esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Motivated by the scarcity of research on the welded joint of a fiber laser welding aluminum alloy with beam oscillation, we conducted a laser welding experiment on 5056 aluminum alloy specimens, and analyzed the appearance, microstructure, hardness, and strength properties of welded joints. The following conclusions were made after the experiment.  
1. Beam oscillation causes great improvements in the weld formation of laser butt welding of aluminum alloy. The surface of the weld with circular oscillation is well formed and has good consistency without spatter, undercut, and root sagging.
2. Beam oscillation results in a significant improvement of the weld microstructure of laser butt welding of aluminum alloy. The circular oscillating weld has the features of fine grain and uniformly dispersed dendrites in the strengthening phase, and the largest microhardness is observed in the fusion zone.
3. Beam oscillation is significantly inhibitory to the porosity of the weld, and the inhibitory effect is even stronger with circular oscillation than with transverse oscillation. There is no pore in the tensile fracture of the circular oscillating weld, but a small number of pores can be seen in the corresponding position of a transversely oscillating weld, and many more pores with a large volume are noted without oscillation. Beam oscillation has few effects on the tensile strength of the weld, but exerts a great influence on the elongation of the weld, which is evidenced by the fact that the largest elongation of the weld is associated with circular oscillation.  
Conceptualization, M.Z.; Funding acquisition, M.Z.; Investigation, X.P. and J.D.; Methodology, S.C.; Supervision, S.C.; Writing--original draft, X.P. and J.D.; Writing--review & editing, M.Z.  
This research was funded by the National Natural Science Foundation of China grant number [51605045] and the Natural Science Foundation of Hunan Province of China grant number [2018][J2437].  
The authors declare no conflicts of interest.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Analysis of Feasibility  
This chapter aims to analyze the feasibility of the method, which is elaborated from the following three perspectives:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Formula Expression** & **Accuracy** \\ \hline _T-V-concatenate_ & \(Concatenate(T,V)\) & 62.44\% \\ _T-V-add_ & \(T+V\) & 58.95\% \\ _T-V-weight_ & \(\alpha T+(1-\alpha)V\) & 65.60\% \\ _T\({}^{T}\)V-max_ & \(Maxpooling(T^{T}V)\) & 57.94\% \\ _T\({}^{T}\)V-avg_ & \(Avgpooling(T^{T}V)\) & 48.72\% \\ _W\({}_{I}\)V-W\({}_{T}\)T-add_ & \(\alpha f(T)+(1-\alpha)g(V)\) & 65.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experimental results of different fusion methods.  
First, at the theoretical level, the method is consistent with the cognitive mechanism of human emotion analysis. Humans construct mental scenarios based on text to judge emotions, and SeeEmoNet simulates this process by using the Stable diffusion model to generate visual scenes from text. This theoretical correspondence provides a reasonable basis for the method, as it draws on the idea that multimodal information (text and generated images) can enhance understanding, just as humans integrate contextual and experiential cues.  
Second, from a technical perspective, the Stable diffusion model is a mature latent diffusion model that can reliably generate semantically relevant images from text prompts. Its ability to compress image information into the latent space reduces computational overhead, making it practically feasible to integrate into the emotion analysis workflow. This enables the conversion of pure text tasks into multimodal tasks without relying on existing image-text pairs. The CLIP model, pre-trained on 400 million image-text pairs, can effectively extract aligned features from text and generated images. Freezing its parameters during training reduces computational costs while ensuring the consistency of cross-modal features, mitigating data heterogeneity issues.  
Finally, from the perspective of practical application, the method addresses the limitations of pure text models by introducing the visual modality, and it does not depend on external image data, making it applicable to pure text datasets. This universality allows it to be extended to various emotion analysis tasks (such as sentiment recognition and hate speech detection). Additionally, the use of pre-trained models minimizes the need for large-scale retraining, facilitating practical deployment.  
In conclusion, the SeeEmoNet method is feasible because it aligns with human cognitive theories, effectively integrates mature models, yields positive experimental results, and is suitable for pure text scenarios.
