esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Database Solution to Process BIM and GIS Data
## 4 Discussion  
The originality of the proposed solution lies, among other things, in that it allows BIM and GIS applications to \"see\" their source data as they did before integration, while gaining access to other data consistently. The BIM application sees GIS data and vice versa. This limits the necessary modifications of the BIM and GIS software, because it is sufficient to modify only the data writing format and not the data model. The storage of BIM and GIS data in a uniform database environment is another advantage of this solution. Conversions can be performed very efficiently and even imperceptibly to users.  
The authors' solution involves the use of the spatial data-processing mechanisms available in popular environments of relational database management. Therefore, BIM data can be stored in a relational database management system (e.g., in PostgreSQL with the PostGIS module, Oracle Spatial, and Graph or in the SQL server). For the prototype and testing, it was decided to use the PostgreSQL relational database management system and the PostGIS extension supporting spatial data, spatial indexes, and spatial functions. The advantage of PostgreSQL is the ability to create a hierarchy of dependent tables, which makes it easier to represent the IFC model. Therefore, the IFC class inheritance hierarchy can be restored in the form of inherited tables in the database.  
The scenario involving the use of dynamic data conversion requires the implementation of stored procedures that are able to return the data collected from the tables of an integrated spatial database for the given GUID building, transforming them \"on the fly\" into the IFC model using the perspective mechanism and stored procedures. The advantage of this solution is its continuous access to the current state of data through BIM and GIS applications.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 3 Scheme Design
It should be noted that stability is independent of the zero location. Table 2 shows the closed-loop system poles obtained by combining Equations (4), (7), and (8). As shown in Table 2, and since all poles of the closed-loop system are located in the left half plane, the circuit is stable. Figure 3: Loop structure: (**a**) passive fourth-order RC filter and (**b**) passive fourth-order RLC filter. Figure 2: Frequency synthesizer system architecture.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 1 Introduction
Structural analysis is a novel model-based fault diagnosis method that identifies the subsystem containing the analysis redundancy relation (ARR) based on structural decomposition [14]. Diagnosability, a critical design attribute representing the fault diagnosis capacity, plays an unignorable function in the design stage. Structural analysis is an essential device in fault diagnosis and plays a significant part in the design of diagnosability. Currently, there is less research on the diagnosability design based on structural analysis. First, this paper establishes the diagnosability structure based on structural analysis to further clarify the profound principle of fault diagnosis and diagnosability design. Secondly, the current situation of diagnosability research based on structural analysis is summarized and analyzed. The diagnosability index system based on structural analysis is designed. Finally, the problems of the current study and the future development direction are pointed out.
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Copper-Based Electrocatalyst for the eNO\({}_{3}\)R: A Case Study for Ammonia Production
#### 4.1.2 Facet Engineering  
Copper-based catalysts exhibit notable catalytic activity, especially when possessing a special electronic structure. Among these, Cu has demonstrated the highest activity, primarily yielding ammonia as the main product. The performance of Cu electrodes in nitrate electroreduction is closely linked to their structural characteristics. Additionally, the specific atomic structure of Cu catalysts, such as Cu(100) and Cu(111) surfaces, significantly influences their electrochemical behavior. In alkaline media, Cu(100) is more active in forming hydroxylamine compared to Cu(111), and the selectivity for nitrate reduction products can be controlled by adjusting the ratio of Cu(200) to Cu(111), which can be further tuned using surfactants to optimize nitrogen selectivity [162; 163; 164]. Studies on Cu\({}_{2}\)O electrocatalysts have shown that manipulating exposed facets, particularly the (100) facet, enhances ammonia yield compared to other facets like (111), demonstrating the importance of surface structure in catalytic performance [165]. [PERSON] and [PERSON] [166] compared eNO\({}_{3}\)R activity on Cu(111) and Cu(100) surfaces in acidic electrolytes. As a result, Cu(111)  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Cathode**} & **Ammonia Production** & \multicolumn{2}{c}{**Nitrogen Selectivity**} & **Refs.** \\ \cline{2-5}  & **Conditions** & **Performance** & **Conditions** & **Performance** \\ \hline
