esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of the Radius of Plastic Zone and Radial Displacement  
The radii of the plastic zone in the surrounding rock of the unlined loess tunnel of the modified Fenner formula based on M-C strength and the newly modified Fenner formula based on joint strength can be calculated is shown in Figure 6.  
The radii of the plastic zone calculated by the modified Fenner formula based on joint strength and the modified Fenner formula based on M-C strength increased with the increase in tunnel depth. However, the radius of the plastic zone determined by the modified Fenner formula based on joint strength was larger than that based on the latter under the same depth.  
The radial displacement at the interface of elastic-plastic zone in the surrounding rock of the unlined loess tunnel for the modified Fenner formula based on M-C strength and the newly modified Fenner formula based on joint strength can be calculated and is shown in Figure 7. In the meantime, the radial displacement of the tunnel wall determined by the two strength theories is shown in Figure 8.  
Figure 6: Relationship between radius of plastic zone and depth of loess tunnel.  
The radial displacements determined by the two strength theories increase with the increase in buried depth. However, the radial displacement determined by the joint strength theory is slightly larger than that determined by M-C strength. In a word, the M-C strength overestimates the tensile strength of structural loess, which leads to the smaller radius of the plastic zone and the smaller displacement of the surrounding rock of the unlined loess tunnel calculated by the modified Fenner formula based on M-C strength.  
The joint strength can be used to reasonably evaluate the tensile and shear strengths of structural loess. Thus, the modified Fenner formula based on joint strength is more practical in the analysis of the stress environment and displacement field of sthe surrounding rock of a loess tunnel. Therefore, a reasonable evaluation of the tunnel excavation impact not only can eliminate potential collapse accidents in advance but also can avoid damage caused by ground subsidence to groundwater systems and ecological environments.
# Effects of Heavy Rainfall on Shallow Foundations in Bukit Timah Granite in Singapore
## 2 Methodology  
### Geographical and Geological Setting  
Residual soils in Singapore are characterized by a parent rock formation that mainly comprises at least three formations, such as Jurong Formation, Bukit Timah Granite, and Old Alluvium [22]. Figure 1 illustrates a simplified Singapore geological map outlining the distribution of geological formations. In particular, the Bukit Timah Granite historically dominates Singapore's geological formation, which comprises five discrete and contiguous plutons [23]. As discovered mainly from visual observation, the residual soil is the final product of the in-situ weathering of underlying rocks and its shear strength is dependent on the degree of weathering [24]. Numerous researchers in Singapore [22; 25; 26; 27] explored variabilities of residual soil properties in terms of mechanical and hydraulic characteristics along with depths. [PERSON] et al. [25] stated that the typical effective cohesion of residual soils in Bukit Timah Granit decreases with depth due to the reduction in fine fractions and ranges from 8 kPa to 24 kPa over Singapore. [PERSON] et al. [22] pointed out that the variability of effective cohesion in Bukit Timah Granit can be clearly differentiated with the incorporation of artificial intelligence techniques, ranging from 4 kPa to 16.5 kPa. [PERSON] et al. [26] observed the variability of hydraulic properties of the residual soil in Bukit Timah Granit, having a wide range of air-entry values due to the large variety of particle sizes. In addition, [PERSON] et al. [27] proposed upper and lower limits for residual soil's mechanical and hydraulic properties in Bukit Timah Granit.  
This study focuses on the area of Bukit Timah Granit to explore the settlement behavior of shallow foundations situated in residual soil in Bukit Timah Granit.
# Re-Keying Scheme Revisited: Security Model and Instantiations
###### Abstract  
The re-keying scheme is a variant of the symmetric encryption scheme where a sender (respectively, receiver) encrypts (respectively, decrypts) plaintext with a temporal session key derived from a master secret key and publicly-shared randomness. It is one of the system-level countermeasures against the side channel attacks (SCAs), which make attackers unable to collect enough power consumption traces for their analyses by updating the randomness (i.e., session key) frequently. In 2015, [PERSON] et al. proposed two kinds of re-keying schemes. The first one is a scheme without the beyond birthday security, which fixes the security vulnerability of the previous re-keying scheme of [PERSON] et al. Their second scheme is an abstract scheme with the beyond birthday security, which, as a black-box, consists of two functions; a re-keying function to generate a session key and a tweakable block cipher to encrypt plaintext. They assumed that the tweakable block cipher was ideal (namely, secure against the related key, chosen plaintext, and chosen ciphertext attacks) and proved the security of their scheme as a secure tweakable block cipher. In this paper, we revisit the re-keying scheme. The previous works did not discuss security in considering the SCA well. They just considered that the re-keying scheme was SCA resistant when the temporal session key was always refreshed with randomness. In this paper, we point out that such a discussion is insufficient by showing a concrete attack. We then introduce the definition of an SCA-resistant re-keying scheme, which captures the security against such an attack. We also give concrete schemes and discuss their security and applications.  
