esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards the Internet of Smart Trains:
## 4 Long Term Evolution (LTE): One Step Ahead of Broadband Communication Systems
This will be performed by introducing a framework for Control, Command and Signaling Technical Specifications for Interoperability (CCS TSI) that will enable the migration of technologies that can be used by the trackside and on-board systems from GSM-R to a next-generation system. In 2011, the LTE-Advanced (LTE-A) specification (Release 10) was introduced. LTE-A meets formally the requirements of International Telecommunication Union-Telecommunication Standardization (ITU-T) 4G technology definition known as IMT-Advanced, and the needs set by the operator-led alliance Next Generation Mobile Networks (NGMN). Release 10 provided a substantial uplift to the capacity and throughput and also took steps to improve the system performance for mobile devices located at some distance from a BS. Main features included: up to 3 Gbit/s (DL) and 1.5 Gbit/s (UL); carrier aggregation (CA), allowing for the combination of up to five separate carriers (20 MHz) to enable bandwidths up to 100 MHz, higher order MIMO antenna configurations, relay nodes to support heterogeneous networks deployments and enhanced Inter-cell Interference Coordination (eICIC). Release 11, functional freeze date including stable protocols in early 2013, included refinements to existing capabilities: enhancements to Carrier Aggregation, MIMO, relay nodes and eICIC, introduction of new frequency bands, and coordinated multi-point transmission/reception to enable simultaneous communication with multiple cells. Release 12 (functional freeze date in March 2015) introduces novel procedures for supporting diverse traffic types, a number of features to improve the support of HetNets, enhanced small cells for LTE, inter-site carrier aggregation, new MIMO and beamforming techniques and advanced receivers to maximize the potential of large cells Proximity Services (ProSe), MBMS enhancements, M2M applications, Self-organizing Networks and interworking between HSPA, Wi-Fi and LTE.
## 2 Results
### Heterologous Protein Expression and Localization in the Diatom P. tricornutum
#### 2.1.2 Heterologous Protein Detection and Localization  
The successful expression of cannabis genes and the accumulation of the corresponding enzymes was observed in _P. tricornutum_ transconjugants. Indeed, the accumulation of the CsTKS-T2A (45 kDa band) protein in _Pt_OA1C1 (Figure 5A), a 68 kDa uncleaved protein corresponding to CsTKS-His-T2A-CsOAC-cMyc in _Pt_OA2C2 (Figure 5B), and an 85 kDa band corresponding to YFP-CsTKS-3(GGGGS)-CsOAC-cMyc in _Pt_OA3C1 (Figure 5C) was detected via Western blot analysis in PCR-positive _P. tricornutum_ transconjugants (Appendix A, Figure A1B). However, the construction with the self-cleavable sequence did not yield detectable successful single proteins in _Pt_OA2. This could be caused by the tag (6 xHis) used in the OA2 construct but not in the OA1 construct, changing the efficiency of cleavage (Figure 5B). Then, we studied the localization of _Cs_TKS in _PTOA3_, taking advantage of the presence of the fluorescent YFP fusion protein. We observed under confocal microscopy that the heterologous recombinant protein (YFP-CsTKS-3(GGGGS)-CsOAC-cMyc) produced in _Pt_OA3 accumulated and was localized to the cytosol (Figure 5D).  
