esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 2 Related Work
### Deep Learning-Based Methods  
[PERSON] [11] tried to sharpen the classification of small patches produced by laser printers through the use of multiple CNNeural Network (CNN) models that analyze the characteristics of these patches. To do this, the data were broken down into three different representations--high-frequency imperfections (banding), image pixels, and border effects--and fed into a set of lightweight CNNs called a Raw Image Network, a Median-Residual Network, and an Average-Residual Network. The results from these networks were then concatenated and fed into a classifier, with a final decision contributing to the identification results. However, this method requires a significant amount of time and energy to collect and process the data.  
[PERSON] [21] proposed using Generative Adversarial Networks (GANs) to decompose the image of a halftone color channel of photographed color documents and transfer the data of the decomposed image into CNNs for the purpose of identifying the source printer. However, this method cannot be used to identify the source printer of scanned images due to the differences in illumination and clarity between the scanning and photographic environments. In contrast, [PERSON] et al. suggested splitting the printed image into smaller blocks to assist CNN models extract better results in printed documents.  
[PERSON] [22] proposed using a customized bottleneck residual model to identify the source printer using black and white QR codes as datasets scanned at 300 dpi. [PERSON]'s segmentation was used to convert the QR codes from grayscale to black and white to minimize variance, resulting in the whole dataset being converted into binary images. A morphological opening operation (erosion followed by dilation) was then used to fill in the inside of the QR code and dismiss a few unintended connected domains. The goal of this approach was to achieve spectacular identification results while maintaining low-complexity CNNs. Nevertheless, the use of image filtering significantly modified the dataset prior to training.  
In the field of printed source identification, very few studies have investigated the use of black and white (BW) QR codes as an input dataset, not to mention the more personalized, colorful QR codes. Most existing methods utilize either grayscale text documents or photographed color images. With this in mind, we decided to study both BW and color QR codes as training datasets for input into CNN models.
## 4 Case Study
In this context, the PROMETHEE-SAPEVO-M1 modeling was chosen as the tool to support decision-making, allowing an integrated algorithm and enabling to perform qualitative and quantitative data, providing the structuring and analysis of variables where it is not possible to define a precise numerical input. Regarding the analysis, fourteen criteria were considered in this assessment:  
* _Technological level_: Aggregate technology of the aircraft, concerning radars, sensors, safety devices in flight, increasing efficiency and noise reduction;
* _Main Cannon system_: A weapon widely used in close combat, consisting of aerial actions by fixed-wing and rotating aircraft against hostile targets that are close to friendly forces. This component can be used against ground troops, vehicles, buildings, or even hostile aircraft. This study will compare the aggregate technology, robustness, reliability, and destructive power of helicopter cannon systems;
* _Missiles System_: The missiles have great power of destruction and are usually laser-guided against armored vehicles, buildings, and other types of targets. However, due to their larger size, they are transported in smaller numbers than rockets in combat aircraft. In this criterion, the accuracy and firepower of missiles of each helicopter will be compared;
* _Weapons subsystems_: In this criterion, the combat control systems of each helicopter are evaluated, concerning the integration between sensors, components, and flexible armaments such as machine guns;
* _Armor level_: Sheet metal coating (such as steel or another resistant alloy) to protect the aircraft from projectiles, combat devices, or electrical discharge from lightning;
* _Employment flexibility_: Capability to use the aircraft in activities beyond combat, such as aeromedical evacuations and other logistics operations;
* _Maximum Speed_: Speed developed with the maximum engine regime, essential for reconnaissance, interception, and fire support to troops in critical situations;
* _Cruising speed_: Operating speed at which combustion engines have an optimum efficiency level for fuel consumption and power output;
* _Range_: Whereas a theater of operations may have large dimensions, the greater the range of an aircraft, the greater the capacity to operate in different missions;
* _Autonomy_: Maximum time that an aircraft can spend in cruising flight. In other words, it is how long an aircraft can stay on-air with one load of fuel;
* _Service ceiling_: Maximum usable altitude of an aircraft;
* _Maximum Payload_: Measure obtained by reducing the weight of the aircraft from the maximum take-off weight of the helicopter. Such a measure is relevant because it will directly influence the range, quantity of fuel, and ammunition/armament that the helicopter will be able to carry;
* _Troop Capacity:_ Maximum number of passengers transported in addition to aircraft pilots;
* _Length:_ Distance from the bow (front) to the stern (reverse part) of the helicopter. Considering that some regions often present difficult maneuverability, it is sought the minimum value for this criterion. Therefore, the shorter the length, the better.
