esa-sceva/satcom-chunk-collection huggingface dataset:
# Floquet Modification of the Bandgaps and Energy Spectrum in Flat-Band Pseudospin-1 Dirac Materials
## 2 Low-Energy Electronic States of the Considered Materials  
The electronic states of the \(\alpha-\mathcal{T}_{3}\) model are described by the following low-energy \(\phi\)-dependent pseudospin-1 Hamiltonian:  
\[\hat{\mathcal{H}}_{\tau}^{\phi}(\mathbf{k})=\hbar\
u_{F}\left\{\begin{array}{c}0 \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\  
where the three solutions (8) are related to the valence (\(\gamma=-1\)), conduction (\(\gamma=+1\)), and flat (\(\gamma=0\)) bands of the electronic structure. The energy dispersions (8) do not directly depend on \(\alpha\) and, therefore, are also the same for a dice lattice.  
The wave functions for the \(\alpha-\mathcal{T}_{3}\) model, obtained as the eigenfunctions of Hamiltonian (1), are calculated as [57,69]  
\[\boldsymbol{\
abla}_{\gamma=\pm 1}(\boldsymbol{k}\,|\,\tau,\phi)=\frac{1}{ \sqrt{2}}\left\{\begin{array}{c}\tau\cos\phi\,\,\mathrm{e}^{-i\tau\theta_{ \boldsymbol{k}}}\\ \gamma\\ \tau\sin\phi\,\,\mathrm{e}^{i\tau\theta_{\boldsymbol{k}}}\end{array}\right\}, \tag{9}\]  
and  
\[\boldsymbol{\
abla}_{\gamma=0}(\boldsymbol{k}\,|\,\tau,\phi)=\left\{ \begin{array}{c}\tau\sin\phi\,\,\mathrm{e}^{-i\tau\theta_{\boldsymbol{k}}}\\ 0\\ -\tau\cos\phi\,\,\mathrm{e}^{i\tau\theta_{\boldsymbol{k}}}\end{array}\right\}. \tag{10}\]  
Unlike dispersions (8), wave functions (9) and (10) directly depend on phase \(\phi\) and valley index \(\tau\).  
For the case of a dice lattice, wave functions (9) and (10) are reduced to  
\[\boldsymbol{\
abla}_{\gamma=\pm 1}^{(d)}\Big{(}\boldsymbol{k}\,\Big{|}\,\tau, \frac{\pi}{4}\Big{)}=\frac{1}{2}\left\{\begin{array}{c}\mathrm{e}^{-i\tau \theta_{\boldsymbol{k}}}\\ \sqrt{2}\,\tau\,\gamma\\ \tau\,\mathrm{e}^{i\tau\theta_{\boldsymbol{k}}}\end{array}\right\}, \tag{11}\]  
and  
\[\boldsymbol{\
abla}_{\gamma=0}^{(d)}\Big{(}\boldsymbol{k}\,\Big{|}\,\tau, \frac{\pi}{4}\Big{)}=\frac{1}{\sqrt{2}}\left\{\begin{array}{c}\mathrm{e}^{- i\tau\theta_{\boldsymbol{k}}}\\ 0\\ -\mathrm{e}^{i\tau\theta_{\boldsymbol{k}}}\end{array}\right\}. \tag{12}\]  
Such a symmetric form of wave functions (11) and (12) has a strong effect on several crucial electronic properties, such as Klein tunneling.
## 3 Materials and Methods
### Dataset Description  
This study considers three datasets to investigate the performance efficiency of the proposed framework for intrusion detection. The details of KDD-CUP 99 and NSL-KDD datasets are summarized in Table 3. KDD-CUP 99 [38] is a benchmark dataset designed by the KDD competition held in 1999. It has been extensively utilized by researchers in investigating intrusion detection systems. It was prepared over nine weeks by simulating  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Parameters** \\ \hline RF & n\_estimator = 200, max\_depth = 20, random\_state = 50. \\ SVM & C = 1.0, kernel = ’rbf’, degree = 3, gamma = ’scale’ \\ LR & Penalty = '12’, solver = ’lbfgs’ \\ MLP & Dense (neurons = 300), dense (neurons = 200), dense (neurons = 100), activation = ’relu’, dropout (0.5), softmax (4) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for the machine learning models.  
the network environment of the military and consists of 42 attributes. The dataset comprises 4898 samples for the train set and 311 samples for the test set. The testing set belongs to 14 attack families. The labels are mainly divided into normal and four types of attacks. NSL-KDD [39] is also widely used for evaluating intrusion detection models. Each record of intrusion has symbol features (3 dimensional) and digital features (42 dimensional). The labels are mainly divided into normal, DoS, Prob, U2R, and R2L types of attacks. It contains a total of 125,973 samples in the train set and 22,544 samples in the test set.  
