esa-sceva/satcom-chunk-collection huggingface dataset:
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 5 Conclusions  
Fog-base MANET is a special field of an Ad hoc network, with the internal architecture continuously extending in different directions based on instrumentation and a private network as basic components. Therefore, its own routing technology may be different from that of the general Ad  
Figure 5: (**a**) Energy Consumption Comparisons, (**b**) throughput comparisons, (**c**) BPUE comparisons.  
hoc network. The fog-based MANET also produces higher security requirements, in addition to the technical features of highly heterogeneous, mobile characteristics. Therefore, fog-based MANET has a higher standard than a traditional Ad hoc network in terms of the security architecture, network security technology, secure routing protocol, privacy protection, security management, and guarantee measures. In this paper, it is proposed that the SAL-SAODV protocol is combined with fog-based MANET application scenarios based on SAODV protocol. By using a cyclic redundancy check instead of a digital signature to reduce the SAODV's complexity and improve the energy efficiency of the protocol, the tamper-proof of packets is guaranteed by a random delayed transmitting mechanism. The simulation results show that the energy consumption is about 35% lower and BPUE is about 60% higher in the SAL-SAODV protocol than the SAODV protocol. At the same time, the schemes of increasing the energy efficiency and information tamper-proof could be used in the improvement of other AODV protocols. It is worth noting that the end-to-end latency will increase due to the random delayed transmitting mechanism. Hence, the SAL-SAODV protocol is suitable for the scenarios, which are delay-insensitive, but have a high security requirement.
## 3 Methodology  
This section outlines the methodological framework employed to systematically evaluate the efficacy of phishing threat detection techniques in an organizational setting. A mixed-method research approach was used, combining quantitative and qualitative methods to provide comprehensive and triangulated insights into phishing detection capabilities and educational outcomes.  
The study's participants were selected from the organization's employees, with a specific focus on individuals who had access to organizational email accounts and essential data resources. A purposive sampling strategy was employed to ensure participant diversity in terms of professional roles, technical proficiency, and prior experiences with phishing incidents. The sample consisted of 237 participants, offering a representative cohort for strong analytical generalization.  
To accurately assess the effectiveness of phishing detection techniques, controlled simulation environments were carefully created to mimic realistic operational scenarios typically encountered in organizational contexts. This simulated environment precisely replicated the organization's genuine digital infrastructure. In this controlled environment, various phishing attack scenarios were methodically conducted, encompassing fraudulent emails with malicious hyperlinks, harmful file attachments, and advanced social engineering techniques. Each scenario was deliberately designed to represent authentic cybersecurity threats faced in actual corporate settings.  
An integrated e-learning platform was implemented to complement the threat detection assessment, providing interactive educational modules that enhance employees' awareness and response skills regarding phishing attacks. The organized curriculum included theoretical instruction, practical exercises, and evaluative quizzes to assess and reinforce ongoing knowledge. During this study, participants maintained continuous access to the e-learning resource, with comprehensive metrics on engagement, progression through content modules, and assessment results systematically recorded and analyzed.