s _applied_  
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# A Comparative Study of Weighting Methods for Local Reference Frame
## 3 Evaluation Methodology
### Implementation Details  
For each dataset, we apply the uniform sampling method [31] to extract 1000 points from the model as the keypoints. Then, the ground-truth transformation is used to transform these keypoints, and these transformed keypoints are used to find the nearest points in the scene. Finally, these nearest points are treated as the corresponding keypoints in the scene. The aim is to remove the effect of keypoint localization error on the LRF because keypoint localization error should be tested independently, as inspired by reference [19]. For the datasets with incomplete shape (i.e., Laser Scanner, Kinect, Space  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Scenario** & **Challenge** & **Modality** \\ \hline Retrieval & Retrieval & Gaussian noise and point density variation & LiDAR \\ Laser Scanner & Object recognition & Clutter and occlusion & LiDAR \\ Kinect & Object recognition & Clutter, occlusion and real noise & Kinect \\ Space Time & Object recognition & Clutter, occlusion, real noise, and outliers & Space Time \\ LiDAR Registration & Registration & Self-occlusion and missing regions & LiDAR \\ Kinect Registration & Registration & Self-occlusion, missing regions, and real noise & Kinect \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental datasets and relevant properties [2 0].  
Figure 2: Experimental datasets. (**a**) Retrieval, (**b**) Laser Scanner, (**c**) Kinect, (**d**) Space Time, (**e**) LiDAR Registration, and (**f**) Kinect Registration. A model and a scene (from left to right) are presented as examples for each dataset.  
Time, LiDAR Registration, and Kinect Registration), we find out the keypoints in the overlapping region. The LRFs are only calculated on the keypoints in the overlapping region. The support radius \(R\) is set as \(15 pr\) (\(pr\) denotes the point cloud resolution, i.e., the average shortest distance among neighboring points) in all the experiments except the experiments for testing varying support radii.
## 2 Objective Function Formulation
### Demand Side Management  
The study of microgrid energy management, particularly focusing on economic operation, has been a significant area of research in recent years and is expected to be a major issue in the future. Nevertheless, the economic operation of a microgrid system remains incomplete until the demand side management (DSM) approach is taken into account. The adoption of DSM would reduce costs across the board for the articles discussed in the review of the relevant literature. DSM priorities shift elastic loads on the studied network to cheaper times of day for the grid. Despite the fact that the overall load demand remains the same at the conclusion of the forecast period, which is characteristically a day, there is a substantial decrease in peak demand, resulting in an increase in the load factor. Load shifting, peak clipping, strategic expansion, strategic conversion, variable load shape, and valley filling are just a few of DSM's load-shaping tactics.  
Listed below are the specific measures that should be taken while putting DSM into practice:  
Step 1: provide the dynamic duration of the time T of the load data in hours.  
Step 2: enter the TOU price of energy consumption on the market for T hours.  
Step 3: enter the total DSM involvement in % (if elastic loads are not specified).  
Step 4: categorize the loads as either elastic or inelastic according to the percentage of engagement in DSM.  
For example, when we say x% DSM, it means that x% of the hourly load demand may be adjusted based on elasticity, but the remaining (\(100-\mathrm{x}\)) % is not adjustable. Optimal planning is required for elastic load requirements.  
Step 5: Calculate the minimum and maximum values, and the total, for the inelastic load. It is important to note that the control variables that need to be optimized have a flexible load prerequisite.  
