esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiscale Analysis for Improving Texture Classification
## 4 Experimental Results
### Experiments with Texture Datasets
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Reference** & **Approach** & **Accuracy** \\ \hline [PERSON] et al. [27] & Shallow & 97.56 \\ \hline [PERSON] et al. [28] & Shallow & 63.04 \\ \hline [PERSON] et al. [29] & ResNet50 & 75.68 \\  & ResNet101 & 75.60 \\ \hline  & ResNet101 & 98.13 \\  & ResNet50 & 98.35 \\  & VGG VeryDeep19 & 94.93 \\ Napoletano [30] & VGG M128 & 85.56 \\  & GoogleNet & 92.65 \\  & VGG M1024 & 92.58 \\  & VGG M2048 & 93.30 \\ \hline Alpaslan and Hanbay [31] & Shallow & 97.20 \\ \hline TiO + LDA & Shallow & **98.48** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy (%) of shallow and deep approaches on the ALOT dataset. The best result is shown in boldface. \begin{table}
\begin{tabular}{c c l c} \hline \hline
**Reference** & **Type** & **Approach** & **Accuracy** \\ \hline \multirow{3}{*}{[PERSON] [9]} & \multirow{3}{*}{Shallow} & GLCM + LightB & 95.52 \\  & & Haralick + \(k\)-NN & 96.92 \\  & & BiT + SVM & 99.88 \\ \hline [PERSON] and [PERSON] [25] & Shallow & LBP-based + \(k\)-NN & 96.23 \\ \hline [PERSON] et al. [26] & Shallow & LSP + \(k\)-NN & 86.12 \\ \hline TiO & Shallow & LDA or CatBoost & **100.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy (%) of the proposed method and related works on the Outex dataset. The best result is shown in boldface. Likewise, the KTH-TIPS dataset has been used to evaluate texture characterization and classification approaches. [PERSON] et al. [32] introduced an approach that integrated the genetic programming and the fusion of HOG and LBP features, which achieved an accuracy of 91.20% with a \(k\)-NN classifier. Such an approach did not use color information and global features. [PERSON] et al. [33] put forth rotational and noise-invariant statistical binary patterns, which reached an accuracy of 97.73%, lower than the accuracy achieved by the proposed method by about 2.3%. This approach was resolution-sensitive and presented a high computational complexity. [PERSON] et al. [34] proposed a rotation-invariant multiscale cross-channel LBP (CCLBP) that encoded the cross-channel texture correlation. The CCLBP computed the LBP descriptors in each channel and three scales and computed co-occurrence statistics before concatenating the extracted features. Such an approach achieved an accuracy of 99.01% for three scales with an SVM. Nevertheless, this method was not invariant to scale. Table 5 shows that the proposed approach outperformed other works that also used the KTH-TIPS.
## 2 Materials and Methods
### Hexanal Extraction and Quantification  
The hexanal concentration was analyzed in the supernatant and in the cell extract. For aldehyde extraction, 2 mL of supernatant was taken and 50 \(\upmu\)L of isoamyl acetate (as an internal standard) was added and extracted with 2 mL of diethyl ether. The organic phase was collected for GC analysis. For quantification of hexanal in cells, 2 mL of medium was taken and cells were separated by centrifugation (10,000\(\times\)\(g\), 5 min, 4 \({}^{\circ}\)C). After centrifugation, the cells were washed with distilled water and resuspended in 1 mL of Tris HCl buffer (100 mM, pH = 7.5). Next, 0.2 g of glass beads (50-100 \(\upmu\)m) were added and the suspension was homogenized into FastPrep (3 cycles, 60 s). The obtained cell extract was made up to 2 mL of Tris-HCl buffer, and the internal standard was added (50 \(\upmu\)L) and extracted with 2 mL of diethyl ether.  
The extracted sample was automatically injected (1 \(\upmu\)L) into an HP6890 gas chromatograph with an HP-Innowax capillary column (0.25 \(\upmu\)m film thickness, 30 m \(\times\) 0.25 mm). The oven temperature was initially 35 \({}^{\circ}\)C, and increased after injection up to 40 \({}^{\circ}\)C at a rate of 0.5 \({}^{\circ}\)C/min and then at a rate of 10 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C. Finally, the temperature was kept at 100 \({}^{\circ}\)C for 1 min. Nitrogen was used as a carrier gas at 4 mL/min. The split injector and the flame ionization detector were adjusted to 250 and 300 \({}^{\circ}\)C, respectively.
