esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
###### Abstract  
This paper presents a numerical investigation on the improvement mechanism of dynamic compaction (DC) in saturated soil of a weak layer with high levels of groundwater, using an improved fluid-solid coupling method with a Drucker-Prager-Cap soil model. The numerical model is verified by comparing with published test data at first, which show that the dual-phase coupling method can approximately reflect the development law of excess pore water pressure and the improvement effect of layered saturated soil foundation under DC. Then, based on the numerical model, the influences of the thickness and depth of the weak layer as well as damping energy on the development and dissipation of excess pore water pressure, effective stress, and the relative degree of reinforcement during DC were investigated. The results showed that the embedment depth and thickness of the weak interlayer may greatly affect the effective reinforcement depth of DC. Meanwhile, the damping energy and the groundwater table also play a great role in the improvement of the layered saturated soil under DC. The groundwater table should be lowered by dewatering or adding a drainage layer to achieve a better compaction effect during DC.  
dynamic compaction; non-homogeneous saturated soil foundation; improved fluid-solid coupling cap model; improvement effect +
Footnote †: journal: Medical Imaging
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 3 Algorithms
### Baseline Algorithm
#### 3.1.3 Selecting Features  
Let \((F_{\pi(1)},\ldots,F_{\pi(k)})\) be the sorted list of features. We first compute a sequence of bit strings \((Z_{2},\ldots,Z_{k})\) of length \(mn\) each such that \(Z_{i}[h]=\bigvee_{j=i+1}^{k}\textbf{B}_{\pi(j)}[h]\) for any \(2\leq i\leq k\) and \(1\leq h\leq mn\); namely, \(Z_{i}\) is the bit array storing the cumulative or each position \(h\) for \(\textbf{B}_{\pi(i+1)},\textbf{B}_{\pi(i+2)},\ldots,\textbf{B}_{\pi(k)}\). Note that \(Z_{i}[h]=0\) indicates that the set \(\{F_{\pi(i+1)},F_{\pi(i+2)},\ldots,F_{\pi(k)}\}\) of features is inconsistent with regard to a pair \((x_{p},y_{q})\) satisfying \(h=m(p-1)+q\), and \(\{F_{\pi(i+1)},F_{\pi(i+2)},\ldots,F_{\pi(k)}\}\) is inconsistent if and only if the bit string \(Z_{i}\) contains 0. See Table 5 for \(Z\)s in our running example. The computation requires \(O(kmn)\) time and space.  
We simulate Algorithm 1 on encrypted **B**s and \(Z\)s for feature selection. Furthermore, we use two 0-initialized bit arrays, \(R\) of length \(k\) and \(S\) of length \(mn\). \(R[i]\) is meant to store 1 if the \(i\)-th feature (in sorted order) is selected. \(S\) is used to keep track of the cumulative or for the bit strings of the currently selected features. Namely, \(S[h]\) is set to \(\bigvee_{\alpha=1}^{\ell}\textbf{B}_{\pi(i_{h})}[h]\) if \(\ell\) features \(\{F_{\pi(i_{1})},\ldots,F_{\pi(i_{l})}\}\) have been selected at this time.  
Assume that we are in the \(i\)-th iteration of the loop of Algorithm 1. Note that, at this time, \(F\) contains features \(\{F_{\pi(i)},F_{\pi(i+1)},\ldots,F_{\pi(k)}\}\) and currently selected features, and \(F\setminus\{F_{\pi(i)}\}\) is consistent if \(\bigwedge_{h=1}^{mn}(Z_{i}[h]\lor S[h])\) is 1. Because we keep \(F_{\pi(i)}\) in \(F\) if \(F\setminus\{F_{\pi(i)}\}\) is inconsistent, the algorithm sets \(R[i]=-\bigwedge_{h=1}^{mn}(Z_{i}[h]\lor S[h])\). After computing \(R[i]\), we can correctly update \(S\) by \(S[h]\gets S[h]\lor(R[i]\wedge\textbf{B}_{\pi(i)}[h])\) for every \(1\leq h\leq mn\) in \(O(mn)\) time. Therefore, the total computational time is \(O(kmn)\).