**Iron-based electrocatalys** & & & & \\ \hline Fe@C-1 & & & & \\ iron nanoparticles & & & 100 mg/L NO\({}_{3}^{-}\)-N\({}_{3}^{-}\) & 75.9\% nitrate & \\ uniformly & & - & 0.02 M NaCl and & conversion with & \\ embedded in the & & & 48 h of electrolysis. & 98\% nitrogen & \\ carbon & & & selectivity & \\ \hline Fe/Fe\({}_{3}\)C-NCNF-2 & & & & \\ Fe/Fe\({}_{3}\)C & & & 100 mg/L NO\({}_{3}^{-}\)-N\({}_{3}^{-}\) & 95\% nitrate & \\ nanoparticle- & & & 0.02 M NaCl and & conversion with & \\ decorated & & & 24 h of electrolysis. & 100\% nitrogen & \\ N-doped carbon & & & selectivity. & \\ nanofibers & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._demonstrated superior activity thanks to the easier formation of surface oxides, as evidenced by cyclic voltammetry (CV) showing significant reduction currents at less negative potentials compared to Cu(100) (Figure 11a). In addition, in situ electrochemical scanning tunneling microscopy (EC-STM) revealed that surface oxidation on Cu(111) enhanced its deoxygenation activity towards NO\({}_{3}^{-}\). The Cu\({}_{2}\)O formation Cu surfaces influences their NO\({}_{3}^{-}\)-to-NO\({}_{2}^{-}\) conversion efficiency which exhibits higher susceptibility to oxidation and facilitates oxygen transfer from NO\({}_{3}^{-}\) and subsequent reduction to NO\({}_{2}^{-}\) at less negative potentials. Thus, highlighting Cu\({}_{2}\)O role in catalyzing the eNO\({}_{3}\)R process [166].
## 4 Presentation and Discussion of the Results
### Transparency  
Within the selected literature, we find a common motivation in all works, i.e., the lack of transparency resulting from the centralization of data on a single platform, managed by an authority. According to the authors, the current reputation systems based on a central authority are prone to data manipulation and errors. As a solution to this problem, the authors propose a blockchain approach due to its immutability and tamper-proof characteristics.  
Indeed, the blockchain paradigm ensures that all data stored in the chain is immutable. Therefore, the reputation data (calculated scores and all records of manual feedback, direct and indirect observations, and inferred data [10]) are preserved in the chain ensuring the desired transparency, and avoiding any type of data manipulation. However, from a blockchain point of view, the e-commerce platforms are oracles that feed them, which can potentially introduce false and/or unfair data [26, 27, 28, 29], as well as biased reputation data [11, 45]. This problem, also presented as a limitation of the smart contracts in [28], potentially compromises this desired transparency. As such, in terms of transparency, the blockchain-based approaches partially solve the problem, so additional means are needed in order to avoid low quality data from entering the chain.  
Based on the literature, we state that this transparency in reputation systems mainly concerns data about the users' reputations. The lack of transparency enables malicious users to perpetrate ballot-stuffing and bad-mouthing frauds. Moreover, this problem is extended to the data about the user identities, because a noneffective identity management enables means to Sybil and whitewashing attacks. In the following two sections, we discuss these types of problems.
## 2 Materials and Methods
### Methodology of the Study and Objective Functions Used
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Step**} & \multirow{2}{*}{\(x_{1}\)/\(y_{1}\)} & \multicolumn{5}{c}{**Design Vector**} & \multirow{2}{*}{**Obj. Value**} & \multirow{2}{*}{**Notes**} \\ \cline{3-3} \cline{5-8}  & & **1** & & **2** & & **3** & **4** & **5** \\ \hline \multirow{2}{*}{**1**} & \(x_{1}\) & \(-34.32\) & \(-5.13\) & \(-58.68\) & 1.29 & \(-22.26\) & \(5.15\cdot 10^{3}\) & No improvement \\  & \(y_{1}\) (\(j=4\)) & \(-34.32\) & \(-5.13\) & \(-58.68\) & **[14.73]** & \(-22.26\) & \(5.36\cdot 10^{3}\) & keep \(x_{1}\) for Step 2 (\(x_{2}=x_{1}\)) \\ \hline \multirow{2}{*}{2} & \(x_{2}\) & \(-34.32\) & \(-5.13\) & \(-58.68\) & 1.29 & \(-22.26\) & \(5.15\cdot 10^{3}\) & Improvement \\  & \(y_{2}\) (\(j=3\)) & \(-34.32\) & \(-5.13\) & **[35.52]** & 1.29 & \(-22.26\) & \(2.96\cdot 10^{3}\) & keep \(y_{2}\) for Step 3 (\(x_{3}=y_{2}\)) \\ \hline \multirow{2}{*}{3} & \(x_{3}\) & \(-34.32\) & \(-5.13\) & 35.52 & 1.29 & \(-22.26\) & \(2.96\cdot 10^{3}\) & No improvement \\  & \(y_{3}\) (\(j=5\)) & \(-34.32\) & \(-5.13\) & 35.52 & 1.29 & \([-43.44]\) & \(4.36\cdot 10^{3}\) & keep \(x_{3}\) for Step 4 (\(x_{4}=x_{3}\)) \\ \hline \multirow{2}{*}{4} & \(x_{4}\) & \(-34.32\) & \(-5.13\) & 35.52 & 1.29 & \(-22.26\) & \(2.96\cdot 10^{3}\) & Improvement \\  & \(y_{4}\) (\(j=1\)) & **[11.97]** & \(-5.13\) & 35.52 & 1.29 & \(-22.26\) & \(1.93\cdot 10^{3}\) & keep \(y_{4}\) for Step 5 (\(x_{5}\)=\(y_{4}\)) \\ \hline \multirow{2}{*}{5} & \(x_{5}\) & 11.97 & \(-5.13\) & 35.52 & 1.29 & \(-22.26\) & \(1.93\cdot 10^{3}\) & No improvement \\  & \(y_{5}\) (\(j=5\)) & 11.97 & \(-5.13\) & 35.52 & 1.29 & \([-88.95]\) & \(9.35\cdot 10^{3}\) & keep \(x_{5}\) for Step 6 (\(x_{5}\)=\(x_{5}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example case: the 5 first steps of the PROS algorithm for the F01-Sphere function (sphere_func) in [–100, 100]2.  
