esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
#### 2.2.1 Correlation Analysis  
After the experiment, a set of laboratory data detailing the CS of cement mortars with various substitution ratios of burned joss paper ash (SR-BJPA; 0%, 5%, 10%, 20%, and 30%) and W/B ratios (0.4, 0.5, and 0.6) was obtained. Each mixture was cured at different temperatures (T = 25, 50, and 75 \({}^{\circ}\)C). As all other conditions were fixed during the experiment, at the analysis stage, the CS was strongly correlated with the number of curing days (#days = 3, 7, 28, 56, 90, or 120 days) and negatively with the W/B ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CaO** & **SiO2** & **Al2O3** & **MgO** & **Na2O** & **K2O** & **SO3** & **Fe2O3** & **LOI** \\ \hline
62.83 & 15.98 & 9.39 & 3.24 & 2.82 & 0.39 & 2.46 & 1.13 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of BJPA determined by XRF analysis (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specific Surface** & **Specific Gravity** & **Strength Activity Index** \\
**(cm2/g)** & **(g/cm3)** & **(28 Days, \%)** & **Particle Size (\(\mu\)m)** \\ \hline
6753 & 2.75 & 79.7 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of BJPA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**W/B Ratio** & **SR-BJPA** & **Cement** & **Sand** & **Water** \\ \(\in\) [0, 1]** & **(\%)** & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** \\ \hline \hline \multirow{4}{*}{0.4} & 0 & 234.49 & & 0 \\  & 5 & 222.77 & & & 11.72 \\  & 10 & 211.04 & 644.85 & 93.80 & 23.44 \\  & 20 & 187.59 & & & 46.89 \\  & 30 & 164.14 & & & 70.34 \\ \hline \multirow{4}{*}{0.5} & 0 & 221.88 & & & 0 \\  & 5 & 210.78 & & & 11.94 \\  & 10 & 199.69 & 610.17 & 110.94 & 22.18 \\  & 20 & 177.50 & & & 44.37 \\  & 30 & 155.31 & & & 66.56 \\ \hline \multirow{4}{*}{0.6} & 0 & 210.55 & & & 0 \\  & 5 & 200.02 & & & 10.52 \\ \cline{1-1}  & 10 & 189.50 & 579.02 & 126.33 & 21.05 \\ \cline{1-1}  & 20 & 168.44 & & & 42.11 \\ \cline{1-1}  & 30 & 147.38 & & & 63.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportions of the cement mortar test samples.  
This latter correlation was weaker in terms of the correlation coefficient (r). Thus, at the data analysis stage, we conducted correlation tests to elucidate the direction and strength of the relationships between CS and each 'variable' (i.e., #days, T, SR-BJPA, and W/B ratio).
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 1 Introduction
Taking Beijing as an example, as of June 2017, Beijing has 1730 bus lines, a 500 m station coverage rate of 80%, and a line network coverage rate of 78%; in 2015, the bus coverage within the Fourth Ring Road had reached more than 97%, and the proportion of urban public electric vehicles that transported passengers was 48.2%. Therefore, the spatial and temporal activity distribution of residents in the central urban area can be effectively described based on bus card data.
## 5 Simulation and Evaluation
### Impact of Nodes Mobility
It instantaneously selects the neighbor with the highest distance progress toward a given destination to forward the packet. The main factor that has a direct impact on the packet delay performance metric is the local minima phenomenon, which occurs when a sender becomes the closest node to the destination, and no further progress towards the destination can be made. In such a scenario, the packet is dropped, which increases the packet delay. Finally, FQ-AGO algorithm outperforms all the routing protocols in this study, by taking advantage of the broadcast medium and involving all available next-hop nodes in the candidate set to choose the most reliable as the next-hop forwarder. Furthermore, selecting the candidate node with a high transmission range has a direct impact on minimizing the packet delay transmission due to the long-distance progress it can achieve. Figure 10 illustrates the impact of the independent factor nodes speed on the packet delivery ratio. We observe that the packet delivery ratio decreases drastically at higher node mobility in DSDV and AODV. The frequent breaking of links requires initiating a route search action to update the routing table, which increases the control message overhead and creates congestion in the network; the time-out it takes to update the routing table increases the end-to-end packet delay and decreases the network throughput. In a real-time application, the source node generates a high constant bit rate traffic, which increases the number of packets injected into the network. Hence, DSDV and AODV are unable to tolerate the packets streams with limited node memory resources. Consequently, the ratio of the dropped packets is high due to high node mobility. On the other hand, the proposed protocol is more resilient to frequent topology  
Figure 7: Hop-Count vs. nodes speed. changes, thanks to the candidate selection mechanism, which allows the construction of the path on-the-fly, which makes it independent of node mobility. Moreover, the presence of asymmetric links creates shorter paths toward the destination, which increases the probability of reaching the destination with a minimum number of intermediate nodes. Overall, the FQ-AGO outperforms all other protocols due to link stability along the path, and its independents from any network topology change.
# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
###### Abstract  
The core challenge of speech synthesis technology is how to convert text information into an audible audio form to meet the needs of users. In recent years, the quality of speech synthesis based on end-to-end speech synthesis models has been significantly improved. However, due to the characteristics of the Mongolian language and the lack of an audio corpus, the Mongolian speech synthesis model has achieved few results, and there are still some problems with the performance and synthesis quality. First, the phoneme information of Mongolian was further improved and a Bang-based pre-training model was constructed to reduce the error rate of Mongolian phonetic synthesized words. Second, a Mongolian speech synthesis model based on Ghost and ILPCnet was proposed, named the Ghost-ILPCnet model, which was improved based on the Para-WaveNet acoustic model, replacing ordinary convolution blocks with stacked Ghost modules to generate Mongolian acoustic features in parallel and improve the speed of speech generation. At the same time, the improved vocoder ILPCnet had a high synthesis quality and low complexity compared to other vocoders. Finally, a large number of data experiments were conducted on the proposed model to verify its effectiveness. The experimental results show that the Ghost-ILPCnet model has a simple structure, fewer model generation parameters, fewer hardware requirements, and can be trained in parallel. The average subjective opinion score of its synthesized speech reached 4.48 and the real-time rate reached 0.0041. It ensures the naturalness and clarity of synthesized speech, speeds up the synthesis speed, and effectively improves the performance of the Mongolian speech synthesis model.  
Mongolian speech synthesis; non-autoregressive; Ghost module; vocoder +
Footnote †: journal: Applied sciences
## 1 Introduction
For example, around 70% of the population in Europe will live in urban areas in 2050 [10]. This growth will increase the number of deliveries; which current resources will not be able to satisfy. For instance, UDCs facing problems with their delivery capacity will communicate and coordinate with other UDCs. The concerned UDC can coordinate with different UDCs in order to minimize delayed products. Delayed products are products delivered with a delay. This can happen when the demand exceeds the sum of the available capacity of the concerned UDC and the available capacity of the UDC to supply. The delivery rate can be affected by maintenance operations. This idea is inspired by the work of [PERSON] el al. [11] where the authors presented an integrate production/maintenance/transportation plan with the strategy of switching and collaboration between several productions sites in order to satisfy random demands with a high service level. Regarding maintenance policy, [PERSON] et al. combined production and maintenance plans for the manufacturing system in order to satisfy random demand with a high service level [12]. In this case, the failure of the manufacturing system depends on the time and the production rate. Moreover, [PERSON] et al. [13] integrated the possibility to use a subcontractor in the case of maintenance actions for the manufacturing system in order to satisfy demand. Furthermore, [PERSON] et al. [14] have extended their work by developing a joint maintenance and production policy for a manufacturing system that can call several subcontractor machines. In this paper, we will study the problem of an efficient distribution and maintenance policy based on two sequential plans, a distribution plan correlated to a maintenance plan, for an urban distribution center (UDC) using the subcontracting strategy. This work consists of integrating a collaborative approach to allow making a call if needed to other UDCs from different potential possibilities to satisfy customers' demands with a high service level. We aim to find the best distribution, maintenance plan and subcontractor plan to minimize the total cost. The choice of UDC subcontractor will depend on two criteria: (1) the distance between the platform and the urban area and (2) the impact on the environment. The total cost is defined as the sum of the transportation cost, maintenance cost and subcontracting cost. The paper is structured as follows. Section 2 introduces the problem statement. Section 3 presents the mathematical model for searching for the best production and maintenance plans using the subcontracting strategy. In Section 4 we present a numerical example illustrating the benefits of our approach. Finally, we conclude and discuss future direction in Section 5.
