attributed Aroma** \\ \hline  & C1 & ethanol & alcoholic \\  & C2 & 3-methyl butanol Isoamyl alcohol & balsamic fruit \\ alcohols & C3 & 2-pentanol & green, fruity, sweet, pungent, plastic–floral \\  & C4 & 2-phenyl ethanol Phenylly alcohol & flowery, rose, spicy, honey-like, caramel \\  & C5 & alpha-terpine & floral, lilac \\ \hline  & C6 & ethyl acetate & pineapple \\  & C7 & 3-methyl butyl acetate Isoamyl acetate & sweet fruit banana solvent, pear \\  & C8 & methyl propyl acetate & banana, apple \\  & C9 & ethyl Benzoate & fruity, sweet, medicinal, cherry, grape \\ esters & C10 & ethyl Ocanoate & fruity, sweet, musty, floral, pineapple \\  & C11 & 2-phenyl ethyl acetate & flowery (rose), honeyfruity, Sweet \\  & C12 & ethyl decanoate & fruity, sweet, pear, grape, brandy \\  & C13 & ethyl 3-phenyllybenzoate & fruity \\  & C14 & ethyl 3-phenyl-2-propenoate & fruity, cinnamon \\  & C15 & ethyl dodecanoate & fruity floral, sweet \\ \hline aldehydes & C16 & 3-methyl butanal & malty, sweet, chocolate \\  & C17 & phenylacetaldehyde & nuty, honey-like \\ \hline acids & C18 & acetic acid & acid, vinegar \\  & C19 & 3-methyl butanoic acid & sweet, rancid \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Volatile compounds and their perceptions for the Principal Component Analysis.
## 7 Conclusions  
In this review, a use-case of using CS measurements of ECG signal in the IoT framework is developed. The basic research and technical aspects are considered on this use-case based on the CS need. The theoretical key concepts of CS with mathematical formulations are discussed to shed some light on selection strategies of sensing matrix and reconstruction algorithms, as well as compression ratio to assure the quality of a reconstructed ECG signal. Numerous CS construction (Measurement and Transformation matrix) and reconstruction algorithms with performance metrics aiming to achieve better classification accuracy, computational efficiency, high speed, low power and less area usage are also analyzed. The most appropriate sensing matrix, which has been examined by several CS approaches and techniques adapted to enhance the sparsity of the signal in order to improve the reconstruction quality, were also highlighted. Furthermore, deep learning algorithms, datasets, hardware implementation, and IoT framework on CS are also studied. Based on the review, recommendations and future research directions are given to select the best sensing matrix, novel deployment of sparsifying process, compressive learning algorithms and faster hardware implementations that consume less power. The results are tabulated, and the tables shed light on the research and technical aspects we considered. Some of the important recommendations given are to use novel compression techniques such as 1D-DAST as a sparsifying method, QRS based sparsification, DCT and DFT based sensing methods, as well compressed domain ML models. Specifically, this paper proposes a generalized CS based ECG framework and provides open research directions that should be considered when developing a CS algorithm for achieving better classification and reconstruction quality of the ECG signal.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 5 Analysis of Numerical Calculation Results on Support Effect for Fractured Loose Rock Slopes
### Characteristic Analysis of Monitoring Points After Implementation of Support Measures
#### 5.4.2 Displacement Monitoring Curve in Z Direction  
As shown in Figure 23, the trend of the displacement monitoring curve in the \(Z\) direction before and after the implementation of support remains essentially unchanged. However, the A-2 and A-3 monitoring points produce small vertical upward displacements because of the low strength and loose structure of the Q\({}^{\text{edl}}\) soil during support application. In addition, the installation of preliminary shaft micropiles induces the compaction of the surrounding soil and vertical upward displacement. The cumulative displacement of the A-5-7 monitoring points decreases by approximately 4.6 cm, and the final displacement of the A-8-10 monitoring points in the \(Z\) direction decreases by approximately 2.4 cm.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 3 Preliminaries
### Weighted Access Tree  
A weighted access tree is an image representation of a weighted access policy. Let \(\Gamma\) be a weighted access tree. Let \(N_{j}\) be the nodes of \(\Gamma\) and \(N_{1}\) be the root of \(\Gamma\). There are two kinds of nodes in \(\Gamma\), non-leaf node and leaf node. The former represents a threshold gate, for example \"AND\", \"OR\", \"_a_ of \(b\) (\(a<b\))\". \(num_{j}\) and \(V_{j}\) describe the threshold gate, where \(num_{j}\) denotes the number of children of \(N_{j}\) and \(V_{j}\) denotes the threshold value, \(0<V_{j}\leqslant num_{j}\). When \(V_{j}=num_{j}\), the threshold gate is an AND gate, and when \(V_{j}=1\), it is an OR gate. A leaf node is described by a data consumer's attribute with weight, and \(V_{j}=1\). We denote the parent of \(N_{i}\) by \(parent(N_{i})\) and denote the index for \(N_{i}\) in its parent's children by \(index(N_{i})\), where \(1\leqslant index(N_{i})\leqslant num_{i}\).  
