esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Coping Retention Based on Various Surface Roughness  
Various sandblasting particles resulted in a variety of retention capabilities as shown in Table 1 below. Each variation was divided into several groups with specimens sandblasted by a 63 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\) particle called SB63 T200, a 102 \(\upmu\)m particle called SB102 T200, a 254 \(\upmu\)m particle size called SB254 T200, a 686 \(\upmu\)m particle called SB686 T200, and untreated (not sandblasted) specimens called NS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Sample** & **R\({}_{\mathbf{a}}\) (\(\upmu\)m)** & **Retention (N)** \\ \hline \multirow{6}{*}{Not Sandblasted (NS)} & Sample 1 & 0.04 & 153.82 \\  & Sample 2 & 0.04 & 129.28 \\ \cline{1-1}  & Sample 3 & 0.04 & 191.93 \\ \cline{1-1}  & Sample 4 & 0.04 & 177.05 \\ \cline{1-1}  & Sample 5 & 0.04 & 165.02 \\ \hline \multirow{6}{*}{Sandblasted 63 \(\upmu\)m} & Sample 1 & 0.35 & 135.36 \\ \cline{1-1}  & Sample 2 & 0.44 & 102.20 \\ \cline{1-1}  & Sample 3 & 0.49 & 209.68 \\ \cline{1-1}  & Sample 4 & 0.47 & 179.87 \\ \cline{1-1}  & Sample 5 & 0.44 & 168.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The retention force of each group in the sandblasting using various sizes of particles in the experiment.  
Figure 6: Tensile test for cement-retained dental implant with custom coping.  
Figure 7 shows the comparison of R\({}_{\rm a}\) among the groups based on the particle sizes used in the sandblasting. It shows that the average R\({}_{\rm a}\) from the highest to the lowest is shown by SB686 T200 with 0.67 \(\pm\) 0.04 \(\mu\)m, SB254 T200 with 0.58 \(\pm\) 0.04 \(\mu\)m, SB102 T200 with 0.56 \(\pm\) 0.03 \(\mu\)m, SB63 T200 with 0.44 \(\pm\) 0.05 \(\mu\)m, and NS with 0.04 \(\pm\) 0.002 \(\mu\)m.  
Figure 8 shows the retention force of each group, with the highest retention force being SB686 T200 with 317.87 \(\pm\) 21.08 N, followed by SB254 T200 with 304.38 \(\pm\) 34.30 N, SB102 T200 with 249.78 \(\pm\) 8.06 N, and NS with 163.420 \(\pm\) 23.76 N. Moreover, the lowest retention force on average was SB63 T200 with 159.106 \(\pm\) 41.47 N. The one-way analysis of variance (ANOVA) shows a \(p\)-value < 0.05, in which all of the groups were significantly different, with an exception for SB686 T200 compared to SB254 T200 and NS compared to SB63 T200, where both pairs have a \(p\)-value > 0.05.
# Multimodal Biometric Template Protection Based on a
## 6 Non-Invertibility Analysis
### False Acceptance Attack  
An FA attack, also called a dictionary attack, is an attempt to gain illegal access to a biometric system [43]. This attack is realistic for any biometric system that relies on a decision threshold value. In other words, if the matching score of the authentication instance \(s^{a}\) and the transformed template \(s\) is less than a pre-defined threshold value \(\tau\), the right to access the biometric system is obtained. In the stolen token scenario, \(s^{a}\) is a CSMoFN hashed vector generated with a biometric vector \(\mathbf{X}^{a}\) and stolen user-specific keys. The decision rule for authentication is then \(Dist_{H}(s^{a},\,s)>\tau\), where \(Dist_{H}()\) is the Hamming distance and \(\tau\) is the threshold value when the False Acceptance Rate (FAR) is equal to the False Rejection Rate (FRR).  
Figure 11: Revocability results from the proposed method on the (**a**) Ethnic, (**b**) IMDB Wiki, and (**c**) AR datasets.  
To mitigate the FA attack, the threshold value should be set high, to achieve FAR = 0%. However, this implies a GAR (Genuine Acceptance Rate) reduction that suggests a degradation in accuracy. To balance the performance with security, a suitable threshold value \(\tau\) should be carefully calibrated.  
In this paper, the distance between the fake template \(s^{*}\) and \(s\) is calculated via \(Dist_{H}(s,s^{*})=UB_{imp}\), where \(UB_{imp}\) is the upper bound on the imposter scores for the considered dataset, and represents the worst scenario. To succeed in this approach, an attacker can attempt to find \(s^{*}\) to satisfy \(Dist_{H}(s^{a},\,s^{*})=\tau\). That is, the goal is to generate a fake template such that the distance score with \(s^{a}\) falls into the interval \([\tau,\,UB_{imp}]\). Hence, the complexity of an FA attack can be estimated as \(q^{m(\tau-UB_{imp})}\).  
