esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Stop Criteria  
Regardless of the environment, or the particular use case of interest, experimenting with routing protocols exhibits a peculiar challenge: determining when a protocol has converged. Tackling this problem is crucial to have accurate stop criteria for a set of use cases, e.g., checking the expected behavior of protocols at bootstrap or determining when a protocol has recovered from a failure.  
This task can be undertaken in multiple ways, some dependent on the control messages of each protocol and others independent of the protocol. Of course, there is an intuitive way to solve the problem: wait long enough to be sure of protocol convergence. This might not be a problem for non-dense topologies or even fat trees with few nodes. However, if emulation of massive-scale data centers (eventually in a distributed environment) is to be performed, it is difficult to determine how long is enough.  
Three ways to solve this problem have been explored: (i) pre-computing the routing tables of each node, (ii) feeding a stopping flag, and (iii) moving a sliding window. Due to the nature of an emulation, where a centralized program is responsible for starting and finishing the experiment, all these solutions are aware of a centralized entity. This entity, from now on the _controller_, comprises the knowledge of the entire topology, and it can send and receive messages from every emulated node.
## 5 Potential Implementation  
### Performance Optimization  
The long-term HV curves of multiple wind turbines can be determined using the PAM method, which can be used to monitor and optimize the performance of wind turbines. In Figure 14, two HV curves is shown to illustrate the performance change of the T01 and T04 wind turbines from July 2014 to 1 November 2017 by using the proposed method. It can be seen that the T01 wind turbine is never in the optimal state before March 2015; however, after repair in January 2015, the turbine shifted towards an optimal state. This change in the performance can be quantified in terms of the difference in HV. For example, the difference in the health value on two different dates is 0.4, which means that the deviation between the actual data and the baseline data reaches 40%. The HV curve of T07 indicates that this wind turbine undergoes notable performance fluctuations, which means that its deviation with the baseline data is large. The baseline data represents the optimal state of the T01 wind turbine in terms of the optimal operation state of the wind farm. After the wind turbine is repaired in March 2017, its performance improved and developed toward the optimal state. Thus, the proposed method can be effectively used to diagnose whether a given wind turbine operates in the optimal state or whether other wind turbines in the wind farm operate under optimal conditions.  
Figure 14: Comparison of two wind turbines.  
Figure 13: Power time series for different dates (dashed line represents the mean, and the black line indicates the rated power).
## 3 Methodology
### PSInSAR Full Stack Processing
#### 3.2.1 Reference Image Selection and Coregistration  
The reference image, crucial for coregistration and interferogram formation, was selected to minimize spatial and temporal decorrelation. This selection was based on its optimal position in terms of both temporal and perpendicular baselines relative to the other images in the dataset. The perpendicular baseline refers to the spatial separation between satellite positions, while the temporal baseline measures the time difference between acquisitions. By optimizing these parameters, we minimize the cumulative decorrelation effects, which are essential for reliable interferometric analysis [41].  
The selected reference image from 18 May 2016 optimally represents the dataset's central tendencies, with minimal average temporal and perpendicular baselines (731 days and 24 m, respectively) relative to all other images. This image serves as the reference point for all 13 stacks, each containing 28 images. The consistent application of this reference image across different temporal segments is crucial in maintaining coherence, especially given Wuhan's significant urban changes during the study period.
