esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applying Predictive Models to an eLearning Messaging System
### The Implementation Phase
#### 3.2.2 The Dashboard  
The prediction formula/algorithm from the prediction model could reside with the dashboard, which would interact with the current database, calculate predictions, and communicate them to the student. Figure 3 illustrates. The dashboard queries the current database to obtain values of the input variables for each student. It calculates predictions and then messages that information to each student. Alternatively, the predictions could be calculated and stored in the current database and the dashboard would be a conduit for relaying those predictions to students.  
The type of information to be displayed necessarily influences the design of the dashboard. Figure 3, for example, illustrates a rudimentary situation in which one message is sent to each student. Ideally, multiple contacts will be made with each student throughout the term. Figure 4 illustrates a  
Figure 3: The implementation phase.  
repeated-messaging system in which a student receives ongoing estimates of the probability of completing a certification course at six critical junctures throughout the course: in each of the first three discussions and in subsequent major assignments. A longitudinal display such as this might encourage students to understand more clearly how their efforts can impact final outcomes. The final probability is absent from Figure 4 because the student has not yet completed the final assignment; _i.e._, the dashboard display in Figure 4 is reactive. A more complex design would be both reactive and prospective. Figure 5, for example, plots changes in probabilities after each activity has been completed, but also estimates a range of outcomes on an upcoming assignment. Dashboard displays such as those in Figures 4 and 5 could mean multiple prediction formulas, one for each of the different assignments in the course; _i.e._, dashboard complexity impacts project complexity.  
The system as described here begins with the historical database and progresses to the eventual dashboard displays. In reality, the _design_ of the system must work backwards; _i.e._, the information that is intended to be provided to students dictates the dashboard design, which impacts the variables needed in the current and historical databases.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Model Agnostic Meta-Learning  
In the context of video anomaly detection, each task \(T_{\text{i}}\) corresponds to a different set of video frames, representing a unique scenario or environment. The collection of all such tasks is denoted by \(T=\{T_{1}\), \(T_{2}\),...,\(T_{n}\}\). For each task \(T_{\text{i}}\), there exists a dedicated dataset \(D_{\text{i}}=\{(x_{\text{j}},y_{\text{j}})\}\text{mi for j}=1\), where \(x_{\text{j}}\) is the input and \(y_{\text{j}}\) is the corresponding level. The model used for anomaly detection is parameterized by \(\theta\), which represents the set of all learnable parameters. During meta-training, the goal is to find an optimal initialization of \(\theta\) such that, after a few gradient updates using data from any task \(T_{\text{i}}\), the model quickly adapts and performs well.
###### Abstract  
This study investigated the optimum NaOH concentration treatment for rubber crumbs that improves adhesion between the polymer matrix and rubber filler in rubber polyester composites. The composite was prepared by mixing rubber crumbs from waste rubber gloves with unsaturated polyester matrix. Rubber crumbs were cryogenically ground from waste gloves and treated with 1%, 4%, 7%, and 10% NaOH (by volume). Treatment with 7% and 10% NaOH provides better wettability and hydrophilicity for rubber as it decreases the surface contact angle by approximately 27%. Higher concentration of NaOH intensively etched the rubber and made the surface rougher with more microcracks, providing a larger surface area for greater polyester coverage and holding the rubber firmly. It also induced more functional groups that increased the rubber surface energy and removed the hydrophobic layer on the rubber. These factors strengthened the interfacial rubber-polyester adhesion, as shown by the SEM micrograph of the tensile fracture which the rubber crumbs adhere well to the polyester matrix. The FTIR analysis of rubber treated with higher NaOH concentration showed a higher peak intensity, which demonstrated more polar groups were generated on the rubber surface. More polar groups created further connections to the polar groups in the polyester matrix, thereby enhancing adhesion between the rubber filler and the matrix.  