In \(\Gamma\), every non-leaf node \(N_{j}\) corresponds to a function \(V_{j}/num_{j}\). \(V_{j}/num_{j}\) is a Boolean function, and it is TRUE if \(N_{j}\) has at least \(V_{j}\) child nodes whose Boolean functions are evaluated to be TRUE. We say that a data consumer's attribute set \(\mathbb{S}\) satisfies \(\Gamma\) if \(\Gamma(\mathbb{S})=\text{TRUE}\), which is defined as described below.  
For any leaf node \(N_{j}\) that is associated with an attribute \(a_{i}\in\mathbb{S}\), if its Boolean value is TRUE, the weight of \(a_{i}\) from \(\mathbb{S}\) must be greater than or equal to the weight of \(N_{j}\). For any non-leaf node, its Boolean value is the value of its Boolean function. If and only if the tree's root node's Boolean value is TRUE, then \(\Gamma(\mathbb{S})=\text{TRUE}\).  
For instance, as shown in Figure 1, two weighted access trees correspond to two weighted access policies. \(\Gamma_{1}:\) [Attr3(3) AND (Attr1(2) AND Attr2(3))], \(\Gamma_{2}:\) [Attr4(2) AND (Attr1(2) AND Attr2(3))]. Considering a data consumer whose attribute set \(\mathbb{S}\) is {Attr1(3), Attr2(3), Attr3(4), Attr4(1)]. We could calculate that \(\Gamma_{1}(\mathbb{S})=\text{TRUE}\), \(\Gamma_{2}(\mathbb{S})=\text{FALSE}\). Table 1 gives several outcomes of \(\Gamma(\mathbb{S})\) with different attribute sets \(\mathbb{S}\).
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion  
This section explains the results of all routing protocols. We compare the proposed PMHRP (Static-PHCS, Random-PHCR, and Centroid-PHCC) schemes with GEO (Greedy-GEOD, Compass-GEOC, Most Forward-GEOMF) and AODV for performance evolution. We have generated results of four basic parameters, i.e., packet delivery ratio, delay, throughput, and normalized routing load, by varying the mobility speed of UAVs (20, 40, and 60 m/s). Furthermore, each QoS result generates 20 connection stress, which is a medium traffic load scenario. The graphs evaluate the impact of mobility and stress on the PDR, delay, throughput, and normalized routing load QoS metrics for data transfer.
## 2 Background and Related Work
### Non-LLM SQLI Black-Box Detection Methods  
In the initial stages of academic research, SQLI black-box scanners primarily relied on predefined rules and patterns to detect vulnerabilities through heuristic methods [20; 21]. These scanners would first identify potential injection points within web applications and generate numerous inputs for testing purposes. Subsequently, they would refine their approach by monitoring the responses from the application and adjusting the inputs according to predefined rules to enhance the detection of SQLI vulnerabilities. Notable heuristic SQLI black-box detection scanners in the industry include OWASP ZAP, Burp Suite, Arachi [22], and Sqlmap [23]. However, these scanners typically suffer from inefficiency, low accuracy, and a heavy dependence on the quality of the predefined rules [24; 25; 26].  
With advancements in AI, researchers have begun incorporating AI technologies into SQLI black-box detection to achieve better detection results. For instance, [PERSON] et al. [27] and [PERSON] et al. [28] have integrated machine learning with fuzzing, employing machine learning algorithms to guide the mutation of fuzzing seeds, thereby enhancing SQLI detection efficiency. [PERSON] et al. [29] introduced DeepSQLi, incorporating deep learning into SQLI black-box detection, which leverages the semantic knowledge of SQLI attack payloads to boost detection performance. Moreover, several studies have explored the application of reinforcement learning in SQLI detection [1; 30; 31]. [PERSON] et al. [2] developed a gray-box SQLI scanner that uses reinforcement learning by conceptualizing SQLI as a game within this paradigm. While these intelligent approaches offer certain benefits, they often require intricate modeling of SQLI and are dependent on high-quality training datasets. Furthermore, the limited transferability of these methods restricts their broader application.