STIN security dataset [40] contains attacks of different types in satellite and terrestrial networks. It contains two types of satellite and nine terrestrial-type attacks. Flow-based features are considered in building the STIN dataset. Table 4 presents the characteristics of the dataset.
# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 5 Classification Experiment
### On-Spot Tweets Detection
#### 5.1.2 On-Spot Tweets Detection Using Baseline Classifiers
In this experiment with baseline classifiers, we applied the method to identify on-spot tweets from collected geotagged tweets by building a classifier that uses location clustering and SVM which learns the geotagged tweets information. SVM classifier achieved an average F1 score of 0.858 when compared with other applied classifiers. There was data imbalance, however, only between classification categories in the training dataset, not in test dataset, which suggests that there is a need to collect more data to assure a balance of classification categories in the future study, to improve the reliability of results and decrease potential bias. Table 5 shows the confusion matrix, where we can see 156 instances were incorrectly classified, 120 instances come from \"not on-spot\" class. Despite the achievement, there is a need to improve the model by collecting more geotagged tweets for training purposes. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classifiers** & **P** & **R** & **F1** \\ \hline Decision Tree & 0.797 & 0.808 & 0.801 \\ Naïve Bayes & 0.821 & 0.801 & 0.808 \\ KNN & 0.860 & 0.851 & 0.827 \\ SVM & 0.875 & 0.872 & 0.858 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: On-spot detection results obtained from multiple classifiers. In the next experiment, we attempted to compare the efficacy of SVM to a deep learning approach.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 2 Glider Performance Method with VBS Sizing  
Figure 1 shows the flow chart of the proposed method to perform the glider performance study to calculate the VBS budget volume for underwater gliders, using the analytical equations presented in the next sections.  
In the first line of Figure 1, it is observed the process to estimate, by CFD modeling the polynomial coefficients required for the glider performance study [23,25]. The validation process for CFD in a laboratory with a towing tank [23] is preferred but not mandatory. To achieve the estimation of coefficients in nominal operation of the vehicle, it is recommended to perform the CFD analysis with the water density at depth rating \(\rho_{d}\), the nominal dive velocity of design \(U^{\prime}\), and the variation of the angle of attack to be between \(-10^{\circ}\) to \(10^{\circ}\). The use of a higher range of angle of attack for the analysis could result in a deviation of the linear fitting caused by the stall effect at low Reynolds number [29], and subsequently in the estimation of the lift polynomial coefficients.  
In the center line of Figure 1, it is observed the main process of the glider performance study. With the hydrodynamic forces estimated with polynomial coefficients, the limit flight angles are calculated: the optimal attack angles \(\alpha_{1,2}\) and the minimal operating glide angles \(\gamma_{1,2}\). With the limit flight angles, the theoretical glide range is defined from [\(-90^{\circ}\),\(\gamma_{1}\)] for the descent (dive) state and [\(\gamma_{2}\),\(90^{\circ}\)] for the ascent (climb) state. The output functions for the attack angle and the pitch angle (measured with the internal compass of the vehicle) are obtained as a function of the glide angle.  
Then, the reference volume of the VBS as a function of the desired velocity and the glide angle \(V_{d}(U,\gamma)\) is obtained. With the main specifications defined above, the reference displaced volumes at the main operating states, defined in Section 3, are computed to calculate the VBS budget volume for the propulsion system.  
Finally, the velocities of the vehicle as a function of the displaced volume and the glide angle are obtained, generating the charts that estimate the performance of the vehicle at different points of operation. In the third line of Figure 1, the outputs of the proposed method are shown.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### Problem Motivation
#### 1.2.2 Manual and Error-Prone Record-Keeping  
Especially in low-resource countries, paper logs remain a very common method during the farming and export stages [8]. These records are vulnerable to human error, misplacement, and even fabrication. For example, a farm might overstate the quantity of beans harvested to meet export quotas or under-report losses to avoid scrutiny. These inaccuracies introduce discrepancies that spread throughout the supply chain and ultimately erode the reliability of exported data.
# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 6 Experimental Campaign
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(Env_{1}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 49,954 & 105 & 3789 & 1,067,387 & 89,904 & 419,777 & 1,630,916 \\ \(Conf_{2}\) & 59,981 & 71 & 1324 & 757,514 & 96,846 & 382,959 & 1,298,695 \\ \(Conf_{3}\) & 98,722 & 100 & 1034 & 677,758 & 86,452 & 19,197 & 883,263 \\ \(Conf_{4}\) & 92,101 & 114 & 777 & 3,840,793 & 45,144 & 381,507 & 4,360,436 \\ \(Conf_{5}\) & 114,270 & 76 & 1353 & 5,143,232 & 473,317 & 401,541 & 6,133,789 \\ \(Conf_{6}\) & 95,432 & 87 & 709 & 4,405,712 & 41,550 & 33,902 & 4,577,392 \\ \(Conf_{7}\) & 117,618 & 116 & 866 & 22,666,491 & 31,347 & 319,117 & 23,135,555 \\ \(Conf_{8}\) & 124,554 & 89 & 1184 & 18,525,784 & 393,088 & 448,029 & 19,492,728 \\ \(Conf_{9}\) & 95,573 & 89 & 914 & 18,047,275 & 47,537 & 32,170 & 18,223,558 \\ \hline \(Env_{2}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 12,170 & 64 & 819 & 182,765 & 15,345 & 341,675 & 552,838 \\ \(Conf_{2}\) & 12,144 & 60 & 225 & 172,772 & 16,442 & 344,312 & 545,955 \\ \(Conf_{3}\) & 22,020 & 86 & 205 & 123,625 & 15,536 & 16,169 & 177,641 \\ \(Conf_{4}\) & 19,871 & 83 & 141 & 890,814 & 10,057 & 305,142 & 1,226,108 \\ \(Conf_{5}\) & 22,414 & 49 & 261 & 1,110,737 & 83,602 & 337,270 & 1,554,333 \\ \(Conf_{6}\) & 16,904 & 65 & 127 & 941,497 & 9991 & 28,069 & 996,653 \\ \(Conf_{7}\) & 20,189 & 89 & 148 & 4,631,174 & 6872 & 304,278 & 4,962,750 \\ \(Conf_{8}\) & 23,781 & 59 & 289 & 4,561,780 & 81,840 & 378,912 & 5,046,661 \\ \(Conf_{9}\) & 22,619 & 71 & 156 & 4,445,672 & 11,872 & 27,817 & 4,508,207 \\ \hline \(Env_{3}\) & Step 1 & Step 2 & Step 3 & Step 4 & Step 5 & Step 6 & Total \\ \hline \(Conf_{1}\) & 6211 & 77 & 856 & 97,479 & 9511 & 188,150 & 302,284 \\ \(Conf_{2}\) & 6156 & 62 & 247 & 78,478 & 9512 & 204,319 & 298,774 \\ \(Conf_{3}\) & 11,540 & 102 & 207 & 64,805 & 10,804 & 7431 & 94,889 \\ \(Conf_{4}\) & 11,196 & 84 & 147 & 487,033 & 3423 & 101,937 & 603,820 \\ \(Conf_{5}\) & 11,051 & 56 & 285 & 460,746 & 42,592 & 101,783 & 616,513 \\ \(Conf_{6}\) & 8784 & 67 & 127 & 388,326 & 5643 & 14,703 & 417,650 \\ \(Conf_{7}\) & 11,148 & 99 & 152 & 2,076,886 & 4416 & 152,356 & 2,245,057 \\ \(Conf_{8}\) & 10,979 & 66 & 305 & 1,923,165 & 51,733 & 156,590 & 2,142,838 \\ \(Conf_{9}\) & 12,386 & 75 & 157 & 2,219,314 & 3836 & 13,466 & 2,249,234 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution times for the corpus of 1000 data sets (first Map-Reduce version).
# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 4 Prediction Results
### Relative Error of the RF Model  
This section further evaluates the performance of the RF model by the relative error as defined by Equation (4). Figure 12 reflects the process in more detail from the perspective of relative error using box plots under the self condition. The closer the relative error is to 0, the closer the predicted value of the model is to the true value. The horizontal axis is the output data o1, o2, o3, and o4, which represent _minlail_, _maxchainFC_, _maxchainFC_DNV_, and _maxoffsetC_, respectively. The vertical axis is the relative error of the RF model calculated by each output data. The value and distribution of the relative error can be observed from the box plots. For the model trained by dataset 1, the relative error of the output data except o1 is far from 0. With the addition of a batch of global data, the relative error of the output data under dataset 2 becomes much smaller with the relative error of o1\(\sim\)o3 bounded by 0.2 and o4 by 0.5, and the box plot is symmetrically distributed around 0. For the model generated by dataset 3 with more local data around the test points, the absolute value of the relative errors of o1, o2, o3, and o4 is under 0.0366, 0.0985, 0.1215, and 0.0546, respectively. The relative errors of the four output data are closer to zero. Sampling more data around the test points, the absolute value of the relative error of the model trained by dataset 4 is almost 0, suggesting little deviation of the predicted output value from the true value.  
