esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 3 The Joint Receiving Scheme of Correlator
### Optimal Coefficient Estimation of Carrier Loop
Therefore, \(\gamma\) and \(\omega\) can be written as follows [16; 17; 18]:  
\[\lambda=\frac{A_{d}^{2}}{A_{d}^{2}+A_{p}^{2}},\ \omega=\frac{A_{p}^{2}}{A_{d}^{2}+A_{p} ^{2}} \tag{31}\]  
Putting Equations (26) and (27) into (28), we can obtain  
\[\arctan(\frac{\alpha n_{d,Q}+\beta n_{p,Q}}{\alpha A_{d}+\beta A_{p}})=\lambda \cdot\arctan(\frac{n_{d,Q}}{A_{d}})+\omega\cdot\arctan(\frac{n_{p,Q}}{A_{p}}) \tag{32}\]  
\(n_{x,Q}\) is the noise in the quadrature branch of the x component, which includes white noise and the cross-correlation \(R_{x,Q}\) between the pseudo-code of the x component and the quadrature branch signal. The value of \(R_{x,Q}\) depends on the amplitude of the quadrature branch signal. The orthogonal branch of the data component is the pilot, and vice versa. Therefore, the relationship of the cross-correlation between the data and the pilot can be obtained as \(R_{d,Q}=A_{p}/A_{d}\cdot R_{p,Q}\) according to the design of the satellite signal, and the mean value of the noise component is zero. Therefore, the relationship between \(n_{d,Q}\) and \(n_{p,Q}\) can be obtained as \(\mu_{n_{d,Q}}=A_{p}/A_{d}\cdot\mu_{n_{p,Q}}\). The above formula can be written as follows:  
\[\Delta\hat{\phi}_{p}=\arctan(\frac{Q_{p,p}}{I_{p,p}})=\arctan(\frac{A_{p}\cdot \sin\delta_{q,p}+n_{p,Q}}{A_{p}\cdot\cos\delta_{q,p}+n_{p,I}}) \tag{24}\]0  
\(x\) is the infinitesimal equivalent of \(\arctan x\) when \(x\to 0\). \(\Delta\hat{\varphi}_{d}\), \(\Delta\hat{\varphi}_{p}\), and \(\Delta\hat{\varphi}_{l}\) tend to 0 when the carrier loop is stable. Therefore, Equation (31) can be equivalently written as Equation (32). \[\Delta\hat{\phi}_{p}=\arctan(\frac{Q_{p,p}}{I_{p,p}})=\arctan(\frac{A_{p}\cdot \sin\delta_{q,p}+n_{p,Q}}{A_{p}\cdot\cos\delta_{q,p}+n_{p,I}}) \tag{24}\]1  
By solving Equation (34), we can obtain \(\frac{\alpha}{\beta}=\frac{A_{d}}{A_{p}}\). Based on the above derivation, we can reach the conclusion that the optimal combination coefficient of the data and pilot correlators joint in the carrier loop is the amplitude ratio of the two. It is worth noting that this conclusion is not only applicable to the two-quadrant arctangent phase detector, but also to other phase detector functions. If the approximation that arctan is equivalent to \(\chi\) is omitted, it can be proved that the conclusion is still suitable for the point-division discriminator function.
###### Abstract  
The aim of this study was to compare the mechanical and physicochemical properties of Ti6 Al4V alloy samples produced using 3D printing (Direct Metal Laser Sintering) and bar after plastic working. Both sets of samples were subjected to various surface-processing methods, including sandblasting, heat treatment (hardening for 120 min at 820 \(\pm\) 10 \({}^{\circ}\)C, followed by cooling to room temperature), mechanical polishing, and steam sterilization. This research included macroscopic surface evaluation before and after pitting corrosion resistance tests, metallographic microscopic research, scanning electron microscopy, and energy-dispersive spectroscopy, as well as measurements of hardness, roughness, and surface wettability. The results showed that heat and surface treatment (grinding and mechanical polishing) significantly increased the material's hardness and corrosion resistance. Furthermore, the steam sterilization process had a positive effect by increasing surface wettability, which is important for biomedical applications, as higher wettability promotes better integration with biological tissues. This is especially relevant in implantology, where surface properties influence osseointegration and overall biocompatibility. In summary, these findings indicate that the selection of manufacturing method and the application of subsequent treatment processes significantly affect the mechanical and physicochemical properties of Ti6 Al4V alloy, thereby influencing its performance and suitability for diverse engineering and biomedical applications.  