## 3 Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Description** & **Reference** \\ \hline Deep Learning for Time Series & This approach uses specialised deep learning models for & [58] \\ Anomaly Detection & detecting anomalous patterns in time series data. & \\  & Variational autoencoder reconstruction along the projection & \\ Deep Generative Models & pathway (VAE-RaPP) and a fence generative adversarial & [59] \\  & network (Fence GAN) are used to deal with imbalanced time & \\  & series data obtained from manufacturing applications. & \\ Deep Metric Learning Model & This model considers imbalanced fault data and a quadruplet & [60] \\  & data pair design manner. & \\ ATT-1D CNN-GRU Model & This model combines a one-dimensional CNN, GRU, attention & [61] \\  & mechanism, and KG (knowledge graph) for fault diagnosis. & \\ Deep-Learning-Based Adaptive & This approach integrates a comprehensive convolutional & \\ Fault Prediction & feature extraction module, a customised gating module, and a & [62] \\  & multi-layered progressive extraction module. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep Learning Methodologies. artificially generated samples. Subsequently, the dataset is divided into separate subsets for training and validation purposes. The data were split in the ratio 7:3 for testing the model, with 70% being the training data and the remaining 30% being the testing data. These data were reshaped into a three-dimensional format, with the dimensions being the samples, time steps, and features. Constructing the LSTM model: Developing the LSTM structure was crucial. An LSTM neural network is built using input layers representing sequential data, LSTM layers capturing temporal dependencies, and an output layer conducting binary classification. The model was compiled by selecting a loss function, such as binary cross-entropy, an optimiser, such as Adam, and an evaluation metric, such as accuracy. The model had a batch size of 50 and was trained for 40 epochs, consisting of 2 LSTM layers and one dense layer for the final output. A seed value was provided at the start, which helped generate reproducible results for the model so that randomly different results were not generated each time the model ran. Once the model was trained, its performance was assessed on the validation set, and any adjustments to the hyperparameters were made, if necessary. Model Evaluation: The model was evaluated using a range of metrics, such as accuracy, precision, recall, and F1-score. The confusion matrix offers a comprehensive breakdown of predictions, including true positives, true negatives, false positives, and false negatives. The evaluation metrics analysed the model's behaviour and made any required modifications. The custom-built algorithm flowchart is shown below in Figure 3.  
Figure 3: Custom-built algorithm flowchart based on SMOTE and LSTM.
## 3 Results
### THz Enhancement  
Figure 3a-d shows dynamic changes of the enhancement factors of the total THz wave emission in \(\,\mid\!\!E\!\mid^{2}\) estimated over the frequency range in 0-3 THz and X-ray emission normalized to the emission intensity under the single-pulse irradiation condition in transmission and reflection, respectively. In the previous report [32], we estimated such enhancement factor only based on the peak intensity of the original TDS waveform as _E_-field intensity. However, the estimation in the current manner is more reliable considering all the frequency components. Furthermore, we have confirmed that the same enhancement factors are obtained even from the data in the previous paper [32]. With the pre-pulse (s-pol.) and the main pulse (p-pol.) intensities at 0.1 mJ/pulse and 0.4 mJ/pulse, respectively, and the transform-limited pulse at 35 fs, the enhancement of the THz emission shows its highest at the 2-5 ns window, while that of  
Figure 2: Normalized THz emission spectra in (**a**) the transmission and (**b**) the reflection sides under the single pulse (in black on the top) or the double-pulse excitation conditions. The vertical axes represent THz wave \(\,\mid\!\!E\!\mid^{2}\) intensity. The insets in the single-pulse excitation case and \(\Delta t\) = 4.7 ns represent the original signal waveforms from \(-\)3 ps to 7 ps in TDS measurements.  
the X-ray shows its peak at 0 ns. Evidently, this data set shows the different enhancement mechanisms for both emissions.  