To analyze and respond to FA attacks in our context, it is necessary to determine the threshold value according to each GAR. Table 8 shows the complexity of an FA attack calculated for the six datasets used with the proposed method, with \(m\) = 4096 and \(q\) = 64. The complexity of the FA attack is evaluated based on GAR = 85%, 90%, and 95%. The \(UB_{imp}\) for each dataset can be obtained from the largest value of the imposter scores. Note that if \(\left(\tau-UB_{imp}\right)\) is negative, the complexity of the attack cannot be estimated.  
In summary, the complexity of an FA attack can be increased in two ways: by increasing the value of m or reducing the GAR. However, the latter also implies a compromise in the accuracy performance. For GAR = 90%, our proposed method is reasonably robust in resisting FA attacks. In addition, an FA attack can be prevented by restricting the number of attempts at the authentication stage.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 2 Related Work  
With the evolution of technology and the demand for diversification, more and more requirements have emerged in Underwater Wireless Sensor Networks (UWSN). [PERSON], [PERSON] described an energy-aware acoustic modem and tested its performance in underwater experiments in [23]. An omni-directional underwater acoustic micro-modem based on a low-power microcontroller unit has been described in [24]. In 2012, [PERSON], [PERSON] et al. made a breakthrough in low power consumption by inventing an ultra-low power and flexible acoustic modem which requires only 10 uW in standby mode and consumes extremely low power during reception and transmission [25]. Due to the high cost of modems, some researchers have focused on reducing their cost. [PERSON], [PERSON] et al. [26] started their study with the most critical component from a cost perspective--the transducer followed by other similar works in [27; 28]. A few companies are actively researching UAC modems and have developed many commercial modems. The Atm series modem [29] made by Teledyne Benthos can support a maximum data rate of up to 15.36 kbps and the maximum working distance can reach 6 km. LinkQuest UWM series modem [30] can support a maximum data rate of up to 17.8 kpbs and the maximum working distance can reach 10 km. EvoLogics has developed a series of modems which employs Sweep-Spread Carrier (S2C) technology to realize communication [31]. Based on the comparative analysis of an underwater acoustic modem collated by [PERSON] and [PERSON] in [32; 33], we have selected some representative devices and made a comparison table as shown in Table 1. These comparisons are performed in terms of modulation, areas of application and characteristics. As we can see from Table 1, the main developing trends of the UAC modem applied to UWSN are low power consumption, low cost, miniaturization and so on. However, basically, no modem is concerned about the Covert Underwater Acoustic Communication (CUAC) between them. Different from the modems presented above, our PBC modem adopts unconventional modulation mode. Additionally, the PBC modem focuses on the application of CUAC among UWSN nodes, divers or UUVs by using real dolphin or whale sounds and mimicking the sounds of sea creatures which is different from existing modems.
## 3 Materials and Methods
### Resampling Strategies
#### 3.1.2 Condensed Nearest Neighbor  
Condensed nearest neighbor (CNN) is a strategy initially proposed by [PERSON] [69] as a data reduction method used to improve the efficiency in the implementation of the k nearest neighbor decision rule (k-NN) for classification problems. In the context of data reduction methods when k-NN is used as classifier, the examples in the training set fall into one of three categories:  
* Outliers: points whose k nearest neighbors do not belong to the same class. That is, examples that are incorrectly classified by k-NN.
* Prototypes: the minimum set of points of the training set needed to classify correctly nonoutlier points.
* Absorbed points: points that would be correctly classified from the set of prototypes.  
CNN uses the basic approach of the NN rule but does not meet all its constraints. This method selects a consistent subset of prototype points from the original dataset, which will be used to classify new instances, but not necessary the minimum subset. The algorithm works as described below:  
1. \(D\) denotes the original dataset and \(E\) the resulting condensed set containing prototypes.
2. An arbitrary point from \(D\) is selected and placed in an original empty set \(E\).
3. The remaining points in \(D\) are classified by the 1-NN rule (\(k=1\)) using E, and those that are classified incorrectly are added to \(E\).
4. This procedure is iterated until no more data points are transferred from \(D\) to \(E\).
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Metal Nanomaterials  
When the metal-based nanomaterials used for imprinting are reduced to nanometer size, the metal exhibits many unique properties that the bulk metal atoms do not have, among which gold nanomaterials are the most explored [30].