Figure 5: Expression and localization of heterologous proteins in transconjugant _P. tricornutum_ strains. (**A**) Whole-cell extraction of four _Pt_OA1 (TKS-T2A-OAC) strains 1-4; TKS protein detection (45 kDa) was performed using anti-T2A antibodies. (**B**) Whole-cell extraction of two _Pt_OA2 (TKS-His-T2A-OAC-cMyc) clones 1 and 2; TKS:T2A-OAC protein detection (68 kDa) was performed using anti-His antibodies. (**C**) Whole-cell extraction of _Pt_OA3 (YFP-TKS-3(GGGGS)-OAC-cMyc) strains; YFP:TKS:OAC protein detection (85 kDa) was performed using anti-YFP antibodies. Mk: protein ladder; whole-cell extracts of the pPtGE30 culture were used as negative control (empty vector, EV);  
C+: Multi Tag was used as a positive control. All Western blots were obtained from 12% SDS-PAGE gel. For each construct, four clones were characterized and the ones showing protein production are presented in this figure. (**D**) Confocal microscopic images of _Pt_OA3 and pPtGE30 cells in a bright field, with autofluorescence of the chloroplast, YFP:TKS:OAC fluorescence, and the merging of three fields are shown.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### Description of the Evaluation Criteria for Stitching Algorithms
#### 2.3.2 Stitching Run Time and Memory Usage (Quantitative Criteria)  
Two quantitative criteria are common for the assessment of any software: run time and RAM memory usage. These two pieces of information are important, especially for large stitching, as a high run time and memory usage would limit the stitching maximum size since these resources are limited. Next, stitching quality is evaluated through two other criteria: mean repositioning error and stitching quality estimator. These values are proposed by the authors of this paper. Their calculation is based on the hypothesis of the linearity of the accumulated repositioning errors in stitching.
## 6 Conclusions  
This study addressed the design for reliability (DFR) methodology by incorporating physics of failure (PoF) into the verification of the design of electronic circuit boards and troubleshooting of legacy boards. Numerous physics-based models and equations were explored, and how they may be used with simulated FEA stresses to produce a life assessment of the system for given testing standards or operational parameters. It was also shown how an existing PCB design may be used as the basis for the FEA model, by importing an ODB++ manufacturing model from the PCB layout.  
The results from a test subject in the form of a power electronics evaluation board were used to demonstrate the process, and the board's design was imported from the PCB layout and subjected to numerous tests. The results of these tests were then discussed and various conclusions around the design were drawn, illustrating the DFR process.  
In summary, this physics of failure approach is part of a design for manufacturability (DfM) and design for reliability (DFR) methodology. By understanding the manufacturing risks, such as solder reliability, strain measurement, suppliers, material selection, and post-assembly operations, in addition to understanding the processes and mechanisms that induce failure, product performance can be improved. An early insight into the various reliability problems translates into almost immediate identification of areas of concern at the design stage, thus allowing issues to be rectified and designs to be retested before certification. This results in improvements in metrics such as safety, flight hours and landings, component removals and failures, component no fault founds (NFFs), airplane schedule reliability, and maintenance action rates.  
Conceptualization, A.W., S.P. and S.A.; Investigation, A.W.; Methodology, A.W., S.P. and S.A.; Formal Analysis, A.W. and S.P.; Writing--original draft, A.W.; Preparation, S.A.; Writing--review and editing, S.P. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
## 3 Numerical Simulations  
### Experimental Region  
The Daya Bay, a semi-closed bay on the northern coast of the South China Sea, is selected as the experimental field for numerical simulations. Autonomous platforms equipped with environmental sensors are planned to be deployed for environmental monitoring as a complement to traditional means, such as research vessels, buoys, etc. The coastline of Daya Bay is retrieved from the Global Self-consistent, Hierarchical, High-resolution Geography database (GSHHG) and the bathymetry from a digitalized nautical chart (Figure 3). The geographic coordinates are first transformed to the Cartesian coordinates. Different from land-based robots, obstacles encountered by AUVs in the ocean are offshore islands, bottom topography, and ships. The ships can be avoided using real-time marine traffic information, such as barrier sonars or ship tracking systems. The AUV maintains a certain distance from the seafloor using single-beam sounders. Since the AUVs are usually deployed in the open ocean, only coastline and islands are considered as obstacles in our simulation. Obstacles due to bottom topography and ships are ignored. The travel time for the AUV arriving at the target from its initial location is used to evaluate the performance of the MDP method.  