Ti6 Al4V titanium alloy; mechanical properties; physicochemical properties; steam sterilization; heat treatment; grinding and mechanical polishing +
Footnote †: journal: Applied Sciences
## 3 Methodology
### SCRM Process for IFMSC Risk Mitigation
#### 3.2.5 Risk Mitigation  
By resolving the AHP model and ranking the options, we better understood how to mitigate the risks regarding the IFM's quality. The steps used to achieve the results from the AHP were:  
* Performing a pairwise comparison of elements.
* Normalizing the matrix. \[r_{ij} =\frac{x_{ij}}{\sum_{i=1}^{n}x_{ij}}\] (1) \[w_{j} =\frac{1}{n}\sum_{i=1}^{n}x_{ij}\] (2)
* Calculating the consistency index (CI) \[\text{Consistency Index (CI)}=\frac{\lambda_{max}-\text{n}}{\text{n}-1}\] (3) where \(\lambda_{max}\) is the largest eigenvalue, and n represents the number of mitigation attributes.
* Calculating consistency ratio (C.R.) \[\text{Consistency Ratio (C.R.)}=\frac{\text{CI}}{\text{RCI}}\] (4) As a result of dividing the CI by the RCI, we obtain the C.R. The upper limit should not exceed 0.1 to ensure the C.R. condition is satisfied [72].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Numerical Rating** & **Definition** & **Explanation** & **Reciprocal** \\ \hline
1 & Equal Importance & Two activities contribute equally to the objective & 1 \\
3 & Moderate Importance & Experience and judgment slightly favor one activity over another & 1/3 \\
5 & Strong Importance & Experience and judgment strongly favor one activity over another & 1/5 \\
7 & Very Strong Importance & An activity is favored very strongly over another & 1/7 \\
9 & Absolute Importance & Evidence favoring one activity over another is of the highest possible & 1/9 \\  & & order of affirmation & \\
2,4,6,8 & Intermediate values & Used to represent a compromise between the priorities listed above & 1/2, 1/4, 1/6, 1/8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The measurement scale for pairwise comparisons.  
Random consistency index (RCI) = chosen following the order of the indicators (n) in the comparison matrix with the [PERSON] proposed table, as shown in Table 3, where for n = 3, it equals 0.58, and for n = 8, it equals 1.41.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion
### Result of the Discrete HMM on Other SMS Dataset in Chinese  
The HMM presented in this paper can be applied to other languages. In order to evaluate its performance in different languages, we apply the proposed HMM method on a Chinese SMS dataset containing 2000 SMS messages. The dataset is derived from the production environment of our cooperated SMS service company that provides the SMS service in the East China area.  
We choose 700 spam SMS and 700 ham SMS from the Chinese SMS dataset as the training set and the rest 300 spam SMS and 300 ham as the testing set. The statistics of the training and testing datasets are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Class** & **Accuracy (A)** & **Precision (P)** & **Recall (R)** & **F-Measure (F1)** \\ \hline The proposed HMM & Spam & 0.959 & 0.892 & 0.816 & 0.852 \\ \hline \multirow{2}{*}{\begin{tabular}{c} LSTM [11] \\ \end{tabular} } & Spam & 0.953 & 0.849 & 0.777 & 0.811 \\  & Ham & 0.972 & 0.976 & 0.973 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} CNN [11] \\ \end{tabular} } & Spam & 0.979 & 0.988 & 0.858 & 0.922 \\  & Ham & 0.982 & 0.996 & 0.988 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **The proposed HMM** \\ \end{tabular} } & Spam & **0.959** & **0.892** & **0.816** & **0.852** \\  & Ham & **0.969** & **0.983** & **0.976** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the experimental results with different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Actual** & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Prediction \%**} \\ \hline The proposed HMM & Spam & Ham & Spam & Ham & AUC \\ \hline \multirow{2}{*}{
\begin{tabular}{c} The proposed HMM \\ \end{tabular} } & Spam & 222 & 50 & 0.892 & 0.031 \\  & Ham & 27 & 1559 & 0.108 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of the results.  
The confusion matrix turned out to be that shown in Table 9.  
