esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 3 Results and Discussion
### Mechanical Properties of Cement Mortars  
Figure 9 shows the compressive strength and flexural strength of cement mortars with different types of early strength agents. For all curing ages, the cement mortar with CNA obtains the largest compressive strength and flexural strength. For 1 day of age, the compressive strength of cement mortars with CNA (34.5 MPa) is 54.7% and 94.9% higher than that of mixture with TEA (22.3 MPa) and the blank sample (17.7 MPa); the flexural strength of cement mortars with CNA (5.5 MPa) is 44.7% and 57.1% higher than that of mixture with TEA (3.8 MPa) and the blank sample (3.5 MPa), respectively. For 3 days of age, the compressive strength of cement mortars with CNA (46.0 MPa) is 29.2% and 39.4% higher than that of mixture with TEA (35.6 MPa) and the blank sample (33.0 MPa); the flexural strength of cement mortars with CNA (7.6 MPa) is 40.7% and 35.7% higher than that of mixture with TEA (5.4 MPa) and the blank sample (5.6 MPa), respectively. This indicates that both CNA and TEA may significantly increase the early (1\(\sim\)3 days)  
Figure 9: Compressive strength (**a**) and flexural strength (**b**) of cement mortars with different types of early strength agents.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Hydration Degree** & **Fractal Dimension of** \\  & & **Hydration Products** \\ \hline BLANK & 0.632 & 1.62 \\ CNA & 0.641 & 1.67 \\ TEA & 0.615 & 1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The hydration degree of the hardened cement slurry at 28 days.  
compressive strength and flexural strength of cement mortars. In the hydration process, CNA provides nucleation sites for C-S-H and reduces the potential energy required for C-S-H nucleation, which promotes the form of C-S-H gel, fills up the remaining pores evenly and leads to a relative dense microstructure. This may explain why the cement mortars with CNA obtain the largest compressive strength and flexural strength. Meanwhile, as a traditional organic early-strength agent, TEA can chelate the aluminate phase to promote the formation of ettrinigite and ultimately improve the early strength of cement mortars [39].  
For 28-days of age, the compressive strength of cement mortars with CNA (59.4 MPa) is 2.6% higher than the blank sample (57.9 MPa), while the compressive strength of cement mortars with TEA (56.4 MPa) is 2.7% lower than blank sample (57.9 MPa). The flexural strength has the same trend. This indicates that CNA has little effect on the mechanical properties of cement in the later stage, while TEA has certain side effects on the mechanical properties of cement in the later stage. This is consistent with the research results of [PERSON] et al. [40].
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats  
As an experimental platform for the validation of localization algorithms, we used two identical DDboats--see Figure 1--that were originally constructed as a students' educational platform at ENSTA Bretagne, Brest, France.  
These DDboats are equipped with a GPS receiver, as well as with a compass module, that allow for the implementation of a trajectory tracking control procedure. Moreover, these sensor data are used for modeling uncertainties in the motion of both robots in an a posteriori analysis of an experimental trajectory tracking controller and for the derivation of a bounded-error uncertainty model for the sensors in Section 4.
## 4 Human-Machine-Logistics Collaborative Scheduling Method Considering Workers' LFE
### Adaptive Real-Time Scheduling Considering Workers' Learning and Forgetting Effects
#### 4.3.3. Human-Machine-Logistics Collaborative Process  
The human-machine-logistics collaboration process consists of three main parts: human-machine collaborative processing, production logistics collaboration for real-time tasks, and material transportation. To further demonstrate the detailed process of each part and the relationship between them, an example service group is described, as shown in Figure 3.  
Human-Machine Collaborative Processing: When the workpiece arrives at the buffer area of the processing equipment, the worker confirms the task, while the machine calculates the worker's real-time efficiency based on their working time and idle time. Once the worker confirms the task, the human-machine collaboration starts, and the next task is triggered. After processing is completed, the workpiece undergoes a quality inspection and is placed in the buffer area.  
Production-Logistics Collaboration: Once the next operation of the task is triggered, the task slackness is calculated, and the task is broadcast to the available production groups. The selected production group broadcasts the logistics task to the surrounding AGVs, forms a service group with the AGV, and evaluates and selects the optimal service combination to place in the execution pool.  
