esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Power Shift  
#### 3.2.1 Optimized Base Attractor for Power Shift  
We demonstrate optimized power-shift conditions for a base attractor in Figure 7. Figure 7 plots the largest tranches (time intervals) that transition to AV as a function of \(Y\) in AIII's domain when power shifted to \(Y_{PS}\) = 1.84; the domain is divided into steps of \(\Delta Y_{0}\) = 0.01. We find the power shift at \(Y_{o}\)=1.63 has the largest tranche for transitioning to the desired AV. Figure 7 uses the same conditions as Figure 7 except the power shift changes to \(Y_{PS}\) = 1.96 to access the desired AVI. This optimization scheme can be achieved for any valid set of base and goal attractors.  
Figure 8 shows results beginning on base AIII at \(Y_{0}\) = 1.63 and power shifted to \(Y_{PS}\) = 1.84 every 1\(\tau\) along the period of motion. The resultant attractors, AV or AII, are recorded. The blue and red segments illustrate along the temporal period, 163.551\(\tau\), the system's viable tranches where either AV or AII can be found, respectively. In Figure 8 we show the results beginning on base AIII at \(Y_{0}\) = 1.72 and power shifted to \(Y_{PS}\) = 1.96 for every 1\(\tau\). The resultant attractors, AVI or AII, are recorded. Note, at the end of one period is the beginning of the other, so we can combine the points on both ends to configure the total tranche size. In this case we had 101 contiguous points in a single period of AIII going to the desired AV in Figure 8 and 55 contiguous points going to the desired AVI in Figure 8.  
Figure 8: Power-shift method from AIII at **(a)**\(Y_{0}\) = 1.63 transitioning to coexisting AII and AV via \(Y_{PS}\) = 1.84, and **(b)**\(Y_{0}\) = 1.72 transitioning to coexisting AII and AVI via \(Y_{PS}\) = 1.96.  
Figure 7: The plot for the largest time interval for each \(Y_{0}\) value in AIII which when power shifted to \(Y_{PS}\) = 1.84 (a) or \(Y_{PS}\) = 1.96 (b) takes the system to AV (a) or AVI (b), respectively. \(Y_{0}\) = 1.63 has the largest tranche for \(Y_{PS}\) = 1.84. \(Y_{0}\) = 1.72 has the largest tranche for \(Y_{PS}\) = 1.96.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 4 Sawing Experiment  
The experiment was conducted by employing a diamond multi-wire saw machine. Figure 7 shows the appearance and schematic of the machine sawing unit in the experiments. The diamond wire is wrapped around two wire wheels, and during the sawing process, the two wire wheels respectively complete the tasks of unwinding and rewinding. When the diamond wire finishes running in a certain direction, the control system can change the wire direction, and at the same time, the roles of the two wire wheels can be switched to achieve the reciprocating moving of the diamond wire. In the cutting area, the diamond wire is wound around three wire guides to form a parallel wire web, and is tensioned by tensioning  
Figure 5: Calculation method of wafer warpage based on finite element method [5; 8].  
Figure 6: Schematic diagram of selected measurement points for sapphire wafer warpage.  
rollers. During the sawing process, the diamond wire runs and the sapphire crystal is fed upwards perpendicular to the diamond wire to achieve cutting. In this experiment, deionized water was used as the coolant. The nozzle sprays coolant to the cutting area to achieve cooling and lubrication of the workpiece, and the coolant flow rate is 17.5 L/min. The sapphire crystal size is 10 mm \(\times\) 10 mm \(\times\) 40 mm, cut along the C plane with a slice thickness of 0.5 mm. The electroplated diamond wire with a nominal diameter of 0.28 mm was used, and four sets of cutting experiments were conducted, and the process parameters are shown in Table 2.  
After the cutting is completed, five wafers are randomly selected from the sawing area middle position of each set, and after ultrasonic cleaning, the wafer warpage is measured using a KEYENCE laser plane measuring instrument (LJ-X8000) (Osaka, Japan). This method scans and the surface accurately measures the shape and height changes of the wafer, and calculates the warpage of the wafer by analyzing the scan data. The experimental measurement principle of wafer warpage is consistent with the method of extracting thermal deformation information point by point in the simulation model to calculate wafer warpage. The average of the measured values is taken. The experimental results are used to validate the results of the simulation calculation model.