The evaluation results of the proposed HMM are shown in Table 10.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Year** & **Authors** & **Focus Area** & **CC** & **EC** & **SC** & **LCCA** & \multicolumn{3}{c}{**Overall Findings/Tools**} \\ \hline \multirow{3}{*}{2000} & Apeldoorn & Env. restoration; & & & & & - & Long-term savings of 30–50\% \\  & [60] & Social disruptions & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & for trenchless} \\  & & & & & & & & - & Higher upfront costs \\ \hline \multirow{3}{*}{2002} & [PERSON] et al. & \multirow{3}{*}{User-delay costs} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & - & Urban pipeline renewals \\  & [75] & & & & & & - & Surface restoration is costly \\  & & & & & & & - & Open cut with trenches \\ \hline \multirow{3}{*}{2002} & [PERSON] and & Cost curve analysis & & & & & - & CIPPRT costs rise exponentially \\  & [PERSON] [34] & for CIPPRT and & & & & & & with diameter \\  & & OCPI & & & & & - & 5100–5800/linear foot \\ \hline \multirow{3}{*}{2003} & Gangavarapu & OCPI and CIPPRT & & & & & - & OCCM costs grow linearly \\  & et al. [90] & (Traffic delay) & & & & & - & Cost of fuel \\ \hline \multirow{3}{*}{2004} & \multirow{3}{*}{[PERSON] [29]} & OCPI and & & & & & - & Time delay (major contributors.) \\  & & Trenchless & & & & & & - & OCCM costs 70\% of the total \\  & & (Construction) & & & & & & project costs \\ \hline \multirow{3}{*}{2005} & Gilchrist and & Traffic congestion; & & & & & - & Trenchless methods reduce \\  & [PERSON] [27] & Business & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} & \multirow{3}{*}{�} \\  & interruption & & & & & & & - & Lowered social costs by \\  & & & & & & & & & \$15,000–520,000/day \\ \hline \multirow{3}{*}{2005} & [PERSON] [91] & CIPPRT cost & & & & & - & CIPPRT costs \\  & scalability with & & & & & & - & Range from $100/LF for 18-inch pipes \\  & & pipe diameter & & & & & & to over $800/LF for large diameter \\ \hline \multirow{3}{*}{2006} & \multirow{3}{*}{[PERSON] [77]} & Open-cut and & & & & & - & OCPI was cost-effective for large \\  & CIPPRT; LCCA for & & & & & & & - & diameter pipeline ranges (i.e., \\  & & water mains & & & & & & - & \(>\)30 inches”) \\ \hline \multirow{3}{*}{2007} & \multirow{3}{*}{[PERSON] et al. [76]} & Open-cut and & & & & & & - & Used Monte Carlo analysis \\  & & & & & & & & & - \\ \cline{1-1}  & & & & & & & & & - & Open-cut method cost \$380/LF for \\ \cline{1-1}  & & & & & & & & 18” pipe \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies related to the cost of trenchless technology (�: indicates that the corresponding literature includes the cost; ✗: indicates the literature does not include the cost.)
## 4 Results and Discussion
### Measurements for Virtualizing an Existing Event Source
#### 4.1.1 Time to Virtual Representation of an Existing Event Source  
As explained before, the time required to have a virtual representation of an existing event source as a REST service depends on three independent times:  
\[t_{simple\ virtualization}=t_{discovery}+t_{registering}+t_{deploying} \tag{1}\]  
In Equation (1), _discovery time_ involves the transmission time from the sensor to the gateway whenever the service is announced (passive discovery) or queried (active discovery); _registering time_ involves the time required for storing in the database the information relevant to the service to be virtually represented, as the event source presented here; finally, _deploying time_ involves the time required to expose the virtualized representation of the event source as a REST service.  
Discovery time is outside the scope of this paper, as it depends on other issues like the time required for the transmission of the messages between the nodes and the gateway. The environmental conditions have also an influence in the transmission, and therefore in discovery time. For the other two, Figure 5 shows the relation between both of them after 230 tests.  
As it can be watched in the previously mentioned figure, the most time consuming task is precisely the one required to register the information of the event source in the database. Indeed, the total time required to have a virtual representation of the event source has a maximum value of 641 milliseconds and a minimum of 221, with an average value of 384.63 milliseconds and a standard deviation of 59.09. Another remarkable piece of information is the time required to register the event source; it shows a maximum value of 590 milliseconds and a minimum of 221 with an average one of 336.2 milliseconds and a standard deviation of 56.54. This represents between a 77.83% and a 92.04% of the total time.  
Figure 5: Time required getting a virtual representation of an existing event source in the gateway.