Logistics Execution and Priority Checking: The system checks the tasks in the execution pool for AGV conflicts. If multiple tasks contend for the same AGV, the system determines the task priority. If the current task has the highest priority, the AGV acquires the workpiece information and moves to the pickup point (i.e., the output buffer area). After loading the workpiece, the AGV transports it to the delivery point (i.e., the input buffer area of the optimal service combination). If the current task does not have the highest priority, the system reselects a service group.  
Repeat the Process: The process repeats until no tasks remain or all workpieces have been processed.  
Figure 3: Human–Machine–Logistics Collaborative Process Diagram.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 5 Analyzing Surface Codes Under Noise
### Surface Codes Under Various Noise Models  
Next, we analyze the performance of surface codes under three distinct noise models as shown in Figure 6, respectively. Beginning with the code-capacity model (Figure 6a), it can be discerned that the threshold for this model is similar to that of gate errors. This similarity arises since the threshold of this model is determined by the lower of the two error types, which in this case, is the gate errors. Moving on to the phenomenological model (Figure 6b), which is only about the syndrome measurements in ancilla qubits, exhibits high similarity with the plots of readout and reset errors. The threshold condition for this model also aligns closely with these two measurement-type errors. Finally, the circuit-level (Figure 6c) noise model encompasses all types of errors across the QECC including both data and ancilla qubits, making it the most representative of real-world scenarios. Accordingly, the threshold for these combined errors is the lowest among all the thresholds we have examined so far.  
We also mapped the logical error rate against code distance while varying physical noise levels in rotated surface codes for the circuit-level noise model (which is the most realistic scenario) as depicted in Figure 6d. We utilized a linear regression model to fit a line to the sampled data, establishing a relationship between the code distance and the logical error rate under various physical noise levels. This enabled us to predict the code distance necessary to achieve a specified logical error rate in the code-capacity model. The plots indicate that a reduction in physical noise directly correlates to a steeper slope. In other words, when the quantum system is inherently less noisy, enhancing the code distance has a pronounced impact in diminishing the logical errors. Hence, it is essential to consider the native noise level of the quantum system when optimizing the code distance for error correction. However, we identified a noise level threshold, beyond which the performance of QECCs' plateaus improved, regardless of distance. It is noteworthy that the lowest error  
Figure 5: Comparison between threshold values of repetition and surface codes for (**a**) different error types; (**b**) various noise models. While the threshold of repetition codes is markedly higher than that of surface codes, this is largely due to its inability to detect or correct Z errors, thus ignoring half of potential errors. The threshold of the rotated surface code is better than the unrotated surface code.  
threshold that the surface code can achieve is around \(10^{-2}\), a figure that is 10 times higher than what is achievable by state-of-the-art quantum processors without error correction. This underscores the pivotal role of QECCs in the advancement of quantum computing.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 4 Experimental Methodology
### Arithmetic Performance and Energy Model  
We made and used an arithmetic performance and energy model to estimate the total training time and Energy-Delay Product (EDP) of CNN. With only an _arithmetic_ model, we can estimate the computation time of the DaDianNao accelerator and the inter-accelerator communication time on the network for the following reasons:  
* The computational unit of DaDianNao has a simple pipeline structure with only three stages and works by in-order execution.
* The clock frequency of the accelerator is constant, so dynamic frequency scaling is not used.
* Every inter-accelerator communication is _all-to-all broadcast_ through a 2D-mesh network so that the execution time can be calculated by a mathematical model [15].
* The throughputs and delays of the computational unit, eDRAM, and interconnect are clearly defined.  
Our model consists of three parts: computation, eDRAM, and communication, which are based on Equations (1)-(3), respectively.  