Step 6: apply the optimization approach [52],  
\[Minimize\;[\mathrm{cost}^{t}_{grid}\times(load\_elastic^{t}+load\_inelastic^{t})] \tag{12}\]  
where  
\[0\leq load\_elastic^{t}\leq load\_inelastic^{max} \tag{13}\]  
\[\mathrm{Total\;load\;demand}=\sum_{t=1}^{T}{(load\_elastic^{t}+load\_inelastic ^{t})} \tag{14}\]  
Step 7: the total load demand model that has been redesigned incorporating the approach of demand side management (DSM) is determined by summing the load demand in terms of inelastic for each hour with the elastic loads with better optimized values.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 2 Materials and Methods
### Configuration of Biasing Circuit
Where the electrode penetrates the RF cavity wall, a capacitor to ground is introduced, acting as a lowpass filter, 2. Those wires of the harness belonging to opposite electrodes (hence carrying signal with opposite voltage) are twisted, 3. There is shielding around the harness which is connected to the housing wall. It is an advantage of liquid crystal technology that due to its relatively low switching speed (in the order of minutes), it is not sensitive to faster time-varying disturbances overlayed to the command voltages. Such voltages can arise from the command electronics and can be difficult to control.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cleaning and Upgrading** & **Key Parameter** & **Average Data** \\ \hline \multirow{3}{*}{Water scrubbing (WS)} & Electricity & 0.20 kWh/N m\({}^{3}\) biogas \\  & Upgrading yield (88\% Methane) & 68\% \\  & Methane losses & 5.13\% \\ \hline \multirow{3}{*}{Pressure swing absorption (PSA)} & Electricity & 0.24 kWh/N m\({}^{3}\) biogas \\  & Upgrading yield (91\% Methane) & 65\% \\  & Methane losses & 4.00\% \\ \hline \multirow{3}{*}{Membrane separation (MS)} & Electricity & 0.19 kWh/N m\({}^{3}\) biogas \\  & Upgrading yield (91\% Methane) & 65\% \\ \cline{1-1}  & Methane losses & 6.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The inventory data of biomethane upgrading processes adapted from [1]. Moreover, the previous studies simulated the PSA process using experimental data at a lab scale for a simulated gas. As a consequence, it is difficult to scale-up for commercial processes. In this study, we focused on the commercial scale PSA unit at the company REBiofuel in Thailand, which has the six-step separation process and CMS adsorbent. The PSA unit was simulated by using an extended Aspen Plus software known as Aspen Adsorption\({}^{\circledR}\). The objective of the current study is to validate of the model with experimental breakthrough data. The simulated results are then compared with operating data to optimize the working conditions.
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Optical Cavity Boundary Layer Control
#### 4.1.3 Mainstream Ejection Slot Test Results  
Figure 9 shows the pressure distribution along the optical cavity under different combinations of the mainstream ejection slot and exhaust volume. The pressure distribution curve was similar to the results of the slotted plate experiment, showing that the pressure in the front part of the optical cavity increased and the pressure in the back part decreased. The pressure of the optical cavity was lower with the combination of mainstream ejection and boundary layer control, and the pressure decreased further with an increase in the exhaust volume. The boundary layer can be effectively controlled by mainstream ejection or a combination of mainstream ejection and air extraction.  
Table 4 lists the chemical reaction platform power under different combined conditions of the mainstream ejection slot and pumping volume. When the combination of mainstream ejection and pumping was used, increasing the pumping volume to 6% led to a significant decrease in the chemical reaction platform power.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **N/N\({}_{\text{D}}\)** \\ \hline mainstream ejection + 0\% pumping & 0.688 \\ mainstream ejection + 2\% pumping & 0.694 \\ mainstream ejection + 4\% pumping & 0.689 \\ mainstream ejection + 6\% pumping & 0.603 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy power at different states of the mainstream ejected slot.  
Figure 9: Distribution of cavity pressure at different states of the mainstream ejected slot.
