esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparative Analysis of Different Models
#### 4.2.3 Evaluation of Model Generalization Using an Application Dataset  
Sections 4.2.1 and 4.2.2 demonstrate the accuracy and efficacy of our methodology, though the test set represents only a subset of scenarios. The trained framework was deployed to predict 120-day temperatures for 10 additional pouring blocks under varying operational conditions, including diverse materials, pouring timelines, and temperature control strategies, to further evaluate the proposed model's generalization capability. Comparative analyses with the established models were conducted to compare their performances. Quantitative comparisons of prediction accuracy between the proposed model and the other models (BP, LSTM, ISV, and SSA-ISVR) using this extended dataset are detailed in Figure 19.  
The results indicate that the BP model achieved average \(RMSE\) and \(MAE\) values of 1.71 and 1.42 \({}^{\circ}\)C, respectively, with negative \(R^{2}\) values for the three blocks. Negative \(R^{2}\) values typically indicate severe underfitting or poor compatibility with the data characteristics; thus, negative values were set to zero in this study. The LSTM model demonstrated similar limitations, yielding an average \(RMSE\) of 1.78, an average \(MAE\) of 1.42 \({}^{\circ}\)C, and negative \(R^{2}\) values for the same blocks. By contrast, the ISVR model significantly improved prediction accuracy, achieving an average \(RMSE\) and \(MAE\) of 0.27 and 0.13 \({}^{\circ}\)C, respectively, and an average \(R^{2}\) of 0.93. The proposed SSA-ISVR model further enhanced prediction performance, achieving an average \(RMSE\) of 0.17, an average \(MAE\) of 0.10 \({}^{\circ}\)C, and an average \(R^{2}\) of 0.97. These results demonstrate that offline models with fixed parameters possess limited predictive capability for concrete blocks, whereas the SSA-ISVR online model significantly improves prediction accuracy and better captures complex data characteristics.  
Figure 19: Model evaluation in the extended application set.
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 2 Design of Shaking Table Test Model
### Design of the Curved Bridge Test Model  
In previous earthquakes, the main girder was rarely damaged and usually showed rigidity, so a steel girder was used instead of an RC girder (Figure 1b). Owing to the quality difference between the two materials, artificial mass is used; i.e., a total of 4.3 tons of artificial mass is arranged in the shaking table model. The piers are double-column piers, which achieve a 5% longitudinal slope of the curved bridge by setting different pier heights. The piers are numbered from pier 1 to pier 4, are arranged symmetrically, and their heights are 1.5, 1.7, 1.9, and 2.1 m (Figure 2a). The longitudinal reinforcement and stirrup adopt the same reinforcement ratio as the prototype structure: the longitudinal reinforcement adopts  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Type** & **Physical Quantity** & **Similarity** & **Similarity Coefficient** \\ \hline \multirow{2}{*}{Geometric properties} & Size, \(l\) & \(S_{l}\) & 0.1 \\  & Displacement, \(x\) & \(S_{x}=S_{l}\) & 0.1 \\ \hline \multirow{3}{*}{Material properties} & Strain, \(\varepsilon\) & \(S_{\varepsilon}\) & 1 \\  & Elastic modulus, \(E\) & \(S_{E}\) & 1 \\  & Equivalent mass density, \(\rho_{e}\) & \(S_{\rho_{e}}\) & 2 \\ \hline \multirow{3}{*}{Dynamic characteristics} & Time, \(t\) & \(S_{t}=S_{l}\sqrt{S_{\rho_{e}}/S_{E}}\) & 0.141 \\  & Frequency, \(f\) & \(S_{f}=1/S_{t}\) & 7.071 \\ \cline{1-1}  & Acceleration, \(a\) & \(S_{a}=S_{E}S_{\rho_{e}}\) & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Similarity relation of the curve bridge test model.  
