esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network  
### Deep Convolutional Neural Network  
The neural network is a mathematical algorithm model that imitates the behavioral characteristics of animal neural networks for distributed and parallel information processing. Convolutional neural networks (CNNs) are composed of multiple layers of neural networks. Each layer contains many two-dimensional planes with multiple independent neurons. Its essence is a mapping from input to output. It self-learns many mapping relationships between inputs and outputs through the networks and does not require any precise mathematical expressions. The CNNs adopt the idea of a sparse connection and parameter sharing. For example, the one-dimensional time series network and two-dimensional pixel image data are obtained by regularly sampling on the time axis. Unlike the traditional neural network connecting each output feature map with each input feature map, the sparse connection makes each output feature map correlate with the neighboring area of the previous layer feature map. Parameter sharing refers to using the same parameters in multiple model functions; one convolution kernel is required to traverse the entire input image each time. It not only dramatically reduces the number of stored parameters and improves computational efficiency, but also gives the CNNs the characteristics of translation invariance. The CNNs automatically extract features at different levels in the image and perform classification and recognition tasks accordingly. They have a high degree of abstraction and generalization, thereby better reflecting the nature of data. The CNN is considered as one of the best technologies for image content learning, demonstrating the best outcomes in terms of image recognition, segmentation, monitoring, and retrieval [108]. The deep CNNs further reduce the parameter number of the neural network architecture. The low-level features are local, so the convolution window is used for feature extraction. The low-level features (e.g., boundaries) of different image regions are similar, and they share a set of filters. The convolution replaces the full connection for feature extraction, and the low-level features are combined to obtain high-level features with more semantic information. The deep CNNs are generally composed of the input layer, convolutional layer, pooling layer, fully connected layer, and output layer [109]. The LeNet-5 structure of the classic convolutional neural network is shown in Figure 6.
## 2 Materials and Methods  
For our retrospective study, we selected all of the patients with NMSC who underwent HDR interventional radiotherapy (IRT) at our center over the past two years. Patients were identified through our internal electronic database system, which allows us to retrospectively retrieve anonymized patients' data, thus, fully respecting the General Data Protection Regulations (GDPR). We collected the data of 25 patients with NMSC, 2 of which had lesions on the thorax, while all the others had lesions on the head. These patients were treated using the Freiburg applicator (Elekta, Stockholm, Sweden). The selected treatment modality was high-dose-rate (HDR) \({}^{192}\)Ir IRT with a Flexitron afterloader (Elekta). Each patient underwent a CT scan with a slice thickness of 0.625 mm using a Discovery CT590 RT CT scanner (GE Medical Systems, Chicago, IL, USA). Contouring of the clinical target volume (CTV) and organs at risk (OAR), i.e., the brain, was manually performed by an experienced radiation oncologist using Oncentra Brachy TPS v. 4.6 (Elekta). Three prescription doses were delivered: group A (54 Gy in 18 fractions), group B (50 Gy in 20 fractions), and group C (40 Gy in 8 fractions).  
Catheters were manually reconstructed, and the IRT treatment plans were created using Oncentra Brachy TPS v. 4.6 (Elekta) by activating all the source positions on top of the CTV with a 3 mm extra margin. The dose was manually optimized by an experienced planner to achieve the best coverage of the CTV, while ensuring that the 150% isodose does not extend into the body, and calculated with the TG-186 algorithm.  
Additionally, the CT scans were exported to Eclipse TPS v.15.1 (Varian, Palo Alto, CA, USA) to perform external beam radiotherapy (EBRT) plans. For each patient, two EBRT plans were performed by an experienced planner: one using a 6 MeV electron field and another using a 6 MV VMAT (prescribed to the target mean with the same prescription doses of the IRT plans) with either one complete arc or two partial arcs, depending on the position of the CTV. The EBRT plans were prescribed to the CTV to better compare the results with IRT, and a digital water bolus of 0.5 cm was used for both VMAT and electron treatments following clinical practice guidelines.  
Dosimetric parameters in the Dose-Volume Histogram (DVH) for each plan were examined to assess the differences in dose distribution, particularly focusing on the D\({}_{\text{mean}}\) CTV (Gy) and the V\({}_{95\%}\)CTV (%). The D\({}_{2\text{cc}}\) of the brain was also considered. Additionally, the dose profile distribution from the skin into the patient's body was evaluated to assess dose distribution differences relative to the distance from the skin. Statistical analysis was performed using a Mann-Whitney U test with a significance level of \(p<0.05\).
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 3 Experimental Verification  
To verify the performance of the proposed lane change algorithm, this section presents numerical calculation results in a simulation environment. Firstly, in Section 3.1, a simulated traffic flow with each vehicle having random initial position, time varying speed and acceleration is designed for the lane change maneuver. This is used to test the reliability and effectiveness of the algorithm with random traffic situation. Secondly, in Section 3.2, three kinds of specified traffic environment with unexpected road events are employed to test the robustness of proposed method. For simplicity, all traffic vehicles are set with the same size. Table 2 shows the predefined parameters used in the simulations.