## 3 Robot Error Prediction and Accuracy Compensation Method
### Spatial-Interpolation-Based Unbiased Error Estimation Method with Joint Weights Optimization
The selection of joint weights directly impacts the prediction accuracy of robot errors, and a particle filter method for searching the optimal joint weights is proposed, alongside the preparation of a new test dataset \(\mathbf{Q}^{test}\) consisting of \(m\) sampling points and the corresponding robot error dataset \(\mathbf{E}^{test}\). During the search for optimal joint weights, the particle filter method regards the probability distribution of the optimal joint weights in configuration space as the state distribution of a six-dimensional vector particle swarm, employing a cyclic searching process based on particle importance update and resampling, as shown in Figure 11. First, an initial particle swarm containing \(N\) particles is generated, following the normal distribution defined by the initial joint weights \(\mathbf{W}^{init}\) and the initial standard deviation \(\sigma^{init}\). According to the distribution characteristics of the robot error spatial similarity across the joint dimensions described in Section 2.3, the initial joint weight for prediction of the x-axis error component is defined as \(\mathbf{W}^{init}=[0.5,\,0.5,\,1.5,\,0.5,\,2.0,\,0.5]\), while the initial joint weights of the y- and z
# Language Model Using Neural Turing Machine Based on Localized Content-Based Addressing
## 3 Neural Turing Machine
### Content-Based Addressing and Location-Based Addressing  
To generate the read and write weighting vectors, content-based addressing and location-based addressing are performed sequentially in the NTM architecture [14]. The interface vector \(\xi_{t}\) is used for addressing these methods. \(\xi_{t}\) consists of the following elements: \(k_{t}\), \(ks_{t}\), \(g_{t}\), \(s_{t}\), \(\gamma_{t}\), \(e_{t}\), and \(v_{t}\). \(k_{t}\) (\(\in\mathbb{R}^{M\times 1}\)) is a key vector at time \(t\). \(ks_{t}\) (\(\in\mathbb{R}\)) is a key strength at time \(t\). \(g_{t}\) (\(\in\mathbb{R}\)) is an interpolation factor at time \(t\). \(s_{t}\) (\(\in\mathbb{R}^{3x1}\)) is a shift vector at time \(t\). \(\gamma_{t}\) (\(\in\mathbb{R}\)) is a sharpening factor at time \(t\). \(e_{t}\) (\(\in\mathbb{R}^{M\times 1}\)) is an erase vector at time \(t\). \(v_{t}\) (\(\in\mathbb{R}^{M\times 1}\)) is a converted input vector at time \(t\).  
For content-based addressing, a cosine similarity is measured with \(k_{t}\) and a vector of each external memory address. The cosine similarity is normalized with the softmax function. The value of content-based addressing in the \(i\)-th memory address at time \(t\) is determined as follows:  
\[CA_{t}[i]=\frac{exp(CS(EM_{t}[i,\cdot],k_{t})^{ks_{t}}}{\sum_{j=1}^{N}exp(CS( EM_{t}[j,\cdot],k_{t})^{ks_{t}}} \tag{2}\]  
where \(exp\) is an exponential function, \(CS\) is the cosine similarity function, and \(EM[i,\cdot]\) is the \(M\)-dimensional real-valued vector of the \(i\)-th external memory address. After content-based addressing, location-based addressing is performed. \(CA_{t}\) is interpolated with \(\omega_{t-1}\) using scalar \(g_{t}\). \(\omega_{t-1}\) is a weighting vector generated at time \((t-1)\). Interpolated weighting \(w_{t}^{ip}\) is calculated as follows:  
\[w_{t}^{ip}=(1-g_{t})\omega_{t-1}+g_{t}CA_{t} \tag{3}\]  
\(w_{t}^{ip}\) is used to calculate a convolution \(w_{t}^{cv}\) with the shift vector \(s_{t}\). The \(i\)-th element of \(w_{t}^{cv}\) is determined as follows:  
\[w_{t}^{cv}[i]=\sum_{j=0}^{N-1}w_{t}^{ip}[j]s_{t}[i-j] \tag{4}\]  
After shifting, sharpening is performed with \(w_{t}^{cv}\) and \(\gamma_{t}\) to obtain the final weighting vector \(\omega_{t}\). The \(t\)-th element of \(\omega_{t}\) is calculated as follows:  
\[\omega_{t}[i]=\frac{(w_{t}^{cv}[i])^{\gamma_{t}}}{\sum_{j=0}^{N-1}(w_{t}^{cv}[ j])^{\gamma_{t}}} \tag{5}\]  
Figure 1: Overview of NTM architecture. The controller interacts with the external memory using read and write operations. The external memory stores information, which is a high-dimensional vector of the input vector.  
\(k_{t},k_{s},g_{t},s_{t}\), and \(\gamma_{t}\) are assigned to each read and write weighting. In the NTM architecture, the number of read and write weightings is \(R\) and one, respectively. Therefore, a dimension of \(\xi_{t}\) is \(3\times(|k_{t}|+|k_{t}|+|s_{t}|+|\gamma_{t}|)+|e_{t}|+|v_{t}|=3\times(M+1+1+3+ 1)+M+M=5M+18\).