## 5 Discussion  
This study proposed an ACI model capable of quantitatively evaluating the aging condition of thermoplastic insulation based on mechanical properties and estimating the remaining lifetime under specific operating conditions. An exponential aging model was derived from tensile strength and elongation data obtained through accelerated thermal aging at various temperatures. The resulting model demonstrated high goodness-of-fit, with coefficients of determination (R\({}^{2}\)) of 0.845 for tensile strength and 0.834 for elongation.  
Analysis of the aging coefficients revealed that the temperature coefficient (0.01) for tensile strength was significantly higher than the time coefficient (0.0003), indicating that temperature has a much stronger influence on degradation than time. This suggests that temperature control is a critical factor in maintaining the long-term reliability of insulated cables during operation.  
The proposed ACI framework not only enables a quantitative assessment of the current aging state of thermoplastic insulation but also facilitates accurate lifetime prediction until a predefined ACI threshold (e.g., ACI = 0.5) is reached. A key contribution of this study lies in its ability to evaluate the aging condition and remaining life of cable insulation even in the absence of a detailed operational history. This feature significantly enhances the practical applicability of the model, supporting the implementation of condition-based maintenance strategies. The originality of the work is further demonstrated by the introduction of a mechanistic aging modeling approach that links accelerated aging data with real-time reliability assessment, providing a novel pathway for insulation health diagnostics and lifecycle asset management in power cable systems.  
However, the current model considers only mechanical aging indicators, excluding electrical factors such as partial discharge, space charge, and dielectric loss. In future work, various electrical diagnostic techniques, including partial discharge, space charge, dielectric loss, and conduction characteristics, will be quantitatively evaluated under combined thermal and electrical aging conditions. Each diagnostic parameter will be normalized and indexed to reflect its degradation behavior, and appropriate weighting factors will be assigned based on its correlation with insulation breakdown strength. Furthermore, the selection of the ACI threshold was based on engineering judgment; future studies should incorporate failure history data to establish more objective, reliability-based thresholds. Future work should focus on integrating electrical aging diagnostics with mechanical models to establish a comprehensive health index framework. Additionally, validation of the model's effectiveness through long-term field data will be essential for practical deployment.
# Ten Years of TerraSAR-X Operations
## 5 Radar Experiments with TerraSAR-X
### Starting Spotlight  
The azimuth steering angles of TerraSAR-X were initially kept below \(\pm 0.75^{\circ}\), especially in the sliding spotlight modes. Up to this limit, the impact of the grating lobes is tolerable as their antenna gain keeps 20 dB below the gain into the desired steering direction. The azimuth resolution for TerraSAR-X has been enhanced by commanding staring spotlight with steering angles up to \(\pm 2.2^{\circ}\). In this post-launch introduced operational TerraSAR-X mode, the azimuth resolution is about 20 cm and it improved by a factor of 5 when compared to the sliding mode HS of TerraSAR-X. The problem that was connected to the enlargement of the steering angles is the grating lobes increase. As Figure 5 shows, for a steering angle of \(2.2^{\circ}\), the largest grating lobe in red shows equal gain as the steered lobe in green. In order to maintain the SAR performance in the presence of strong grating lobes, two main incidence angle dependent adaptions were introduced, i.e., a PRF optimization in commanding, and a scene size optimization in the SAR processing [23; 24; 25].  
Figure 6 shows an example staring spotlight image that was acquired at the test site at DLR in Oberpfaffenhofen, Germany. The corner reflectors in the scene were used to verify the expected geometric resolution, which was found to be within 2% of the expected values. From the comparison of a zoom into the staring spotlight image with the corresponding zoom into a 300 MHz HS sliding spotlight image, the improvement that was achieved by multilooking five azimuth looks is obvious, and the higher level of detail is apparent.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 3 Methodology
### Hybrid Neural Network Based on a GCNN and Two-Layer BiLSTM
#### 3.2.2 Two-Layer BiLSTM with Residual Connections  
LSTM networks extend a traditional RNN by introducing specialized gates that manage the flow and preservation of information across time steps, thereby improving the modeling of long-term dependencies in sequential data. Each LSTM cell comprises an input gate, forget gate, output gate, and a memory cell that together coordinate what information to retain, discard, or expose. The input gate filters incoming signals, the forget gate removes irrelevant historical data, and the output gate determines the exposure of the updated memory content. This gated architecture enables LSTM to better address issues like vanishing or exploding gradients and significantly enhances its ability to learn semantic relationships over extended text sequences.  