## 3 Methodology  
The proposed model in Figure 1 uses RSS and timestamps to predict tag movement and direction. Data pre-processing was done to remove inconsistent data and missing values were replaced with the mean. Outlier detection based on iForest has been applied to the RFID readings dataset so that the outliers can be eliminated. Statistical features were extracted from RSS and timestamps and the SMOTE technique was applied to generate new instances of the minority class. The RF algorithm was used for prediction and performance was evaluated by comparing the proposed model to other machine-learning models. The trained model was then integrated into a web-based application for easy use by end users. The evaluation of the model was done using stratified 10-fold cross-validation, a variation of k-fold cross-validation where each subset contains the same proportion of class labels as the original dataset.
## 3 Results and Discussion
### N-H Vibrations  
The N-H stretching modes typically occur in the 3500-3300 cm\({}^{-1}\) region. The asymmetric -NH stretching mode appears in the range from 3500 to 3420 cm\({}^{-1}\), and the symmetric -NH stretching mode occurs in the 3420-3340 cm\({}^{-1}\) region. In the current investigation, the title compound exhibited one N16-H38 asymmetric stretch at 3590 cm\({}^{-1}\) and one N18-H39 symmetric stretch at 3433 cm\({}^{-1}\), which are in agreement with the B3 LYP/6-311G++(d,p) calculated values. The NH\({}_{2}\) in-plane deformation vibrations occur in the 1650-1580 cm\({}^{-1}\) wavelength region of the spectrum. Therefore, a very strong band was observed in the FT-IR spectra of (2\(E\))-HIPC at 1315 cm\({}^{-1}\), which was assigned to the in-plane deformation mode of the -NH group. The -NH out-plane bending was observed at 655 cm\({}^{-1}\) in the FT-IR spectrum.
## 2 Methodology
### Bayesian Binary Search (BBS)
#### 2.2.2 Problem Formulation  
Given a search space \(S\) and a target value \(t\), our aim is to locate \(t\) in \(S\), or produce a specified range for t with a given tolerance. Unlike classical binary search, we assume that the distribution of potential target locations is not uniform across \(S\). We represent this non-uniform distribution using a probability density function (PDF) \(p(x)\), where \(x\in S\). We formulate the binary search algorithm in two parts: (1) search space density estimation, which produces a PDF that is fed into a (2) modified binary search algorithm that begins at the median of the PDF and bisects in probability density space. BBS is equivalent to basic binary search when the search space PDF estimation process is replaced with the assumption of a uniformly distributed target space. This formulation maps to a binary search problem on a sorted array in the following way: the search space S corresponds to the sorted array, the target value t is the element being searched for, and the PDF p(x) represents the probability of finding the target at each index in the array using interpolation.
# Shared-Custodial Wallet for Multi-Party Crypto-Asset Management
## 6 Business Model
### Value Proposition
#### 6.1.1 Shared-Custodial Model  
Our shared-custodial model offers a balanced approach to crypto-asset management by combining the security and control of non-custodial wallets with the convenience and services of custodial wallets. The values provided in the shared-custodial model over traditional wallet models are as follows:  
1. User Empowerment and Control: In fully custodial wallets, users must trust the custodian entirely, often sacrificing control over their assets. In contrast, our shared-custodial model ensures that users retain control over their assets and transaction authorisations. Our solution cannot initiate transactions without the user's explicit consent, aligning with the principles of user sovereignty.
2. Decentralised Recovery Mechanisms: Traditional non-custodial wallets place the entire key management responsibility on the user, risking permanent loss of assets if keys are lost or forgotten. In addition, users are required to protect seed phrases in addition to private keys. Our platform integrates decentralised recovery mechanisms, allowing users to securely recover their key shares without relying on a central authority or exposing their private keys.
3. Mitigation of Single Points of Failure: Most traditional wallets present a single point of failure, making them vulnerable to certain attacks. Our model's distributed architecture mitigates this risk by requiring collaboration among multiple parties for critical operations, enhancing overall system resilience.
4. Diverse Service Offerings with Private Key Control: Users benefit from convenient accessibility to various financial services such as staking, lending, and wealth management tools, while controlling the private key.