# Real-Time Spatial-Division Multiplexing Transmission with Commercial 400 Gb/s Transponders
## 3 Real-Time MDM Transmission Using Non-Degenerate LP Modes of the WC-FMF

Table 5: Insertion loss (diagonal data) and inter-mode crosstalk (nondiagonal data) of the 150 km FMF with mode MUX (Unit: dB). FMF link are shown in Figure 6d,e, respectively. It can be seen that the BER curves of the 20-roll 3 km FMF link are flatter than the those of the single-span 60 km FMF link. This is because the extra modal crosstalk caused by the 19 splice points slow down the increase of the OSNR. At the sensitivity limit of the receiver, the 20-roll MDM transmission system ran for 6 h, and the BER was still below the threshold of 4.75 \(\times\) 10\({}^{-2}\) during this time. Figure 6f shows the BERs of the 20-roll MDM transmission at six frequencies, in which the input power of LP\({}_{01}\) and LP\({}_{02}\) modes are 7.85 dBm and 10.1 dBm, respectively. It can be seen the BERs are all below the FEC threshold as well. The BER and OSNR performance of the 150 km FMF link are shown in Figure 6g,h, respectively. It can be seen that in both single-mode and MDM transmission, the performance of LP\({}_{02}\) mode is worse than that of LP\({}_{01}\) mode. This is because no EDFA is employed within the 150 km FMF, the total loss of LP\({}_{02}\) modes is larger than LP\({}_{01}\) mode which leads to a small OSNR. Figure 6i shows the BERs of the 150 km MDM transmission at six frequencies, in which the input power of LP\({}_{01}\) and LP\({}_{02}\) modes are 16.15 dBm and 16.32 dBm, respectively. It can be seen the BERs are all below the FEC threshold in which the margin of LP\({}_{02}\) mode is minor. The experiment proves that the proposed non-degenerate LP mode MDM system with all-fiber MSCs can achieve 150 km repeaterless transmission using commercial 400 G transponders. Figure 6: **(a)** OSNR curves versus input power of 60 km single-mode and MDM transmission. **(b)** BER curves versus input power of 60 km single-mode and MDM transmission. **(c)** BER over the C-band of 60 km MDM transmission. **(d)** OSNR curves versus input power of 20 \(\times\) 3 km single-mode and MDM transmission. **(e)** BER curves versus input power of 20 \(\times\) 3 km single-mode and MDM transmission. **(f)** BER over the C-band of 20 \(\times\) 3 km MDM transmission. **(g)** OSNR curves versus input power of 150 km single-mode and MDM transmission. **(h)** BER curves versus input power of 150 km single-mode and MDM transmission. **(i)** BER over the C-band of 150 km MDM transmission.
## 2 Methodology
### RTM SOG Test Using the 2D Marmousi Model  
The aforesaid cumulative process can be validated using the 2D Marmousi model. First, modulation calculation is performed to all of the 240 single-shot records by multiplying each trace data by the shot to receiver distance, the single-shot records before and after modulation, as shown in Figure 1. Second, RTM is performed on the original shot data and modulated shot data, leading to two migrated data of each shot being obtained. Third, the offset values can be calculated with two migrated results with Equation (6), the original shot data migrated data can be sorted to an offset gather by these values. Finally, every singleshot is summed up to the offset gather andobtain the final SOGs.  
The original data and modulated data are migrated using the same imaging parameters, respectively (e.g., Figure 1a,b) and solve the ratio of the results (Figure 2a,b) from the two migrations to obtain the value of the offset at the imaging point.The imaging result is sortedby the calculated values of the surface offsets (Figure 2c) into SOGs, and the records of all the shot gathers are calculated and inserted following the aforesaid processes to obtain the SOGs of the imaging results.  
Figure 1: (**a**) The original single-shot records from the Marmousi model and (**b**) the results from the modulation of offsets.  