# The Invisible Threat of Antibiotic Resistance in Food
## 3 Antibiotic Resistance in Traditional Foods
### Antibiotic Resistance in Vegetables and Fruits
Carbapenem resistance was detected in 35.3% of _Pseudomonas aeruginosa_ and 66.8% of _Acinetobacter_ spp. isolates from fresh vegetables sold at a retail market in Kyiv (Ukraine) [179]. Carbapenemase-producing bacteria were observed in 2.4% of the vegetables analyzed in Romania [55]. Carbapenemase production was detected in 4 (0.47%) of the 856 bacterial isolates from vegetable samples. Carbapenem-resistant _Klebsiella pneumoniae_ was detected from leafy vegetables from Gondar, Ethiopia [192]. All isolated _K. pneumoniae_ strains were resistant to the carbapenem drugs. Among the carbapenem genes, _NDM-1_, _blaOXA48_, _blaVIM_, and _blaIMP_ were found. [PERSON] et al. [193] examined the carbapenem resistance in _E. coli_ from ready-to-eat fresh-cut fruits in Accra, Ghana. A total of 5.9% of the 34 _E. coli_ isolates exhibited resistance to carbapenem and contained the carbapenemase gene _blaIMP_. It can be concluded that the consumption of fresh vegetables and fruits may significantly contribute to the dissemination of antibiotic resistance in humans.
## 4 Problem Formulation  
In this paper, our objective is to maximize the user association (\(\psi_{u,m}\)) and the sum rate of the prioritized ABS under the constraints of ABS 3D location \((x_{m},y_{m},h_{m})\), power budget, and users' priority (\(\delta_{u,m}\)).  
The 3D ABS optimal placement plays a significant role in maximizing the number of associated users' matrix \(\Psi=(\psi_{u,m})\), due to the joint optimization of the ABS 3D positions \((x_{m},y_{m},h_{m})\). Hence, the proposed optimization framework is mathematically expressed as  
\[\max_{x_{m},y_{m},h_{m},p_{m}}\sum_{u=1}^{U}\sum_{m=1}^{M-1}\delta _{u,m}\psi_{u,m}C_{u,m},\ \ \forall m\in\mathcal{M},m\
eq 0, \tag{11a}\] \[C_{m}\geq C_{\text{min}},\forall m\in\mathcal{M}\] (11b) \[\sum_{m}\psi_{u,m}\leq 1,\ \ \forall u\in\mathcal{U},\ \ \forall m \in\mathcal{M}\] (11c) \[\psi_{u,m^{*}}=1,\ \ m^{*}=\max_{m}\mathcal{R}_{m},\forall u\in \mathcal{U}\] (11d) \[|d_{u,m}|^{2}\leq R^{2},\ \ \forall m\in\mathcal{M}\] (11e) \[h_{\text{min}}\leq h_{m}\leq h_{\text{max}}\ \ \forall m\in \mathcal{M},m\
eq 0\] (11f) \[P_{\text{min}}\leq P_{m}\leq P_{\text{max}},\forall m\in \mathcal{M}\] (11g) \[\delta_{u,m}\in\{0,1\},\ \ \forall u\in\mathcal{U},\ \ \forall m \in\mathcal{M}\] (11h) \[\psi_{u,m}\in\{0,1\},\ \ \forall u\in\mathcal{U},\ \ \forall m\in \mathcal{M} \tag{11i}\]  
We aim at maximizing the users' sum rate in Equation (11a) under the constraints of ABS location, associations, and transmit power. Constraint (11b) maintains the minimum sum-rate requirements of the users associated with the GBS. Constraint (11c) imposes that each user is associated with at most one ABS at a time and the constraint (11d) guarantees that a user is associated with the ABS that maximizes the reward function \(\mathcal{R}\). Constraint (11e) ensures that the user \(u\) lies inside the ABS coverage when located within a distance \(R\) from the ABS center \((x_{m},y_{m})\). Constraints (11f,g) impose that the ABS altitude and transmit powers must be within the feasible region. \(psi_{u,m}\) and \(\delta_{u,m}\) are the association and priority binary variables, respectively.
## 1 Introduction
The work described in this paper contributes to the research in the fields of virtual reality applications in industrial systems and technical communication. The study investigates the potential of VR as a collaborative review and risk assessment platform and specifically addresses the following research question:  
**RQ: What are the benefits of collaborative virtual reality to maintenance documentation reviews and risk assessments in industrial companies? **  
To address the research question, we conducted user tests with domain experts that are representative of the intended users of the VR system. A total of nine users from KONE Corporation tested the VR environment and gave their feedback on the usefulness and benefits of the system for technical documentation reviews and risk assessments. The tasks performed during the user testing were designed to replicate an actual review and risk assessment of a maintenance method and instructions related to a product from KONE. Even though this study is focused on a single industrial company's documentation and risk assessment processes, the documentation review and risk assessment are universal collaborative processes that are very similar in other industrial companies. Therefore, this case study is representative of the generic maintenance documentation review and risk assessment processes used in many industrial companies.