## 2 Materials and Methods
### Surface Characterization  
SEM is an efficient technique for the investigation of organic and inorganic materials from the scale of a nanometer (nm) to a micrometer (\(\upmu\)m). EDX produces qualitative and semi-quantitative data in conjunction with SEM [28]. Raman spectroscopy is an effective technique for determining the characteristics of carbonaceous structures, such as the phases, functions, and defects [29]. A potent nondestructive method for describing crystalline materials is XRD, which offers details on the phases, preferred crystal orientations (texture), and other structural characteristics, such as the average grain size, strain, crystallinity, and crystal defects [30]. AFM offers high-resolution images with surface height data that can be used to obtain three-dimensional sample morphology information. Thus, the carbon-based structures deposited on the substrates were analyzed with reference to the related analyses. The morphology of the coated sample surfaces was analyzed via SEM, and XRD was utilized to procure comprehensive details about the coated surface's chemical composition, physical characteristics, and crystallographic structure. Raman spectroscopy was used to analyze the intramolecular and chemical bonding of the coating. In order to determine the elemental and chemical composition of the coated surface, EDX analysis was carried  
Figure 1: Schematic of the ECR-CVD process.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Sample** \\ **ID** \\ \end{tabular} } & \multirow{2}{*}{**Process**} & **Process** & **N\({}_{2}\) Flow** & **CH\({}_{4}\)** & **Plasma** & **Bias** & **Rotation** & **Process** & **Coating** \\  & & **Pressure** & **Rate** & **Flow Rate** & **Current** & **Voltage** & **Rate** & **Duration** & **Thickness** \\  & & **(Torr)** & **(sccm)** & **(sccm)** & **(mA)** & **(V)** & **(\({}^{\circ}\)/min)** & **(min)** & **(\(\upmu\)m)** \\ \hline US & Etching & 7.0 \(\pm\) 0.5 \(\times\) 10\({}^{-3}\) & 11.28 \(\pm\) 1.0 & - & 26 & \(-\)580 \(\pm\) 30 & 5 & 60 \(\pm\) 2 & - \\ SCN & Coating & 5.1 \(\pm\) 0.2 \(\times\) 10\({}^{-3}\) & 11.00 \(\pm\) 1.0 & 6.82 & 26 & \(-\)580 \(\pm\) 20 & 5 & 60 \(\pm\) 2 & 2.72 \(\pm\) 0.2 \\ SC & Coating & 5.2 \(\pm\) 0.2 \(\times\) 10\({}^{-3}\) & - & 6.74 & 26 & \(-\)580 \(\pm\) 20 & 5 & 60 \(\pm\) 2 & 3.55 \(\pm\) 0.2 \\ \hline \hline \end{tabular}  
* US: uncoated sample; SC: sample coated with only carbon; SCN: sample coated with carbon in the presence of nitrogen.  
\end{table}
Table 1: Etching and coating process parameters.  
out. The surface topography was analyzed via AFM and the profilometer. The technical specifications of the equipment for the aforementioned analyses are tabulated in Table 2.
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
## 2 Study Area and Methods
### Parameters and Evaluation Metrics  
**Parameters** During the training process, the parameters of the proposed MaRNN and baseline methods are initialized using the same strategy. Specifically, we utilize the truncated normal distribution method to initialize the parameters of the above model and optimize the parameters using the Adaptive Momentum Estimation (Adam). The learning rate of Adam is set to 0.0001, and the number of iterations of the model is adjusted based on the set [500, 1000, 1500, 2000, 2500]. The LSTM in each model is set to the same number of hidden units and determined by performing a grid search method on sets [15, 20, 25, 30, 35, 40]. The above models all use RMSE as the loss function, and the optimal parameters of the model are determined by minimizing the value of the loss function.  
**Evaluation metrics** We use Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) to evaluate MaRNN and baseline methods. If the value of RMSE or MAPE is closer to 0, the model's prediction results will be superior. The calculation of MAE and MAPE is shown in Formula (8):  
\[\begin{array}{l}\mathrm{MAE}=\frac{1}{\tau}\sum\limits_{t=1}^{\tau}\left( \frac{1}{N}\sum\limits_{i=1}^{N}\left|\hat{y}_{t}^{i}-y_{t}^{i}\right|\right)\\ \mathrm{MAPE}=\frac{1}{\tau}\sum\limits_{t=1}^{\tau}\left(\frac{1}{N}\sum \limits_{i=1}^{N}\left|\frac{\hat{y}_{t}^{i}-\hat{y}_{t}^{i}}{y_{t}^{i}}\right| \right)\end{array} \tag{8}\]where \(\mathbf{\tau}\) is the size of forecasting horizon, which is the length of the output sequence of the model. \(N\) is the number of samples. \(y_{t}^{i}\) (\(1\leq i\leq N\), \(1\leq t\leq\tau\)) is the ground true of sample \(i\) at time \(t\), \(\hat{y}_{t}^{i}\) is the corresponding predicted value.