**Keywords: rubber crumbs; waste gloves; sodium hydroxide treatment; surface modification; surface analysis; wettability; interfacial adhesion; recycle rubber**
# Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking
###### Abstract
In this paper, we propose a cross-modal image registration algorithm to solve the problem of pixel coordinate misalignment between visible-light and infrared images. The proposed model contains four modules. The first is a Feature Extraction Module (FEM) that uses the VGG convolutional neural network [28] to encode feature pyramids for a reference image (visible light) and a moving image (infrared). The second module is the Registration Parameter Prediction Module (RPPM), which predicts a homography matrix for each raster in all layers of the feature pyramids and outputs a homography matrix group for both images in order to fuse multimodal images and enhance the representation of high-resolution images. The third module is the Image Transformation Module (ITM), which transforms the homography matrix groups into a kind of transform image representation via sampling point transformation and bilinear sampling for image alignment. The last module is the Similarity Measurement Module (SMM); it first pre-trains two encoders to generate the similarity representation of images, then uses the similarity scores as the cost function for training an image registration model. We conducted experiments on the MS-COCO and FLIR datasets, and the experimental results show that our method achieves state-of-the-art performance on most metrics. Our main contributions can be summarized as follows:  
* We propose a cross-modal image registration method that solves the problem of pixel coordinate misalignment between visible-light and infrared images. * We propose a deep learning-based multi-modality and multi-scale feature transformation parameter prediction method. * We introduce a contrastive learning-based cross-modal similarity measurement model for semi-supervised image alignment.
## 2 Overview of SDN Architecture
### Flow Table of SDN Switches
The software switch is designed based on the general-purpose central processing unit (CPU). In contrast, commodity switch is based on application-specific integrated circuit (ASIC), which is purposely designed for high-speed lookup [31]. In addition, software switches stored forwarding rules in the conventional random-access memory (RAM), which has a relatively lower cost with high storage capacity. OpenFlow switches store forwarding rules in TCAM, which is designed for matching flexibility and high lookup performance. Moreover, as explained by [32], installing forwarding rules in software switch instead of ASIC reduces the OpenFlow switch performance from 940 to 14 Mpbs. To further speedup switching operation in software switches, it is possible to store a flow table in CPU caches. However, these caches inherit similar shortcomings of storage limitation, which was introduced in ASIC [32]. Consequently, the shortage introduces a lot of problems, which include but is not limited to extra packet processing delay, update operation, and signaling overhead. Therefore, the switch flowtable limitation and frequent flow setup request to controller present great concern, which further needs to be investigated, especially in a large scales network environment such as data center and IoT. Section 4 extensively surveys the existing solutions proposed in the literature to cope with the limitations of the switch flow table memory.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 5 System Overview  
The proposed system is designed to continuously and comprehensively evaluate the quality of service provided by a regular road passenger transport operator. This evaluation is conducted using metrics commonly used by operators and transport agencies, measuring punctuality--e.g., with the AD and RTV parameters--or measuring regularity--e.g., with the HV or EWT parameters. Continuous evaluation of quality of service means that it may be performed at any given time, thus permitting its evolution to be analysed in different calendar periods (working days, holidays, weekends, time of year, etc.). Comprehensive evaluation means that the metrics can be obtained with different levels of granularity, i.e., for a set of routes, for specific routes, or for a set of individual stops.  
The system primarily uses four resources that are usually employed in regular public passenger transport by road: GPS vehicle tracking system, the computing and storage systems used in vehiclesto record and monitor the activities carried out on board, the communications system used to transfer data between the head office and the vehicles, and the transport database (TDB). The system consists of two modules: the AVL Module (AVLM) deployed on the vehicles and the Service Quality Control Module (SQCM) deployed at the operator's or transport agency's data processing centre. The AVLM continuously records and automatically transfers the vehicle's position. The SQCM evaluates the quality of service by calculating the punctuality and regularity metrics for the planned services. Figure 2 provides an overview of the system.
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 4 Numerical Simulation and Results
### Impact of Oscillatory Flow on Vortex-Induced Vibration of Deep-Sea Mining Pipes  
This section will study the vortex-induced vibration characteristics of deep-sea mining pipe under the action of oscillating flow and reveal the vortex-induced vibration characteristics of deep-sea mining pipe under the special constraint condition that the upper end is hinged and the lower end is free (z-direction constraint) through numerical simulation.  
Four oscillating flow frequencies, f = 0.1f1, f = 0.2f1, f = 0.3f1, and f = 0.4f1, were selected for the oscillating flow conditions selected in the numerical simulation in this section, as shown in Figure 9 for details.  