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
## 2 Principle
### Dataset Establishment in Virtual FPP System
\tag{16}\]  
All parameters in the intrinsic and extrinsic matrixes of the camera and projector can be calibrated by [PERSON]'s [34] and [PERSON]'s method [35]. Since the Euler angles along the \(x\), \(y\), \(z\) axes are required in Blender to describe the object's rotation, the calibrated rotation matrix should be considered as the multiplication of three parts:  
\[R=R_{z}(\alpha)R_{y}(\beta)R_{x}(\gamma). \tag{17}\]  
where the Euler angles \(\alpha\), \(\beta\), \(\gamma\) can be solved by [PERSON]'s method [36]. So far, the one-to-one relationship between the virtual and actual FPP system has been built. The next step is to determine the location and motion type of objects and perform image rendering. As shown in Figure 2c, the most common rigid motion type of a measured object can be categorized into translation, rotation, and mixture of them, while the object remains static without any external force. To simulate a dynamic scene realistically, a dataset generation pipeline is proposed:  
1. to load a 3D model as the measured object and randomly rescale it to 1/2-2/3 of the calibration volume. The coordinate of the object's centroid represents its position. 2. to randomly select the initial position of the object at first frame and final position at \(F\)th frame. \(F\) is the number of frames the object motion. For the stationary and rotation around the centroid, the initial position equals the final one. The selected position should make the bounding box of the object inside the calibration volume. 3. to choose the corresponding interframe interpolation methods according to the desired motion trajectory. For example, linear interpolation corresponds to uniform linear motion, and the nodes of Bezier interpolation can be controlled to implement different accelerative motion. 4. to cyclically project three-step phase-shift fringe patterns in \(F\) frames and synchronously render the corresponding deformed patterns. The object poses (location and rotation) are simultaneously recorded at every frame. One phase map affected by motion is calculated from three adjacent images, so \(F\)-2 phase maps can be obtained from the total \(F\) frames. (e) for each pose recorded in (d), to project standard 12-step phase-shift patterns and calculate the phase maps of \(F\)-2 poses as the corresponding ground truth for the phase maps obtained in (d). (f) to remove the object and return to (a) for the next object. After processing all objects with different motion types, the obtained synthetic dataset can be used to train the model mentioned in Section 2.2. Instead of complex manually marking, the data collection using virtual FPP system and computer graphics can be performed automatically. In addition to data generation, the proposed method can also be used for the analysis of non-rigid movement or other unstable measurement scenes.
# An Improved Winkler Foundation Modulus for a Beam in a Full Space
## 2 The Formula of the Winkler Foundation Modulus
### Beam Bending Response of the Beam on a Three-Dimensional Elastic Foundation
#### 2.2.3 The Response of the Beam under Unit Bar Displacement  
Underground structures, such as tunnels, are subject to free-field deformation. The displacement of the free field is assumed to be \(w_{f}\) from Equation (8). The contact force at the interface of the beam and foundation has the form  
\[Q_{avg}=-Q_{avgf}cos(\lambda x) \tag{36}\]  
where \(Q_{avgf}\) is amplitude.  
According to the theory of the elastic beam, when the load expressed by Equation (36) is applied to the beam, the deformation of the beam satisfies the following expression:  
\[w_{b}=\frac{Q_{avg}}{E_{b}I_{b}\lambda^{4}} \tag{37}\]  
Using the relationship derived from Equation (34), the displacement of the ground under the force given by Equation (36) has the form  
\[w_{s}=-Q_{avg}\frac{C\big{(}1-{\
u_{s}}^{2}\big{)}}{E_{s}\beta\psi(\beta)} \tag{38}\]  
As shown in Figure 5, the displacement of the beam and ground should meet the displacement coordination. Thus, the following expression is given:  
\[w_{b}=w_{f}+w_{s} \tag{39}\]  
The term \(Q_{avg}\) is eliminated by combining Equations (37) and (38). By using the result in Equation (39), the displacement of the beam is obtained as follows:  
\[w_{b}=\frac{a\cos(\lambda x)}{C(1-v^{2})\frac{E_{b}I_{b}}{E_{s}\beta}\frac{ \lambda^{4}}{\psi(\beta)}+1} \tag{40}\]  
By using Fourier integration on Equation (40), the displacement response of the beam under the unit bar displacement shown in Figure 1 can be written as follows:  
Figure 4: A simple cosine load acting on a foundation.  