population-based methods is set to 550. Equivalently, for the two methods that are not population-based (PRS and PROS), the convergence criterion has to do with the maximum number of objective function evaluations _MaxFE_ which is set to 165,000.
## 2 Superiorities of Near-Space Radar Platform
### Persistent Region Coverage or Fast Re-Visiting Frequency  
The first superiority of near-space platform is persistent region coverage or fast re-visiting frequency. Space technologies have revolutionized modern battlefield and remote sensing techniques greatly [16]; however, persistent coverage, which is highly desired, is still unavailable through satellites or airplanes. Table 1 shows the observation time for selected LEO orbits. Fortunately, these can be achieved by using near-space platforms. The altitude of near-space is above troposphere and atmosphere region where most weather occurs. There are no cloud, thunderstorm, or precipitation [17]. Moreover, propulsion technique can be applied to offset possible mild winds in near-space. Near-space stationary vehicles can stay at a specific site for a long time, so a persistent region coverage is possible. This advantage has a particular value for applications that require persistent monitoring. Such platforms are the functional cross between satellites and airplanes. They can fast-fly or station-keep over a specific position, to provide large footprint and long mission duration.  
Figure 1: Near-space definition and its advantages while compared to space including geosynchronous earth orbit (GEO), middle earth orbit (MEO) and low earth orbit (LEO), and airspace.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Construction of Seq2 Seq-Based Restoration Model
#### 4.3.3 Evaluation Indicators  
To verify the accuracy of the experimental results, a variety of evaluation indicators were used to assess the accuracy of the restoration results. These indicators include root mean square error (RMSE), mean absolute error (MAE), Frechet distance (FD), and average Euclidean distance (AED). RMSE reflects the accuracy of the repair result, MAE visualizes the repair error, FD reflects the matching degree between the repaired trajectory and the  
Figure 7: Basic network model structure: (**a**) RNN model structure; (**b**) LSTM model structure; (**c**) GRU model structure; (**d**) BILSTM/BIGRU model structure.  
real trajectory, and AED reflects the positional accuracy of the repaired trajectory points. The specific calculation of the evaluation indexes is as follows (Equation (13)).  
\[\left\{\begin{array}{l}\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{{ \left({Z_{\text{true},i}-Z_{\text{re},i}}\right)}^{2}}}}}\\ \text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{{{\left|{Z_{\text{true},i}-Z_{ \text{re},i}}\right|}}}\\ \text{FD}=\text{max}_{\left[{{{\left|{{{x_{\text{true},i}}-{x_{\text{re},i}}} \right)}^{2}}+\left({{y_{\text{true},i}}-{y_{\text{re},i}}}\right)^{2}}}}\\ \text{AED}=\frac{{\sum\limits_{i=1}^{n}{{{{\left({{x_{\text{true},i}}-{x_{\text {re},i}}}\right)}^{2}}+\left({{y_{\text{true},i}}-{y_{\text{re},i}}}\right)^{2}} }}}{n}}\\ \end{array}\right. \tag{13}\]  
The RMSE and MAE units are given in degrees, and the maritime common unit (nautical miles) is used as the FD and AED units. \(n\) is the number of samples; \(Z_{\text{true},i}\) and \(Z_{\text{re},i}\) are the true and restored values of the features; and \(x_{\text{true},i}\), \(y_{\text{true},i}\), \(x_{\text{re},i}\), and \(y_{\text{re},i}\) are the projected latitude and longitude of true and restored values, respectively.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 3 Results
### Model Selection and Training  
For the results of the selection and training of anomaly detection models. Two different data sets were used to train and validate the models. The training set consisted of 1500 regular and 200 anomalous examples, while the validation set included 500 every day and 50 abnormal examples. The division of the data was carried out in a stratified manner to guarantee the representativeness of the classes. During the training process, regularization techniques, including L1 and L2 regularization, were applied to avoid overfitting the models.  