Figure 1: Section parameters of main girder (unit: cm): (**a**) Girder dimensions of the reinforced concrete girder; (**b**) Girder dimensions of the steel girder.  
an HRB335-grade reinforcement with a diameter of 10 mm, the spiral stirrup adopts an R235-grade reinforcement with a diameter of 6 mm, and the spacing between the stirrups is 5 cm. The 28-day average compressive strength of concrete material is determined to be 35.16 MPa. Pier 2 is installed with fixed pot bearings (GPZ(II)0.8 MN-GD), and the other peers are installed with sliding pot bearings (GPZ(II)0.8 MN-DX). The parameter of the bearings is shown in Figure 3. To prevent the impact of concrete cracking between the concrete platform and the shaking table caused by vibration on the test results, the concrete platform is fixed on the shaking table with channel steel and h bolts (Figure 4e). The installed curved bridge shaking table model is shown in Figure 4a.  
Figure 3: Details of bearing parameters (unit: mm): (**a**) Fixed pot bearing; (**b**) Sliding pot bearing.  
Figure 2: Layout of the bridge model (Unit: cm): (**a**) Side view; (**b**) Plan view.
###### Abstract
Archaeologists have only recently begun to search for more complex models, drawing on innovative analysis of spatial and statistical computing and machine learning methods [36, 37]. To date, these studies are increasing when dealing with the detection of archaeological sites based on high-resolution satellite or drone imagery and in pottery classification [38, 39, 40]. Several studies have applied different methods for the identification of buried structures [5, 41]. For instance, in arid or desert environments, satellite remote data provides very reliable results that can be used to analyse archaeological sites and their environments. According to previous research, there are more Roman civilisation sites in this area [8], but only a few have been uncovered by remote sensing methods and identified the major distinguishing features of Roman settlements, such as villa and tower walls. In this context, the discovery of archaeological features using machine learning classification algorithms has emerged as one of the most important topics regarding remote sensing; it is an important method to better understand and reconstruct ancient landscapes. Thus far, these topics have mainly been studied by experts, sometimes with the help of automated tools, which results in a very costly and time-consuming process. Therefore, in recent years, many automation methods combining satellite image processing with machine learning models have been developed [19, 42, 43, 44, 45]. Due to the difficult geography, the investigation of ancient sites in this region is not easy; therefore, machine learning via satellite can play an important role in the detection and documentation of archaeological sites. The aim of this study is to evaluate the potential offered by the combined use of remote sensing data and machine learning to discover fortified sites in arid areas. An innovative aspect of our approach is the combination of very high spatial resolution satellite (PB1) technology with SAR data for better detection and to help obtain enhanced results.
# Intelligent Transportation System Technologies, Challenges and Security
## 3 The Impact of Innovative Technologies on Intelligence Transportation Systems
### Quantum Information Technologies in 6G Networks  
Quantum information technologies are poised to revolutionize the landscape of telecommunications, particularly in the context of 6G networks. These are Quantum Key Distribution (QKD), Quantum-Secure Communication Protocols, Quantum-Secured Internet of Things (IoT), Quantum-Sensing for Network Optimization, Quantum Machine Learning for Network Intelligence, and Quantum-Secured Satellite Communication. Incorporating quantum information technologies into 6G networks holds immense potential for enhancing security, reliability, and performance, ushering in a new era of ultra-secure and efficient telecommunications infrastructure [39].