Figure 8: Envelope diagram of instantaneous dimensionless displacement in the cross-flow direction of the riser.  
2.1 Analysis of Vibration Displacement Trajectory Characteristics of Mining Pipe by Oscillation Frequency  
The displacement trajectory of the mining pipe under different oscillating flow frequencies was drawn. The movement trajectory at the centroid of the mining pipe at 13 places was intercepted from top to bottom, and the drawing software Origin2020 was used to draw the following figure.  
The change in vibration displacement at different positions of the deep-sea mining pipe with the oscillation frequency is analyzed by observing Figure 10. It is found that the moving distance of the mining pipe increases continuously along the current, which accords with the feature of free bottom end of the mining pipe. Initially moving in the negative direction of the x-axis, the riser soon reverses its course to the positive x-axis due to the phenomenon known as \"Hysteresis\". The maximum vibrational displacement of the riser in the cross-flow direction exhibits a unique pattern: it in-creases from the top downwards, diminishes, and then rises again around 0.3 z/L. This pattern is likely induced by the second-order vibration mode of the mining pipe in the cross-flow direction. It is found that the instantaneous amplitude in the downstream direction is much larger than the instantaneous amplitude in the transverse direction, and the vibration track of the mining pipe has no fixed shape when the transverse direction displacement increases gradually. With the increase in down-flow displacement, the instantaneous amplitude of mining pipe in the down-flow direction gradually decreases and gradually changes into down-flow instantaneous amplitude less than cross-flow instantaneous amplitude. When the forward displacement reaches the maximum value, the mining pipe appears to have an \"8\" shape vibration track, which is similar to that when the oscillating flow acts on the traditional riser, because the two have the same physical appearance and belong to the category of cylindrical flow.
## 4 Efficiency and Fairness Signal Optimization Model
### Optimization Model Construction  
Considering the idea of feasibility analysis in Section 4.1, in order to realize the organic combination of fairness and efficiency, this paper proposes to take the conversion rate as the objective function and at the same time take the degree of delay sacrifice as the constraint.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi
### Pigments  
Carotenoids are well known pigments that are also produced as a response to stressful salinity conditions by halophilic microorganisms such as bacteria, archaea, algae, and yeasts, but in the case of halophilic fungi, they are not common and were only found in the species _Fusarium_ sp. T-1 [99]. Strains were isolated from seawater collected from Japan and were grown with an artificial seawater as medium while the highest concentration of carotenoids was detected at 1/10 of an artificial seawater making this species rather halotolerant than halophilic. Found carotenoids were identified as \(\beta\)-carotene, \(\gamma\)-carotene, torulene, neurosporaxanthin, and neurosporaxanthin \(\beta\)-D-glucopyranoside [100].  
Melanin, on the other hand, is the pigment that is widespread in halophilic fungi and has not been found in other halophiles. Melanins are high molecular mass pigments that typically are dark brown to black but could also have other colors. They are insoluble in aqueous and organic solvents because they are negatively charged and hydrophobic [101]. They are found in all organisms--microorganisms, plants, and animals. In fungi, melanins are present in cell walls as a distinct layer or extracellular as polymers formed by enzymes or autooxidation in the medium [37]. Most of fungal melanins from _Ascomycetes_ and _Deuteromycetes_ are termed DHN-melanins derived from 1.8-dihydroxynaphthalene (DHN) [37; 102]. Those pigments help microorganisms to withstand extreme environmental factors such as a high UV radiation level, extreme temperatures, and osmotic stress [102].  
As stated above, the halophilic filamentous fungi are mainly capable of synthesizing melanins; however, the species _Periconia_ sp. has also been found to produce a rare blue pigment [103]. On the other hand, the quinone compounds (variecolorquinones), which showed a yellow color, were obtained from the halotolerant fungus strain _Aspergillus variecolor_[104].
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 1 Introduction
This method effectively suppresses noise while minimizing damage to the effective signal. Specifically, a lightweight convolutional attention module is introduced in the generator to better extract signal features. When evaluating the denoising results, instead of directly comparing the denoised output with the target at the pixel level, our method incorporates adversarial loss, perceptual loss, and gradient loss from the discriminator to comprehensively assess the denoising results, making the denoised output more closely match the feature distribution of the real signal. Compared to other deep learning-based methods, DeepDenoiser and ARDU, the proposed method obtains better denoised results, especially for low SNR signals, and causes less damage to the true signal.
