esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### EMR Results
Due to the minimization of magnetostatic energy by dipolar forces, both of a single aggregate and the entire ensemble of MNPs, the average magnetic moment of the aggregate was only about two moments of a single MNP. The EMR measurements showed a break in these magnetic correlations in an external magnetic field of 110 kA/m [26]. The estimation of the dipolar field created by a magnetosome at the neighboring one in the chain (in the \"heavy\" fraction), based on the estimated magnetosome moment of 1.7 \(\times\) 10\({}^{6}\) u\({}_{\text{B}}\) and the average distance between magnetosome centers in the TEM images of 40-50 nm (Figure 1), yields the crossover field \(H_{\text{cross}}\)\(\approx\) 250-350 kA/m. This means that each magnetosome follows the external field independently in the fields above \(H_{\text{cross}}\). The analyzed extrema in the EMR spectra are localized in the lower fields (Figure 7) and arise from magnetosomes that are ferromagnetically linked in the chains by dipolar forces. Since the dipolar correlations in the \"light\" fraction have, at least partly, an antiferromagnetic character, the magnetic moment of the \"light\" fraction is an order of magnitude smaller than that of the \"heavy\" one (Table 1). Therefore, the broad weak signal from the highly dispersed \"light\" fraction is not actually observed against the background of the signal from the \"heavy\" fraction with much larger moment. This suggests that the observed EMR signals should be attributed to the \"heavy\" fraction, whose magnetosome chain is attached to the membrane [1; 2; 3; 4; 5; 29]. As shown in Figure 7, the amplitudes of the experimental signals for the \"old\" and \"new\" fractions differ little for different strains. At the same time, the signal from the \"old\" fraction is somewhat less than that from the \"new\" fraction for one and the same strain. The positions of the first of two main fitting signals (magenta Gaussians) near \(g\)-factor 2 are practically the same for all strains, BB-1, MSR-1, and AMB-1, both for the \"new\" and \"old\" samples, as also shown in Table 2. Hereafter, the \(g\)-factor refers to the effective \(g\)-factor characterizing the position of the signal in the magnetic field. The closest values of the first Gaussian \(g\)-factors in the \"new\" and \"old\" samples are found in the AMB-1 strain, while some difference is observed in the BB-1 strain, and some more difference is found in the MSR-1 strain. As for the second main fitting signals (blue Gaussians), their field positions are rather close for all the strains in the sample \"old\", while the positions of the second Gaussians are noticeably different in the trials \"new\". In a magnetosome chain, one can expect the orientation of the moments of magnetic core and, probably, their anisotropy axes along the chain due to their dipolar correlations,
## 3 Method
### Selective Kernel Convolutions  
In the introduction, we mentioned that correctly detecting different objects requires varying effective receptive field sizes. In order to guarantee the network's adaptive adjustment of effective receptive field sizes, we utilize a multi-branch structure to model a series of long-range contexts for adaptive selection. We propose to combine larger kernel convolutions and depth-wise convolutions to construct larger effective receptive field sizes. Increasing kernel size is more effective than stacking more layers in expanding the Effective Receptive Field (ERF) [44]. Therefore, a small number of large kernel layers can be utilized to build a sufficient ERF, thus saving computational budget. The saved computational budget can be used to add other effective structures, effectively enhancing the abstraction level of spatial patterns or network depth. Therefore, we introduce Wavelet Domain Channel Selection Module (WDCSM) and the Wavelet Domain Spatial Selection Module (WDSSM) to increase the network depth and enhance representational capacity.  
As illustrated in Figure 5, it represents an WDARF module with dual-branch case, yet this module can be easily extended to multiple-branch case. For any given feature map  
Figure 4: Illustration of a block of Wavelet-Domain Adaptive Receptive Field Network.  
Figure 5: Illustration of Wavelet Domain Adaptive Receptive Field Module.  