To strengthen the modeling of bidirectional context, we employ a BiLSTM architecture composed of two LSTM layers operating in opposite temporal directions: one encoding the input sequence from left to right, and the other from right to left, as illustrated in Figure 4. The forward and backward hidden states are combined to produce a rich representation encompassing both historical and future context. Given an input sequence \(v_{t}\), the bidirectional hidden representations at time step \(t\) are obtained through the following computation [40]:  
\[\overset{\rightarrow}{h_{f}}=LSTM(v_{t},h_{t-1}^{\rightarrow}) \tag{14}\]  
\[\overset{\leftarrow}{h_{b}}=LSTM(v_{t},h_{t-1}^{\leftarrow}) \tag{15}\]  
The final hidden representation is formed by merging the outputs from both the forward and backward passes:  
\[H_{t}=[\overset{\rightarrow}{h_{f}},\overset{\leftarrow}{h_{b}}] \tag{16}\]The output sequence \(H_{t}\) produced by the BiLSTM captures bidirectional contextual features, thereby enabling richer semantic representation. To further extract higher-level, more abstract semantic features, a second BiLSTM layer is stacked on top of the first. To preserve the original embedding information and mitigate degradation caused by deep stacking, we introduce a residual connection by concatenating the original input \(V\) with the output \(H\) of the first BiLSTM layer [41]:  
\[R=[H,V] \tag{17}\]  
The concatenated result \(R\) is then passed to the second BiLSTM layer. The final global feature vector, denoted as \(Out_{BiLSTM}\), is formed by merging the terminal hidden states generated from the forward and backward directions of the second BiLSTM layer.
## 3 Real-Time Small Pit Defect Detection Network
### Extracting Candidate Image Patches for Pit Detection
#### 3.2.3 Pit Classification  
The extracted pit candidate regions are entered as an input into a CNN-based binary classifier. This study used typical CNN models such as ResNet [27], MobileNet [28], and SqueezeNet [29] for binary classification. ResNet uses a skip-connection, which adds the output value x of previous layer L \(-\) 1 to the output value F(x) of a certain layer L. This resolves the learning problems of deep-layer models. ResNet is a typical CNN-based model with markedly improved performance. MobileNet is a typical lightweight CNN model employing depth-wise convolutions and using a small number of parameters to achieve an excellent image classification performance. SqueezeNet is another typical lightweight CNN model. By introducing the Fire Module, this model achieves the same performance as existing CNN-based models with a greatly reduced model size. In this study, experiments were conducted using these three CNN models as binary classifiers, which have their own unique features, and their performances were compared.  
Figure 8: Candidates of pit defects through extracting features and localizing bounding boxes.  
Figure 7: Flow chart of contrast ratio-based filtering.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Production Phase  
#### 4.2.1 Digital Manufacturing  
Digital manufacturing encompasses the utilization of digital tools such as 3D printing and Computerized Numerical Control (CNC) to enhance productivity, reduce material waste, and improve the customizability in precast element production. This advancement enables the production of more complex, high-performing, and sustainable precast elements with greater accuracy.  
Three-dimensional printing technology demonstrates remarkable potential in digital manufacturing, particularly in dry assembly, implementation speed, and recyclability. [PERSON] et al. [43] employed extrusion-based 3D concrete printing technology to fabricate building envelope components. Despite the geometric flexibility afforded by 3D printing in crafting non-standard components, concerns persist regarding geometric accuracy. Therefore, [PERSON] et al. [44] illustrated how robotic fused deposition modeling can efficiently fabricate large, lightweight, ready-to-pour freeform work for non-standard concrete components. Moreover, [PERSON] et al. [45] proposed an automated 3D concrete printing platform tailored for fabricating custom columns with intricate geometries and textures, highlighting its advantages in bolstering constructability.  
Regarding sustainability, [PERSON] et al. [46] quantified the environmental impact of 3D printing technology and identified that, while 3D printing generates fewer greenhouse gas emissions than conventional solutions, there is still room for improvement in economic indicators for competitive solutions. [PERSON] et al. [47] integrated 3D printing with lean manufacturing techniques, prioritizing on-demand production and bi-directional digital workflows to reduce material waste. [PERSON] et al. [48] explored the cost-effectiveness of using CNC machines for concrete formwork, and concluded that CNC machines can amplify labor productivity, manufacturing quality, and worker safety. [PERSON] et al. [49] pioneered lightweight concrete floor slabs through hybrid 3D-printed and CNC-manufactured formwork, achieving a 70% weight reduction. This quantitatively illustrates how digital manufacturing can facilitate the development of more sustainable structures.