In the case of X-ray, the pre-pulse irradiation induces the plasma formation and its fast decay at around 0 ns delay time, then the main pulse irradiates this pre-excited region leading to jump in plasma temperature and electron density. High temperature is the cause of X-ray emission and its enhancement [33]. In the case of THz emission which may depend on temperature gradient [53; 54], such hot plasma formation may interfere with THz emission since the critical density of plasma [55], \(n_{c}=\omega_{\mathrm{THz}}^{2}m_{e}e_{0}/e^{2}\), where \(n_{c}\), \(\omega_{\mathrm{THz}}\), \(e_{0}\), \(m_{e}\) and \(e\) are the critical electron density, THz frequency, permittivity, electron mass and charge, respectively, makes the excited region opaque. In the nanoseconds time delay after the pre-pulse irradiation, various laser ablation phenomena described above have already started. The acoustic space-time scaling of sub-\(\upmu\)m features formed in a few tens of \(\upmu\)s as described above. Importantly, the highest enhancement in THz wave emission under the double-pulse excitation is \(1.5\times 10^{3}\) times in intensity \(\,\mid\!E\!\mid^{2}\) at 4.7 ns delay in the transmission direction, while the X-ray enhancement is only 15-22 times at the highest.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 3 Establishing IRT Database
### Specimen Corrosion
#### 3.2.2 Building a Rebar Corrosion Automation System  
In this study, all steps were programmed according to the sequential process detailed in Figure 4, and corrosion was induced. A rebar corrosion automation system was built using a power supply with program control. This study facilitated bidirectional control between the power supply and the computer, as well as the individual output of the two power output ports. A feature was added to turn the output off via program control when the desired cumulative voltage and current values were attained. The program system was built such that the real-time voltage and current data were automatically entered into an Excel sheet. The data acquisition intervals can be adjusted to allow the long-term progression of corrosion, without any hindrance. The program that controls the power supply was created in Microsoft Visual Basic 6.0.  
In past experiments, the amount of voltage was continuously checked and directly entered into the Excel sheet as the corrosion progressed. The Excel sheet was used to frequently predict the time at which the current would be cut off, using the mensuration by parts method (Equation (3)), as the corrosion progressed. When the desired extent of corrosion was attained, the power supply was manually switched off. In this experiment, a new system was introduced in which the desired corrosion time was pre-set and current was supplied. The corrosion progressed until the pre-set extent and then the current supply was automatically cut off by the program.  
\[Amount\;of\; corrosion\;(g)=\;\frac{55.847}{2\times 96500}\sum_{i=1}\frac{q_{i}+q_{i +1}}{2}(t_{i}+t_{i+1}) \tag{3}\]  
where the molecular weight per mole of steel molecules = 55.847 g, Faraday's constant (\(F\)) = 96,500 C/mol, \(q\) is the amount of current (A), and \(t\) is the time (s).  
The corrosion automation system that was built during this study automatically stops the corrosion process after it reaches the desired extent (see Figure 6). Therefore, it can be used to induce corrosion more accurately and easily than the existing corrosion inducing methods.  
Figure 5: Rebar corrosion system of this study.  
Several rounds of preliminary experiments were performed. The results of the induced corrosion in the specimens are shown in Table 5. In the case of the 10 mm specimen, the difference between the expected corrosion ratio and the actual corrosion ratio was minor. However, as the cover thickness increased, the actual corrosion ratio became lower than the expected corrosion ratio. It is believed that this is because the current intensity was lost as it passed through the concrete, which is a material with high specific resistance. For the specimens used in the experiment, current loss was considered, and the corrosion ratio was increased based on the corrosion results of the preliminary test specimens in order to obtain the desired corrosion rate and eliminate the difference between the expected and actual corrosion ratios.
## 2 System Architecture
We chose an integrated solution for the LED and PD pair (SFH7050 BioMon Sensor, 4.7 \(\times\) 2.5 \(\times\) 0.9 mm\({}^{3}\), OSRAM Opto Semiconductors GmbH, Regensburg, Germany), which includes one PD and three LEDs with different wavelengths (green at 525 nm, red at 660 nm, and infrared at 950 nm) along with a light barrier in between to minimize optical crosstalk with the PD. The BioMon sensor is also placed on the bottom side to ensure tissue contact. In order to fit the two ASICs within the dimensional constraints of the capsule PCB, we wire-bonded these directly to the board and covered with glob top epoxy (EPO-TEK(r)T7109-19, Epoxy Technology, Inc., Billerica, MA, USA). As mentioned earlier, the two system blocks that we designed and integrated into the two ASICs are: (1) the AFE for reading and conditioning the photocurrent from the PD; and (2) the frequency-shift keying (FSK) transmitter. The following subsections describe the design considerations and implementation techniques of the PPG signal conditioning, the control logic, and the FSK transmission in detail.