\(X\in\mathbb{R}^{H\times W\times C}\), we first use depth-wise convolutions with kernels of different sizes to obtain a series of features:  
\[\left\{\begin{aligned} \widetilde{\mathcal{F}}^{1}&:X \rightarrow\mathrm{U}_{1}^{\prime}\in\mathbb{R}^{H\times W\times C}\\ \widetilde{\mathcal{F}}^{2}&:X\rightarrow\mathrm{U}_{2}^ {\prime}\in\mathbb{R}^{H\times W\times C}\end{aligned}\right. \tag{6}\]  
where \(\widetilde{\mathcal{F}}^{1}(\cdot)\) and \(\widetilde{\mathcal{F}}^{2}(\cdot)\) are depth-wise convolutions with kernel sizes of 5 and 7, respectively. The purpose is to explicitly generate a series of features with different receptive field sizes, facilitating subsequent kernel selection operations.  
Subsequently, use \(1\times 1\) convolution to reduce dimensionality for each branch:  
\[\left\{\begin{aligned} \hat{\mathcal{F}}^{1}&:X \rightarrow\mathrm{U}_{1}\in\mathbb{R}^{H\times W\times C/2}\\ \hat{\mathcal{F}}^{2}&:X\rightarrow\mathrm{U}_{2}\in \mathbb{R}^{H\times W\times C/2}\end{aligned}\right. \tag{7}\]  
where \(\hat{\mathcal{F}}^{1}(\cdot)\) and \(\hat{\mathcal{F}}^{2}(\cdot)\) are \(1\times 1\) convolutions, the purpose is to reduce the dimensionality of feature maps for each branch and allow mixing of features from different channels. Due to the multi-branch structure generating a series of feature maps, causing a significant increase in computational resource requirements, we reduce the channel dimension of each group of feature maps by half to mitigate computational complexity. In the following section, we propose the WDSSM and WDCSM. This mechanism dynamically selects suitable receptive fields for various objects while also enhancing the noise robustness of the network.
## 2 Materials and Methods
### Survival Prediction

Table 7: Results obtained by the models trained with radiomic + recurrence features, evaluated on the test sets. The metric used for evaluation was the concordance index. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature (CT)** & **Coef.** & **Std. Err.** & **Coef. lower 95\%** & **Coef. Upper 95\%** & _p-Value_ \\ \hline SmallDepHighGLEmph & 1.425 & 0.563 & 0.32 & 2.529 & 0.011 \\ JointAverage (CT) & \(-2.226\) & 1.078 & \(-4.338\) & \(-0.113\) & 0.039 \\ Imc (CT) & \(-2.545\) & 1.073 & \(-4.648\) & \(-0.442\) & 0.018 \\ Imc1 (CT) & \(-4.401\) & 1.389 & \(-7.123\) & \(-1.678\) & \textless{}0.005 \\ Skewness (CT) & \(-1.114\) & 0.754 & \(-2.592\) & 0.365 & 0.140 \\ Energy (CT) & 0.676 & 0.339 & 0.011 & 1.341 & 0.046 \\ Contrast (CT) & \(-4.655\) & 1.329 & \(-7.260\) & \(-2.049\) & \textless{}0.005 \\ Max2 DDiamSlic (PET) & 1.650 & 0.680 & 0.317 & 2.983 & 0.015 \\ SurfaceVolumeRatio (PET) & 2.126 & 0.679 & 0.796 & 3.456 & \textless{}0.005 \\ Maximum (PET) & 1.336 & 0.699 & \(-0.034\) & 2.705 & 0.056 \\
10 Percentile (PET) & \(-1.350\) & 0.705 & \(-2.732\) & 0.031 & 0.055 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Combined selected CT and PET features after further selection with the GA, with corresponding Cox coefficients, standard errors, 95% confidence intervals, and _p_-values.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 3 Comparison of Maximum Diameter and Particle Size Parameters
### Correlation between Coarseness Index and Maximum Diameter  
The maximum diameter (\(d_{\text{max}}\)) is a particle property that does not require a mathematical calculation. Therefore, it can be said that this is the most intuitive characteristic of rock fragments that does not require additional calculations.  