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 1: Modification in IEEE 802.11p  
#### 3.1.1 The Midamble Based Channel Estimation Scheme (MBCE)  
This work was proposed in [10; 11] to mitigate the effects of time-varying channels. The midamble pilots are placed periodically in the data symbols. If the number of these midamble sequences is sufficient, then there is no limit on the length of Data section. At first, the channel estimation is performed based on the two LTS, and then the estimated channel response is used to equalize the next data symbols. Afterward, the midamble pilots are used to update and track the channel, using the  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Channel Model** & \begin{tabular}{l} **Doppler Shift** \\ **(Hz)** \\ \end{tabular} & \begin{tabular}{l} **Multipaths** \\ **(km/h)** \\ \end{tabular} & \begin{tabular}{l} **Velocity** \\ **(km/h)** \\ \end{tabular} & \begin{tabular}{l} **Average Path Gains (dB)** \\ \end{tabular} & \
\begin{tabular}{l} **Path Delays (ns)** \\ \end{tabular} \\ \hline VTV Express Oncoming & 1000–1200 & 11 & 104 &
\begin{tabular}{l} [0, 0, 0, -6.3, -6.3, -25.1, -25.  
same channel estimation procedure as that in the case of LTS. The ratio of the number of data symbols and the number of midambles is called \(c\), and the simulation results have shown that a minor value of \(c\) can effectively enhance the performance. However, improvement in the performance is achieved at the expense of spectrum efficiency, which results in lower transmission rates. In addition to this, the optimized selection of parameter \(c\) is still not resolved.
# The Difficulty of Measuring the Roughness of Glossy Surfaces Using the Triangulation Principle
## 1 Introduction
This method utilizes a laser beam emitted by an optical source of electromagnetic radiation and  
Figure 2: Scheme of the laser triangulation experimental setup. a sensing device in the form of a camera, the basis of which is a CCD or CMOS chip, to capture the profile of the object's surface [5]. Just like other methods of roughness measurement, laser profilometry has its pros and cons. One of the advantages of contact-free laser profilometry is that there is no wear on the measuring tool or damage to the surface of the measured object during measurement. It is capable of detecting cracks and micro-unevenness, which is a problem when using contact roughness meters that rely on tip geometry. Laser profilometry provides better repeatability of measurements and allows for measuring even highly curved surfaces. It can also evaluate multiple parameters of roughness and undulation and measure the entire surface area of the sample rather than just one line. Problems often associated with non-contact laser profilometry include difficulties in measuring glossy surfaces, problems in measuring multi-layered surfaces, the high cost of equipment acquisition, and the need for skilled operators [24; 25]. After conducting a comprehensive study of the existing literature, the objectives of the paper were established. When choosing a specific topographical method, several criteria should be considered, and these criteria depend on the nature of the experiment. Some of the most important criteria to consider include the measurement speed, size of the measurement area, sensitivity of the method, accuracy of results, and financial difficulty. Part of the experiment was to assess whether laser profilometry was a suitable method for measuring the roughness of materials with a glossy surface or not. The test samples were made from stainless steel material A304 and aluminum alloy AW 2017 by cutting the respective material using laser technology. The main requirement for the samples was that their cleavage surface showed a very glossy surface when laser cleaved, and that was the reason why this cleaving procedure was used. The experiment of measuring the selected roughness parameters was carried out with a contact-free laser profilometer, and to verify the accuracy of the measured values and compare the measured values, the Mitutoyo Surftest SJ-400 contact roughness meter was used as well. At the end of the experiment, the experimental data from individual measurements were presented in graphs and analyzed. Another objective of the study was to compare two methods of measuring roughness: contact-free laser profilometry and the Surftest SJ-400 contact roughness meter. The experiment provided practical insights into the suitability of laser profilometry for measuring the roughness of glossy material surfaces based on the analysis of the sample surfaces.
## 4 Data Processing
### Creating 3D Models from TLS  
The original 3D model from the Faro laser scanner was used as a reference (Phase 1, January 2018). The individual scans were processed using the Faro Scene (v 7.2.) software into a complete model by automatic scans correlated with a standard deviation of 3 mm. Ground control points (GCPs) in the form of reflective targets were used to verify the quality of the model and its possible distortion. A total of 16 GCPs were installed and the distances between them were independently measured geodetically using a Leica Disto (Figure 6). For transport reasons, a total station was not available. Due to the nature of the building and the quantity of debris and rubble, more accurate measurements were not necessary, so the distances between the GCPs from the geodetic hand measurement and from the identical targets in the point cloud were compared. Model distortion was not present, and deviations in the measured and calculated distances were typically in the order of 3-5 mm, with a maximum deviation of 12 mm for one distance.  