Tidal currents are predicted using the Finite Volume Community Ocean Model (FVCOM) developed at the University of Massachusetts [28], which is a regional ocean model solved using the finite volume technique in unstructured grids. The model is driven by the tidal flows on the open boundaries which are provided by the TPXO global prediction [29], including eight primary tidal constituents (M\({}_{2}\), S\({}_{2}\), N\({}_{2}\), K\({}_{2}\), K\({}_{1}\), O\({}_{1}\), P\({}_{1}\), Q\({}_{1}\)). The model performance has been validated through comparison with the tide gauges in Daya Bay. The AUV initially starts at 17:00:00 6 October 2013 (UTC) corresponding to an ebb tidal course, shown in Figure 4. The tidal current flows offshore and reveals a spatial variation related to water depth. The predicted tidal currents are provided to the MDP method so that the environment is fully observable.  
Figure 3: The MDP algorithm is tested in Daya Bay. The coastline is acquired from GSHHG, and the bathymetry is from a nautical chart.  
Three typical experiments are designed to examine the performance of the MDP path planning algorithm. Coordinates of the starting and target points in each experiment are given in Table 2. The starting and target points in Case 1 are in a north-south direction without any obstacles along their direct connection. In Case 2, there are islands between the direct connection between the starting and target points. In Case 3, the direct connection between the starting and target points crosses the coastline.
## 4 Analysis And Assessment
### Influence of Code Biases  
#### 4.2.1 Influence of IFB Bias  
In all observation models with BDS/GPS, the model, which has IFB and do not have ISB, is the uncombined model with triple-frequency BDS-only (UC3-BDS). The positioning errors in UC3-BDS are shown in Figure 4, and Figure 5 illustrates the IFB in UC3-BDS. The results show that differences in positioning errors are small. The reason is that the IFB in third frequency carrier phase \(L_{3}\) is absorbed by ambiguity. The IFB in third frequency pseudorange \(P_{3}\) has little effect on the positioning results, because the weight of pseudorange is small. Meanwhile, the results may be effected by the precise satellite orbit and clock products in BDS. But IFB can not be eliminated from estimated parameters. If IFB is eliminated, the third frequency ambiguity absorbs the IFB, then the convergence time and fixed success rate of the third frequency ambiguity will be not good for triple-frequency ambiguity-fixed PPP. IFB can not be omitted as a parameter to be solved. As a part of receiver DCB, each constellation has a corresponding IFB. According to the Section 2.2, each IFB is considered as a constant, the initial values of IFBs are one, the initial variances of dynamic noise in IFBs are zero.  
Figure 4: Positioning errors with or without IFB in UC3-BDS at different stations. (**a**) The positioning errors in UC3-BDS with IFB; (**b**) The positioning errors in UC3-BDS without IFB.  
Figure 3: The number of BDS/GPS satellite with triple-frequency observation data.
## 2 Materials and Methods
### Performance Ratio Calculation  
The IEC 61724 norm constitutes the European Standard that describes the general recommendations for performance monitoring and analysis of PV systems [19], both for grid tied and for isolated forms. Thus, in order to provide information on the energy efficiency and reliability of a PV installation, the standard defines a performance ratio (_PR_) as the quotient of the system's final yield (\(Y_{J}\)) to its reference yield (\(Y_{r}\)), and indicates the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Manufacturer** & **Technology** & \begin{tabular}{c} **Nominal** \\ **Power (W)** \\ \end{tabular} & \begin{tabular}{c} **N\({}^{\circ}\) of PV** \\ **Units** \\ \end{tabular} & \
\begin{tabular}{c} **N\({}^{\circ}\) of** \\ **Modules** \\ \end{tabular} &
\begin{tabular}{c} **Cell Size** \\ **(mm)** \\ \end{tabular} \\ \hline \multirow{5}{*}{SW} & \multirow{3}{*}{m} & 155 & 1 & 684 & \\ \cline{3-6}  & & 165 & 24 & 15,526 & \\ \cline{3-6}  & & 175 & 40 & 24,529 & \\ \cline{3-6}  & & 155 & 1 & 684 & \\ \cline{3-6}  & & 165 & 9 & 5814 & \\ \cline{3-6}  & & 175 & 15 & 9158 & \\ \hline \multirow{3}{*}{KC} & \multirow{3}{*}{P} & 167 & 7 & 4522 & \\ \cline{3-6}  & & 170 & 4 & 2584 & \\ \cline{3-6}  & & 175 & 21 & 12,938 & \\ \hline YL & & 175 & 1 & 648 & \\ \cline{3-6} ST & & 162 & 5 & 3190 & \\ \hline DK/ST & & 175 & 1 & 616 & \\ \hline IT & & 170 & 1 & 667 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the modules installed in SOLTEN I, considering manufacturer (SW, KC, YL, ST, DK/ST, and IT); technology (“m” for monocrystalline and “p” for polycrystalline); nominal power, number of PV production units installed with these modules; and the total number of modules installed for each type.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Street \#** & **SOLTEN I** & **SOLTEN II** & **Total \# of PV Units** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Number of PV production units per street in SOLTEN I. Note that from street number 16 to 19, there are units that belong to SOLTEN I and SOLTEN II. The former are located on the west side, and the latter on the east side of each street.  