## 4 Results and Discussion
### Discussion
#### 4.4.3 Information Flow: Gathered from Sensor Robot and Provided to Mobile App for the User  
Additional functions for projecting messages to a specific dangerous vehicle is available with supervised learning in real-time. According to the signals from sensor robots, and real-time data from APIs, customized recommendations for users to ensure safe driving experiences were given. This resulted in three types of mobile app function module from the perspective of:  
* Causal inference to data from sensor robot
* Sensor robot data to cloud network DB
* DB to information architecture (IA)
* IA to layout
* Layout to display
* Display to state-transition in the mobile app of the user.  
The result of customized/alternative path finding service via ad-hoc networks can share information to alert uncommon sudden events on the road as quickly as drivers want to bypass.
## 6 Cost Analysis of the Sensor4 PRI Platform  
The previous sections have studied the main components that could be integrated in the system proposed in this paper. Taking into account their characteristics, the final prototype is composed by the components shown in Table 11. The components have been selected taking into account the accuracy of the sensors, the energy consumption and the reduced costs. The Sensor4 PRI platform is pictured in Figure 13.  
It is important to highlight that the cost of the final monitoring device could be reduced a lot for two obvious reasons. First, because all the components can be integrated into a single electronic board and second, because the device is designed to be installed along the railway, which means that a high number of devices will be produced and the costs can be reduced.  
Figure 12: Energy generated by the Peltier cell outside a “tunnel”.
## 4 Simulation Results
### Simulation Tests Conducted
#### Advantages, drawbacks and limitations of the IQV controller  
* Because only two controller parameters and three design parameters are changed, it may therefore be difficult to achieve very accurate tracking (however, this may be because according to the mathematical proof only uniform stability is guaranteed).
* The control scheme is suitable for studying hovercraft with rudders, while so far there is no way to directly extrapolate to marine vehicle models with rudders.  
The simulation verification involved testing a control algorithm using a model based on a velocity transformation containing components due to the fact that the geometric center does not coincide with the center of mass. New variables including the parameters of this model were incorporated into the control algorithm resulting in a useful tool for estimating vehicle behavior during trajectory tracking. Based on the comparison with the TSMC-type algorithm, better values of the adopted indicators were obtained, while in comparison with the original algorithm the results did not always yield a better performance. Nevertheless,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Sine Trajectory**} & **Complex Trajectory** \\
**Index** & & **with Friction** & **with Friction** \\ \hline MIA & \(\Delta p_{x}\) & \(-4.7\%\) & \(-53.2\%\) \\  & \(\Delta p_{y}\) & \(-6.1\%\) & \(5.1\%\) \\ RMS & \(||\mathbf{e}||\) & \(1.9\%\) & \(0.7\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: IQV tracking performance vs. original algorithm [22].  
a feature of the IQV controller is that it is a useful tool for estimating the impact of dynamic parameters during trajectory tracking.
## 4 Design Scheme and VISSIM Simulation
### Operational Efficiency and Environmental Assessment
#### 4.4.2 Analysis of Simulation Results  
The four auxiliary lane design forms proposed in Section 4 helped establish VISSIM models and input of the measured traffic parameters. Therefore, the indexes of each model in terms of traffic efficiency and environment can be obtained (see Table 7).  
Upon examining the simulation results in Table 7, it is apparent that Scheme 2 exhibits comparatively unfavorable outcomes regarding travel time, delay, and number of stops compared to the other three schemes under the measured traffic volume. This indicates that Scheme 2 has the lowest traffic efficiency under the measured traffic volume. This situation occurs because the auxiliary lanes of Scheme 2 are tapered from two to one, and the vehicles on the outer auxiliary lanes need to change lanes centrally, reducing traffic efficiency.  
On the remaining efficiency indexes, Scheme 3 performs best, with varying degrees of improvement in all aspects. Notably, Scheme 3 exhibits the most significant improvement in delays, with a decrease of 49.6%. This improvement in delays is the most notable among all the indexes measured. Scheme 4 has a slightly lower efficiency improvement than Scheme 3.  