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 2 Train-Track-Soil Interaction Model
### Track Irregularities  
The main source of ground vibrations is the loads of the wheels acting on the rail. The track irregularities are considered to increase the ground vibrations. In the present study, the track irregularities are numerically generated using the power spectral density function (PSD), developed by the USA Federal Railway Administration [32], and is given as:  
\[S(\phi_{k})=\frac{A\phi^{2}{{}_{2}}(\phi^{2}{{}_{k}}+\phi^{2}{{}_{1}})}{\phi^ {4}{{}_{k}}(\phi^{2}{{}_{k}}+\phi^{2}{{}_{2}})}, \tag{17}\]  
where \(A\), \(\phi_{1}\) and \(\phi_{2}\) are the constants defined in the actual measurement depending on the type of irregularity and rail quality. According to the practice by the Federal Railway Administration, USA, the level of track irregularities is categorized into six classes ranging from class1 (bad quality) to class6 (good or finest) [33]. The random vertical track irregularities _irr_ are modeled as a stationary Gaussian random process based on the PSD function and can be written as:  
\[irr(y)=\sum\limits_{k=1}^{N_{k}}{{a_{k}}\cos(\phi_{k}y+\theta_{k})} \tag{18}\]  
\[a_{k}=2\sqrt{S(\phi_{k})\Delta\phi} \tag{19}\]  
\[\phi_{k}=\phi_{\text{min}}+(k-0.5)\Delta\phi \tag{20}\]  
\[\Delta\phi=\frac{\phi_{\text{max}}-\phi_{\text{min}}}{N_{k}}\, \tag{21}\]  
where \(a_{k}\) is the amplitude of the wave, \(\phi_{k}\) is the circular spatial frequency within the range of upper (\(\phi_{\text{max}}\)) and lower (\(\phi_{\text{min}}\)) limits of route frequency, \(\theta_{k}\) is the random phase angle with a uniform distribution between 0 and 2 \(\pi\), \(N_{k}\) represents the number of harmonic functions, and \(y\) is the global coordinates along the rail.
## 3 Preliminary  
In order to understand the basic elements of the proposed scheme, in order to increase its ability to be implemented in various other subcategories by third-party users, it is necessary to define some important terms. In this sense, blind signature schemes allow users to obtain valid signatures of messages while hiding the content of the message from the signatory. The system assumes a certain degree of confidentiality of the messages through its signature mechanism, while maintaining a high degree of anonymity of the sender. The scheme proposed by us addresses these aspects by tempering the parameters with certain elements of primality, thereby being able to extend the parameterization principle and various levels of anonymity are implementable, which was ready to be implemented in the systems needed in research such as [32; 33], customizable according to the specific requirements of a beneficiary, to satisfy the different application requirements in specific cases of function.  
In order to create a coherent and understandable flow of the presentation, in this article, the following symbols will be used for the calculation of cryptographic primitives:  
* \(M\): The message to be signed.
* \(K_{priv}\): The private key used by the signer to generate the signature.
* \(K_{pub}\): The public key of the signer, used to verify the signature.
* \(\sigma\): The signature generated on the message \(M\).
* \(T\): The temperature parameter, which controls the level of anonymity in the signing process.
* \(S(M,T)\): The tempered signing function, which applies the signature to the message \(M\), taking into account the parameter \(T\).
* \(V(\sigma,M,T)\): The verification function, used to validate the signature \(\sigma\) against the message \(M\) and the parameter \(T\).  
Each of these notations will be used to formalize the proposed algorithm in the next section.  
In developing the proposed scheme, we consider the following assumptions:  
1. Particular subspaces: All subspaces used are finite, well-defined, and compatible with the proposed mathematical structure.  
2 Secure keys: All participants possess securely generated keys, according to accepted standards.
3 Active attacker model: We assume a security model that includes attackers capable of intercepting, modifying, and manipulating messages in transit.
4 Limited resources: The scheme is designed to work efficiently even in environments with low computing resources, such as IoT devices.  