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 3 Methodology for Quantifying Uncertainties
### Special Cases  
Let us now consider the special case of Equation (7): \(\mathbf{\hat{S}}(\mathbf{x})=(\mathbf{J}(\mathbf{x})^{T}\mathbf{S_{e}}^{-1} \mathbf{J}(\mathbf{x})+\mathbf{S_{a}}^{-1})^{-1}\). If the measurement error term, \(\mathbf{J}(\mathbf{x})^{T}\mathbf{S_{e}}^{-1}\mathbf{J}(\mathbf{x})\), approaches zero then Equation (7) becomes:  
\[\mathbf{\hat{S}}(\mathbf{x})\approx\mathbf{S_{a}}. \tag{15}\]  
This would cause the standard uncertainty estimate of the posterior to become:  
\[\sigma_{a}\approx\sqrt{\text{diag}(\mathbf{S_{a}}.)}\approx\text{APE}\times \mathbf{x}_{Ref}. \tag{16}\]  
This simplified case, i.e., Equation (16), is in essence the same as the approach used by the C2 RCC [25] and SWARM [26] algorithms to attach uncertainties to IOPs. If the diagonal elements of the measurement error term are positive then:  
\[\text{diag}[(\mathbf{J}(\mathbf{x})^{T}\mathbf{S_{e}}^{-1}\mathbf{J}(\mathbf{ x})+\mathbf{S_{a}}^{-1})^{-1}]<\text{diag}[\mathbf{S_{a}}], \tag{17}\]  
which would lead to a smaller uncertainty than implied by Equation (16). From testing we found that the partial derivative of the forward model, \(\mathbf{F}(\mathbf{x})\), and state vector, \(\mathbf{x}\), produced values near zero causing the measurement error term to result in a positive value near zero. This circumstance also makes it less important to have the best approach for forming the measurement error covariance matrix term, \(\mathbf{S_{e}}\), as it will be effectively neutralized by the Jacobian, \(\mathbf{J}(\mathbf{x})\).
###### Abstract  
**Background/Objectives**: The aim of this study was the early identification of endometriosis-associated ovarian cancer (EAOC) versus non-endometriosis associated ovarian cancer (NEOC) or non-cancerous tissues using pre-surgery contrast-enhanced-Computed Tomography (CE-CT) images in patients undergoing surgery for suspected ovarian cancer (OC). **Methods**: A prospective trial was designed to enroll patients undergoing surgery for suspected OC. Volumes of interest (VOIs) were semiautomatically segmented on CE-CT images and classified according to the histopathological results. The entire dataset was divided into training (70%), validation (10%), and testing (20%). A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. The results of each CNN model for each slice within a VOI were combined using three rival machine learning (ML) models, i.e., Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbor (KNN), to obtain a final output distinguishing between EAOC and NEOC, and between EAOC/NEOC and non-tumoral tissues. Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. Each model's performance was assessed based on the area under the receiver operating characteristic (ROC) curve (AUC), F1-score, sensitivity, and specificity. These metrics were then integrated into a Machine Learning Cumulative Performance Score (MLcps) to provide a comprehensive evaluation on the test dataset. **Results:** An MLcps value of 0.84 identified the VGG19 + majority voting ensemble as the optimal model for distinguishing EAOC from NEOC, achieving an AUC of 0.85 (95% CI: 0.70-0.98). In contrast, the VGG19 + SVM model, with an MLcps value of 0.76, yielded an AUC of 0.79 (95% CI: 0.63-0.93). For differentiating EAOC/NEOC from non-tumoral tissues, the VGG19 + SVM model demonstrated superior performance, with an MLcps value of 0.93 and an AUC of 0.97 (95% CI: 0.92-1.00). **Conclusions**: Hybrid models based on CE-CT have the potential to differentiate EAOC and NEOC patients as well as between OC (EAOC and NEOC) and non-tumoral ovaries, thus potentially supporting gynecological surgeons in personalized surgical approaches such as more conservative procedures.  
**Keywords:** CNN; machine learning; ovarian cancer; personalized surgery; endometriosis
## 4 Conclusions  
The following conclusions can be drawn from this study:  
1. The activation of CFA was achieved by the Na\({}_{2}\)CO\({}_{3}\) calcination effectively at 850 \({}^{\circ}\)C for 80 min. The following washing with water and alkali could significantly reduce the silicate aluminate constituents in the activated CFA sample. The REEs contained in the treated solid residue can be easily and successfully extracted while greatly reducing acid consumption during the acid leaching process. The acid saving rate was achieved by 74.5%, and the extraction rate of total REEs was achieved by 91.24%.