Figure 6 shows the relationship between \(d_{\text{max}}\) and \(CI\) obtained in this study; the details are listed in Tables 3-5. Similar to \(d_{50}\), \(d_{\text{MPS}}\), and \(d^{\prime}\), \(d_{\text{max}}\) had an exponential relationship with the \(CI\), with a strong correlation (R\({}^{2}>0.8\)). The range of change in \(CI\) was 480-620 in the rock with a UCS of 20 MPa and 530-640 in the rock with a UCS of 40 MPa. This is similar to the phenomenon in which \(CI\) is affected by rock strength in the relationship between \(d^{\prime}\) and \(CI\)[19]. Thus, \(d_{\text{max}}\) is sufficient to replace particle size parameters such as \(d_{50}\), \(d_{\text{MPS}}\), and \(d^{\prime}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(p\) (mm)} & \multirow{2}{*}{\(s\) (mm)} & \multirow{2}{*}{\(s\)/\(p\)} & \multicolumn{8}{c}{**Cumulative Probability (\%)**} & \multirow{2}{*}{**CI**} & \multicolumn{2}{c}{\(d_{\text{max}}\)} \\ \cline{3-11}  & & & \#4 & \#10 & \#20 & \#40 & \#60 & \#100
## 2 Application of Ribosome Engineering to Increase Antibiotic Production
### Combination of Genome Shuffling and Ribosome Engineering  
Genome shuffling is an approach involving recursive genomic recombination within a population of phenotypically selected bacteria to generate new strain libraries, which may result in pronounced improvement in the screened phenotype. The combination of genome shuffling and ribosome engineering has been widely used in antibiotic development, including avilamycin (**5**) [36], daptomycin(7) [39], nosiheptide (**1**) [43], and virginamycin (**2**) [52]. For example, avilamycin (**5**), a feed industry antimicrobial agent approved by the European Union, is used to inhibit the growth of multidrug resistant Gram-positive bacteria. [PERSON] et al. [36] combined genome shuffling with _str_ screening to obtain an improved recombinant strain _Streptomyces viridochromogenes_ E-219. The yield of avilamycin in this strain reached 1.4 g/L, a 36.8-fold increase in comparison with that of the ancestor _S. viridochromogenes_ strain.
# MEC: A Mesoscale Events Classifier for Oceanographic Imagery
## 5 Results and Discussion
A further extension of MEC will be achieved by conceiving a set of general classification rules, conceived in such a way that they can be applied to a general marine scenario without requiring any specific fulfilment on the considered area. Future work will address this extension. Finally, it is suitable to briefly report about the MEC computational performance. Considering that the satellite sensor resolution is about 0.01\({}^{\circ}\) (see Table 1), more than 10\({}^{4}\) SST samples are expected to fall within a sea square surface with side size \(\delta=1^{\circ}\). As mentioned in Section 4, the SST data are projected on a spatial grid with cell side typically selected to be larger than the sensor nominal resolution. The algorithm performance mostly depends on the time required to open the data files, access the stored sensor measurements and assign a signal intensity value to the cell, computed as the mean value of the SST measurements falling within that cell. Once the resolution value is chosen by fixing the size of the cell side (\(r=\delta/d\), with \(d\) integer), the number of average operations to be performed roughly amount to \(d^{2}\), which is approximately confirmed by observing the values reported in the second column of Table 2. The spaghetti plot generation stage thus represents the task with the highest computational burden in the algorithm pipeline, while the number of operations for the remaining pipeline stages (statistics extraction and rules application) grows only linearly with the number of images captured per day. On the other hand, a reasonable choice for \(d\) must ensure a suitable execution of the feature extraction stage, so that the grid cell size has to be comparable with the typical size of the sought phenomena (tens of km), which is why 0.25\({}^{\circ}\) has been identified as a proper trade off value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resolution** & **Sbaghetti Plot** & **Statistics** & **Application** \\
**Generation** & **Extraction** & **of Rules** \\ \hline
0.05\({}^{\circ}\) & 261.363 s & 0.114 s & 0.216 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Times required for each step of the classifier at different resolutions, for the analysis of an area of 1\({}^{\circ}\times 1^{\circ}\) for 15 days. All computations were performed on a processor Intel® CoreTM i5-8250U @ 1.60 GHz with 8 GB RAM. Figure 11: **Left**: event of 7 October 2017, classified as E4 in the ground truth (but images with events classified as E1 are present around that day in the dataset). **Right**: heatmap generated by MEC. The figures are cropped to the area between 36\({}^{\circ}\) and 39\({}^{\circ}\) N and between 11\({}^{\circ}\) and 7\({}^{\circ}\) W for a better visualisation.