Figure 2: (**a**) Results from the migration of the original single shots; (**b**) results from the migration the of modulated single shots; (**c**) attributes of the surface offsets obtained from ratios of (**a**,**b**).  
In the calculation of SOGs, for gathers, the minimum offset was set at 200 m, the maximum offset was set at 2600 m, and the step of the offset was 100 m. Figure 3 shows two SOGs obtained from a calculation at common middle point CMP 150 and CMP 350, respectively. RTM SOGs were mainly used to update the velocity model in the offset calculations, and the events in gathers were horizontal, indicating that the velocity model used in the migration was accurate. Where the velocity was low, the event features warped upward, and where it was high, the event tilted downward. Figure 4 shows the SOGs calculated after the values of the velocity model value were adjusted up and down by 5%, respectively, from which we can see the changes of the gathers; when the velocity is higher than the correct velocity, the events in the gathers will bend down, and when the velocity is lower than the correct velocity, the events will bend up, which also demonstrates that the gathers calculated with RTM can be used for velocity updates. Some methods, such as tomography or residual velocity analysis, will convert the event curvature to the velocity, and acquire the updated velocity model.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 3 Signal Processing to Improve Deep Learning Results
### Pre-Processing of Train, Validation, and Test Data Using the Signal Processing Method
For this reason, as shown in Figure 17a, it was not possible to clearly confirm that there were four flat signals in the red and blue circles when compared to (b). Therefore, it was found that as the number of wheel flats increased, the time resolution became more important. Figure 16: Definition of overlap [24]. Figure 10:0 Comparison of (**a**): window length 128 and (**b**): window length 64. Finally, Figure 18 shows the result of the difference in overlap when the window length is the same as 64. At this time, the cases of overlap 50% and 80% were compared. In addition, the motivation for the comparison with 80% overlap is as follows. First of all, an increase of 10% overlap each from 50% were reviewed. It was confirmed that the case of four wheel flats from 80% overlap is better. Therefore, the cases with 50% and 80% were compared. As a result, it was decided that it is appropriate to set it to 80% as shown in (b) of Figure 10:1 The reason is that it shows more clearly in four wheel flats in (b) than in (a). The following parameter values were selected through comparative analysis; First, the window length of 64, the overlap of 80
## 3 Background and Theory
### CART Classifier  
The second supervised machine learning classifier applied here is the classification and regression tree (CART) [32], which uses decision trees for both classification and regression tasks. In EEG signal analysis, CART is often employed to classify various mental states or detect patterns related to specific neurological conditions. The algorithm can be outlined using the following pseudo-code:  
Given a set of EEG features \(X\) and a target variable \(Y\), the following procedure applies:  
1. Start with the root node containing all instances.
2. If all instances have the same value for \(Y\), stop. Otherwise, proceed.
3. Select feature \(x_{i}\) and threshold \(\theta\) to split: \[x_{i},\theta=\underset{x,t}{\text{argmin}}\text{Impurity}(X,Y,x,t)\]
4. Split the node into two child nodes: \[X_{\text{left}}=\left\{x\in X|x_{i}\leq\theta\right\}\] \[X_{\text{right}}=\left\{x\in X|x_{i}>\theta\right\}\]
5. Repeat steps 2-4 recursively for \(X_{\text{left}}\) and \(X_{\text{right}}\).
6. Stop if a maximum tree depth is reached or if further splitting does not improve impurity measures significantly. where the following apply: * \(X\) is the set of input variables (EEG features); * \(Y\) is the target variable (e.g., type of brain activity); * \(x_{i}\) is a feature from the EEG features; * \(\theta\) is the threshold value used for splitting a node; * \(\text{Impurity}(X,Y,x,t)\) is a measure of the homogeneity of the target variable within the nodes after the split (it can be Gini impurity, entropy, or another suitable metric); * \(X_{\text{left}}\) is a subset of the data where the value of \(x_{i}\) is less than or equal to \(\theta\); * \(X_{\text{right}}\) is a subset of the data where the value of \(x_{i}\) is greater than \(\theta\); * \(\underset{x,t}{\text{argmin}}\) is the argument of the minimum, the values of \(x\) and \(t\) that minimize the impurity.