In terms of CO emissions, the improvement effect of Schemes 3 and 4 increased by 7.53% and 6.22%, respectively, compared with Scheme 1. Schemes 3 and 4 have high traffic efficiency and stable vehicle speed, greatly reducing fuel consumption and controlling pollutant emissions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flow** & **Through Traffic** & **Diverging Traffic** & **Emerging Traffic** \\ \hline Investigated capacity (veh/h) & 1732 & 1684 & 724 \\ Simulated capacity (veh/h) & 1576.8 & 1620 & 648 \\ Individual MAPE (\%) & \(-8.96\%\) & \(-3.8\%\) & \(-10.5\%\) \\ MAPE (\%) & & 0.28\% & \\ \hline \hline \end{tabular}
\end{table}
Table 6: VISSIM simulation calibration results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Scheme 1** & **Scheme 2** & **Scheme 3** & **Scheme 4** \\ \hline Travel time (s) & 77.81 & 84.2 & 70.96 & 74.86 \\ Delay (s) & 2.58 & 4.73 & 1.30 & 1.44 \\ Number of stops & 0.036 & 0.10 & 0.009 & 0.007 \\ CO emissions (g) & 5967.36 & 5350.25 & 4948.78 & 5019.775 \\ Fuel consumption (gallon) & 85.39 & 76.54 & 70.78 & 71.78 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation results for transportation efficiency and environmental indexes.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 5 Verticality Monitoring Result and Analysis of the W Tower
### Verticality Monitoring Result of the W Tower
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**From**} & \multirow{2}{*}{**To**} & \multicolumn{2}{c}{**GZGEOID**} & \multicolumn{2}{c}{**EGM2008**} & \multicolumn{2}{c}{**EIGEN-6C4**} \\ \cline{3-6}  & & \(\Delta\)**N/m** & \(\xi\)/\(\eta\)(\(\eta^{\prime\prime}\)) & \(\Delta\)**N/m** & \(\xi\)/\(\eta\)(\(\eta^{\prime\prime}\)) & \(\Delta\)**N/m** & \(\xi\)/\(\eta\)(\(\eta^{\prime\prime}\)) \\ \hline \multirow{4}{*}{XT} & A1 & \(-0.018\) & \(3.80\) & \(-0.014\) & \(3.20\) & \(-0.013\) & \(2.99\) \\  & C1 & \(0.019\) & \(4  
hand, the corrected difference may be the actual difference caused by construction and measurement errors. As shown in Table 10, the difference in position between the GNSS measurement and the laser plumbing measurement is within 2.0 cm after correction. Therefore, when considering such factors as the wind, the operation of a tower crane, and the installation of a curtain wall, one may conclude that the verticality of the W Tower is within 2.0 cm.
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 3 Background
### Narrowband IoT  
Narrowband IoT (NB-IoT) is a new radio interface for LPWANs standardized by the 3 GPP. It is one of the most promising IoT standards defined in the _3 GPP Release 13_, together with Long-Term Evolution (LTE) for Machines (LTE-M), and has been extended in Releases 14 and 15. The NB-IoT design requires support for a massive number of low-throughput devices connected to the same site cell. This equates to 40 devices per household in a densely populated area such as London [32]. NB-IoT was designed as a communication technology for power constrained devices that are expected to operate during the months or years supplied by a single battery charge with a capacity of 5 Wh. In this regard, NB-IoT implements two power saving schemes, namely, Power Saving Mode (PSM) and Extended Discontinuous Reception (eDRX).  
NB-IoT employs a single narrowband of 200 kHz with low baseband complexity to maintain a lower cost and a large coverage range. Technically, NB-IoT has a link budget of 164 dB, significantly better than previous standards. For instance, compared to legacy GPRS technology, NB-IoT counts with an extended coverage of 20 dB. This grants NB-IoT extended coverage ranges of up to 10 km in line-of-sight scenarios and of up to 1 km in densely populated areas. The radio modulations of NB-IoT in the Physical layer (PHY) are Binary Phase Shift Keying (BPSK) and Quadrature Phase-Shift Keying (QPSK) for the uplink and downlink, respectively. The access scheme employs Single-Carrier Frequency-Division Multiple Access (SC-FDMA) for the uplink communications and Orthogonal Frequency Division Multiplexing Access (OFDMA) for the downlink. In both cases, it employs subcarriers of 15 kHz in half-duplex communication mode.  
Data rates range from 160 bps to 250 kbps, depending on scenario conditions. Most IoT applications have permissive latency requirements, with a target latency of 10 s or less. It is expected that by 2025 the number of deployed NB-IoT devices will surpass 5 billion devices. As a consequence, NB-IoT devices are targeted to be marketed with prices of US$5 per unit. NB-IoT development is based on LTE and supports all the signaling needed to establish communications with a LTE base station. Thus, any LTE compatible base station can be upgraded via software to support NB-IoT. This characteristic greatly reduces the deployment time and complexity of operations. NB-IoT can be deployed in three different operation modes, namely (i) Inband, (ii) Guardband, and (iii) Standalone. In Inband mode, the device employs a resource block of 180 kHz within a LTE carrier band. In Guardband mode, the energy is irradiated in the unused guard resource blocks between two LTE carriers. In Standalone mode, it occupies one GSM channel, and not LTE radio band resources.