## 2 Related Work and Contributions
### 802.11 Power Management  
Two modes of power management, as well as various power save mechanisms that can be employed in each mode, are defined by the 802.11 Standard. An active mode allows an STA to receive frames at any time, i.e., the STA typically remains in a full-power awake state. In addition, a Power Save (PS) mode enables an STA to enter a low-power doze (or sleep) state for selected periods of time, thus the STA cannot always receive frames.  
When operating in the PS mode, an STA can enable either the PSM, APSD, or PSMP mechanism, being PSMP the most advanced power save mechanism. This mechanism is an extension of PSM and APSD aimed at reducing the energy consumption of an STA spent in overhearing during awake periods. When PSMP is employed, an STA with buffered data at an AP, as indicated in a received beacon frame from the AP, remains awake waiting to receive a PSMP frame from the AP. The PSMP frame signals the beginning of a PSMP sequence and includes a schedule of transmission and reception times for various STAs within the PSMP sequence. When an STA is identified in a received PSMP frame, it can transmit and receive at scheduled times and sleep at the other times during a PSMP sequence (i.e., microsleep).  
TXOP PSM adapts the microsleep operation of PSMP to be employed by an STA operating in the active mode, hence saving a significant amount of energy for an STA during overhearing periods. When TXOP PSM is employed, an STA can go to sleep at the beginning of data transmissions to other STAs and wake up at the end of the data transmissions. The decision to sleep is made after an STA identifies in a received frame from an AP that it is not the intended destination. The AP specifies the duration of data transmissions in transmitted frames, thus allowing the STA to determine the time to sleep.  
In contrast to PSMP, TXOP PSM neither causes an AP to store data frames for an STA in the sleep state nor requires an STA to process the information included in beacon and PSMP frames. This avoids delays of frame transmissions from the AP to an STA, loss of frames at the AP, and the need of the large number of PSMP frames. In addition, TXOP PSM causes no increase in the size of beacon and PSMP frames as a function of the number of STAs, as does PSMP.
## 4 Research Case
### Independent Variable Data  
#### 4.3.1 Independent Variable Data Processing  
Transportation-Oriented Development (TOD) plays a crucial role in urban construction and development. TOD is a method of public transportation planning and design that maximizes the use of residential and commercial areas [37]. The rail station buffer zone is the core area of TOD and is also the urban space where close urban development and mixed land use are concentrated. Rail station buffers are important nodes in urban development and key components of urban rail transit growth. Defining the buffer zone is the premise and basis for studies, such as assessing the built environment around stations and forecasting passenger flow at rail stations.  
The area accessible to people within a 10 min walk from a rail transit station is generally regarded as the attraction area for rail transit passengers [38]. Combined with previous studies, the buffer zone radius of the rail transit station involved in this paper is set to 800 m [39; 40; 41], as shown in Figure 2(a) below. Considering the high-density characteristics of the built environment in Shenzhen, to avoid the overlapping of buffer zones, this paper utilizes the Tyson polygon to divide the buffer zone range [24], as shown in Figure 2(b) below. Through the division of the buffer zone, the factors affecting passenger flow at subway stations and related data are obtained.  
Based on the multi-center characteristics of Shenzhen's urban areas and the commuting patterns of urban rail transit, and drawing from previous studies [21], this paper selects 15 built environment factors as the independent variables \(X_{1}\)-\(X_{15}\). These variables are classified into three categories: socio-economic variables, built environment variables, and station characteristics [37]. To examine whether the average housing price affects the morning peak passenger flow of Shenzhen rail transit, the average housing price is included as an economic variable, and Table 2 describes these independent variables.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Experimental Results on the Breast Cancer Prediction Dataset
#### 5.4.1 Varying Privacy Budget Using Breast Cancer Prediction Dataset  
Experiments were performed with different values of epsilon to examine the protection level provided by the DP mechanism with identical attributes but setting different values for privacy parameter. Results were evaluated by selecting different values for epsilon (0.02, 0.01, 0.2, 0.4, 0.6, and 0.8). It can be seen in Figure 7 that decreasing \(\varepsilon\) value added more noise and vice versa.  