\[w_{b}(x)=\frac{g_{0}}{\pi}\int_{0}^{\infty}\frac{1}{\lambda}\frac{2\sin(\lambda l )\cos(\lambda x)}{C(1-v^{2})\frac{E_{b}I_{b}}{E_{s2}\beta}\frac{\lambda^{4}}{ \psi(\beta)}+1}d\lambda \tag{41}\]  
The normalized displacement and bending moment of the beam under displacement can be derived as follows:  
\[\frac{w\tau}{s_{0}}=\int_{0}^{\infty}\frac{2\sin(m\eta\kappa)\cos(m\eta\vec{x} )}{\frac{\eta^{4}}{2\psi(m\eta)}+\eta}d\eta \tag{42}\]  
\[\frac{M\pi b^{2}}{s_{0}E_{b}I_{b}}=m^{2}\int_{0}^{\infty}\frac{2\sin(m\eta \kappa)\cos(m\eta\vec{x})}{\frac{\eta^{2}}{2\psi(m\eta)}+\frac{1}{\eta}}d\eta \tag{43}\]  
where  
\[c^{3}=C\left(1-v^{2}\right)\frac{E_{b}I_{b}}{E_{s}b} \tag{44}\]  
\[m=\sqrt[3]{E_{s}b^{4}/\left[C(1-v^{2})E_{b}I_{b}\right]} \tag{45}\]  
\[\eta=c\lambda \tag{46}\]
## 3 Field Case Study of Pumping Test
### Optic Fiber Measurement  
The fiber cable was custom-made for this study to improve the coupling strength between casing and cement. The frequency shifts were taken using a Neubrescope NBX-8000 device (2012, manufacturer, city, country), which can measure both Rayleigh and Brillouin as in the experimental study [21]. Unfortunately, only the Rayleigh shifts were thoroughly recorded during the pumping test because the signal to noise ratio of Brillouin shift was too low for measuring the strain data. During water pumping the Rayleigh frequency shift was recorded every 5 cm along the fiber cable and the frequency shift measurement was repeated in a time interval of 5 minutes. Deformation of the target reservoir formation caused by water pumping was estimated from the Rayleigh frequency shift following the same approach demonstrated in the experimental study [21].
# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
## 3 Result Analysis
### Accuracy Analysis  
In our proposed parking space detection method, there are some hyperparameters that need to be determined. First, it should be noted that the YoloV11 framework we use specifies the size of the input image. Therefore, when obtaining the panoramic image of the vehicle, we need to first unify the size of the input image. All hyperparameters are empirically set to be compatible with this resolution. We specify the specific values of these hyperparameters in Table 2. The specific meaning of hyperparameters can be understood from the context.  
The parameter selection in this paper is based on the best parameter selection obtained in the actual experiment. For example, p represents the size of the target box detected by YoloV11, j represents the angle of each image increase during image enhancement. These parameters are selected according to the hardware equipment of the computer and the convenience of the subsequent actual operations. For example, the data \(t_{1},\ t_{2},\ t_{3}\) are determined according to the size of the actual parking space.  
In parking space detection, key point detection is a crucial step. In order to complete this task, we propose a method based on YoloV11. In this experiment, its performance was evaluated and compared with other classical object detection algorithms HOG+SVM, ACF+Boosting, Faster-RCNN, SSD, and DETR on the data set ps2.0.  