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method
### Structure-Emphasizing Rendering
#### 3.3.2. Shading and Blending
Combining color with shading is immediately done by multiplying the luminance values produced by the profile textures by the color values computed by mapping the trail directions at every sample point \(\mathbf{x}_{j}\). Directly splatting trail sets however creates artifacts where the borders of the circular splats overlap--see Figure 9a and its insets. A better solution is to use alpha-blending to render the splats. This would require depth-sorting all splats every time the viewpoint changes. For a dataset of hundreds of thousands of trails, each rendered with hundreds of splats, this is not (easily) done at interactive frame rates. Solutions such as Order-Independent Transparency (OIT) [57; 58] do not require depth-sorting the splats and are as such faster. However, OIT works well typically for a few overlapping surfaces with high transparency. Our datasets, in contrast, contain hundreds of  
Figure 8: Conical shading with (\(\mathbf{a}\)) fixed light direction and (\(\mathbf{b}\)) lighting orthogonal to local fiber tangent. The latter method (\(\mathbf{b}\)) shows more shading contrast than the former (\(\mathbf{a}\)). See Section 3.3.2.  
overlapping fibers at a single location. More importantly, we do not need high-transparency rendering, but rather low-transparency, sufficient to allow the few overlapping splats in the 'topmost' layer, i.e., closest to the viewpoint, to blend. We achieve this by a simple two-pass rendering technique: First, we render the entire trail set \(T\) using no shading but with \(Z\) buffering on. This delivers a depth buffer \(\mathbf{z}\) recording the \(z\) value of the closest splat to the viewplane at every pixel. In the second pass, we visit (again) all splats, but only render those whose depths, i.e., the \(z\) components of the sample points \(\mathbf{x}_{j}\) where they are centered, are not larger than the \(\mathbf{z}\) value at that pixel plus a small offset \(\delta=0.005\). This effectively renders only those splats that are within a 'peel', or thin layer, of \(\delta\) units from the closest one to the viewer. Since there are only a few splats per pixel within this layer--typically under 10--we can use standard blending with a transparency value of \(\alpha=0.1\). Figure 9b shows the same dataset as in Figure 9a rendered with peel blending. As visible, peel blending removes the shading artifacts we had seen earlier.
## 5 Online Web Service  
The presented calibration method is implemented as an online web tool ([[https://gnss-antcal.city.tudelft.nl](https://gnss-antcal.city.tudelft.nl)]([https://gnss-antcal.city.tudelft.nl](https://gnss-antcal.city.tudelft.nl)), accessed on 9 February 2022). It calculates the baseline between the base and rover antenna and provides the estimated PCVs of the rover antenna for GPS \(L_{1}\) and \(L_{2}\). An antenna calibration can therefore be directly performed by the user in the field.  
To calibrate a rover antenna with the online calibration tool, a simple base-rover setup is required. The base station antenna must be an already calibrated antenna with available calibration patterns in the current type mean IGS ANTEX file or in an external ANTEX file. For the best results, both antennas are placed over a very short baseline and the rover antenna is installed at the location as it is intended to stay after the calibration. In any case, both antennas should be installed in a preferably multipath-free environment with no or only small height differences between them. The tool in its current state only accepts RINEX3 files that cover a maximum of one full day. The user is advised to format the input data accordingly to avoid rejection of the input data by the software. On the website, an explanation page highlighting the requirements is provided. The basic specifications and features of the tool are illustrated in Figure 4.  
The user can provide the base antenna name separately or specify it in the RINEX header. If no custom ANTEX file is supplied, the currently available IGS ANTEX I14.ATX  
Figure 4: Overview of the web tool requirements and functions.  