# Magnetoelectric Nanoparticles for Wireless Peripheral Nerve Stimulation: A Computational Study
## 2 Materials and Methods
### Configurations and Data Analysis  
We considered the MENPs placed within a fascicle of the peripheral nerve and for each case study, we analyzed different configurations. In each of those, the single MENP or MENPs cluster was hypothesized to be in proximity and aligned to the center of a Ranvier's node of one of the neural fibers of the fascicle. Table 1 summarizes all the analyzed configurations. Regarding the orientation, the direction of the dipolar distribution used to model the MENPs' electrical behavior is the same as the direction of the hypothesized external AC magnetic field. In fact, when subjected to an external magnetic field, the MENPs' core domains align along the direction of the stimulating magnetic field in a synchronous way, thus creating a net core magnetization directed parallel to the magnetic field. Through the strain-mediated ME effect, this magnetization results in a local dipole electric field distribution over the MENP surface. Therefore, by specifying the orientation of the exerted external magnetic field, it becomes possible to control the orientation of the MENPs dipoles. Among the great variability in possible MENP directions, in our work, we considered two different scenarios: the parallel and the perpendicular orientations. The parallel orientation denotes the MENPs' dipole oriented parallelly to the neural fiber (Figure 1a), whereas the perpendicular orientation assumes the dipole perpendicular to the neural fiber (Figure 1b), with the negative hemisphere close to the fiber when the dipolar distribution shows its maximum amplitude. The MENP-fiber distance defines the separation from the center of the single MENP or MENPs clusters to the neural fiber, and R represents the radius of the MENPs of the corresponding case study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dipole Diameter** & **Parallel Orientation** & **Perpendicular Orientation** \\ \hline  & MENP–Fiber Distance & MENP–Fiber Distance \\ \hline
80 nm & 1R & 1R, 2R, 3R \\
80 μm & 1R, 2R, 3R & 1R, 2R, 3R \\
250 μm & 1R, 2R, 3R & 1R, 2R, 3R \\
500 μm & 1R, 2R, 3R & 1R, 2R, 3R \\ \hline \hline \end{tabular}
\end{table}
Table 1: MENPs’ configurations.  
To assess the effectiveness of the various configurations, we examined how the distance between the MENPs and the fiber, as well as the orientation of the MENPs, affected the electric potential distribution \(\varphi\), the \(MF\), and the injected current. Specifically, we evaluated the electric potential distribution \(\varphi\) obtained by the electromagnetic simulation along the fiber length, we analyzed the \(MF\) values, conducting an analysis in terms of fascicle recruitment, i.e., an estimation of the area elicited inside the fascicle injected with the MENPs, and finally, we investigated the behavior of the injected current within the intracellular space of the neural fiber.
## 2 Materials and Methods
### Experimental Design and Data Analysis  
#### 2.3.1 A Tissue-Mimicking Phantom Experiment  
Volumetric ultrasound data acquired from a 100 mm \(\times\) 100 mm \(\times\) 70 mm oil-in-gelatin phantom were also used to test the above-mentioned three different NCC calculation methods. The inclusion in the ultrasound data was 5 times stiffer than the background. As shown in Figure 3, a 9-MHz CMUT transducer connected to a Siemens Antares (Siemens Health Care, Inc. Ultrasound Division, Mountain View, CA, USA) was used to acquire radio frequency (RF) echo data. A robotic arm (see Figure 3) was used to move the CMUT transducer downward to compress the phantom. The volume-to-volume deformation was approximately 1.5%. Then, ultrasound echo data before and after the compression were first acquired using ultrasound research interface (URI, Siemens Health Care, Inc. Ultrasound Division, Mountain View, CA, USA) installed on the Siemens scanner. Each echo data represented a 40 mm (axial) \(\times\) 37 mm (lateral) \(\times\) 30 mm (elevation) volume. The RF sample size, line spacing and distance between two parallel image planes were 0.0193-mm, 0.119-mm and 0.214-mm, respectively. More details can be found elsewhere [19].  
Figure 3: A photograph showing the CMUT ultrasound transducer and the tissue-mimicking phantom. The transducer is attached to a fixture that can be moved in the axial direction in order for the transducer to compress the phantom. The original picture was published in [19] and reuse permission has been granted for work presented in this paper.