Additionally, specific hyperparameter tuning was carried out. The number of hidden layers in the neural network was set to 3, with 128, 64, and 32 neurons in each layer. A learning rate of 0.001 and a ReLU activation function were used in the hidden layers.  
The models were trained for 100 epochs using the Adam optimizer. During training, the loss on the validation set was monitored to avoid overfitting. It was observed that the models achieved an accuracy of 95% on the validation set, indicating their practical ability to detect anomalies.
# Shaking Table Testing of Liquefied Soil Layer Located in the Bottom Slab of a Subway Station
## 3 Analysis of Test Results of Foundation Soil
### Analysis of Spectral Properties of Soil  
The spectral pattern of the soil under the action of the Beijing Hotel wave is analyzed, and the primary frequency of points A4, A5, A6, A7, and A8 are shown in Table 6.  
The points A4 and A5 are located in the soil near the top and bottom slabs of the accessory structure, respectively. When the peak acceleration of the input seismic wave is below 0.5 g, there is minimal alteration in the primary frequencies of A4 and A5, suggesting that the soil at that location remains in the elastic phase. When the input seismic wave reaches a peak acceleration of 0.5 g, the main frequencies of A4 and A5 abruptly fall to 5.126 Hz and 1.467 Hz, respectively. At this point, the stiffness of the modeled soil decreases, and its nonlinearity increases, indicating that the soil enters the plastic phase at this location.  
Point A6 is situated within the soil on the right-hand side of the base plate of the main structure. Additionally, this point is positioned at the division between the saturated and unsaturated sand. When the peak acceleration of the input seismic wave is below 0.4 g, there is minimal alteration in the primary frequency of A6, suggesting that the soil at the current location remains within the elastic phase. When the input peak acceleration is greater than or equal to 0.4 g, the primary frequency of point A6 suddenly decreases, indicating that the stiffness of the soil is weakened, and the soil at this location enters the plastic working stage.  
Points A7 and A8 are located in saturated sand 700 mm and 900 mm below the model surface. Their principal frequencies do not change with the peak acceleration of the input seismic wave.  
Generally, as the seismic intensity gradually increases, the primary frequency of points in close proximity to the structure and situated within the unsaturated soil demonstrates a transition from high frequency to low frequency. This may be due to the influence of the structure on the primary frequency of the soil. The points in the saturated sandy soil did not show a significant change in their primary frequency, which may be due to the effect of the saturated sandy soil on them.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 2 System Model
### Network Model  
The sensor network is \(200\times 200\times 200\) m cube: The top of cube is considered to be the surface of water and bottom as a seabed. The nodes are deployed one by one in 3D space having random location and depth to address a realistic scenario, including a single sink node on the surface having depth zero. The nodes are randomly placed to assure the flexibility of proposed routing protocol. Once the nodes are placed, they are considered to remain static and do not flow because of marine currents and waves. Each node is placed at a minimum of 40 meters separation from the surrounding nodes. This is done to prevent nodes from sending packets of similar measured events to the sink. The separation between nodes is calculated through Euclidean metric using the well-known equation as follows,  
\[d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}+\left(z_{2}- z_{1}\right)^{2}}, \tag{1}\]  
where \(\left(x_{i},y_{i},z_{i}\right)\) are the location coordinates for the \(i\) node. Since the nodes are placed randomly, is assumed that each node uses a power control mechanism to alter and save the transmission power based on the distance between two nodes. Figure 1 presents an example of network model where the nodes are randomly located in 3D space.