Figure 3: The 2D variogram maps of the residuals for the antennas TRM5 (**left**), AV 28 circular ground plane (**middle**), and AV28 rectangular adaptor (**right**), before (**top**) and after (**bottom**) removing the elevation pattern.  
is used. The name of the base antenna must be in the ANTEX file. The user is also required to supply the rover RINEX header with an antenna name. The recommended maximum sampling frequency should not exceed 1 Hz. The user is encouraged to upload GPS-only observations with \(L_{1}\) and \(L_{2}\) observations as these are the only frequencies that are currently supported by the tool. However, multi-GNSS observations can also be uploaded but will not be utilized by the current processing scheme. The program will output the computed baseline between base and rover antenna in local coordinates (North, East, and Up), as well as an ANTEX entry containing the PCVs. To be consistent with the ANTEX notation, an averaging of \(5^{\circ}\) is chosen. If the height difference between the ARP of the base antenna and the ARP of the rover antenna is provided by the user (mm level), the estimated median height offset will be reported as PCO in the ANTEX entry on both supported frequencies.
## 2 Materials and Methods
### LiDAR and Digital Terrain Model (DTM) Data  
An objective of the 3 DEP LiDAR project is to obtain full national coverage with a specific quality level defined by The National Enhanced Elevation Assessment (NEEA) with less than eight years between each cycle (Table A1) [22]. As of 2021, two sets of Indiana's statewide 3 DEP LiDAR data were made available for public access. The first Indiana statewide LiDAR dataset was produced during 2011-2013 with 1.0 or 1.5 m average post spacing. The post spacing is defined as the smallest distance between two points that can be explicitly represented in a gridded elevation dataset [27]. The second statewide LiDAR data in Indiana was acquired during 2017-2020. The average post spacing for this dataset has not been reported in detail, but IGIC (Indiana Geographic Information Council) reported that nominal pulse density is over 2.0 pulses/m\({}^{2}\)[28].  
For the two statewide LiDAR datasets in Indiana, the bare-earth hydro-flattened digital elevation model (DEM) was also publicly available and used as input data in this study. For brevity, we used the term digital terrain model (DTM) instead of bare-earth hydro-flattened DEM in the rest of the paper. The spatial resolution of DTM is 1.524 m for 2011-2013 LiDAR data and 0.762 m for 2017-2020 LiDAR data. The original 3 DEP LiDAR and DTM data are delivered in an individual 1524-by-1524-m tile (Indiana State Plane East/West coordinate system). It should be noted that the unit of length used for 3 DEP LiDAR and DTM was originally U.S. survey foot (1 ft = 0.3048 m), and the size of DTM tile corresponds to 1000-by-1000 pixels and 2000-by-2000 pixels for the first and second 3 DEP LiDAR project. Data size of LiDAR and DTM was 4.9 and 0.5 terabyte for 2011-2013 3 DEP data and 13.0 and 0.7 terabyte for 2017-2020 3 DEP data. A total of 42,539 and 48,602 tiled datasets were available for 2011-2013 and 2017-2020 3 DEP data.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
###### Abstract  
There is a strong correlation between the geometry of the weld pool surface and the degree of penetration in arc welding. To measure the geometry of the weld pool surface robustly, many structured light laser line based monitoring systems have been proposed in recent years. The geometry of the specular weld pool could be computed from the reflected laser lines based on different principles. The prerequisite of accurate computation of the weld pool surface is to segment the reflected laser lines robustly and efficiently. To find the most effective segmentation solutions for the images captured with different welding parameters, different image processing algorithms are combined to form eight approaches and these approaches are compared both qualitatively and quantitatively in this paper. In particular, the gradient detection filter, the difference method and the GLCM (grey level co-occurrence matrix) are used to remove the uneven background. The spline fitting enhancement method is used to remove the fuzziness. The slope difference distribution-based threshold selection method is used to segment the laser lines from the background. Both qualitative and quantitative experiments are conducted to evaluate the accuracy and the efficiency of the proposed approaches extensively.  
Image processing; segmentation; spline; grey level co-occurrence matrix; gradient detection; threshold selection Article
## 4 Experimental Results
### Result and Discussion
#### 4.4.4 Flow Channel Design  
Furthermore, the investigation was extended by using the flow channel designs, namely two serpentine, three serpentine, serpentine zigzag, straight parallel, and straight zigzag with machine learning models. Here, the datasets were considered based on the flow channel designs. All the humidification levels are considered with respect to flow channel designs.The tested results illustrated in the Figure 11 show the algorithm gives different results based on the nature of designs.