## 4 Discussion
### Analysis of the Reconstruction Process and Additive Manufacturing of Models
When using the laser head, it was crucial to determine the laser power and the measurement resolution. The laser power setting was critical for the efficiency of making geometry measurements. If the laser power was not precisely adjusted, each measurement generated cavities in the triangle mesh structure of the 3D-STL model. Additional measurements of the cavity area geometry further generated the overscan formation. In the case of measurement resolution, as with the Atos II Triple Scan system, we ensured that the measurement resolution for both systems was better than 0.06 mm to adjust with the manufacturing tolerance of the scanned object. Additionally, we placed markers on the surfaces of the models before scanning them with both systems. This process helped to streamline the measurement and enabled the alignment of the scans afterward. To reduce reflectivity, the surface of the scanned model was coated with a matting agent--3D Helling. The publication specifies that the maximum thickness of the matting agent applied to the model surfaces should not exceed 0.016 mm [65]. However, the article's authors verified the layer thickness using an Alicona InfiniteFocusG4 optical microscope. Its average value was 0.01 mm. The choice of optical digitizing method affects the number of scans obtained during the measurement. Because the measurements on the measuring arm--laser head system were carried out manually and without configuration with a rotary table, a considerable amount of time was spent aligning the scans into one single-point cloud. The profile method is commonly used to evaluate surface roughness. However, using a profilometer can lead to technical limitations when dealing with complex geometric models like pelvic ones. To address this, an Alicona InfiniteFocusG4 optical microscope measurement procedure was developed. Due to the optical properties of the PLA material used, surface replicas were created to reduce measurement errors. Although replicas were produced, some errors at the edges of the scans were only partially mitigated. To address the noise generated during measurement, a 15 \(\times\) 15 median filter was applied. Additionally, the precise value of the Gaussian filter, \(\lambda\)c = 0.8 mm, was determined. This allowed for the effective separation of long-wave components, facilitating the distinction between roughness and waviness.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 2 Preliminaries
### Knowledge Graph Embedding
#### Linear Models  
Linear models assume that entities are represented as vectors and relations as matrices. They measure the plausibility of a fact as the similarity between two entities, usually after a linear mapping operation via the relation. In this case, for a true triple, the head entity can be linearly mapped, by the relation matrix, to somewhere close to the tail entity in the embeddings' vector space.  
ComplEx: ComplEx [18] is the most commonly used linear model. Entities and relations are represented as vectors in complex space \(\mathbb{C}^{d}\). Given any triple \((h,r,t)\), a multi-linear dot product is used to score the triple. Thus, the score function is defined as follows:  
\[f(h,r,t)=\text{Re}(\mathbf{h}^{\top}\text{diag}(\mathbf{r})\bar{\mathbf{t}})= \text{Re}(\sum_{i=0}^{d-1}[\mathbf{r}]_{i}\cdot[\mathbf{h}]_{i}\cdot[\bar{ \mathbf{t}}]_{i}), \tag{3}\]where the \(\text{Re}(\cdot)\) function takes the real part of a complex value and the \(\text{diag}(\cdot)\) function constructs a diagonal matrix from \(\mathbf{r}\), \(\mathbf{\bar{t}}\) is the conjugate of \(\mathbf{t}\); \([\cdot]_{i}\) is the \(i^{\text{th}}\) entry of a vector. The higher the score, the more likely the triple holds. For the prediction of triples, ComplEx further devises a function \(\phi:\mathcal{E}\times\mathcal{R}\times\mathcal{E}\rightarrow(0,1)\), which maps the score \(f(h,r,t)\) to a continuous truth value from the range of \((0,1)\), i.e.,  
\[\phi(h,r,t)=\sigma(f(h,r,t))=\sigma(\text{Re}(\mathbf{h}^{\top}\text{diag}( \mathbf{r})\mathbf{\bar{t}})), \tag{4}\]  
where the \(\sigma(\cdot)\) function denotes the sigmoid function. ComplEx learns the entity and relation embeddings by minimizing the logistic loss function, i.e.,  
\[\sum_{(h,r,t)\in\mathcal{T}\cup\mathcal{T}^{\prime}}\log(1+\exp(-y_{htt}\cdot f (h,r,t))), \tag{5}\]  
where \(y_{htt}=\pm 1\) is the label of a positive or negative triple \((h,r,t)\) and \(\mathcal{T}^{\prime}\) is the same one defined in Equation (2).