## 5 Experiment Result and Comparative Study  
Extensive research has been carried out to prove the superiority of the proposed method. In Section 5.1, the similarities and differences between the time-domain diagram and time-frequency image of typical manufacturing defects are introduced, proving that it is difficult to diagnose types of manufacturing defects. Section 5.2 uses confusion matrix and t-distributed stochastic neighbor embedding (t-SNE) to demonstrate the classification ability of the proposed method. In Section 5.3, four evaluation indicators are used to evaluate different diagnostic methods to illustrate the superiority of the proposed method.
# A Geometric Model for a Shield TBM Steering Simulator
## 4 Example of Curved Excavation Simulation
### Comparison of the X- and V-Type Systems  
A comparison of the reference points used in the X- and V-type systems is provided in Figure 9. The articulation angles in the horizontal and vertical directions were set at 0.5\({}^{\circ}\), with only the initial and final reference points being marked in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline TBM shield diameter (\(D\)) & 8000 mm \\ Length of front shield and cutterhead (\(l_{F}\)) & 3000 mm \\ Average thickness of segment ring (\(d_{s}\)) & 1500 mm \\ Articulation jack speed (\(=v_{u}=g_{d}=v_{l}=v_{r}\)) & 1 mm/s \\ Shield jack speed (\(v\)) & 10 mm/s \\ Initial front reference point (\(F_{x}^{0},F_{y}^{0},F_{z}^{0}\)) & (0 mm, 3000 mm, 0 mm) \\ Initial rear-right reference point (\(R_{x}^{0},R_{y}^{0},R_{z}^{0}\)) & (4000 mm, 0 mm) \\ Initial rear-left reference point (\(L_{x}^{0},L_{y}^{0},L_{z}^{0}\)) & (\(-\)4000 mm, 0 mm) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of simulation conditions for curved excavation.  
Figure 9: Comparison of reference points from a single cycle simulation with the X- and V-type systems.  
Deviations in reference points are described in Table 2. As the locations of articulation points in the two systems are different, deviations can arise during articulation. Errors in the y coordinate (\(L_{y^{\prime}}\), \(R_{y^{\prime}}\), \(F_{y^{\prime}}\)) are generally the highest of the three reference points, with \(L_{r}\) and \(R_{r}\) having almost identical error values. Such errors may be changed depending on the direction and angle of articulation.  
The X-type system is clearly more appropriate for use in curved excavation as it has the advantages of articulation sealing and water resistance during articulation. The mathematical model of the X-type system is also less complex than that of the V-type. Therefore, we mainly focused on simulations of curved excavations with the X-type articulation system under predetermined simulation conditions (Table 1), with the simulation involving 100 cycles (100 rings), and with pitch and yaw being considered.
## 3 Results
### Sec  
SEC is one of the major assessment parameters during the drying operation. The diminishing fossil fuel sources and their environmental concerns (greenhouse gas emissions), as well as the increase in the energy costs have motivated the industries to reduce their energy consumption [38]. The SEC of all dryers is listed in Table 4. The highest SEC was observed in the HD method (\(297.29\pm 11.21\) MJ/kg) which could be due to the lower moisture loss of the product and hence, a prolonged drying time [14; 22]. The lowest SEC was observed in US+MWD (\(23.75\pm 2.22\) MJ/kg) which is only \(1.73\%\) of the total energy consumed during the drying (\(p<0.05\)). By polarizing the water molecules, MW can increase the internal temperature of the product, destroy the product tissues, and form larger channels. This can prevent the surface hardening during the drying process and hence, better moisture loss; thus the SEC decreases [7; 25]. According to Table 4, the methods involving the US pretreatment had a lower SEC compared to their corresponding US-free counterparts. This could be due to the fact that products, such as carrot, form a hard surface layer which may slow down the moisture removal from the surface. US pretreatment prevents the formation of such a layer which reduces the drying time and hence, the SEC [27; 29]. Single dryers (MWD and INFD with respective SEC of \(3.09\) and \(8.81\%\) of the total energy consumption) had a lower SEC compared to the hybrid MW-HD and INF-HD methods.  
[PERSON] et al. [44] used five types of dryers (HD, INF-HD, MW-HD, INF+MW-HD, and HD with MW and INF pretreatments) to dry green pepper. They reported that the highest energy consumption was observed in the HD method. [PERSON] et al. [38] employed HD, MW-HD and US pretreatment to dry respberries; they observed that the highest SEC was in the HD method. Moreover, the US pretreatment decreased the energy consumption.  