Phase 2 was mainly about statically securing the shrine and clearing the debris. Only photogrammetric documentation was taken, to be used for analyses at a later date.  
In September 2021, in the third phase, two models were captured from laser scanners. The first using the TLS BLK360 laser scanner, the second using the PLS GeoSlam Go mobile laser scanner.  
For Phase 3, mainly the TLS BLK360 was already used. The accuracy of the BLK360 as stated by the manufacturer is 4-6 mm per 10 m and it is therefore lower than the Faro scanner. The advantage of the BLK360 is its ease of transport. The Leica Cyclone software version 1.6.0 was used to calculate and align the point cloud from each site.  
Figure 5: Flow chart of the general workflow.  
The point clouds are used here as reference data for photogrammetry, especially for the purpose of determining scale. The incoming points were taken from the clouds and used in the following calculations. Since no significant features changed between Phase 1 and Phase 2, the point cloud from the 1 st phase was used as the Phase 2 reference. The properties of the resulting point clouds are shown in Table 2.  
The use of the PLS GeosSlam Go was mainly for the processing of working plans and cross-sections. For this purpose, the device is perfect and sufficient for construction and reconstruction purposes. However, in terms of accuracy and detail, its measurement capabilities cannot compete with the above from TLSs.
# Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules
## 3 Scoring Scheme
### Accumulated Distance Cost  
This cost function is used for finding the sequence of tasks for agents that minimizes total distance. To apply this cost function, bundle construction phase of CBBA should be changed as in Algorithm 3.  
```
1:\(\boldsymbol{y}_{i}(t)=\boldsymbol{y}_{i}(t-1)\)
2:\(\boldsymbol{z}_{i}(t)=\boldsymbol{z}_{i}(t-1)\)
3:\(\boldsymbol{b}_{i}(t)=\boldsymbol{b}_{i}(t-1)\)
4:\(\boldsymbol{p}_{i}(t)=\boldsymbol{p}_{i}(t-1)\)
5:while\(|\boldsymbol{b}_{i}|<L_{i}\)do
6:\(c_{ij}=d_{accum}+d_{p_{i}}^{j}\), \(\forall j\in J\backslash\boldsymbol{b}_{i}\)
7:\(h_{ij}=\left\{\begin{array}{ll}1&(c_{ij}<y_{ij})\\ \infty&(otherwise)\end{array}\right.\), \(\forall j\in J\)
8:\(J_{i}=argmin_{j}c_{ij}h_{ij}\)
9:\(\boldsymbol{b}_{i}=\boldsymbol{b}_{i}(+)_{end}\{J_{i}\}\)
10:\(\boldsymbol{p}_{i}=\boldsymbol{p}_{i}(+)_{end}\{J\}\)
11:\(y_{i,J_{i}}(t)=c_{ij}|\)
12:\(z_{i,J_{i}}(t)=i\)
13:endwhile
```  
**Algorithm 3** CBBA Phase 1: Bundle construction for Accumulated Distance Cost  
In line 6 of Algorithm 3, \(d_{accum}\) is total accumulated distance and \(d_{p_{i}^{ind}}^{j}\) is distance between agent \(i\) and point of task in \(\boldsymbol{p}_{i}(end)\). Note that the inequality sign and argument of the maxima in lines 7-8 of Algorithm 3 are changed for finding the sequence of tasks that minimizes total accumulated distance. To allocate tasks to agents, sequential greedy algorithm in Algorithm 2 also should be changed as described in Algorithm 4.  