## 4 Experimental Results and Analysis
### Statistical Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Target Model** \\ **Mean Error** \\ \end{tabular} & \begin{tabular}{c} **Comparison** \\ **Model** \\ **Mean Error** \\ \end{tabular} & \begin{tabular}{c} **Mean Error** \\ **Difference** \\ \end{tabular} & \begin{tabular}{c} **T-Statictic** \\ **\(p\)-Value** \\ \end{tabular} & \begin{tabular}{c} **Wilcoxon** \\ **Statistic** \\ \end{tabular} & \begin{tabular}{c} **Wilcoxon** \\ **\(p\)-Value** \\ \end{tabular} & \
\begin{tabular}{c} **Cohen d** \\ **Significance** \\ **(\(\alpha\) = 0.05)** \\ \end{tabular} &
\begin{tabular}{c} **T-Test** \\ **Significance** \\ **Significance** \\ **(\(\alpha\) = 0.05)** \\ \end{tabular} \\ \hline BLSTMModel & 0.00275269 & 0.08020279 & -0.07799202 & -20.053164098 & 0 & 2191,422 & 0 & -2.78899267 & Significant & Significant \\  & 0.00275269 & 0.01675279 & -0.07799202 & -20.053164098 & 0 & 20.52880213 & 0 & -2.78899267 & Significant & Significant \\  & 0.00275269 & 0.01675279 & -0.07799202 & -20.053164098 & 0 & 102.5288 & 0 & -2.78992643 & Significant & Significant \\  & 0.0275269 & 0.1487519 & -0.148591098 & -14.29723837 & 0 & 94.5251 & 0 & -1.39792643 & Significant & Significant \\  & 0.0275269 & 0.129429828 & -0.27727618 & -14.3628376 & 0 & 25.364 & 0 & -2.29952043 & Significant & Significant \\  & 0.00275269 & 0.127843251 & -0.135681681 & -19.5888822 & 0 & 26.607 & 0 & -2.26988754 & Significant & Significant & Significant \\  & 0.0275269 & 0.1278013 & -0.13415444 & -22.067884 & 0 & 70.486 & & -3.0960608 & Significant & Significant & Significant \\  & 0.00275269 & 0.136183782 & -0.133431213 & -186.1417087 & 0 & 65.410 & 0 & -2.51974888 & Significant & Significant & Significant \\  & 0.0275269 & 0.13157097 & -0.13082401 & -166.908467 & 0 & 116.673 & 0 & -2.252803018 & Significant & Significant & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Paired t-test of MDFFE model and other models. 2304.543389, and the degree of freedom is 193,536. The residual represents the error inside the model, that is, the difference between the predicted value and the actual value of the model. Although the sum of squares of residuals is large, the difference between models is more significant than the error within the model, which further confirms that there are significant differences in prediction accuracy between different models.
Early orthodontic intervention or treatment was recommended for patients to prevent subsequent disease [2; 6]. Previous studies mentioned that scissors bite can adversely affect jaw growth and appearance [3]. However, few studies accurately evaluated the association between unilateral premolar scissors bite and the mandibular morphology and symmetry. Previous investigations reported the potential association between unilateral scissors bite and mandible position using conventional analysis methods. For example, [PERSON] et al. [7] evaluated the lateral cephalograms and found that patients with premolar scissor bite were observed to have less mandibular length. However, this method can only evaluate the mandible in a sagittal position. Due to the complexity of anatomical structures, two-dimensional (2D) X-ray techniques suffer from superimposition. The information obtained from 2D measurements is relatively limited for accurate evaluation [8]. Current CBCT scanners can offer higher contrast and spatial resolution for bone images [8; 9]. [PERSON] et al. [10] evaluated the morphologic and positional features of the mandible of patients with unilateral posterior crossbite via CBCT. Although the 3D models were constructed, the study still followed the 2D measurement rule to calculate the linear distance and angles. However, the bone itself is not a flat plane; the length of the line cannot completely represent the real bone surface. Furthermore, the manual work was time-consuming. Based on the above literature, it was found that there were limitations of the previous methods, and few of them evaluated the mandibular symmetry of scissors bite via 3D measurements. Therefore, we attempted to propose a computer-aided method for more visual and accurate assessment, which was hypothesized to outperform the previous ones in the literature. The purpose of this study was to introduce the 3D distance mapping technique to evaluate the mandibular symmetry and morphology of adult patients with unilateral premolar scissors bite.