overall effect of losses on the system output due to both array temperature and system component inefficiencies or failures, including balance of system components.  
The PR is defined as  
\[PR=\frac{Y_{f}}{Y_{r}}=\left(\frac{E_{out}}{P_{0}}\right)/\left(\frac{H_{i}}{G_{ i,ref}}\right) \tag{1}\]  
where the \(E_{out}\) is the net energy output of the entire PV system (AC in kWh); the \(P_{0}\) is the rated kW (DC in kWp) of the installed PV array; the \(H_{i}\) is the total in-plane irradiation (in kWh\(\cdot\)m\({}^{-2}\)) during the considered period; and, finally, \(G_{i,ref}\) is the module's reference plane of array irradiance (1 kW\(\cdot\)m\({}^{-2}\)), which is the irradiance at which \(P_{0}\) is determined.  
Thus, since the available data had monthly resolution, the monthly performance ratio, \(PR_{monthly}\) or simply PR henceforth, is the performance ratio, evaluated for a reporting period of one month.
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Surface Templating
#### 5.2.3 Nanofabrication of Plasmonic and Nanoelectric Devices
Over time, the scale of DNA-templated nanoparticle assemblies has also become increasingly complex by utilizing the modular assembly of DNA nanostructures into multicomponent, superstructure assemblies. [PERSON] et al. reported a modular strategy in which, first, gold nanoparticles are placed anywhere within a half-hexagonal DNA origami trough, then those troughs, each as a module, could be linked in series to form a specific, linear assembly of gold nanoparticles [159]. The Gang group demonstrated a strategy for utilizing polyhedral DNA origami as a frame for fabricating gold nanoparticle clusters with high spatial complexity. Attachment of gold nanoparticles on selected vertex points of the polyhedral DNA frame produced specific geometric arrangements of gold nanoparticles that can mimic the position of atoms in a crystal lattice unit cell. This could be done either using a single, high-degree polyhedra [160] or a large, crystalline assembly of low-degree polyhedra such as cubes and octahedrons, where gold nanoparticles could be placed at either the vertices or at the center of the structure [44; 161; 162] (Figure 7C). The [PERSON] group demonstrated a deterministic strategy of tethering gold nanoparticles using DNA origami nanoflowers, which wrap around a gold nanoparticle giving it rotational specificity [163]. DNA-assisted fabrication of electronic nanodevices has also garnered significant research attention. Carbon nanotubes (CNTs) have been an enticing candidate to outperform and replace silicon-based electronics. However, they are difficult to individually handle and align to precisely tailor the electrical properties of any CNT connection. [PERSON] et al. used micrometer-scale trench-like structures constructed by DNA bricks as a solution to the challenging problem of aligning and positioning CNTs between two nodes [126] (Figure 7A). DNA strands attached to the CNTs guided and locked them into parallel trenches that were only wide enough for a single CNT, thereby physically filtering and aligning them. The [PERSON] group also later used this technique to create FET transistors [164]. Site-specific growth of metallic nanostructures based on a DNA nanostructure template have also been explored via different metallic growth strategies. [PERSON] et al. used the nanocasting strategy to create gold nanowires by growing a gold crystal through a chain of DNA origami molds [96]. [PERSON] et al. demonstrated how low-valence metal ions (Cu\({}^{2+}\) and Ag\({}^{2+}\)) aggregate to and begin to metallize upon clusters of long ssDNA preferentially over stiffer DNA origami as a seedless approach to the fabrication of metallic nanostructures [165]. [PERSON] et al. used DNA origami nanostructures as a lithography mask to produce similarly shaped patterns of gold upon a silicon nitride chip [166] to create plasmonic nanostructures.