## 6 Conclusions  
In this study, the leveling of geochemical data was performed using the CR method, BL method, and MSC method after compositional data transformation. The following conclusions were drawn:  
1. Between multiple map sheets, inherent differences due to factors such as different units, different time periods, and different instruments may cause a shift effect in geochemical data. Therefore, data leveling is necessary to improve the data's usability and accuracy.
2. The CR method is not suitable for leveling in areas with small scales or significant geological background differences.
3. The BL method works well in eliminating the shift for all elements and is applicable for leveling between different scales. However, its limitation is that the geological conditions at the boundaries need to be consistent.
4. The MSC method yields results similar to the BL method but does not require consistent boundary conditions, making it less restricted. However, it requires geochemical data at multiple scales.  
In summary, this paper used the uneven distribution of geochemical anomalies between multiple map sheets in the Baiyinchagan-Maodeng area as a starting point, thoroughly analyzing various methods for handling the \"shift\" in geochemical data. When confronted with such data exhibiting \"shift\", it is crucial to first perform compositional data transformation to eliminate the closure effect inherent in the data. Subsequently, the optimal data leveling methods should be explored, prioritizing the use of similarities between geological boundaries and potentially incorporating data at different scales to achieve effective data leveling. This exploration aims to provide a valuable reference for future explorers need to address similar issues.  
Methodology, R.T. and C.L.; software, R.T. and C.L.; writing--original draft preparation, R.T. and C.L.; writing--review and editing, R.T. and G.T.; visualization, C.L.; supervision, K.X.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Science and Technology Major Project (2024 ZD1003206), the National Key R&D Program of China (Grants 2023 YFC2906403, 2023 YFC2906404), and the Geological Survey Fund Project of Inner Mongolia Autonomous Region (2021-KY04).  
All data and materials are available on request from the corresponding author. The data is not publicly available due to ongoing research using part of the data.  
**Acknowledgments:** The authors thank the anonymous reviewers and the editors for their hard work on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 3 Crown Decomposition and Applications
### \((n-k)\)-List Coloring  
Banik et al. [26] use Lemma 2 to obtain an FPT algorithm for \((n-k)\)-List Coloring; a generalization of the problem is presented at Box 2 in Section 3.2, where there is a reduction rule that binds the number of colors over all the lists to \(n\). The problem is formally defined below.  
Here, they create an auxiliary bipartite graph with vertices \(V(G)\) on one side, \(\mathcal{C}\), and the colors in every vertex list on the other side, adding edges between a vertex and a color if its list contains it. After that, they invoke Lemma 2. If there is a matching saturating \(\mathcal{C}\), the number of colors is bounded by \(|V(G)|=n\). Otherwise, we have a crown decomposition \((C,H,R)\) with \(C\subseteq\mathcal{C}\) and \(H\subseteq V(G)\). Since there is a matching \(M\) from \(H\) to \(C\) saturating\(H\) and \(N(C)=H\), we can safely color the vertices of \(H\) using the colors \(C\) according to the matching \(M\) and reduce the instance. Thus, the number of colors is reduced to \(n\). Further ideas are used to obtain an FPT algorithm for this problem defined in Box 4.  
\[(n-k)\text{-List Coloring Problem}.\]  
\[(n-k)\text{-List Coloring}\]  
**Input:**_A graph \(G=(V,E)\), an integer \(k\) and for every \(u\in V(G)\), a set \(L(u)\) of exactly \((n-k)\) colors._  
**Parameter:**_k._  
**Question:**_Is there a proper coloring of \(G\) such that for all \(u\in V(G)\), the color assigned to \(u\) is in \(L(u)\)?_
## 2 Background on Personality Research
### Personality Evaluation Tools
#### 2.2.2 Myers-Briggs Type Indicator  
The _Myers-Briggs Type Indicator_[33], or _MBTI_ for short, is another very popular personality evaluation tool. The Myers-Briggs Company ([[https://www.themyersbriggs.com/en-US/Connect-with-us/Blog/2018/October/MBTI-Facts-Common-Criticisms](https://www.themyersbriggs.com/en-US/Connect-with-us/Blog/2018/October/MBTI-Facts-Common-Criticisms)]([https://www.themyersbriggs.com/en-US/Connect-with-us/Blog/2018/October/MBTI-Facts-Common-Criticisms](https://www.themyersbriggs.com/en-US/Connect-with-us/Blog/2018/October/MBTI-Facts-Common-Criticisms)) (accessed on 28 March 2023), which is in charge of distributing the evaluation test, has stated that millions of people use the test annually in over 100 countries.  