In Figure 7, we present statistics on FN and FPPI of two classical machine learning algorithms and four deep learning-based algorithms, including YoloV11, in the identification of key points of parking spaces. We found that in the detection task of key points of parking Spaces, the depth-based algorithm generally showed better performance in the verification set. In particular, YoloV11 and DETR were very close in accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline p & 56 Pixel & \(\lambda_{2}\) & 30\({}^{\circ}\) \\ \(\delta\) & 71.5\% & \(t_{1}\) & 195 Pixel \\ \(\mu_{2}\) & 83 Pixel & \(t_{3}\) & 160 Pixel \\ \(t_{2}\) & 279 Pixel & \(\underline{1}\) & 86 Pixel \\ \(t_{4}\) & 139 Pixel & \(\underline{J}\) & 0.5–1.5 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter assignment table.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method  
It is essential to highlight that investigating different IP flow features (or dimensions) enriches traffic analysis by providing relevant information about the communication. Several different approaches use this characteristic to improve the performance of anomaly detection. Still, most of them use a set of manually selected features, such as bits/s and packets/s rates. However, IP flows can provide a much more comprehensive range of information often unused by traditional methods, unlike most conventional deep learning approaches that can analyze several flow features and automatically give more importance to those dimensions that most impact the classification outcomes.
# A Survey of the Applications of Text Mining for the Food Domain
## 6 Text Mining in Recipe Recommendation and Personalized Nutrition
### Food Knowledge Discovery  
Techniques for data mining are used in the field of knowledge discovery in modern food science to uncover underlying patterns and guiding principles from massive datasets. This method not only contributes to a deeper knowledge of topics like customer preferences, food safety, nutrition, and supply chain logistics, but it also establishes the framework for extensive and thorough study.  
Ontologies, structured frameworks for categorizing domain-specific concepts, have become indispensable tools in this era of the Semantic Web [107]. Various ontologies, such as FoodWiki, AGROVOC, Open Food Facts, Food Product Ontology, and Foodon, serve distinct purposes in elucidating the complexities of the food domain [108; 109]. For example, FoodWiki analyzes the adverse effects of food additives using Semantic Web technologies to provide tailored advice on safe consumption for different risk demographics [110; 111]. Foodon addresses challenges in disparate datasets within food safety, quality, production, distribution, and consumption, aiming to enhance traceability and transparency [109]. The ISO-FOOD ontology encapsulates metadata and traceability elements, supporting future analytical endeavors and integrating stable isotope data within food science research [107]. It seamlessly integrates with pre-existing ontologies like the Unit Metrics Ontology, the Food Nutrition Ontology, and the Literature Ontology, showcasing the synergy of shared knowledge in this domain. The Nutritional Epidemiology Ontology (ONE) standardizes the outputs of nutritional epidemiology studies, embedding data standards, reporting guidelines, and core concepts from authoritative guidelines within the field [104]. An ontology-based repository for food lexicon and methodologies further enhances the exchange and retrieval of food data [94].  
Text mining, as a tool for extracting latent knowledge from unstructured textual data, is poised to catalyze the evolution of food science research and knowledge-based services.
## 3 Push-Off Tests on Joints between Segments
### Test Design
#### 3.1.3 Test Setup and Testing Procedure  
Since main objective of these tests was to study the strength of the joints under uniform shear and normal stresses, a testing configuration was chosen that eliminated, to the extent possible, bending effects and stress concentrations (Figure 13).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Product** & \(\rho\) **[g/cm\({}^{3}\)]** & \(f_{cm}\) **[N/mm\({}^{2}\)]** & \(f_{ct,\beta}\) **[N/mm\({}^{2}\)]** & **Age [days]** \\ \hline Grout 1 & 2.24 (\(\pm\) 0.7\%) & 75.40 (\(\pm\) 4.8\%) & 10.30 (\(\pm\) 4.7\%) & 55 \\ Grout 2 & 2.26 (\(\pm\) 0.3\%) & 84.30 (\(\pm\) 2.3\%) & 6.00 (\(\pm\) 5.6\%) & 16 \\ Concrete & 2.33 (\(\pm\) 0.7\%) & 81.40 (\(\pm\) 1.0\%) & 4.10 (\(\pm\) 1.3\%) & 116 \\ \hline \end{tabular}
\end{table}
Table 6: Mechanical properties of mortar and concrete.  
Figure 13: Test setup and surface measurements on push-off tests with constant lateral force.  