Figure 13 shows the visualized relative error in a box plot for the operation condition. The horizontal axis includes one more output data from that of the self condition. o1, o2, o3, o4 and o5, respectively, represent _minlail_, _max_hauserC_, _maxchainFC_, _maxchainFC_DNV_, and _maxoffsetC_. For dataset 1 with a small amount of data and sparse distribution, the absolute value of the relative errors of o1, o3, and o5 are overall within the range of 0.5, and the relative errors of o2 and o4 are far from 0, with the values relatively scattered. The absolute value of the relative errors of the five output data on dataset 2 is mostly under 0.3. With further densification of the local data, the absolute value of the relative errors of the five output data of the model trained by dataset 3 is reduced to around 0.  
Figure 12: Box plot of the relative error of the RF model trained by dataset 1–4 under the self condition.
## 1 Introduction  
Two-dimensional (2D) materials have attracted great attention with the birth of graphene in 2004 [1]. 2D materials are planar materials with atomic thickness [2]. They have excellent electrical properties, outstanding flexibility and good transparency [3; 4]. These new physical properties can meet the needs of different applications such as electronics, photo electricity, energy storage and conversion [5]. In recent years, the transition metal dichalcogenides (TMDs), as an important part of 2D materials, have attracted great interest due to their various electrical properties including metal [6], semi-conducting [7], superconducting [8], charge density wave [9] and so on.  
Molybdenum disulfide (MoS\({}_{2}\)) is a typical material of TMDs. It is one of the most promising candidates to be the semiconductor of the next generation [5]. MoS\({}_{2}\) has a direct bandgap of 1.8 eV, which is suitable for developing field-effect transistors (FETs) with low static power consumption and high carrier mobility [10]. In the past decade, studies around MoS\({}_{2}\)-FET were focused on devices preparation [11], electrode contact [12], hysteresis phenomenon [13], heterojunction structure [14], material modification [15], and so on. However, the research on the comparison of different typical electrode materials in MoS\({}_{2}\)-FET are relatively rare.  
In this paper, we prepared MoS\({}_{2}\) materials through a classical mechanical exfoliation method [1], fabricated the devices with different metallic electrodes, and characterized them subsequently. We found that the on-state currents of four devices were of the same order of magnitude with no obvious difference. In order to clarify the internal mechanism, we calculated the Schottky barrier height (SBH) of the four metal-semiconductor contacts by thermionic emission theory and discovered that there may exist Fermi-level pinning (FLP) between the contacts in four devices [16].
## 3 Results and Discussion
Isogro(tm) is composed of an algal lysate that contains metabolic precursors permitting the bacteria to initiate growth more rapidly, thus eliminating the need for de novo production of the various components of the metabolic pathways. Isogro(tm) was then added to MSGM 6 since MSGM 6 resulted in the best bacterial growth amongst all nitrogen sources tested. This modification resulted in MSGM 9; however, _M. magnetotacticum_ growth did not improve when compared to MSGM 6 and in fact remained similar (\(p>0.05\)),possibly due to it not containing the specific nutrient requirements needed for improved growth for this particular bacterial species. Since the highest growth of _M. magnetotacticum_ was obtained in MSGM media containing glucose and sodium nitrate as the sole carbon and nitrogen sources, respectively, these nutrient sources were combined to create the MSGM modification MSGM 10. _M. magnetotacticum_ grown in MSGM 10 reached an OD of 1.25 after 72 h of incubation. However, a rapid decline could be noted once the highest growth had been obtained and thus there was no prolonged sustainable growth as that seen when using glucose alone. Upon addition of ammonium sulphate to the MSGM modification containing glucose and sodium nitrate to produce MSGM 11, it could be noted that the presence of two nitrogen sources resulted in a lower growth with an OD of 0.92 after 72 h of incubation and also resulted in a more pronounced decline after the exponential phase was concluded which was below that of MSGM 10 (modification using glucose and sodium nitrate in combination). Thereafter, glucose, ammonium sulphate, sodium nitrate and yeast extract were combined resulting in MSGM 12. This modification was performed to establish whether the addition of two nitrogen sources having a higher growth rate than MSGM original, together with the best carbon source and an additive, were able to improve _M. magnetotacticum_ growth. When yeast extract was added, an almost two-fold improvement in _M. magnetotacticum_ growth (OD 1.71) was observed when compared to MSGM 11. Comparatively, the highest growth of _M. magnetotacticum_ was obtained using MSGM 12 consisting of glucose as the sole carbon source, two nitrogen sources namely sodium nitrate and ammonium sulphate, and the additive yeast extract.