###### Abstract  
Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user's own data using the RFR achieved a MAE of 0.81 degrees.  
knee valgus angle; knee flexion angle; wearable computing Article
## 3 Results and Discussion
### Correction Factors Used for CO\({}_{2}\) Uptake  
The coefficient used to calculate the CO\({}_{2}\) uptake should be corrected because of the different variations encountered in the diffusion, such as the type of cement used, surface treatment (painted walls or not), and whether the concrete is sheltered indoors or outdoors.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.3.3 Leaf Area Index (LAI) Data  
LAI is a key input data in BEPS model, which is closely related to photosynthesis, transpiration and productivity of vegetation [33]. Remote sensing technology is an important means to obtain forest LAI. In order to obtain high spatial-temporal resolution LAI data and reduce the impact of atmospheric and other factors, MODIS LAI data and Sentinel-2 remote sensing images were combined to generate time-series LAI data with a spatial resolution of 20 m and a time resolution of 8 days through remote sensing inversion and field verification.
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 3 Stiff Hangers  
In some cases, mainly because of structural reasons, the hangers are stiffened. In these cases, the cables are substituted by H or hollow box-sections. In H-sections, the orientation of the hanger determines the bending direction where the effect of the hanger is more relevant. Two examples of Calatrava [16] illustrate this fact. In the first bridge, the Devesa footbridge (Figure 5) built in Ripoll, in northern Spain, the orientation of its H-shaped hangers is defined to provide stiffness in order to support out-of-plane bending. In the Alameda Bridge in Valencia, the hangers are fully fixed at the arch (Figure 6), which provides longitudinal in-plane stiffness.  
Figure 4: Load cases considered (see IAP [14] and EC-1 [15]).  
Figure 5: La Devesa footbridge (See [PERSON] ([17] and [PERSON] [18]) Photo: [PERSON], [PERSON] (reproduced with permission from Structura [7], 2011).  
When hangers are stiffened, the structural behavior of both the arch and the deck is modified, since they introduce concentrated bending moments at their ends. These partially counteract both the bending moments that appear in the arch and the deck of a bridge with pinned hangers. Since the bending moments are reduced, the deflection at the arch decrease accordingly. The structural behavior would be similar to that of a Vierendeel truss, i.e., a frame with no diagonal elements, employing rectangular voids. Unlike the conventional truss, it must resist significant bending forces.  
In Figure 7, the Town Centre Link Bridge, in Stratford near London (UK) is shown, which is an example of a bridge where the Vierendeel truss is the main structural system. In this bridge, the upper chord is straight, as usually happens in Vierendeel trusses, whereas in an arch bridge the upper chord is curved as it is shown in Figures 5 and 6.  
Figure 6: La Alameda bridge. (See [PERSON] [17], [PERSON] [18] and [PERSON] [19]). Photo: [PERSON].  
Figure 7: Town Centre Link Bridge at Stratford (UK). Photo: [PERSON] (reproduced with permission from Structurae [7], 2011).  
Therefore, since stiff hangers are an efficient way to reduce internal forces and deflections both in the arch and in the deck, they can be considered as a tool for the designer, who can decide to use them within a more general design process.  
In Figure 8, the effect of stiffening the hangers in the reference bridge is shown, both for loads distributions upon half (\(q_{1}\)) and the whole deck (\(q_{3}\)). Regardless of the load case, it can be seen how the bending moments are always smaller for the bridge with stiff hangers. All the internal forces and deflections diagrams shown in this paper have been obtained with SAP2000 (see Computers and Structures, Inc. [20]).
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 5 Conclusions  
This paper presents ADDAEIL, an incremental, unsupervised ensemble-based anomaly detection framework. It integrates distribution-based and performance-based drift detection, supported by an incremental updating strategy. ADDAEIL maintains interpretability and avoids full retraining by selectively replacing drift-affected base detectors. Each update decision is based on statistical tests and structure-based performance evaluation.  
In contrast to traditional static or semi-adaptive methods, which operate within a fixed model structure, ADDAEIL continuously evolves its ensemble based on data-driven drift evaluation. This hybrid design enhances robustness and efficiency, making it well suited for edge computing environments. Compared to deep learning-based approaches such as LSTM-VAE and STAD, which rely on offline-trained models or threshold-triggered retraining, ADDAEIL provides superior adaptability and stability in evolving data streams. This design ensures precise anomaly detection in both sudden and incremental drift scenarios.  
ADDAEIL is evaluated on both synthetic and real-world benchmarks. The experimental results show that, compared with existing baselines, ADDAEIL is superior on all evaluation metrics. For example, ADDAEIL obtains the highest average F1-score (0.76), AUC (0.90), and precision (0.75), while maintaining high recall (0.95). An ablation study is also conducted to validate the effectiveness of combining both drift detection modules.  
While the current framework is limited to univariate time series, extending ADDAEIL to multivariate inputs is a promising direction. The base iForest model natively supports multivariate data; however, adapting the drift detection modules poses additional challenges. In particular, current statistical tests (e.g., KS and MWU) are univariate and would need to be replaced or extended with multivariate distributional comparison techniques such as Maximum Mean Discrepancy (MMD), multivariate KL divergence, or copula-based approaches. Moreover, evaluating structural drift in high-dimensional decision trees introduces further complexity in performance-based detection.  