For further demonstration, a graph was plotted between the privacy parameter (epsilon) and the results of queries to compare the exact results and data with introduced noise. In the context of differential privacy, epsilon \(\epsilon\) is a privacy parameter that balances the trade-off between accuracy and privacy. The amount of noise introduced to the query results grows significantly as the \(\epsilon\) value decreases toward zero. This improves privacy but may result in less accurate results (higher count variance). In Figure 8, it can be seen that the actual count for the number of patients between ages 60 and 70 at the time of mammography with true values of history of breast biopsy is 36. Data points such as (\(\epsilon=0.02\), count = 143) demonstrate that a smaller epsilon yields a higher count with increased noise. After decreasing the value of the privacy parameter (epsilon) by applying DP, more noise is added to the actual data. In contrast, the quantity of noise introduced into the results  
Figure 6: Comparison of results using the Breast Cancer Prediction Dataset.  
Figure 7: Varying epsilon values on the Breast Cancer Prediction Dataset.  
decreases as \(\epsilon\) increases, enabling more precise query outputs (e.g., the points (\(\epsilon\) = 0.40, count = 38) and (\(\epsilon\) = 0.60, count = 36) demonstrate that the count values get less noisy as \(\epsilon\) increases). This line chart illustrates the relationship whereby higher \(\epsilon\) values are associated with less distortion in the query results but at cost of less privacy.
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 5 Damage Constitutive Model Considering Seepage Pressure
### Model Validation  
The constitutive model is verified by triaxial hydraulic coupling test results of Cretaceous red sandstone; the relevant model parameters are shown in Table 1. As shown in Figure 11, the theoretical curve basically coincides with the test curve; this model can accurately capture the nonlinearity near the peak. This shows that the damage constitutive model can reflect the strength characteristics and stress-strain relationship of red sandstone under hydraulic coupling, and verifies the rationality and applicability of the hydraulic coupling damage constitutive model. To some extent, it can predict the mechanical behavior of red sandstone under a complex stress state.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\begin{tabular}{c} **Seepage** \\ **Pressure** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cohesion** \\ **Angle** \\ **(\(\circ\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Internal** \\ **Friction** \\ **Angle** \\ **(\(\circ\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Confining** \\ **Pressure** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Basic** \\ **Strain** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **\(m\)** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(k\)** \\ **Ratio** \\ \end{tabular} } \\ \hline S-0-0 & & & & & 0 & 14.5 & 0.00519 & 3800 & 0.325 & 8.8 & 11.3 \\ S-4-0 & &
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments
### Ablation Experiments  
In this section, we conduct ablation experiments to evaluate the performance of the individual modules within the proposed framework.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 4 Experiments and Results
### Case Study  
In Table 6, we present a case study with four conclusions (true or false as predicted by Iterlogic-E), which are inferred with two rules during training. Table 6 shows some conclusions derived from rule inference and the score change (the average of the head entity prediction score and the tail entity prediction score) of the conclusions. Let us take the first conclusion as an example: the true conclusion is (Albany_Devils/hockey_roster_position/posit on, Centerman), which is obtained by the rule \"/sports_team_roster/position(\(x,y\)) \(\stackrel{{ 0.808}}{{\Rightarrow}}\)/hockey_roster_position/position(\(x,y\))\". The Albany Devils are a professional ice hockey team competing in the American Hockey League ([[https://en.wikipedia.org/wiki/Albany_Devils](https://en.wikipedia.org/wiki/Albany_Devils)]([https://en.wikipedia.org/wiki/Albany_Devils](https://en.wikipedia.org/wiki/Albany_Devils)), accessed on 21 September 2023), and the centerman is the center in ice hockey. Therefore, this is indeed a true conclusion. Compared to ComplEx, Iterlogic-E increased the score of this fact from 5.48 to 9.40.  
Figure 4: Results of MRR (**a**) and HITS@1 (**b**) with different confidence thresholds on FB15K. We set all hyperparameters to their optimum values and change the confidence threshold within [0.5, 1] in 0.05-step increments.  
Also, (San_Diego_State_Aztecs_football, /hockey_roster_position/position,Linebacker) can be inferred from the fact (San_Diego_State_Aztecs_football, /sports_team_roster/team, Linebacker) by the same rule. However, the San Diego State Aztecs are a football team, not a hockey team, and this is an incorrect conclusion. Compared to ComplEx, Iterlogic-E decreased the score of this fact from \(-4.65\) to \(-8.35\). This case illustrates that Iterlogic-E can distinguish whether the conclusion is true and improve prediction performance. Furthermore, Iterlogic-E has good interpretability, which allows us to understand the reason behind the inferred conclusion.