## 5 Results and Discussion
### Comprehensive Detection Capability  
In our study, we evaluated five mainstream adversarial example attack methods and provided associated detection parameters for each type of adversarial example. These parameters involve the detection methods used and their corresponding thresholds, as shown in Table 3. First, for the FGSM, PGD, and BIM attacks, we employed the reconstruction error as the primary detection tool. These three attacks yielded different detection rates under the same threshold of 0.0006. The FGSM attack had a detection rate of 91.45%, which is the highest among the three. In comparison, PGD and BIM had detection rates of 81% and 71%, respectively, indicating certain challenges in detecting these two attacks under the given parameters. For the CW and Deepfool attacks, we chose to use KL divergence as the detection tool. At a threshold of 0.0002, the CW and Deepfool attacks had detection rates of  
Figure 7: Detection rates for CW and Deepfool adversarial examples under different KL divergence thresholds.  
99.09% and 99.90%, respectively, which are very encouraging. Particularly, the Deepfool attack was almost entirely detected, suggesting that the detection framework exhibits very high stability against this type of attack at high thresholds.  
Considering all five attack methods, our detection framework achieved an impressive average detection rate of 88.488%. This result not only reveals the efficiency of the framework but also demonstrates its broad adaptability. First, achieving a comprehensive detection rate close to 90% implies that in most cases, adversarial examples will be effectively identified and blocked, thereby significantly enhancing the model's security. In practical applications, such a high detection rate can substantially reduce the success rate of malicious attacks, ensuring that critical tasks maintain normal operation when faced with adversarial attacks. Second, the framework exhibited a high detection capability against five different attack methods, further highlighting its wide adaptability. In real-world scenarios, attackers might attempt various attack strategies, so a comprehensive, multi-strategy detection framework is of paramount importance. This not only saves time and resources required to design separate detection strategies for different attacks but also provides a unified defensive front, reducing the chances for attackers to find vulnerabilities.
## 2 Study Areas and Data
### Data
#### 2.2.3 Reference Data  
The reference data used to evaluate surface conditions for the performance of satellite passive microwave signals are: 90 m Digital Elevation Model (DEM) data from the Shuttle Radar Topographic Mapping Mission [43], 250 m river width data from the Global Width Database for Large Rivers (GWD-LR) [44], 500 m LULC data [45] and 250 m Vegetation Continuous Fields (VCF) [46] from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products, climate zones from the World Map of Koppen-Geiger climate classification [47; 48]. The above data were all upscaled into the same spatial resolution (3.125 km) as the \(T_{b}\) data.
# A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells
## 2 Methodology  
As shown in Figure 1, the architecture of the proposed method consists of contour initialization, touching cell splitting (pink arrow) and overlapping cell segmentation (purple arrow). In Section 2.1, the contour initialization includes nuclei detection and segmentation,as well as the segmentation of the contours formed by the boundary of the cellular clumps. After contour initialization, we obtained the initial contour of cytoplasm for each cervical cell. Based on this initialization, we proposed a new method for the touching cell splitting described in Section 2.2 and overlapping cell segmentation described in Section 2.3. As a major task, the proposed touching cell splitting (pink arrows) can alleviate the effect of misallocation of the pixels on the clump boundary, which may cause the cells slight touching or the isolated ones misjudged as overlapping. After the isolation judgement of the isolated cells, the algorithm proceeds to the overlapping cell segmentation, which is the other major task, aiming to segment the overlapping cell from its neighbor cells iteratively.