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Process Metrics  
The efficiency of biomass hydrolysis was evaluated using two different parameters. Saccharification efficiency (\(\eta_{\text{ssc}}\)) is defined as the ratio of sugars released from the carbohydrates in biomass to the theoretical maximum of releasable sugars and is calculated for microalgae biomass hydrolysis as follows:  
\[\eta_{\text{ssc}}=(c_{\text{glu}}+c_{\text{man}})\cdot(c_{\text{X}}\cdot x_{ \text{X,carb}}\cdot(x_{\text{carb,glu}}+x_{\text{carb,man}}))^{-1}, \tag{3}\]  
where \(c_{\text{glu}}\) and \(c_{\text{man}}\) are the glucose and mannose concentrations released by hydrolysis, respectively, \(x_{\text{X,carb}}\) is the carbohydrate fraction of the CDW of the microalgae, and \(x_{\text{carb,glu}}\) and \(x_{\text{carb,man}}\) are the glucose and mannose mass fractions in the carbohydrates of the microalgae, respectively. Here, only glucose and mannose were taken into account, since these make up over 80% of the total sugar content of _M. salina_ and were the target of enzymatic hydrolysis. [PERSON] et al. [12] determined the sugar composition of _M. salina_ grown in thin-layer cascade photobioreactors in a nutrient-replete ASW medium to be as presented in Table 6, which was used to calculate the saccharification efficiency. CDW concentration (\(c_{\text{X}}\)) was adjusted to 150 g L\({}^{-1}\) in all hydrolysis experiments prior to cell disruption, while the carbohydrate fraction of the dry _M. salina_ biomass (\(x_{\text{X,carb}}\)) was determined through analysis of its elemental and macromolecular composition. Since the enzyme mixes used for hydrolysis contained sugars as well, their contribution to the final sugar concentration of the biomass hydrolysate was measured and subtracted to determine the sugars released from the biomass by hydrolysis.  
Carbon solubilization efficiency (\(\eta_{\text{C}}\)) of the microalgal biomass, considering all carbon sources present in it, was calculated as follows:  
\[\eta_{\text{C}}=(x_{\text{C,MAH}}\cdot\rho_{\text{MAH}})\cdot(c_{\text{X}}\cdot x _{\text{C,X}})^{-1}, \tag{4}\]  
where \(x_{\text{C,MAH}}\) is the mass fraction of carbon in microalgae hydrolysis, \(\rho_{\text{MAH}}\) is the density of microalgae hydrolysate; \(c_{\text{X}}\) is the CDW concentration of microalgae biomass to be hydrolyzed, and \(x_{\text{C,X}}\) is the mass fraction of carbon in dry _M. salina_ biomass. The CDW concentration (\(c_{\text{X}}\)) was adjusted to 150 g L\({}^{-1}\) in all hydrolysis experiments prior to cell disruption, while the mass fractions of carbon in the dry biomass and in the hydrolysate were determined by elemental analysis. Additionally, the density of the biomass hydrolysate (\(\rho_{\text{MAH}}\)) was measured to be 995.8 g L\({}^{-1}\).
## 3 Materials and Methods
### Quantitative Analysis  
For the true quantitation of the selected markers in genuine, spiked and commercial EO, the external calibration method was chosen. Stock standard solutions were prepared via the addition of an aliquot of pure standard to an appropriate volume of cyclohexane (final concentration: 10 mg/mL). Suitable dilutions of each stock standard mixture were then prepared (final concentrations in the range of 5-0.1 mg/mL). The resulting stock and diluted solutions were supplemented with C13 (final concentration a dilution of 0.1 mg/mL), stored at 0 \({}^{\circ}\)C and renewed weekly. A calibration curve was built by analysing the above diluted solutions.  
For the true quantitation of the selected enantiomers, the MHS-SPME approach was adopted by using the same diluted solutions that were sampled by MHS-SPME and using the total vaporization approach [26]. MHS-SPME is the most appropriate approach for volatile component quantitation in liquid or solid matrices that are sampled by the headspace. MHS-SPME is the SPME extension of the MHE-static HS that was developed by [PERSON] et al. [27; 28], and is based on successive dynamic gas extraction from a single sample; the marker analyte peak area decays exponentially with the number of extractions, and the amount present initially in a given matrix (in this case, the EO) is represented by the sum of the areas from each extraction. The total area of the analyte(s) under investigation for quantitation is determined using the following equation:  
\[\text{A}_{\text{T}}=\sum_{\text{i=1}}^{\infty}\text{A}_{\text{i}}=\frac{\text{A }_{\text{1}}}{(1-\text{e}^{-q})}=\frac{\text{A}_{\text{1}}}{(1-\text{Q})} \tag{1}\]  
where A\({}_{\text{1}}\) is the analyte area after the first extraction, A\({}_{\text{T}}\) is the total analyte area (derived from the sum of the areas from each extraction) and Q: e\({}^{-q}\), \(-q\) is a constant calculated from the following linear regression analysis equation:  
\[\text{ln A}_{\text{i}}=-q\;(\text{i}-1)+\text{ln A}_{\text{1}} \tag{2}\]  
where A\({}_{\text{i}}\) is the peak area obtained from the ith extraction. In everyday practice, a few extractions (generally, 3-5) are sufficient to obtain a reliable exponential equation that describes the analyte decay, from which the total area of the analyte in the sample can be successfully extrapolated. The analytes are then quantified using an external standard approach that is performed by submitting standard mixtures of selected markers at different concentrations to MHS-SPME. MHS-SPME can also be carried out under non-equilibrium conditions [29; 30], provided that the sampling parameters are rigorously standardised and the amount of sample is suitable to give linear analyte decay(s).  