## 2 Materials and Methods
### Growth-Related Gene Expression Profiles in Shrimp Using Quantitative Realtime PCR  
Total RNA was extracted from individual shrimp muscle with TriReagent(r) (Molecular Research Center, Cincinnati, OH, USA) according to the manufacturer's instructions and was subsequently treated with 0.5 unit/ug RQ1 RNase-free DNase (Promega, Madison, WI, USA) for 30 min at 37 \({}^{\circ}\)C to remove any contaminated genomic DNA. The concentration of total RNA was measured using a NanoDrop ND8000 spectrophotometer. Total RNA was used for cDNA synthesis with an ImProm-II(r) Reverse Transcriptase System kit (Promega, Madison, WI, USA) following the company's instruction.  
In brief, 1.5 mg total RNA was reverse transcribed to cDNA by priming with an oligo(dT) primer. The transcript levels of six growth-related genes containing _fatty acid synthase_ (_Fas_) [27], _lipase_[28], _carnitine palmitoyltransferase_ (_Cpt-1_) [27], _cytochrome C oxidase_ (_Cox_) [29], _ATP synthase_ (_ATPase_) [29], and _NADH dehydrogenase_ (_NADH_) [30] were determined. _Elongation factor 1 alpha_ (_EF1a_) was used as the internal control for quantitative real-time PCR (qPCR) analysis. A qPCR reaction (20 \(\upmu\)L) composing of cDNA (0.5 \(\upmu\)L), specific primers (200 nM each) and SsoAdvanced(tm) Universal SYBR(r) Green supermix (Bio-Rad, Irvine, CA, USA) according to the company's instruction. The thermal cycling parameters were 95 \({}^{\circ}\)C for 30 s, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s, 56 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 30 s. The melting curve analysis was performed from 65 \({}^{\circ}\)C to 95 \({}^{\circ}\)C with a continuous fluorescent reading with a 0.5 \({}^{\circ}\)C increment. The threshold cycle (Ct) was analyzed using BioRad CFX Manager 2.1 software (Bio-Rad, Irvine, CA, USA). Relative mRNA abundance of each gene in the sample group was calculated based on 2\({}^{\text{-}}\)\({}^{\
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 3 Results and Discussion
### Quantitative Assessment
#### 3.1.2 Baseline Results for Signer Representation #16  
The results are summarized in Table 7, in which we report average values and standard deviation after seven identical simulations, for the binary recognition of FLSs, DSs, PTSs and FBuoys.  
From this table, it appears that the best results are obtained for the recognition of FLSs, with a 64% frame-wise F1-score and a 52% \(I_{pr}\). DSs and PTSs get comparable performance values, while FBuoys are not very well recognized--14% frame-wise F1-score and 11% \(I_{pr}\). Except for FBuoys, one can note that the recall is usually higher than the precision, which means that there are more false positives than false negatives.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Configuration**} & \multicolumn{4}{c}{**Corresponding Feature Vectors and Size (Section 2.3.1)**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-11} \# & \multirow{2}{*}{**Body and Face**} & \multirow{2}{*}{**Hands**} & \multirow{2}{*}{\(x_{\text{raw2D}}^{\text{b}}\)} & \multirow{2}{*}{\(x_{\text{raw2D}}^{\text{b}}\)} & \multirow{2}{*}{\(x_{\text{raw2D}}^{\text{th}}\)} & \multirow{2}{*}{\(x_{\text{raw3D}}^{\text{th}}\)} & \multirow{2}{*}{\(x_{\text{raw2D}}^{\text{th}}\)} & \multirow{2}{*}{\(x_{\text{hapes}}^{\text{hand}}\)} & \multirow{2}{*}{\(x_{\text{hapes}}^{\text{hand}}\)} & \multirow{2}{*}{\(x_{\text{feat2D}}^{\text{th}}\)} & \multirow{2}{*}{\(x_{\text{feat3D}}^{\text{th}}\)} & \multirow{2}{*}{Size} \\ \cline{3  
The differences in terms of performance can be explained first by the discrepancy with respect to the number of training instances: as can be seen in Table 4, FLSs account for about 75% of the manual units, while this drops to 11% for DSs and for PTSs. Only 589 FBuoy instances are annotated in Dicta-Sign-LSF-v2, that is about 2% of the total number of manual units.  
However, other reasons can be proposed. DSs are a very broad category of units--many sub-categories can be listed--with a lot of inner variability. Also, the role of eye gaze is known to be crucial in DSs, however our signer representations include no gaze information. PTSs are very short, sometimes they last only one or two frames in 25 fps videos. As for FBuoys, they correspond to a maintained hand shape at the end of a bimanual sign, when it bears a linguistic function, which is not easy to detect (sometimes the hand shape is held for other reasons, and is not annotated as a FBuoy).