In a hierarchy-based routing algorithm, clusters are formed, and each cluster is assigned a head node (CH) using a certain threshold value _T(n)_. The clustering technique is also effective in minimizing network traffic to the base station and prolonging the lifespan of the network [20]. Cluster networks are of two types: (1) a homogeneous sensor network, in which each node is assigned an identical initial energy value, and (2) a heterogeneous sensor network, where the nodes differ in initial energy from each other [21; 22]. In a homogeneous network, cluster heads remain static after their selection [13]. Those cluster heads can remain operational for the entire life of the network. As the cluster heads perform data aggregation and then transmit that aggregated data to the remote long-distanced base station, they are constantly overloaded. Thus, they expire first and result the instability in the network [23]. In a heterogeneous network, instability in the network performance occurs after the first node death [24]. Tofix these issues and improve the network efficiency, lifetime and stability period, various routing algorithms have been proposed.
## 1 Introduction  
The rationale of interceptive treatment in mixed dentition is to create appropriate space for the alignment of the permanent teeth prior to complete eruption and to solve transverse discrepancies [1]. When crowding is limited to a few millimeters, normal growth could provide adequate space, but when the crowding exceeds this amount, orthodontic expansion could represent an effective procedure [1]. The expansion of the maxillary arch is differentiated into orthopedic or dentoalveolar types [2].  
One of the most common orthopedic procedures is the opening of the mid-palatal suture through Rapid Maximillary Expansion (RME) [3; 4; 5]. The orthopedic management of maxillary transverse deficiency was first reported in 1860 [1] by exerting expansion forces at the mid-palatal and intermaximillary sutures [2; 3]. Since then, the RME has been widely used and proven to be effective for the correction of maxillary posterior crossbites [4], transverse dental arch discrepancies [5], and deficient arch perimeter [1; 3; 4; 5; 6; 7].  
On the other hand, dentoalveolar expansion can be performed using several conventional fixed, removable appliances or aligners. When aligners are used, the force is applied directly to the teeth, producing a lateral displacement of the upper dento-alveolar structures. Clear aligners can be adjusted to expand the molars and anterior teeth differentially, and they have the ability to rotate molars [2]. Few authors have investigated the changesin the upper maxillary arch under the influence of Clear aligners in adults, and to our best knowledge, very few studies have analyzed the effects induced by Clear aligners in mixed dentition [8; 9].  
In 2021, [PERSON] et al. [8] evaluated the dentoalveolar changes in 20 patients treated with Clear aligners in mixed dentition. In this study, the superimpositions of pre- and post-treatment dental models revealed that Clear aligners could be a great alternative to Slow Maximillary Expansion in the case of mild crowding or limited transverse deficiency.  
A further study was published by [PERSON] et al. [9], in 2021. In this prospective investigation, transverse widths were measured in the upper arch on digital models obtained at the beginning and at the end of treatment performed by Clear aligners. The authors concluded that the Clear aligner can be considered in growing patients who require maxillary arch form changes, especially in the anterior and lateral regions.  
Therefore, the objective of this retrospective study was to analyze the dento-alveolar effects induced by two treatment appliances (i.e., RME and Clear aligners) in growing subjects presenting with early mixed dentition and mild maxillary deficiency.
## 4 Propulsion System Design and Selection
### Propulsion System Selection
#### 4.1.3 Selected off the Pulsed Plasma Thruster  
The selected thruster for integration into the CubeSat is the Mars-Space PPTCUP [62]. The thruster has the following performance specification (Table 26):  
This system is also able to perform a graveyard orbit up to 40 km altitude. The selected system can provide a higher \(\Delta V\) than the calculated required amount and is therefore suitable for integration into this CubeSat system. The authors therefore assume a suitable propulsion system has been selected within the scope of this report; however further research could be conducted into the mass of Teflon propellant required, which is outside the scope of this paper.