\[Computation\;time=max\left[\left(\frac{\#\;of\;multiply\;operations}{multiply\;throughput}\right),\left(\frac{\#\;of\;add\;operations}{add\;throughput}\right),\;...\;\right]. \tag{1}\]  
\[eDRAM\;time=\frac{total\;data\;size\;of\;eDRAM\;accesses\;}{eDRAM\;bandwidth}+ eDRAM\;access\;latency. \tag{2}\]  
\[\begin{split} Broadcast\;time=2\times node\;to\;node\;latency \times\left(\sqrt{\#\;of\;nodes}-1\right)\\ +\frac{node\;to\;node\;transfer\;data\;size}{node\;to\; node\;bandwidth}\times\left(\#\;of\;nodes-1\right).\end{split} \tag{3}\]  
In Equation (1), the computation time depends on the operation type causing performance bottleneck. In Equation (2), although eDRAM accesses are pipelined, the access latency should be considered once. Equation (3) from [15] derives all-to-all broadcast time for the 2D-mesh topology. We also considered the pipelined executions between computations and eDRAM accesses. When measuring EDP, we apply the peak power values of DaDianNao chip and its PHY at the proper time interval because all chips and chip-to-chip interconnects are fully utilized and show their maximum throughput during the computation time and communication time, respectively.
## 4 Experimental Evaluation
### Evaluation Metrics  
When evaluating the performance of the model, the evaluation index proposed by the studies of previous unaligned sentiment analysis [9; 39; 40; 41] is selected to illustrate the experimental results of this study from two perspectives of classification and regression.  
As for the classification evaluation index, Accuracy, Recall, and F1-Score are used to evaluate the performance of sentiment analysis. F1-Score is an index that comprehensively considers both Precision and Recall rate, as shown in Equations (41)-(44).  
\[P_{A}=\frac{N_{TP}+N_{TN}}{N_{TP}+N_{FN}+N_{FP}+N_{TN}} \tag{41}\]  
\[P_{R}=\frac{N_{TP}}{N_{TP}+N_{FN}} \tag{42}\]  
\[P_{P}=\frac{N_{TP}}{N_{TP}+N_{FP}} \tag{43}\]  
\[P_{F}=\frac{2\times P_{R}\times P_{P}}{P_{R}+P_{P}} \tag{44}\]  
where \(N_{TP}\) is the number of samples correctly labeled as a positive example, \(N_{FP}\) is the number of samples incorrectly labeled as a positive example when they are actually a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Setting** & **CMU-MOSI** & **CMU-MOSEI** \\ \hline Optimizer & Adam & Adam \\ Batch size & 32 & 32 \\ Learning\_rate\_bert & \(5\times 10^{-5}\) & \(5\times 10^{-5}\) \\ Epochs & 100 & 120 \\ Bi-GRU hidden sizes & 32 & 32 \\ Attention head & 8 & 10 \\ Kernel size (h\({}_{\text{t}}\)/h\({}_{\text{v}}\)/h\({}_{\text{c}}\)) & 1/1/1 & 1/1/1 \\ Dropout & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameter settings adopted in the CMU-MOSI and CMU-MOSEI datasets.  
negative example, \(N_{TN}\) is the number of samples correctly labeled as a negative example, and \(N_{FN}\) is the number of samples incorrectly labeled as a negative example when they are actually a positive example. \(P_{A}\) stands for Accuracy, \(P_{R}\) stands for Recall rate, \(P_{P}\) stands for Precision, and \(P_{F}\) stands for F1-Score.  
For the regression evaluation indicators, the mean absolute error (MAE) and Pearson correlation (Corr) are used to evaluate the performance of sentiment analysis. With the exception of MAE, higher values indicate better performance for all indicators.
## 1 Introduction
These include devices mounted on the Greenhouse Gases Observing Satellite (GOSAT), the Global Ozone Monitoring Experiment 2 (GOME-2) instrument, carried aboard the MetOp-A (Meteorological Operational Satellite A), the Scanning Imaging Absorption spectrometer for Atmospheric CHartograph (SCIAMACHY) onboard Envisat, the Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3), and the TROPOspheric Monitoring Instrument (TROPOMI). Although none of these satellite missions were originally designed for SIF mapping, they provided the initial datasets that revealed the feasibility of satellite SIF retrievals, both at a coarse spatial resolution or at footprint level. Progressive advances in satellite technology and improvements in satellite SIF retrieval technology will allow for a more direct estimation of transpiration and play a more significant role in understanding the carbon cycle of terrestrial ecosystems as a whole. [PERSON] et al. [11] derived a measurement of average global annual SIF at 755 nm with a spatial resolution of \(2^{\circ}\times 2^{\circ}\) using GOSAT data. To obtain higher spatial and temporal resolutions and a continuous SIF dataset, many researchers use MODIS products and meteorological data, employing linear regression techniques to simulate global or regional SIF [12; 26; 27; 28; 29]. Satisfactory results have been obtained by such means. Different plant species typically exhibit different photosynthetic characteristics and different light use efficiencies (LUE) [22]. However, in previous studies, the photosynthetic characteristics of different ecosystems have not been considered while performing SIF simulations. This research aims to (1) understand the seasonal trends of SIF for the ecosystems of cropland, forest, and grassland, (2) identify and quantify the relationships between VIs and SIF at both regional and footprint scales, (3) understand how meteorological factors affect SIF in different vegetation types, and (4) estimate the accuracy of former SIF products of different vegetation types and then compare the difference.