## 3 Results
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**HuG**} & \multicolumn{3}{c}{**CoG**} & \multicolumn{3}{c}{**HuG vs. CoG**} \\ \hline  & \multicolumn{1}{c}{**T0**} & \multicolumn{1}{c}{**T1**} & \multicolumn{1}{c}{**T0**} & \multicolumn{1}{c}{**T1**} & \multicolumn{1}{c}{**_p-Value_**} & \multicolumn{1}{c}{**_p-Value_**} & \multicolumn{1}{c}{**_p-Value_**} \\  & \multicolumn{1}{c}{**(Mean \(\pm\) SD)**} & \multicolumn{1}{c}{**(Mean \(\pm\) SD)**} & \multicolumn{1}{c}{**\(p\)-Value**} & \multicolumn{1}{c}{**(Mean \(\pm\) SD)**} & \multicolumn{1}{c}{**(Mean \(\pm\) SD)**} & \multicolumn{1}{c}{**_p-Value_**} & \multicolumn{1}{c}{**_p-Value_**} \\ \hline
**TIS** & \(5.14\pm 1.66\) & \(11.71\pm 1.82\) & **0.001** & \(4.95\pm 1.22\) & \(9.75\pm 2.14\) & **0.002** & **0.013** \\ \hline
**BBS** & \(4.71\pm 3.43\) & \(39.07\pm 8.97\) & **0.001** & \(4.38\pm 3.32\) & \(38.58\pm 10.41\) & **0.002** & 0.595 \\ \hline
**SPPB** & \(1.50\pm 2.77\) & \(5.86\pm 3.06\) & **0.001** & \(1.58\pm 1.23\) & \(3.92\pm 1.78\) & **0.003** & 0.212 \\ \hline
**MI-LL AAF** & \(51.57\pm 29.94\) & \(79.64\pm 28.02\) & **0.001** & \(40.58\pm 10.49\) & \(83.83\pm 9.19\) & **0.002** & **0.046** \\ \hline
**MI-LL nAFF** & \(79.21\pm 27.53\) & \(90.21\pm 13.61\) & **0.026** & \(75.50\pm 18.96\) & \(85.83\pm 12.43\) & **0.006** & 0.118 \\ \hline
**LCF** & \(5.19\pm 0.91\) & \(7.07\pm 0.83\) & **0.001** & \(4.92\pm 0.29\) & \(6.42\pm 0.67\) & **0.002** & 0.462 \\ \hline
**DRS** & \(16.36\pm 1.28\) & \(5.57\pm 3.63\) & **0.001** & \(16.92\pm 1.13\) & \(6.75\pm 3.39\) & **0.003** & 0.940 \\ \hline
**mBI** & \(12.14\pm 4.26\) & \(63.57\pm 19.26\) & **0.001** & \(14.58\pm 15.44\) & \(41.42\pm 7.23\) & **0.004** & **0.001** \\ \hline
**EQSD\_VAS** & \(21.79\pm 9.53\) & \(64.64\pm 11.34\) & **0.001** & \(29.17\pm 11.45\) & \(58.75\pm 10.03\) & **0.002** & **0.011** \\ \hline
**MFIS\_PHY** & \(25.00\pm 5.10\) & \(15.29\pm 6.37\) & **0.003** & \(21.08\pm 2.71\) & \(19.92\pm 6.69\) & 0.253 & **0.002** \\ \hline
**MFIS\_COG** & \(12.86\pm 4.83\) & \(5.36\pm 4.78\) & **0.001** & \(13.42\pm 3.32\) & \(11.00\pm 8.14\) & 0.304 & 0.118 \\ \hline
**MFIS\_PSY** & \(4.57\pm 1.78\) & \(2.57\pm 1.74\) & **0.006** & \(5.58\pm 1.83\) & \(2.58\pm 1.68\) & 0.719 & 0.193 \\ \hline
**MFIS\_TOT** & \(42.43\pm 10.10\) & \(23.21\pm 11.26\) & **0.002** & \(40.08\pm 6.36\) & \(33.50\pm 13.51\) & 0.116 & **0.017** \\ \hline \hline \end{tabular}
## 3 Underwater Routing Protocols
### Localization-Based Routing Protocols
#### 3.1.1 Protocols Addressing Nodes' Mobility  
These protocols make use of the mobility of the sensor nodes in data routing. The mobility of the sensor nodes is either used as a parameter to be controlled or to help other nodes to select different forwarder nodes during data forwarding. These protocols are described in the lines to follow.  