## 3 Results  
### Individual R.F. Model Performance  
To obtain an overview over the performance of the R.F. models in predicting the metal pollution potential, the OOB error can be accessed. Table 2 shows all metals included in the analysis, the number of cases, and the respective OOB error of the fitted R.F. model. There were apparent differences in the OOB error for the different metals. In this classification model (L.V. or H.V.), the OOB error is the rate of wrong classifications. Hence, in Al, 16.5% of cases were allocated to the wrong group (and 83.5% of cases were classified correctly); in Hg, 28.1% were wrongly allocated (and 71.9% were correctly allocated). The overall performance of the individual models was satisfactory, with distinct differences between metals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metal** & **No. of Cases** & **OOB Error** \\ \hline Aluminium (Al) & 2927 & 16.5\% \\ Arsenic (As) & 2678 & 23.0\% \\ Cadmium (Cd) & 877 & 19.9\% \\ Cobalt (Co) & 2801 & 22.2\% \\ Chromium (Cr) & 2828 & 26.4\% \\ Copper (Cu) & 2675 & 25.8\% \\ Mercury (Hg) & 1677 & 28.1\% \\ Manganese (Mn) & 2775 & 25.8\% \\ Lead (Pb) & 2652 & 28.0\% \\ Tin (Sn) & 896 & 18.9\% \\ Vanadium (V) & 2793 & 22.3\% \\ Zinc (Zn) & 2670 & 26.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of cases per metal and the out-of-bag error.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 4 Experiments
### Semantic Segmentation Evaluation Metrics  
To evaluate the proposed models on semantic segmentation, we use two evaluation metrics: mean intersection over union (mIOU ) and mean pixel accuracy (Pix.acc. ). The definition for those metrics is stated as below:  
\[mIOU=\frac{1}{N}\sum_{c=1}^{N}\frac{TP_{c}}{TP_{c}+FP_{c}+FN_{c}} \tag{7}\]\[Pix.acc.=\frac{1}{N}\sum_{c=1}^{N}\frac{TP_{c}+TN_{c}}{TP_{c}+TN_{c}+FP_{c}+FN_{c}}, \tag{8}\]  
where \(N\) is the number of class categories, \(TP\) (true-positive) represents the pixels that are truly predicted to be belonging to a class (\(c\) ), \(TN\) (true-negative) represents the pixels that are predicted to not belong to that class, \(FP\) (false-positive) the pixels which are falsely predicted to be belonging to a class, and \(FN\) (false-negative) the pixels which are falsely predicted to be not belonging to that class.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 1 Introduction
The emergence of transformer architectures has provided new opportunities for addressing the limitations of CNNs in medical image segmentation. Transformers, with their self-attention mechanisms, excel at capturing long-range dependencies, making them particularly suitable for segmenting complex 3D volumes [27; 28]. Applications of transformers in medical imaging, such as brain tumor and cell instance segmentation, have demonstrated promising results [29; 30]. However, their computational complexity and parameter-heavy nature make them less practical for large-scale datasets. Hybrid models like UNeXt, which combine UNet-like architectures with shifted multi-layer perceptrons, aim to reduce computational demands but often sacrifice segmentation accuracy [31]. Recent advancements in deep learning have revolutionized medical image analysis which enables precise segmentation of complex anatomical structures and improving tasks like tumor segmentation and organ delineation. Transformer-based and hybrid modelshave further enhanced boundary precision and long-range dependency capture, particularly in 3D imaging. Building on these advancements, we propose a novel, end-to-end segmentation framework, Adaptive Multiscale U-Net (AMU-Net), tailored for left atrium segmentation, addressing the limitations of existing approaches. Our model integrates a Contextual Dynamic Encoder (CDE) to extract multi-scale features and capture long-range dependencies within 3D images. To enhance segmentation performance, particularly in challenging regions with subtle boundaries, we introduce an Adaptive Feature Decoder Block (AFDB), which incorporates an Adaptive Feature Attention Block (AFAB) for improved boundary delineation. Additionally, a Spectral Synthesis Fusion Head (SFFH) is employed to synthesize spectral and spatial features, enhancing segmentation performance in low-contrast areas. Unlike traditional two-stage methods, our approach operates end-to-end, eliminating the need for intermediate processing, thereby reducing error propagation and computational overhead. To ensure interpretability, we incorporate uncertainty estimation using Laplacian approximation, enabling clinicians to identify areas of low confidence and prioritize regions requiring further review. Our proposed AMU-Net framework not only addresses the segmentation challenges of low-contrast cardiac images but also introduces uncertainty estimation to enhance interpretability and reliability in clinical applications. The integration of the Adaptive Feature Decoder Block and Spectral Synthesis Fusion Head in our model will play a crucial role in improving diagnostic precision and supporting intelligent healthcare solutions. The remainder of this paper is structured as follows. Section 2 details the methodology of our proposed framework, including its architecture and training process. Section 3 presents the experimental setup and results, followed by a conclusion in Section 4.