# A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing
## 4 Evaluation
### PCT and the Estimation of ATF Time  
A Spearman Rank-Order Correlation Coefficient (SROCC) analysis (Figure 6) confirms that PCT has a high correlation with the objective ATF, but its correlation with the perceived ATF is stronger than the correlation between the objective ATF and the perceived ATF.  
The computed \(SROCC\) for the objective ATF and mean and median of perceived ATF are 0.87 and 0.78, respectively. The same analysis reveals a stronger correlation between PCT versus mean and median of the perceived ATF (\(SROCC=0.89\) and \(SROCC=0.86\), respectively). Whether a web QoE is modeled based on mean or median of perceived ATF, the high correlation of PCT (with both mean and median) with perceived ATF makes PCT a suitable proxy metric for predicting perceived ATF. The correlation between PCT and objective ATF is (\(SROCC=0.87\)). Despite the high correlation between the two metrics, there are still some cases where PCT estimation differs from the objective ATF metric (e.g., websites with animations).  
The left subplot of Figure 7 compares PCT with perceived ATF. It can be seen that, for the majority of the cases, ATF estimation of PCT is close to the mean ATF time reported by the users, i.e., the perceived ATF time is similar to the estimated ATF time. However, the middle subplot shows the objective ATF overestimates the perceived ATF time. Root Mean Square Error (RMSE) is used to measure the performance of the metrics. The computed \(RMSE\) based on all test cases is lower for PCT (\(RMSE=5705.986435\)) and higher for the objective ATF (\(RMSE=18,579.652607\)).  
From the middle subplot, one can also see that, while the distance between two variables increases as the load time increases, this trend is not always true for PCT (left subplot). In fact, PCT stays close to the diagonal line and shows a tight positive linear correlation with the perceived ATF, even when the load time increases (see the orange \(\times\) marker on the left subplot for \(PCT=43\)).  
The right subplot of Figure 7 directly compares PCT with objective ATF. While PCT and the objective ATF have some degree of agreement and positive linear correlation on ATF estimation (see the markers on the diagonal line), disagreement can also be seen for a few websites without animations and the majority of the websites with animated content (see the \(\times\) markers).  
The result illustrates that PCT provides a robust estimation of ATF for the wide range of network conditions and content types tests. PCT provides ATF estimation for animated content without compromising performance for other content.  
Figure 6: The Spearman Rank-Order Correlation Coefficient (SROCC) between PCT, Perceived ATF, Objective ATF, and PLT.
# Enhancing the Privacy of Network Services through Trusted Computing
## 4 Experimental Results
We also evaluate the performance of DNS caching in DoTT and DoT in Figure 6. In DNS implementations, resolving servers often results in the speeding up of response requests. To prevent memory from being sniffed and polluted by privileged applications, such as operating systems, SGX uses a separate memory model. We still perform our domain resolution experiments on 10 well-known websites. Figure 6a,b show the evaluation results for caching 10 and 100 domains, respectively. Note that we have used the error bar to show the test results and the standard deviation in addition to the mean value. As expected, the response speed of DoT is significantly higher when the DNS caching feature is turned on, while the response time is almost constant. The response time also gets a significant improvement for DoTT. Although SGX has to perform encryption and decryption operations to prevent OS intervention when exchanging data between CPU and memory, these encryption operations are efficiently implemented by hardware instructions. Therefore, secure memory access in SGX has less impact on applications compared to normal memory operations. On the other hand, we see a large deviation disturbance in the response time of DoTT, which is because the prototype system we implemented only utilizes SGX features without further optimization beyond those of SGX. Further optimization of DoTT will be our future work.