2. The optimal alkali washing conditions of the activated sample were found to be 20% NaOH as the alkali washing solution, 60 \({}^{\circ}\)C in temperature, 1:20 in a solid-liquid ratio, and 1 h in holding time. The removal or extraction efficiencies of Al and Si were achieved by 62.51% and 88.28%, respectively.
3. For the value-added products obtained, the yield of the precipitated silica product was achieved as high as 257.58 g/kg. The generated class A commercial white carbon black single crystal was around 40 nm in its individual grain size, 338.1 m\({}^{2}\)/g in its BET surface areas, and more than 93% in its purity. The production of the obtained zeolites, mainly sodalite and class A zeolite, was achieved in a yield of 410.3 g/kg after 5 h of crystallization.  
Conceptualization, Y.C.; data analysis, Y.C. and Z.S.; investigation and methodology, Y.C., Z.S., H.L. and Z.T.; project administration, Y.C.; writing--original draft, Y.C.; visualization, B.W., L.G. and Y.X.; writing--review and editing, all co-authors. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Natural Science Foundation of China (Grant No. 22178339), 2023 Innovation-driven Development Special Foundation of Guangxi Province, the University Science Research Project of Anhui Province (No. 2022 AH050076), the Hundred Talents Program (A) of the Chinese Academy of Sciences, and the Polygeneration Cycle Coupling Coal-based Hydrogen Production Project of Shanxi Science and Technology Research Institute of Jinneng Holding Group.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
## 4 Simulation Results
### Complexity Analysis  
Both WT-based and OMP algorithms estimate path parameters iteratively, and for proper selection of \(\varepsilon\), the iteration number is nearly the true channel path number for both of them. Therefore, the mainly differences of computation lie in the process of one iteration. Hence we will focus on the computation of one iteration.  
For OMP algorithm, parameters in both time domain and Doppler domain need to be searched. Let \(K_{L}\) be the length of the training sequence, \(N_{\mathrm{T}}\) be the grid points on the time dimension, \(N_{a}\) be the grid points on the Doppler scale dimension, thus the column numbers of the dictionary is \(N=N_{\mathrm{T}}N_{a}\). Thus, the inner products of the received signal and columns in the dictionary require \(\rho=N_{\mathrm{T}}N_{a}K_{L}\) complex multiplications.  
For WT-based algorithm, WT will be performed twice at each iteration. As mentioned before, HFM signal is Doppler invariant, thus it only needs to search in the time domain. let \(K_{L}\) be the length of the training sequence, and \(N_{\mathrm{T}}\) be the grid points on the time dimension, and then the computation  
Figure 5: Residual signal energy rate versus SNR with sampling rate at 20 kHz.  
Figure 6: Residual signal energy rate versus SNR with sampling rate at 60 kHz.  
is \(\rho=2N_{\tau}K_{L}\).  
Therefore, WT-based algorithm only needs one-dimension searching and the computational complexity is much lower than OMP algorithm, especially when the grid points on the Doppler scale dimension is large.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 2 Quality Control Algorithm and Quality Measures
### Quality Measures
#### 2.3.2 Quality Dilution of Precision (QDOP)  
QDOP is a quality measure that determines how much influence the geometric constellation of satellites has on internal reliability and redundancy. By using the redundancy represented in Equation (50), this can be rearranged as:  
\[r_{i}=(\mathrm{P}^{-1}-AQ_{\delta}A^{T})P=1-A(A^{T}PA)^{-1}A^{T}P \tag{52}\]  
In this equation, a coefficient matrix representing the geometry of the tracking satellites enables the precision of measurement to be expressed as the QDOP value:  
\[QDOP_{i}=e_{i}^{T}A(A^{T}A)^{-1}A^{T}e_{i} \tag{53}\]  
QDOP is in the range of 0 to 1. It is evident that if the measurement estimated from a satellite constellation with good geometry is closer to 0, the ability to detect an outlier can be enhanced because the redundancy was increased and the minimal detection bias (MDB) was decreased. However, if QDOP is closer to 1, in the relation between internal reliability and redundancy, the redundancy is closer to 0. This is because the denominator of the internal reliability is also 0 and the result of the MDBis infinity. The coefficient matrix then becomes the non-singular matrix. In other words, to achieve a higher redundancy, QDOP should be minimized [7].