Unlike the Big Five model, MBTI focuses on personality types, rather than traits, and as such is based on the theoretical works of [PERSON] [34], who is often credited as the progenitor of personality types. Building on the three dichotomies that [PERSON] originally proposed, namely Introverted/Extroverted, Sensing/Intuitive and Thinking/Feeling, [PERSON] and [PERSON] later introduced the fourth dichotomy labelled as Judging/Perceiving, which finalized the initial MBTI model [35]. These dichotomies can be explained as measures in the following way:  
1. _Extroverted/Introverted--_Describes how an individual gains energy. Often abbreviated as _E-I_;
2. _Sensing/Intuitive--_Describes how an individual gains information. Often abbreviated as _S-N_;
3. _Thinking/Feeling--_Describes how an individual makes decisions. Often abbreviated as _T-F_;
4. _Judging/Perceiving--_Describes how an individual observes the world around them. Often abbreviated as _J-P_.  
Personality measured by the MBTI model is based on the idea that every individual has one pronounced value from the four dichotomies mentioned (Figure 4). For example, the type _INTJ_ would refer to an individual who is _Introverted_, _iNtuitive_, _Thinking_ and _Judging_. Thus, the MBTI offers a total of 16 different combinations that describe 16 unique personality types [36].
## 3 Results  
### Patients  
In the present study, we enrolled 28 patients (20 females and 8 males) with a mean age of 58 \(\pm\) 10.1 years. The mean body mass index (BMI) was 26.7 \(\pm\) 4.7 kg/m\({}^{2}\); 14 patients were normal weight (50%), and the other 14 were overweight or obese (50%). However, despite such a relevant number of overweight/obese subjects enrolled in this study, BMI does not appear to have exerted any impact on overall results. All patients underwent a skin pick test to investigate the allergic status. Among them, 25 were positive to at least one antigen (89.3%), and only 3 patients (10.7%) were non-allergic. In particular, 17 (60.7%) patients were taking omalizumab as add-on biological therapy, 4 (14.3%) mepolizumab, and 7 (25.0%) benzlizumab (Table 1). In regard to comorbidities, 11 patients (39.3%) had nasal polyposis, and 19 (67.9%) complained of gastroesophageal reflux disease (GERD). When considering the previous or current smoking habit, 19 patients (67.9%) were never smokers, 3 (10.7%) were former smokers, and 6 (21.4%) were current smokers, characterized by an exposure ranging between 10 and 20 packs/year. Asthma was diagnosed at the mean age of 38 \(\pm\) 13.7 years.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Pre-Pandemic** & **Pandemic** & **\(p\)-Value** \\  & **(Median \(\pm\) SD)** & **(Median \(\pm\) SD)** & **\(p\)-Value** \\ \hline _Onalizumab_ & & & \\ Spirometry & 0 \(\pm\) 0.5 & 0 \(\pm\) 0.6 & 0.99 \\ FEV1 & 1.8 \(\pm\) 0.7 & 2 \(\pm\) 0.6 & 0.94 \\ ACT & 21 \(\pm\) 2.8 & 25 \(\pm\)4.7 & 0.27 \\ Blood tests & 0 \(\pm\) 0.3 & 0 \(\pm\) 2.4 & 0.16 \\ Clinical examination & 6 \(\pm\) 4.6 & 0 \(\pm\) 3.1 & 0.006 \\ Exacerbations & 0 \(\pm\) 0.7 & 0 \(\pm\) 1.9 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical data related to omalizumab, benzlizumab, and mepolizumab administration, sorted for pre-pandemic and pandemic period.
## 1 Introduction  
Cognitive radio is one of the effective solutions to the problem of spectrum scarcity in wireless communications networks. Secondary users (SUs) with cognitive capability can utilize the spectrum bands licensed to primary users (PUs) for reliable and effective data transmission [1]. To achieve this, the SU modifies its parameters to adapt to the time-slotted operation of the PU on the channel of interest, and then senses the presence of the PU on that channel in every time slot. When the PU is sensed as inactive in a particular time slot, the SU can use the licensed channel during that time slot to transmit data. In this paper, the SU uses its limited-capacity battery, powered by a non-radio frequency (non-RF) energy harvester, for spectrum sensing, data encryption, and data transmission.