Figure S1 in the Supplementary Materials shows the GC-MS-extracted ions of linalool (_m/z_ = 71) in a bergamot EO, with three consecutive extractions from a sample (on the left) and its linear decay diagram (on the right).
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Queueing Problems
Every message is associated with some form of EDI transformation, which is defined by the associated \"Action\" and \"Category\". Table 23 shows a data dictionary of some of the \"Category\"-type transformations. We note from the table that there are eight different types of translations. Two of the most common translations are DeEnvelope translation and flat translation. The DeEnvelope translation locates the envelope type, for example, an ACH inbound envelope and calls the DeEnvelope business process. It then uses a data extraction tool to write out the data. The flat translation may take a flat file and reformat the transaction according to some standard format, such as X12 or EDIFACT. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Status Message** & **Count** & **Percentage** \\ \hline Accepted & 194,255 & 40.59 \\ Waiting & 184,632 & 38.58 \\ None & 97,797 & 20.44 \\ Accepted With Errors & 970 & 0.20 \\ Rejected & 563 & 0.12 \\ Received & 303 & 0.06 \\ Partially Accepted & 32 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Queueing system messages by ack status. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Message by Status** & **Transformation** & **Count** \\ \hline Accepted & DeEnvelope & 194,255 \\ Waiting & Flat Translation & 80,819 \\ Waiting & XML Translation & 61,013 \\ Waiting & Batch XML Translation & 41,154 \\ None & XML Translation & 41,198 \\ None & Flat Translation & 38,097 \\ None & Batch XML Translation & 14,279 \\ None & Send & 1570 \\ None & Extraction Translation & 1534 \\ Accepted With Errors & DeEnvelope & 970 \\ Waiting & Flat Potential Translation & 856 \\ None & XML Potential Translation & 632 \\ Rejected & DeEnvelope & 563 \\ Waiting & XML Potential Translation & 463 \\ None & Flat Translation & 460 \\ Received & DeEnvelope & 303 \\ Waiting & Send & 269 \\ Waiting & Flat Translation & 47 \\ Waiting & Null & 11 \\ Partially Accepted & DeEnvelope & 32 \\ None & Flat Potential Translation & 25 \\ None & Null & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Messages by ack status and transformation.
## 4 Results and Discussions
### Results of Clustering Algorithms
#### 4.1.4 DBSCAN Clustering  
DBSCAN clustering was another algorithm tested to create clusters of students. To use the algorithm, the scikit learn DBSCAN library was imported, setting by default the variable \"epsilon\" is equal to 67 and minimal number of samples are equal to 3, which is the minimum number of points that we need to create a cluster. We will store the labels that are formed by the DBSCAN algorithm and also the number of clusters that the algorithm has created by ignoring the noise if it is present. We can also see the noise using the variable \"n_noise\".  
The results of the DBSCAN are 5 clusters:  
* Cluster 0: (1, 17, 24, 34, 38, 42, 44, 52, 53)
* Cluster 1: (0, 19, 39)
* Cluster 2: (6, 7, 8, 9, 22, 23, 25, 29, 30, 33, 35, 36, 37, 41, 43, 48, 49, 50)
* Cluster 3: (15, 18, 27, 46, 47)
* Cluster 4: (2, 3, 4, 5, 10, 11, 12, 13, 14, 16, 20, 21, 26, 28, 31, 32, 40, 45, 51, 54)  
After the execution of the algorithm, we don't have noise because the number of students in 5 clusters is the same as the initial number of students.