Conceptualization, D.L.; methodology, D.L.; software, D.L.; validation, D.L., N.-K.C.N. and K.I.-K.W.; formal analysis, D.L.; investigation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, D.L., N.-K.C.N. and K.I.-K.W.; supervision, N.-K.C.N. and K.I.-K.W.; project administration, K.I.-K.W. All authors have read and agreed to the published version of the manuscript.  
This project was (partially) supported by Te Hiranga Ro QuakeCoRE, an Aotearoa New Zealand Tertiary Education Commission-funded Centre. This is QuakeCoRE publication number 1068.  
Data Availability Statement: The data that support the findings of this study will be publicly available in a suitable repository upon publication.  
The authors declare no conflict of interest.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Specifications** & **Remarks** \\ \hline \hline \multirow{8}{*}{[PERSON] et al. [58] (2017)} & Temperature Range (\({}^{\circ}\)C): 30–50 & \multirow{8}{*}{It was observed that the thermal conductivity increased with temperature and nanoparticle volume concentration, having a more significant increase at higher concentration. It was observed that for the same base liquid, the nanofluid containing nanotubes had a significantly higher thermal conductivity than that containing alumina nanoparticles, demonstrating the superior characteristics of nanotubes. \\ \cline{1-1}  & Temperature Range (\({}^{\circ}\)C): 25–50 & \multirow{8}{*}{It was observed that the thermal conductivity increased with nanoparticle concentration and temperature, having a greater increase at higher temperatures.} \\ \cline{1-1}  & Base Liquid: Water & \\ \cline{1-1}  & Nanoparticle Material: MWCNT, CuO & \\ \cline{1-1}  & Nanoparticle Size (nm): & \\ \cline{1-1}  & CuO diameter-50 & \\ \cline{1-1}  & MWCNT-70 & \\ \cline{1-1}  & Outer diameter-15 & \\ \cline{1-1}  & Inner diameter-5 & \\ \cline{1-1}  & Length-50,000 & \\ \cline{1-1}  & Concentration (\(\varphi\)): 0.05\%, 0.01\%, 0.15\%, 0.2\%, & \\ \cline{1-1}  & 0.4\%, 0.6\% & \\ \hline \hline \multirow{8}{*}{[PERSON] et al. [59] (2017)} & Temperature Range (\({}^{\circ}\)C): 30–50 & \multirow{8}{*}{A non-linear increase with temperature and nanoparticle volume concentration was observed. It was suggested that the increase with temperature was a result of Brownian motion.} \\ \cline{1-1}  & Nanoparticle Material: MWCNT, Graphene, Cu & \\ \cline{1-1}  & Nanoparticle Size (nm): & \\ \cline{1-1}  & Cu diameter-18 & \\ \cline{1-1}  & Outer diameter-20 & \\ \cline{1-1}  & Length-10,000 & \\ \cline{1-1}  & Concentration (\(\varphi\)): 0.045-0.5\% & \\ \hline \hline \multirow{8}{*}{[PERSON] et al. [60] (2017)} & Temperature Range (\({}^{\circ}\)C): 30–50 & \multirow{8}{*}{A non-linear increase in nanofluid thermal conductivity was observed with both increased temperature and nanoparticle concentration, with a stronger dependency on temperature at higher concentrations.} \\ \cline{1-1}  & Nanoparticle Material: SWCNT, Al\({}_{2}\)O\({}_{3}\) & \\ \cline{1-1}  & Nanoparticle Size (nm): & \\ \cline{1-1}  & Al\({}_{2}\)O\({}_{3}\) diameter-20 & \\ \cline{1-1}  & Outer diameter-2 & \\ \cline{1-1}  & Concentration (\(\varphi\)): 0.04–2.5\% & \\ \hline \hline \multirow{8}{*}{[PERSON] [61] (2017)} & Temperature Range (\({}^{\circ}\)C): 25–50 & \multirow{8}{*}{A non-linear increase in thermal conductivity with concentration was observed, likely due to nanoparticle clustering at higher concentrations.} \\ \cline{1-1}  & Base Liquid: Ethylene Glycol & \\ \cline{1-1}  & Nanoparticle Material: MgO, Functionalized & \\ \cline{1-1}  & MWCNT: & \\ \cline{1-1}  & Nanoparticle Size (nm): & \\ \cline{1-1}  & MgO diameter-40 & \\ \cline{1-1}  & Functionalized MWCNT: & \\ \cline{1-1}  & Outer diameter-5 & \\ \cline{1-1}  & Inner diameter-5 & \\ \cline{1-1}  & Content (\(\varphi\)): 0–0.6\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._