## 3 Results
### Organoid Formation from Sorted Cells  
During organoid formation, most single cells in the cultures aggregated and formed organoids. Nevertheless, 14% of OPSCC cells did not form any 3D structure, indicating that there may have been subgroups of cells with different organoid-forming capabilities. Cancer stem-like cells have the ability to form organoids [6]. Thus, the organoid-formation capacity and differential motility of individual OPSCC cells were assessed by sorting them according to their expression of CD44 and NGFR, two well-established CSC-related markers [10]. After sorting, cells from three different phenotypes, CD44\({}^{+}\)/NGFR\({}^{+}\), CD44\({}^{+}\)/NGFR\({}^{-}\), and CD44\({}^{-}\)/NGFR\({}^{-}\), were seeded in hydrogel and tracked using  
Figure 2: Organoid-formation events monitored over 15 h using DHM. (**a**) DHM images captured at different time points (0, 7.5, and 15 h) in a hydrogel-embedded culture system. (**b**) Increase in area and optical-volume patterns of five organoids tracked over 15 h at 30 different time points.  
HoloMonitor M4. In total, for each phenotype, five individual cells from two replicates were tracked over 15 h at 30 different time points.  
As depicted in Figure 3, cells enriched for CD44 expression (Figure 3a) were able to form 3D structures within 15 h after seeding, independently of NGFR expression. CD44\({}^{-}\) tumor cells were unable to aggregate (Figure 3a), consistent with their reported nonstem-cell phenotype. In addition, cells enriched for CD44 expression displayed greater average motility (140-240 \(\upmu\)m), while CD44\({}^{-}\) tumor cells moved less through the hydrogel (\(\sim\)30 \(\upmu\)m; Figure 3b).  
Figure 3: Organoid-formation ability and motility of different oropharyngeal squamous cell carcinoma-cell phenotypes. (**a**) Cells derived from quadrants Q1 and Q2 included cancer-stem-cell (CSC) phenotypes CD44\({}^{+}\)/NGFR\({}^{-}\) (Q1) and CD44\({}^{+}\)/NGFR\({}^{+}\) (Q2). Q4 contained the CD44\({}^{-}\)/NGFR\({}^{-}\) nonstem-cancer-cell population. DHM images obtained for each cell phenotype (Q1, Q2, and Q4) at t = 15 h. (**b**) Average accumulated motility of single cells according to expression of CD44 and NGFR. Five single cells were monitored for each phenotype in duplicates.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Mobile Communications Technologies  
As mentioned before, ref. [48] presents a disaster resilient three-layered architecture for PS-LTE. This architecture consists of an SDN layer to provide centralized control, UAV cloudlet layer to facilitate edge computing or to enable the emergency communication link, and a radio access layer. Indeed, the CIRS communications are based on D2D communications over LTE, and the paper describes how the latest releases of 3 GPP are working in the coexistence between LTE and other existing communication systems such as TETRA.  
Even if [33] implements an emergency network for a maritime area of tens of kilometres, and thus has been previously analysed as a SR category work, many references appear in the paper referring to the shore-based data centre. The coordination of the emergency is conducted by both the \"communication command ship\" (tens of kilometres of the emergency area) and the \"coordination centre on-shore\", and indeed, when the communication link with the shore base fails, the proposed solution based on a tethered UAV platform can also use the local LTE system to operate in the island mode and still play a certain role in the field emergency communication. Furthermore, it is also mentioned that the internet access based on shore can be realized by satellite communication link and microwave line of sight communication equipment, although these technologies are not further developed in the paper.  
In [46], the focus is also on the emergency short range communications based on a privacy-preserving federated learning embedded buses-and-drones mobile edge infrastructure. However, as in the previous references, the coordination is seen as a two-fold work, first conducted by the buses equipped as Emergency Control Centres at a local level--and only in the optimistic case are there still some terrestrial BSs working or accessible--then, the buses-and-drones federated edge infrastructure can be further connected to the global infrastructure through these still-working and accessible BSs and thus connected to CIRS far from the emergency area.