## 4 Discussion
### Research Inspirations and Limitations  
The optimization results demonstrated a high similarity between overall comfort optimization at both block level and street level. However, the complexity and difficulty of the optimization of block level was higher than that of street level. Notably, in an actual urban environment, the building form would be more complex, which brings some difficulties to modeling restoration and simulation optimization. Considering that street space is the main daily travel and public activity space for people, this study has more practical significance in optimizing outdoor comfort at the street level. However, it brings one limitation that this study did not take the real urban block as the optimization basis and also ignored the details of real urban block with its complexity and high computation requirement, which can also be an inspiration for future work.  
The second limitation is that this study considered only two parameters--building form and street width. However, other factors related to urban layout can impact on the microclimate, such as the solar radiation differences caused by block direction, as well as the building height variation between north and south. Although this study can achieve good optimization when comparing the best and the worst urban layouts, such limitations should be overcome as an inspiration for considering additional factors of urban morphology to achieve better urban layout design and a more optimal microclimate.  
Third, the time range in this study was only from 8:00 a.m. to 8:00 p.m., which can represent the duration time for outdoor activity; however, future work can take more optimization time into consideration, such as 24 h, summer day, or even the whole year.  
Finally, as suggested, cities in cold regions should avoid the placement of large-scale unprotected open spaces; however, open spaces are a necessity for outdoor activity. Therefore, cold-area cities should consider locating open spaces under building enclosure protection around the block or dividing open spaces into small pieces arranged in the interior or between the enclosed buildings.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.21. Seecr [44]  
In [44], the authors proposed a secure energy-efficient and cooperative routing protocol (SEECR). It is a multi-path cooperative routing protocol. The nodes accept the data packet only if they know the next two hops. After accepting the data, it amplifies it and forwards the data toward the sink. Each node broadcasts its depth and residual energy using a Hello packet periodically. When the dead nodes reach 20%, the default depth of 60m decreases to 40m. When it reaches 75%, the network adjusts to improve the network lifetime. The neighbor records the data received and sent. A node compares the received data with the neighbor data. In the case of any discrepancy, the author compares the data attack values, and if it exceeds the values, the node is isolated from the network to prevent it from being attacked. The nodes try to send the data directly to the sink. If it cannot perform this task, it refers to the cooperation phase to send the data to the sink. The source node selects the relay node using weights assigned using signal-to-noise ratio (SNR) and residual energy. If the residual energy of the source is more significant than the relay nodes, then direct transmission occurs. Otherwise, relay nodes forward the packet. The authors use signal-to-noise ratio combined (SNRC) to combine the source and relay node signals.  
* Comparison  
Here, all the protocols use a Hello packet except EECOR; the authors assumed that the sink of the protocols is fixed and does not need localization to determine the position of the nodes. All these protocols have a void avoidance mechanism but only SEECR has the ability to detect security threats. Table 9 illustrates the differences between the protocols.  
Table 10 illustrates the key ideas, advantages and disadvantages of all the energy-aware routing protocols discussed in this paper.