```
1:\(I_{1}=I\), \(I_{1}=J\)
2:\(\eta_{i}=0\), \(\forall i\in I\)
3:\(c_{ij}^{(1)}=c_{ij}[\{\varnothing\}]\), \((i,j)\in I\times J\)
4:for\(n=1\) to \(N_{init}\)do
5:\((i_{n}^{*},j_{n}^{*})=argmin_{(i,j)\in I\times J}c_{ij}^{n}\)
6:\(\eta_{i_{n}}^{*}=\eta_{i_{n}}+1\)
7:\(I_{n+1}=J_{n}\backslash\{J_{n}^{*}\}\)
8:\(\boldsymbol{b}_{i_{n}^{*}}^{(n)}=\boldsymbol{b}_{i_{n}^{*}}^{(n-1)}(+)_{end}\{j_{n}^ {*}\}\)
9:\(\boldsymbol{b}_{i_{n}^{*}}^{(n)}=\boldsymbol{b}_{i_{n}^{*}}^{(n-1)}\), \(\forall i\
eq i_{n}^{*}\)
10:\(\boldsymbol{i}\boldsymbol{\eta}_{i_{n}}=L_{t}\)then
11:\(I_{n+1}=I_{n}\backslash\{i_{n}^{*}\}\)
12:\(c_{i_{n},j}^{(n+1)}=0\), \(\forall i\in J\)
13:else
14:\(I_{n+1}=I_{n}\)
15:endif
16:\(c_{i_{n},j}^{(n+1)}=0\), \(\forall i\in I_{n+1}\)
17:\(c_{ij}^{(n+1)}=c_{ij}\Big{[}\boldsymbol{b}_{i}^{(n)}\Big{]}\), \(\forall(i,j)\in I_{n+1}\times J_{n+1}\)
18:endfor
```  
**Algorithm 4** Sequential greedy algorithm for Accumulated Distance Cost  
In line 5 of Algorithm 4, argument of maxima is changed to argument of minimum as line 8 of Algorithm 3. Note that \(d_{accum}\) should be replaced to \(c_{i_{n},j_{n}}^{(n)}\) when calculating the next iteration costs, \(c_{ij}^{(n+1)}\) in line 17 of Algorithm 4. Additionally, note that accumulated distance cost can be only applied to centralized task allocation. This method can be used in the situation; a minimum number of agents are used for completing all tasks with minimum fuel consumption.
# A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks
###### Abstract  
Cooperative spectrum sensing (CSS) is a vital part of cognitive radio networks, which ensures the existence of the primary user (PU) in the network. However, the presence of malicious users (MUs) highly degrades the performance of the system. In the proposed scheme, each secondary user (SU) reports to the fusion center (FC) with a hard decision of the sensing energy to indicate the existence of the PU. The main contribution of this work deals with MU attacks, specifically spectrum sensing data falsification (SSDF) attacks. In this paper, we propose a correlation-based approach to differentiate between the SUs and the outliers by determining the sensing of each SU, and the average value of sensing information with other SUs, to predict the SSDF attack in the system. The FC determines the abnormality of a SU by determining the similarity for each SU with the remaining SUs by following the proposed scheme and declares the SU as an outlier using the box-whisker plot. The effectiveness of the proposed scheme was demonstrated through simulations.  
cognitive radio networks; spectrum sensing data falsification (SSDF); opposite malicious user (OMU); random opposite malicious user (ROMU); box-whisker plot
## 5 Discussion
Thus, the model's performance was not tested on the test set, and no specific error value or rate was reported. Similarly, [PERSON] et al. [10] only used the ARIMA model, and the model's performance was presented graphically as actual, fitted, and residual values. No statistical metrics were provided for the performance of the ARIMA model on the test set. [PERSON] et al. calculated MAPE and RMSE for the training dataset using ARIMA and ARIMA-ANN models. [PERSON] et al. [6] performed EF predictions using different parameters as inputs. In this approach, using input parameters alongside time series improved the models' prediction success. However, the study only tested 2 years (2014\(-\)2015). The most successful model, SVM, had an error rate of 1% for 2014 and 0.50% for 2015. However, the small size of the test set limits the models' generalization ability. This situation makes it challenging for the results to gain overall validity and highlights a limitation that should be considered. [PERSON] et al. [13] used Neural Network methods for EF and BC forecasting. The study reported the use of 11 NN methods for different parameters. MSE and MAE values for training and validation sets were graphically presented. Later, fishing grounds, grazing lands, forests, crops, built-up land, and carbon footprint values were forecasted for African, Asian, Central American, European, North American, Oceanian, and South American countries for the year 2030. As a result, upon examining EF forecasting studies in the literature, it becomes apparent that a limited number of models were used, and the prediction performances of models with different structures have not been compared. In numerous studies, model performances were not assessed on the test or validation set. Focusing exclusively on training performance may prove inadequate for accurately evaluating a model's real-world data performance. The evaluation of model performances through statistical analyses in studies is another noteworthy aspect. Additionally, the studies lack data containing anomalies such as COVID-19, and these anomalous situations remain unaddressed. The distinctions of this study from others in the literature are outlined below:  
* This study provides a comprehensive global ecological sustainability analysis using the worldwide total Ecological Footprint instead of specific geographical regions. * It compares the forecasting performance of classical, deep learning, and hybrid time series models for the Ecological Footprint. * The last 10 years, representing 15% of the time series dataset, have been used to evaluate the forecasting performance of the models. This approach allows for a more reliable assessment of how well the models perform under real-world conditions. * The forecasting performances of the models have been assessed regarding the sudden decrease in Ecological Footprint due to COVID-19 lockdowns. Consequently, the capability of the forecasting models to handle anomalies in the datasets has been evaluated.