Springs with a stiffness \(k\) = 2.4 kN/mm were used to minimise a possible increase of the lateral force caused by dilatation of the joint.  
Each conducted test consisted of the following steps: post-tensioning followed by a push-off test. The joint surface was prestressed with a nominal value of normal stress (\(\sigma\) = 2.0/9.0/16.0 N/mm\({}^{2}\)) in the joint. The post-tensioning forces \(P\) were applied by a hydraulic jack over a frame containing two beams made of U-profiles and two threaded rods with strain gauges applied to them. The strain gauges were used to determine the prestressing force.  
The applied load \(F\) in the push-off tests was determined by measuring the oil pressure in the hydraulic jack. The dilatation \(w\) of the joint was measured with a total of four LVDTs, placed on the top and bottom on each side. The shear deformation \(s\) was measured with LVDTs placed in the middle on each side.  
The experiments were loaded with a slow loading rate of 0.05 mm/min for precise coverage of damage effects. After the first pronounced load decrease, the loading rate was doubled. The test procedure was stopped after a shear deformation of at least 3.0 mm. Some experiments were continued until no further significant change in the post-peak behaviour was observed.
## 2 Materials and Methods  
### Reagents  
CRANAD-2, a commercial near-infrared fluorescent probe highly selective for A\(\beta\) aggregates, was used as an optical reporter of the AD hallmark. CRANAD-2 is described to display a typical 70-fold increase in fluorescence and a 90 nm blue shift when interacting with A\(\beta\)[23]. In our study, CRANAD-2 (abcam, AB141775) was prepared using two solvents: phosphate-buffered saline (PBS, 1\(\times\), pH 7.4) purchased from CORNING (21-040-CMR) and methanol (MeOH, HPLC grade) purchased from Labkem (MTOL-0 GH-2K5). A\(\beta\) peptides (1-40), human (abcam, AB120479), were used as oligomers and fibrils conformations. The oligomeric A\(\beta\) was prepared in PBS (pH 7.4), while fibrils were formed in a 10 mM hydrochloric acid solution (HCL prepared from 1M HCL stock solution diluted in PBS) incubated at 37 \({}^{\circ}\)C during 24 h, following the protocol described by [PERSON] et al. [27].  
For preliminary assessments, CRANAD-2 samples were prepared in both PBS and MeOH to evaluate solvent effects and optimize conditions for fluorescence detection. CRANAD-2 was prepared at concentrations of 0.5 \(\upmu\)M, 1 \(\upmu\)M, and 2 \(\upmu\)M in both solvents to investigate how solvent choice influenced its fluorescence properties. For further optimization, CRANAD-2 was also prepared at concentrations of 5 \(\upmu\)M and 25 \(\upmu\)M, in MeOH. A\(\beta\) peptides were prepared at a series of concentrations--5 nM, 10 nM, 25 nM, 50 nM, 100 nM, 200 nM, 500 nM, and 1000 nM--to establish a concentration gradient for studying both oligomeric and fibrillar A\(\beta\) forms. Both A\(\beta\) conformations were analyzed by dynamic light scattering and zeta potential ([PERSON], Iitesizer DLS 500), focusing on their hydrodynamic size and electrophoretic mobility measurement features.  
For fluorescence assays, CRANAD-2 was mixed with A\(\beta\) oligomers or fibrils at a 1:1 (\(v/v\)) ratio. For macroscopic validations, this translates into a final volume of 2 mL  
Figure 1: A graphical representation of the microfluidic-sensing experimental setup used for A\(\beta\) detection—a proof-of-concept study.  
per sample. At microfluidic scale, the same procedure was used, but with a volume of 150 \(\mu\)L per sample, following the same ratio. All samples' preparations and fluorescence measurements were performed at room temperature (RT).  
To ensure reproducibility, the solutions containing A\(\beta\) and CRNAAD-2 were mixed immediately before the optical analysis (i.e., prior to loading into the cuvette or pumping to the inlet of the microfluidic channel using a syringe pump), allowing adequate interaction and analysis between the molecules. All samples were prepared freshly before each experiment to maintain the structural integrity of A\(\beta\) peptides and ensure reliable fluorescence measurements.