## 3 Case Analysis
### Prediction Accuracy with Factor Ablation
#### 3.2.1 Test Site GPP without Ta  
In this experiment, all Ta data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 9. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy of the AR site is significantly improved, while that of the DXST, DXSW, NMC, and ZF sites is greatly reduced. The prediction accuracy of the GL, HBKO, HBSH, and HBSW sites does not change noticeably.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Site** & **AR** & **DXST** & **DXSW** & **GL** & **HBKO** & **HBSH** & **HBSW** & **NMC** & **ZF** \\ \hline R\({}^{2}\) & 0.663 & 0.853 & 0.843 & 0.879 & 0.935 & 0.868 & 0.856 & 0.879 & 0.758 \\ R\({}^{2}\) (500 km) & 0.827 & 0.778 & 0.678 & 0.889 & 0.933 & 0.928 & 0.898 & 0.706 & 0.601 \\ R\({}^{2}\) (100 km) & 0.877 & 0.780 & 0.717 & / & 0.951 & 0.883 & 0.816 & 0.683 & / \\ R\({}^{2}\) (vt) & 0.945 & 0.833 & 0.751 & 0.801 & 0.902 & / & 0.849 & 0.902 & 0.767 \\ \hline \hline \end{tabular} Numbers in red indicate that the prediction results for the corresponding site have increased in accuracy compared to the previous results without setting a distance range; numbers in blue indicate that the results have decreased in accuracy; and numbers in black indicate that the results have no significant changes.  
\end{table}
Table 4: Results comparing all previous experiments.  
Figure 9: Predicted GPP vs. labeled GPP at a single site (no Ta).
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 1 Introduction
This extension is achieved through the adoption of a Security Service Level Agreement (SLA) agreement (SSLA) as a security intent definition, which, in turn, is orchestrated, scheduled, and enforced via security policies. This paper describes the proactive E2E security slice enforcement as a consequence of an SCLA high-level description, as well as the reactive security enforcement triggered by a security state alteration. This work also presents, implements, and evaluates a use case chosen by ETSI as the sixth Proof of Concept [11] for ZSM, titled ETSI ZSM: Security SLA Assurance in 5G Network Slices. In this use case, the analysis of encrypted traffic is addressed as a consequence of an SCLA, and a VNF infection caused by a malicious attacker is mitigated without human intervention. The contributions of this work are as follows:* Security is integrated as a native component in the process of creating and managing E2E slices. * An automated closed-loop security operation is achieved. * An extensible intent-based approach with different levels of abstraction, security models, and services actions is proposed. * Continuous and autonomous Security Service Level Agreement (SSLA) compliance through tailored monitoring and cross-domain reactive capabilities provided by the framework is achieved. * Real-world validation on a 5G testbed encompassing multiple administrative domains and different security properties is achieved. * As result of the above, we demonstrate the feasibility of ZSM architectures for managing B5G networks. The rest of this paper is organized as follows: Section 2 provides the state of the art. Section 3 provides the security framework overview. Section 4 shows a detailed explanation regarding how the framework provides dynamic 5G core security slice management. Sections 5 and 6 provide the implementation and performance results, respectively. Finally, Section 7 provides the conclusions.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 5 Derivation and Validation of Shipborne GNSS-Based Sea Surface Heights
### Validation and Least-Squares Adjustment of the Results
#### 5.4.1 The Final Data Processing Results  
The results in Figure 16b suggest that around a 5 cm accuracy can be achieved with the presented methodology, but it is also evident that a data bias of 5.2 cm exists simultaneously. According to Figure 17, the bias primarily originates from areas in the Gulf of Finland and Vainameri (refer to Figure 2) and appears to possess a long-wavelength nature. The bias could likely represent the NKG2015 geoid model errors to some extent. Similarly probable is that there are errors in the estimated DT, since even after correcting the used HDM with TG data, the determination of instantaneous DT with an accuracy of a few centimeters is a difficult task. It could also be that the tape measurements were systematically conducted in a way that resulted in lower than actual SSH. Unfortunately, these are all assumptions, and the origin of the bias currently remains unknown.  