## 5 Discussion
### Datasets and Models
On the other hand, during the training and testing models on data DS1, there was no such significant difference in the average accuracy of predictions using LIWC (77.45%) and the combined approach analysis (77.04%) compared to the n-gram analysis (75.27%), but the average results achieved using these methods are still more favorable than those obtained with the n-gram analysis (Table 12). Given the above, it follows that n-grams better detect patterns related to deception by analyzing the DS1 compared to the DS2 dataset. A statistical consideration of the number of above-average predictions that exceed those defined by trends by models (Tables 12 and 13) and trends by methods (Tables 14 and 15) shows that logistic regression proved to be the most reliable model with the most above-average values compared to other methods (Table 9). It also showed good performance in combination with the LIWC analysis, where it achieved the highest number of above-average predictions compared to other models tested with the LIWC analysis on the same datasets. The random forest model achieved the least favorable above-average results while training and testing on the same datasets (Table 9). Examining the possibility of a generalization of the models by training them on DS3 and testing on DS1 and DS2 datasets, the highest number of above-average predictions (in relation to the trend by models and trend by methods) was achieved using the SVM and multinomial Naive Bayes models while the random forest showed the lowest rate of above-average predictions. At this point, it is difficult to conclude as to which model is generally most applicable to the problem of deception detection given that all models have shown different performances on different datasets using different data analysis methods. It is important to note that the procedure for selecting important LIWC features is limited to testing the models on all combinations of subsets up to the size of 11 features obtained using the WEKA tool (due to the factorial time complexity). The feature selection adopted in this study potentially needs to be improved by testing the model performance on feature subsets larger than 11 features or by applying another feature selection method.
# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 1 Introduction
In this work, acoustic datasets from WASN deployed in Barcelona and Madrid cities, Spain, are used for comparison of several machine learning clustering techniques. Machine Learning has been used with acoustic data, both audio signal and sound level indexes, to help cities to manage noise in recent literature. On one hand, supervised learning techniques were applied to identify the main noise source of the acoustic environment using Mel-frequency cepstral coefficients as features with Gaussian Mixture Model and Artificial Neural Networks as algorithms [12]. In reference [13], Convolutional Neural Networks (CNN) were evaluated to classify urban sound events using local features of short-term sound recording features and with long-term descriptive statistics. Additionally, CNN was implemented to detect anomalous noise source detection to remove unrelated road traffic noise events and then generate a noise map [14]. Another study using CNN over acoustic signal recordings developed a system to detect the presence of an unmanned aerial vehicle in a complex urban acoustic scenario focusing on cities security [15]. On the other hand, the unsupervised learning technique Hierarchical Agglomeration was trained to optimize the choice and the number of monitoring sites [16] for defining a methodology to estimate the mean \(L_{\text{d}}\) and \(L_{\text{n}}\) levels in urban roads with the noise profiles detected in the clustering [10]. Additionally, the K-means method was trained in reference [17] to identify sound pressure level patterns. In this paper, the performance of several data clustering techniques is evaluated for discovering and analyzing different behavior patterns of the sound pressure level. A comparison of clustering techniques is carried out using noise data from two large cities, considering isolated and federated data. After this introduction, datasets, applied techniques, and evaluation metrics are described in the next Section 2. Then, the results of the comparison together with a discussion and an analysis of these results are presented in Section 3. Finally, the main conclusions of this work are summarized in Section 4.