## 3 Results and Discussion
### Assessment of Uncertainty
The volume expansion coefficient for a 40% \(v/v\) alcoholic beverage is 6.5 \(\times\) 10\({}^{-4}\)\({}^{\circ}\)C\({}^{-1}\)[22]. Therefore, the change in volume of liquid contained in the bottle is:  
\[\pm(100\times 4\times 6.5\times 10^{-4})=0.26\ \mathrm{mL}\]  
the standard uncertainty of this source, assuming a quadratic distribution, is:  
\[\frac{0.26}{\sqrt{3}}=0.15011107\ \mathrm{mL}\] \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**Measurement** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**recovery Ri\%** & 93 & 94 & 93 & 91 & 95 & 91 & 92 & 97 & 95 & 92 & 91 & 91 \\ \hline
**Measurement** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & \\ \hline
**recovery Ri\%** & 93 & 94 & 91 & 90 & 94 & 91 & 92 & 92 & 93 & 94 & 91 & \\ \hline \end{tabular}
\end{table} 
Table 9: DEHP recovery measurements in Ouzo, in the context of internal quality control of the accuracy of the method. If the two standard uncertainties are combined, the standard uncertainty of the bottle volume is obtained:  
\[\text{u}(\text{V}_{\text{bottle}})=\sqrt{0.04618802^{2}+0.15011107^{2}}=0.157056253\]  
and the corresponding relative standard uncertainty is:  
\[\frac{\text{u}(\text{V}_{\text{bottle}})}{\text{V}_{\text{bottle}}}=0.00157056\]  
Furthermore, a pipette of 0.010 mL to 0.100 mL was used to transfer the concentrated DEHP standard, and in particular V\({}_{\text{pipette}}\) = 0.050 mL were transferred, which according to the manufacturer have a tolerance of a = \(\pm\)0.001 mL. Thus, the standard uncertainty of the pipette volume, assuming a quadratic distribution, is:  
\[\text{u}\big{(}\text{V}_{\text{pipette}}\big{)}=\frac{0.001}{\sqrt{3}}=0.00057 735\text{ mL}\]  
and the corresponding relative standard uncertainty, is:  
\[\frac{\text{u}\big{(}\text{V}_{\text{pipette}}\big{)}}{\text{V}_{\text{pipette }}}=0.001154701\]  
In summary, the relative volume uncertainty, u\({}_{\text{vol}}\) is:  
\[\text{u}_{\text{vol}}=100\sqrt{\left(\frac{\text{u}(\text{V}_{\text{bottle}}) }{\text{V}_{\text{bottle}}}\right)^{2}+\left(\frac{\text{u}\big{(}\text{V}_{ \text{pipette}}\big{)}}{\text{V}_{\text{pipette}}}\right)^{2}}=1.16533257\%\]  
Substituting into Equation (4), the u\({}_{\text{conc}}\), u\({}_{\text{vol}}\) values calculated above, R\({}_{\text{i}}\) from Table 9 and n = 23, the relative standard uncertainty of systematic errors (from recovery) by NORDTEST is [14]:  
\[\text{u}_{\text{bias,N}}=7.67052066\%\]
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 1 Introduction
The low standard deviation achieved in our experimental evaluation serves as evidence demonstrating the effectiveness of dynamic masking in reducing false eliminations and preserving static feature points. 2. A dynamic feature point update mechanism is designed. On the basis of dual-mask filtering, outliers are effectively identified and eliminated through PnP pose optimization to dynamically update the set of geometric outliers. This provides a reliable reference for subsequent frames, further improving the detection and elimination of dynamic areas. 3. Empirical evaluations on public dynamic datasets, including KITTI and TUM, demonstrate that the proposed method significantly outperforms ORB-SLAM3 by reducing both the Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) in dynamic environments, validating its superiority in terms of both accuracy and robustness. Specifically, the proposed method achieves average reductions in error of 3.43% (ATE) and 11.42% (RPE) on the KITTI dataset and 24% (ATE) and 8.27% (RPE) on the TUM dataset. The remainder of this paper is structured as follows. Section 2 reviews the research progress in visual SLAM and focuses on discussing existing dynamic object handling techniques while analyzing the strengths and weaknesses of these methods and the challenges that they face. Section 3 introduces the design and implementation of the proposed system, including core ideas, system architecture, and key technologies. Section 4 evaluates the performance of the proposed method through experiments on the KITTI and TUM datasets and compares it with existing methods to demonstrate the system's performance improvements. Finally, Section 5 concludes the main work of the paper, discusses the limitations of the research, and looks forward to potential future research directions and application prospects.