Figure 16: The (**a**) mean values of residuals (the initial ones coincide with those in Figure 14a) and (**b**) histograms of residuals (all campaigns combined).  
Figure 17: The final filtered, corrected, and least-squares adjusted residuals \(F_{CoM}^{+C+A}\).  
However, recall that this study aimed to derive accurate SSH from shipborne GNSS surveys. As a final step, the SSH was restored from filtered, corrected, and adjusted residuals (Equation (6) modification):  
\[SSH_{CoM}^{F+C+A}(\varphi,\lambda,t)=r_{CoM}^{F+C+A}(\varphi,\lambda,t)+\hat{DT}( \varphi,\lambda,t)+[N(\varphi,\lambda,t_{0})+GC(\varphi,\lambda)\cdot(t-t_{0})], \tag{23}\]  
where \(\hat{DT}\) and \(N\) are correspondingly the DT and NKG2015 geoidal heights initially used to reduce SSH into residuals. The geoid change denoted as \(GC\) is restored to obtain the actual SSH (recall that Equation (7) initially eliminated geoid change for data consistency). Assuming minimal error propagation during data filtering, the derived SSH should be mostly free of DT and geoid model errors. It is, thus, plausible that the expected accuracy of the derived SSH exceeds that of the presented and discussed residual values. Therefore, the derived SSH data are suitable for subsequent applications, such as validation of various marine models and measurements (with a limiting factor that measurements must be conducted at the same time as marine survey campaigns). Notably, the presented methodology works under harsh weather conditions (recall that the second half of the C1 campaign was conducted in stormy conditions), which is often the case in real life when marine campaigns are planned long in advance (i.e., the weather becomes unpredictable).
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods  
### NaSC-TG2 Dataset  
Natural Scene Classification with Tiangong-2 (NaSC-TG2) dataset [7] was used to fine-tune a pre-trained ResNet-50 model. The NaSC-TG2 dataset contains a total of 20,000 images of 128 \(\times\) 128 size with 10 classes for remotely sensed image scenes. Sample images for different classes are displayed in Figure 2. The NaSC-TG2 dataset addresses the limitations of existing remote-sensing image datasets by offering several distinct properties. Firstly, it provides a large-scale dataset that overcomes the shortage of labeled scene images in remote sensing. This allows for more effective training of complex deep-learning networks, making it a valuable resource for the remote-sensing community. The dataset also ensures a balanced distribution of scenes, contributing to improved network training and evaluation. Secondly, NaSC-TG2 exhibits large intra-class differences and high inter-class similarity, mimicking the complex and variable conditions found on the Earth's surface. This challenges classification methods to be more robust and generalize well to accurately classify scene images. Thirdly, the dataset includes natural scenes with novel spatial scales and imaging performance, setting it apart from existing datasets that primarily focus on artificial landscapes. This diversity of natural scenes enables comprehensive algorithm verification and analysis, particularly in the field of natural scene classification. Additionally, NaSC-TG2 includes 14-band multi-spectral scene images alongside true-color RGB images, providing valuable data for high-dimensional scene image classification research. Overall, the NaSC-TG2 dataset offers a large-scale, diverse, and comprehensive benchmark for remote-sensing scene classification methods, addressing the limitations of previous datasets and promoting advancements in the field.  
Figure 2: _Cont._  
The number of images for each class are equally distributed, as shown in Figure 3. This dataset is challenging because of its high intra-class variations (see Figure 4) and inter-class similarities (see Figure 5). All images are obtained by wideband imaging spectrometer from the Tiangong-2 satellite. This contains both RGB and multi-spectral images but for this study, only RGB images were used. The overall benchmark accuracy achieved on this dataset was 89.59% using the VGG-16 model in an 80:20 train-test split.  
Figure 3: NaSC-TG2 Class Distribution.  
Figure 2: NaSC-TG2 samples.