## 4 Analysis of Ground Temperature Monitoring Data
### Overall Change in Ground Temperature
At a depth of 4.5 m, the highest temperature of the soil was 3.99 \({}^{\circ}\)C; the date when the soil temperature reaches the highest value is delayed by nearly 4 months compared with the depth of 0.5 m. The lowest temperature of the soil is \(-\)0.13 \({}^{\circ}\)C; the date when the soil temperature reaches the lowest value is delayed for nearly 3 months compared with the depth of 0.5 m. It can be concluded that the hysteresis effect of the temperature of the soil in the lower part of the subgrade is smaller than that of the warm season in the cold season. The heat transfer is more rapid during the cooling process of the soil under the subgrade. To see the thickness of the active layer at different locations more clearly, the origin software was applied to draw the envelope diagram, as shown in Figure 5. The 0 amplitude depth of the four positions is different. The depth of 0 amplitudes at the medial divider and the right toe of the slope is about 4 m, and the depth of 0 amplitudes at the left natural hole and the left toe of the slope is about 2 m. It can be concluded that the heat absorption of the road surface has a large impact on the medial divider, resulting in a significant perturbation of the permafrost in the lower portion of this location, which is specifically reflected in the increase in the depth of the 0 amplitude in the lower part of the location. Under the influence of the heat absorption of the road surface, the maximum temperature of the surface at the medial divider can be up to 26.37 \({}^{\circ}\)C, and the maximum temperature is significantly higher than in other locations. The minimum temperature of the surface in the medial divider is \(-\)20.37 \({}^{\circ}\)C, and the minimum temperature is smaller than the minimum temperature of other locations. The large difference in ground temperatures at the left and right toe of the slope, i.e., the large lateral difference in subgrade temperatures, is due to the sunny-shady slope effect, which also leads to significant disturbance of the permafrost in the lower part of the right toe of the slope.
## 2 Background
### Risks of Reducing Crew on Crew and Vessel Safety
#### 2.4.4 Ship Regulation, Insurance, and Liability  
The barriers of ship regulation, insurance, and liability could mean that it will be a long time before it is profitable to invest in autonomous ships [49]. According to research conducted by the DMA in 2017, there are seven categories where regulatory barriers exist with the implementation of autonomous shipping. They are: jurisdictional issues, COLREGs, the crew of the future, marine environmental protection, construction requirements, liability and insurance, and cybersecurity [10]. DMA research concluded that MASS should be regulated by the IMO, and additions to COLREGs and STCW have to be addressed to include MASS and Remote Control Operators (RCOs). They also concluded that MASS should be crewed appropriately to maintain the proper safety of the vessel, crew members should be adequately trained as current licensed mariners, passenger ships cannot be unmanned due to fire safety until changes to IMO's Safety of Life at Sea (SOLAS) are made, and cybersecurity has to be addressed by a vessel's safety management system [10].  
From 2011 to 2016, the Allianz Global Corporate and Specialty Global Claims Review analyzed 15,000 liability claims from the marine sector that equated to 1.6 billion USD of losses [50]. These maritime incidents rank high as a cause of liability loss as each individual accident accrues costly expenses such as personnel liability, wreck removal, and pollution [51]. Many shipping insurers are skeptical of the practicality of removing crew from vessels, and there is some consensus that it may take more than just a few years for laws to change for unmanned vessels to operate without crew onboard [2]. Currently, the ship's Master is responsible for all actions related to their ship. If ships remove their onboard crew, ship liability will have to fall elsewhere, such as the ship's owner. MUNIN conducted an analysis of the liability and legal issues with regard to autonomous shipping. They determined that the current legal framework can be adapted, but significant issues exist with navigation and manning laws, liability, and ship design standards and construction. In any case, further research must be conducted [18].
# Consistency Models of NoSQL Databases
## 5 NoSQL Consistency Implementations
### Cassandra
#### 5.2.1. ALL Write Consistency Level or ALL Read Consistency Level  
From Figure 5, we can conclude that the probability of consistency over time is constant, resulting in 100%. That is because each write or read operation is executed on every node available before acknowledging the result to the client. Therefore, this configuration makes the Cassandra cluster strong consistent.