## 2 Problem Formulation  
### Problem Definition and Assumptions  
In the context of the FJSP, we consider \(n\) jobs that need to be processed on \(m\) machines. The set of jobs is denoted as \(J=\{J_{1},J_{2},\ldots,J_{n}\}\), and the set of machines is represented as \(M=\{M_{1},M_{2},\ldots,M_{m}\}\). Each job \(J_{i}\) comprises a sequence of operations that must be executed in a specific order, denoted by the set \(O_{i}\). Each operation \(O_{ij}\) can be processed on a subset of eligible machines \(M_{ij}\subseteq M\).  
The scheduling process involves assigning each operation to an appropriate machine and sequencing the operations on each machine to determine the start time \(S_{ij}\) for each operation \(O_{ij}\). The objective is to establish a comprehensive schedule that minimizes the makespan, defined as the maximum completion time across all jobs [34].  
The proposed model is based on the following assumptions:  
1. Completeness of Job Processing: Every job must complete all its constituent operations.
2. Non-Interruptibility and No Rework: Once a job starts processing, it cannot be interrupted or subjected to rework.
3. Precedence Constraints: There are strict precedence relations between different operations within the same job, enforcing a specific execution order.
4. Machine Assignment: Each operation must be processed on one and only one assigned machine.
5. Deterministic Processing Times: The processing time for each operation is fixed and known in advance for each eligible machine.
6. Inclusive Setup Times: The setup times required for each operation are encompassed within the processing times.
7. Machine Exclusivity: At any given moment, a machine can process only one operation.
## 4 Case Study
### Sensitivity Analysis of \(\beta_{cor}\)  
To evaluate the impact of \(\beta_{cor}\), a sensitivity analysis was conducted with values ranging from 0 to 0.5. The results show that the influence of \(\beta_{cor}\) is minimal across all corrosion levels. Further examination of the cumulative risk ratio, which considers the entire lifecycle of the infrastructure, revealed negligible changes of up to 1%. This finding suggests that the uncertainty introduced by corrosion in our model is overshadowed by other factors affecting seismic vulnerability. The limited impact of \(\beta\)cor implies that efforts to precisely quantify this parameter may not significantly improve overall risk assessments in most cases.
## 5 Utilization Assessment and FGI Results of AROS_\(\alpha\)
### Technical Issue 1: Projection Stability Degradation  
Site management for infrastructure typically involves a large footprint that covers a wide and linearly long area. Therefore, inspection times can be substantive and involve frequently moving around the base of a large structure. Thus, the experts emphasized that the AR projections should account for such structures and provide stability during these inspections. However, as shown in Figure 4a, the projection of the bridge did not always overlap correctly with the actual structure. Moreover, lags occurred when inspectors moved from positions, and in severe cases, the projections disappeared from the screen altogether (Figure 4b). Such issues occurred when the distance between the inspectors and the bridge was over 15 m and after 40 min of using AROS_\(\alpha\).
## 3 Results and Discussion
### Numerical Results of Adsorption and Desorption Rates  
The temporal evolutions of the ion concentrations of Eu(III) and La(III) in the aqueous solution are shown in Figure 8 ((a) Eu(III), (b) La(III)). In general, the ion concentrations in aqueous solution decrease unilaterally with time, which is consistent with Equation (1). In addition, it can be found that the genetic algorithm actually fits the experimental time evolutions of the ion concentrations very well.  
Figure 6: Desorption efficiency of Eu(III) and La(III) ions using HCl or HNO\({}_{3}\).  
Figure 7: The recovery efficiency of La(III) and Eu(III) ions using regenerated L2.  
The adsorption and desorption rates calculated from the fittings in Figure 8 are shown in Table 5. We can see that the adsorption rate is larger for Eu(III) than for La(III), which agrees with the experimental result that Eu(III) has a higher adsorption ability under the identical conditions, as shown in Section 3.3. In addition, Table 5 suggests that Eu(III) desorbs more easily than La(III). As shown in Figure 7 experimentally, the recovery efficiency of La(III) is smaller than that of Eu(III). This means that the rate of desorption is low if the recovery efficiency is small. Therefore, our numerical results reasonably suggest that recovery efficiency is large if the rate of desorption is large.