These assumptions provide an essential basic framework that can support both the implementation and analysis of the scheme, while allowing for the detailed explanation of the algorithmic model and its customization.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 2 Signal Design
### Gaussian Coding with Coding Gain  
The Gaussian-LBC method has a limitation in that the method considers only the real-valued message case. However, for modern communication systems that transmit a variety of finite-field messages, the encoding of a finite-field message must be considered. Unfortunately, the direct application of the Gaussian-LBC method to finite-field messages is inefficient because there is no actual coding gain. In finite-field coding, the coding gain is defined by how much the minimum Hamming distance between two codeword vectors increases when compared to that between message vectors. This is because the finite-field decoder maps the input vectors to the closest codeword in terms of the Hamming distance. In contrast, the coding gain of a real-valued code cannot be defined by the Hamming distance because the closeness of real-valued codewords to non-sparse noise is meaningless. Instead, the Euclidean distance has to be used for real-valued code. It can be noted that the minimum Euclidean distance is constant before and after the Gaussian-LBC, i.e.,  
\[\begin{array}{ll}(\mathbf{c}-\widetilde{\mathbf{c}})^{T}(\mathbf{c}- \widetilde{\mathbf{c}})&=(\mathbf{G}\mathbf{m}-\mathbf{G}\widetilde{\mathbf{m }})^{T}(\mathbf{G}\mathbf{m}-\mathbf{G}\widetilde{\mathbf{m}})\\ &=(\mathbf{m}-\widetilde{\mathbf{m}})^{T}\mathbf{G}^{T}\mathbf{G}(\mathbf{m}- \widetilde{\mathbf{m}})\\ &=(\mathbf{m}-\widetilde{\mathbf{m}})^{T}(\mathbf{m}-\widetilde{\mathbf{m}}), \end{array} \tag{2}\]  
where \((\cdot)^{T}\) is the transpose operator for the vector/matrix and \(\mathbf{G}^{T}\mathbf{G}=\mathbf{I}\) for large \(N\). To benefit from coding gain, we considered two approaches: concatenation coding and the codebook method.
# Exploring Language Markers of Mental Health in Psychiatric Stories
## 2 Related Work
### NLP Techniques for Identifying Language Markers
#### 2.2.1 Lexical Processing  
Research so far on exploring language markers in mental health has been done mainly with Linguistic Inquiry and Word Count (LIWC) [6]. LIWC is a computerised text-analysis tool and has two central features: a processing component and dictionaries [15]. The processing feature is the program which analyses text files and goes through them word by word. Each word is compared with the dictionaries and then put in the right categories. For example, the word \"had\" can be put in the categories verbs, auxiliary verbs and past tense verbs. Next, the program calculates the percentage for each category in the text; for example, 17% of the words may be verbs. A disadvantage of the LIWC program is that it ignores context, idioms, sarcasm and irony. Furthermore, the 89 different categories are based on language research. However, this does not guarantee that these categories represent reality, because categories could be missing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disorder** & **Pron** & **SC** & **Word Use** & **More** & **N** \\ \hline ADHD & 3 pl & - & - & Relativity, more sentences, less clauses & 4 \\ Autism & 1 sg & - & Motion, home, religion and death & - & 5 \\ Bipolar & 1 sg & - & Death & - & 7 \\ BPD & 3 sg & n & Death & Swearing, less cognitive emotion words & 5 \\ Eating & 1 sg & - & Body & Negative emotion words & 10 \\ GAD & imprs & i & Death and health & Tentative words & 4 \\ MDD & 1 sg & i & - & Inverse word-order and repetitions & 11 \\ OCD & 1 sg & - & Anxiety & More cognitive words & 4 \\ PTSD & sg & - & Death & Less cognitive words & 6 \\ Schizophrenia & 3 pl & i & Religion & Hearing voices and sounds & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of associated language markers for ten mental health disorders.