[PERSON] et al. [8] applied HD and MW-HD dryers by US pretreatment to dry raspberries and they showed that the US pretreatment can reduce the SEC. Their results indicated that the largest energy consumption was obtained in HD without US pretreatment; thus, they concluded that US waves were able to decrease the SEC. [PERSON] et al. [11] also employed various dryers (HD, MW-HD, HD-US, MW-HD-US) to dry strawberries and declared that the application of HD can elevate the energy consumption.
## 4 Discussion  
The traditional EnKF and EnKS algorithms provide an ensemble that directly represents the posterior models \(f(\mathbf{r}_{T+1}|\mathbf{d}_{0:T})\) and \(f(\mathbf{r}_{0:T}|\mathbf{d}_{0:T})\), respectively. Hence, the posterior models can be assessed by displaying statistics based on these ensembles. Reliable assessment of the posterior model in the two test cases can be obtained with approximately 1000 ensemble members. The SEnKF and SEnKS algorithms under study provide an ensemble of the augmented posterior models, \(f(\tilde{\mathbf{r}}_{T+1},\mathbf{\
u}|\mathbf{d}_{0:T})\) and \(f(\tilde{\mathbf{r}}_{0:T},\mathbf{\
u}_{0:T}|\mathbf{d}_{0:T})\), respectively. In order to obtain the posterior models of interest,\(f(\mathbf{r}_{T+1}|\mathbf{d}_{0:T})\) and \(f(\mathbf{r}_{0:T}|\mathbf{d}_{0:T})\), the conditioning on \(\mathbf{\
u}\in A\) must be made by empirical sampling based inference, see [21, 22]. This inference requires the estimation of the expectation vector \(\mathbf{\mu}_{P_{\
u}}\) and covariance matrix \(\mathbf{\Sigma}_{\
u}\). The two test cases are defined on a \((21\times 21)\)-grid for both \(\tilde{\mathbf{r}}_{t}\) and \(\mathbf{\
u}\)--hence in dimension 882. The expectation and covariance will have 882 and 389,403 unique entries, respectively. Our experience from this study is that approximately 10,000 ensemble members are required to obtain reliable assessment of the posterior models of interest. To reduce the ensemble size, we have tested various localization approaches [29], without notable success, and leave the subject for further research.
# Logitwise Distillation Network: Improving Knowledge Distillation via Introducing Sample Confidence
## 5 Conclusions and Future Work
### Future Work  
While LDN demonstrates significant improvements in error correction for knowledge distillation, several promising directions remain unexplored:  
1. **Dynamic Confidence Thresholding**: Current fixed thresholds may limit adaptability across diverse tasks. Future work will explore adaptive thresholding mechanisms, such as meta-learning or reinforcement learning, to dynamically adjust thresholds based on task complexity and dataset characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline
**Case** & **CIFAR100** & **CIFAR100-1** \\ \hline CE + KL (baseline) & 70.66 & 68.87 \\ CE + KL + FD & **72.36** & 69.09 \\ \hline \end{tabular} Bold numbers indicate the best performance under this structure.  
\end{table}
Table 6: Ablation experiments of FD loss on different datasets.  
Figure 4: The test error rates of the teacher–student networks ResNet56–ResNet20 using different modules in FD loss on the CIFAR100 dataset.  
2. **Cross-Modal Knowledge Distillation**: Extending LDN to vision language models (e.g., CLIP distillation) is a natural next step. Preliminary experiments suggest that logit ranking could serve as a proxy for cross-modal alignment confidence, enabling more robust distillation in multimodal settings.
3. **Large Teacher Adaptation**: The diminishing gains observed with stronger teachers (e.g., ResNet-152) indicate a need for hybrid ranking-entropy metrics. Future research will investigate combining logit ranking with entropy-based confidence measures to better handle overconfident predictions from large models.  
These directions aim to broaden LDN's applicability while deepening its theoretical foundations, paving the way for more reliable and efficient knowledge transfer in real-world applications.  
Conceptualization, T.S.; Methodology, T.S.; Software, Z.C.; Validation, T.S.; Formal analysis, T.S. and J.Q.; Resources, Z.C. and J.Q.; Data curation, T.S.; Writing--original draft, T.S.; Writing--review & editing, T.S.; Visualization, T.S.; Supervision, Z.C. and J.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
This work was supported in part by the Science Research Project of Hebei Education Department under grant QN2022107, and in part by the Science and Technology Program of Hebei Province under grant 22370301D (corresponding author: [PERSON]).  
The authors declare no conflicts of interest.