VBF  
Vector-based forwarding (VBF) addresses the mobility issue of the underwater sensor nodes [15]. It predefines a virtual pipe from a source to a destination. Only nodes that are located within the pipe qualify for data forwarding. Every packet contains information about the position of the source, forwarder and destination. When a node receives a packet, it calculates its position. If the calculated position is such that it lies within the pipe, the position information is inserted by the node in the packet, and it sends the packet to the next forwarder. Otherwise, the packet is discarded. Since the protocol limits the number of forwarders in data transmission, it achieves energy efficiency. Furthermore, it is not necessary to know the state information of each node that makes the network easily scalable. However, nodes within the pipe die soon due to the frequent selection for data forwarding. The death of these nodes creates energy holes and, in turn, results in packet loss. In addition, there may be no forwarder at all within the pipe when the network is sparse.
# Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses
## 3 Numerical Validation of Methods
### Verification at Different Roughness Levels  
Recognition under different roughness classes is investigated for Class B and C road roughness considering Gaussian white noise with 5% root mean square value. Parameters are set according to Ref. [21]. The vehicle parameters are set to the following: \(m_{v}=1400\) kg, \(I_{v}=3526\) kg \(\cdot\) m\({}^{2}\), \(s=3.4\) m, \(a_{1}=0.485\), \(a_{2}=0.514\), \(v=10\) m/s, \(m_{1}=35\) kg, \(m_{2}=65\) kg, \(k_{s1}=21,284\) N/m, \(k_{s2}=28,835\) N/m, \(k_{t1}=92,727\) N/m, \(k_{t2}=107,592\) N/m, \(c_{s1}=2342\) N * s/m, \(c_{s2}=3795\) N * s/m, \(L=34\) m, Sampling frequency 1000 Hz.  
The spatial domain results of class B roughness identified by the proposed method are shown in Figure 4 and the relative percentage error of identification is 3.14%. PSD (power spectral density) reveals the distribution of surface roughness at different spatial frequencies. The PSD results of the power spectral density of the identified Class B road roughness are shown in Figure 5, where the identification is basically accurate, but with a slight undulation in the high-frequency part affected by noise. Figure 6 illustrates the convergence of suspension stiffness identification under Class B roughness. The spatial domain results of class C roughness identified by the proposed method are shown in Figure 7 and the recognition error for class C roughness is 3.73%. The PSD results of the power spectral density of the identified Class B road roughness are shown in Figure 8. It shows that as the roughness class increases, the recognition accuracy is not greatly affected and the method can accurately identify road roughness. Figure 9 demonstrates the convergence of suspension stiffness damping under Class C roughness. In terms of identifying the vehicle parameters, as shown in Table 2, most of the identified parameters based on the extended Kalman filter with unknown input have an identification error of less than 3%. According to Equation (16), the identification of tire stiffness is dependent on the identification accuracy of the unknown force, with an error of around 1% and 3%. It can be seen that the method can accurately identify the stiffness, damping, and axle distance parameters of the moving vehicle.  
Figure 4: Road roughness identified in the spatial domain (Class B roughness).  
Figure 5: Identified road roughness PSD (Class B roughness).  
Figure 8: Identified road roughness PSD (Class C roughness).  
Figure 6: Convergence of some of the parameters (at Class B roughness). (**a**) Front suspension stiffness \(\text{ks}_{1}\); (**b**) rear suspension stiffness \(\text{ks}_{2}\).  
Figure 7: Road roughness identified in the spatial domain (Class C roughness).  
Figure 9: Convergence of some parameters (at Class C roughness). (**a**) Front suspension damping \(\text{cs}_{1}\); (**b**) rear suspension damping \(\text{cs}_{2}\).