## 2 Related Works
### Lane Detection
#### 2.2.1 Tool-Based Methods
(**2**) Compute Gradient Magnitude and Orientation using the Sobel Edge Mask (Sky blue: 337.5–22.5 and 157.5–202.5 degrees, Red: 22.5–67.5 and 202.5–247.5 degrees, Blue: 67.5–112.5 and 247.5–292.5 degrees, Green: 112.5–157.5 and 292.5–337.5 degrees). (**3**) Non-maximum suppression. (**4**) Hysteresis Thresholding step detection enhances weak edges near strong edges to form consistent edges by distinguishing strong and weak edges. to infer objects, and employs a novel spatial-temporal 3D neural network to enhance the features of object detection [18]. There have been studies that contribute to the extraction, reconstruction, and evaluation of vehicle trajectories based on the cooperative perception of connected autonomous vehicles (CAV) by presenting data acquisition and analytics platforms utilizing not only cameras but also LiDAR sensors, GPS, and communication systems in autonomous driving systems [19]. The Kalman filter is a commonly employed technique for tracking lanes in successive frames after lane detection in an initial frame. It utilizes the newly obtained lane information in the current frame to correct and update the accumulated lane information from the previous frames. By leveraging this information, the Kalman filter estimates the position of the lane in the next frame [20]. Another approach for lane tracking is to utilize a particle filter [21]. This method employs a set of particles to represent the possible positions and characteristics of the continuous lanes. By iteratively updating and resampling the particles based on the observed lane information, the particle filter can track the lane across multiple frames. Furthermore, there exists a fusion method that combines both the Kalman filter and the particle filter for lane tracking [22]. This hybrid approach leverages the strengths of both filters to improve the accuracy and robustness of the lane tracking process. Kalman filters can be utilized not only for lane tracking in video but also for estimating errors in a reduced inertial navigation system (R-INS) by incorporating information from an inertial measurement unit (IMU). The IMU provides acceleration and angular rate measurements, which can be used to estimate velocity and heading errors in the R-INS. By combining these IMU measurements with velocity error measurements between R-INS and the global navigation satellite system (GNSS), a velocity-based Kalman filter can accurately estimate the errors in R-INS, including velocity and heading. This integrated approach, leveraging both visual data and GNSS measurements, enhances the accuracy of position and heading estimation in autonomous driving systems. Therefore, Kalman filters not only facilitate lane tracking but also enable the estimation of velocity and heading errors, improving the overall navigation and perception capabilities in autonomous driving systems [23; 24; 25].
###### Abstract  
Microencapsulation allows for the entrapment, protection, and delivery of sensitive and/or active desired nutrients and ingredients as well as biologically-active agents. The microencapsulating properties of wall solutions (WS) containing 2.5-10% (\(w/w\)) wheat proteins isolate (WHPI) and 17.5-10% (\(w/w\)) lactose were investigated. Core-in-wall-emulsions (CIWEs) consisting of the WS and soy oil were prepared at a wall-to-core (W:C) ratio ranging from 25:75 to 75:25 (\(w/w\)). Microcapsules were prepared by spray-drying the CIWEs. The CIWEs had a mean particle diameter smaller than 0.5 \(\upmu\)m and surface excess that ranged from 1.59 to 5.32 mg/m\({}^{2}\). In all cases, microcapsules with smooth outer surfaces that exhibited only limited surface indentation were obtained. The core, in the form of protein-coated lipid droplets, was embedded throughout the wall matrices. In all but one case, core retention was higher than 83%, and in 50% of the cases, it was higher than 90%. Core retention was significantly influenced the composition of the WS and by W:C ratio (\(p<0.05\)). Except for two cases, microcapsules exhibited very limited core extractability. The microencapsulation efficiency was >90% and was influenced, to a certain degree, by the composition of the CIWEs. Results indicated the potential for utilizing wall systems consisting of WHPI and lactose as effective and highly functional microencapsulating agents in food and related applications.  
Keywords:wheat proteins isolate; microencapsulation; spray drying; lactose; core-in-wall-emulsions; surface oil; microstructure +
Footnote †: journal: Applied Physics Letters
## 3 Finite Element Simulation
### Finite Element Models  
This study examined a numerical VBI system, featuring a simply supported beam and a quarter-vehicle model, as depicted in Figure 4. The design specifications for both the vehicle and the bridge followed the models utilized in prior research [35]. The beam exhibited a uniform cross-section, with the vehicle initially moving at a steady velocity of 5 m/s. Later, the speed was adjusted to simulate different traffic scenarios. Tables 1 and 2 detail the respective physical parameters of the vehicle and the bridge.  
The VBI system was modeled using the commercial software ABAQUS 2020 [39], as illustrated in Figure 4, employing a three-dimensional approach. Both the bridge and the quarter-vehicle were represented with solid elements. The quarter-vehicle model comprises a sprung mass and a wheel, both of which were also modeled using solid elements. These components are linked by a spring element. The wheel is shaped as a cylinder, matching  
\begin{table}
\begin{tabular}{c c} \hline \hline Sprung mass (kg) & 1200 \\ \hline Spring stiffness (N/m) & 500,000 \\ \hline Bouncing frequency (Hz) & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the vehicle.  
Figure 4: Finite element model of the simple VBI system.  
\begin{table}
\begin{tabular}{c c} \hline \hline Mass per meter (kg/m) & 4800 \\ \hline EI (N/m\({}^{2}\)) & \(3.33\times 10^{9}\) \\ \hline First bending frequency (Hz) & 2.08 \\ \hline Second bending frequency (Hz) & 8.33 \\ \hline Length (m) & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of the beam.  
the width of the beam's cross-section. This study excluded road roughness from its considerations, given its minimal impact on the bridge responses [40]. The interaction between the wheel and the beam surfaces was captured using the penalty method. The dynamic responses of the system were computed using the Hilber-Hughes-Taylor (HHT) algorithm with a 0.01 s time step. The accuracy of this simulation methodology was confirmed in prior studies. Further details on the simulation process can be referred to in the previous research [41].
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Unsupervised Method
#### 3.2.2 Generative Adversarial Network  
A generative adversarial network consists of two participants: generator and discriminator. The generator is used to obtain the distribution of sample data, and the discriminator is used to estimate the probability of sample training data. The ultimate goal of this model is to learn the inherent laws of real data, predict and estimate the distribution or density of real data and generate new data according to the learned knowledge, that is, generating adversarial network manufactures data.  
Literature [64] proposed a method for fabric defect detection using DCGAN. First, the GAN discriminator is used to generate the defect distribution likelihood map; then, the encoder is introduced into the standard DCGAN to reconstruct the detected image, which is subtracted from the original image to obtain the residual image highlighting the potential defect area; later, the combined residual map and the defect distribution likelihood map are combined to obtain an enhanced fusion map, and the accurate position of the defect is finally obtained by threshold segmentation on the fusion map. Literature [65] proposed a GAN-based one-class classification method for strip steel surface defects detection, where the generator G adopts codec, and the features of the hidden space obtained by encoding (the penultimate layer output of the GAN generator) are input into the SVM for defect classification, the model has achieved good test results on the images provided by Handan Iron and Steel Plant. Literature [66] proposes a detection method based on GAN. In the first stage, a novel area is detected using a generative network and a statistical-based representation learning mechanism. In the second stage, the Frechet distance is directly used in the latent space to distinguish defects from normal samples. The method achieves an accuracy of 93.75% on the solar panel data set. Literature [67] designed a GAN-based surface vision detection framework, which uses a multi-scale fusion strategy to fuse the responses of the three convolutional layers of the GAN discriminator and then uses OTSU to segment the fusion feature response map to further segment the defect location. Experiments on wood and road crack datasets have proved the effectiveness of the framework. In order to detect various defects on the fabric surface, literature [68] proposed a model based on the GAN framework. The model first uses a variety of textures to fuse to a specific location, and then, the existing fabric defect data set was continuously updated through multi-level GAN; thus, the network model is continuously fine-tuned to achieve better detection effect.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 1 Project Overview Inform@Risk
### Project Area  
In Medellin, the second largest Colombian city, landslides are the most common and deadly natural hazard [15]. Compared to the rest of the larger region (Valle de Aburra), Medellin has by far the highest number of landslide reports [16]. Especially the steep slopes in the east of the city are prone to landslides [17; 18]. The four deadliest landslides in Medellin in the last 100 years took place in this area, where at the same time informal settlements have shown some of the highest growth rates in the city. This lead to a continuously increasing landslide risk and heavy losses of life in the past 100 years. Based on a 2012 study, more than 200,000 inhabitants in the Aburra Valley are subject to medium or high landslide risk and live in precarious settlements [17].  
This is also where the barrio \"Bello Oriente\" (Communa 3) is located, which was selected as a test site for the early warning system of the Inform@Risk project. The informal settlement lies on the urban-rural border of the city and was chosen based on a preliminary qualitative risk assessment at city scale as well as additional social factors (security, community management etc.).