esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 5 Conclusions  
This article proposes using an ED model to depict the relative movement of the b/g face. The full force-deformation curve of rockbolts was divided into three phases: the elastic phase, the elastic-softening phase, and the debonding phase. The credibility of this analytical modelling process was validated with experiment results. Based on the ED model, a series of parameter studies was conducted. The following conclusions were obtained:  
(1) The cross-section area of rocks had an impact on the axial performance of rockbolts. With the cross-section area of rocks increasing, the peak capacity of rockbolts increased. However, for the cross-section area of rocks, there was a critical range. Once the cross-section area of rocks was beyond that critical range, external rocks had a mild impact on the axial performance of rockbolts. For the test scenario used in this article, the critical diameter was 1 m.  
(2) Rockbolt diameter and borehole diameter are two diameter parameters commonly used in the bolting system. The rockbolt diameter had a significant impact on the axial performance of rockbolts. A linear relationship existed between the rockbolt diameter and the peak force of rockbolts. By contrast, a non-linear relationship existed between the rockbolt diameter and the deformation at the peak force. The equations between the peak force of rockbolts, the deformation at the peak force, and the rockbolt diameter were obtained. Borehole diameter had a mild impact on the axial performance of rockbolts.  
(3) With the rockbolt length increasing, the peak force of rockbolts increased linearly. As for the deformation at the peak force, it increased non-linearly. The rockbolt modulus of elasticity had a more apparent impact on the deformation at the peak force. When the rockbolt modulus of elasticity increased, the deformation at the peak force decreased non-linearly. As for the peak force of rockbolts, it increased gently.  
(4) Mechanical properties of the b/g face affected the axial performance of rockbolts. When the b/g face strength increased, the peak force of rockbolts increased linearly. Moreover, the overall force-deformation curve became higher. The slippage at the ultimate load had a more apparent impact on the turning point between the elastic phase and the ED phase. When the slippage at the ultimate load increased in the force-deformation curve, the turning point between the elastic phase and the ED phase became further.  
Conceptualisation: B.Z. and S.W.; writing: J.C., X.Z., J.M. and S.S. All authors have read and agreed to the published version of the manuscript. This study is funded by the National Natural Science Foundation of China (52174093 and 52034009) and the Fundamental Research Funds for the Central Universities (2024 ZKPYNY01, 2023 ZKPYNY03, and 2023 YQTD02). Not applicable. Not applicable. Not applicable.  
**Data Availability Statement:** All data are contained within this article.
## 2 Literature Review  
Accurate traffic prediction is fundamental for Intelligent Transport Systems (ITS), particularly in urban areas where congestion is a major issue. Congestion can be categorised as recurrent (e.g., regular peak-hour traffic) or non-recurrent (e.g., incidents such as crashes, severe weather conditions, or road maintenance) [6; 7; 8; 9]. Non-recurrent events are particularly challenging, as they are difficult to predict and manage. Identifying the time, location, and severity of such incidents has long been a focus of traffic management research, evolving from manual reporting to automated algorithms and neural networks [10; 11]. Early methods like the California Algorithm [12] and statistical approaches such as Autoregressive Integrated Moving Average (ARIMA) and Kalman Filters were widely used but struggled to capture the non-linear and dynamic nature of traffic flow, especially on arterial roads [13; 14].  
Arterial roads present unique challenges for traffic prediction due to frequent interruptions, including passenger pick-ups, vehicle turns, and parking activities, which introduce high variability into the traffic flow [15]. These irregular behaviours make it difficult to apply standard algorithms, as they often fail to account for these complexities [16; 17; 18]. Parametric models such as Autoregressive Integrated Moving Average (ARIMA) and Kalman filters, while effective for structured and stationary data, often fall short in the dynamic and unpredictable conditions found on arterial roads [19; 20]. Kalman filters, in particular, were used for incident detection by measuring flow characteristics before and after incidents. However, they often struggle with the irregular traffic patterns on arterial roads, which require more adaptive models [21; 22; 23; 24].
###### Abstract  
Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO-ICEEMDAN-iTransformer model outperformed the other comparison models, with an R\({}^{2}\) of 0.9873, RMSE of 48.0014, and MAE of 66.2221.  
Keywords:power system; load forecasting; beluga whale optimization; data decomposition; iTransformer +
Footnote †: journal: _articles
## Nanomaterial-Based Membrane
### Polymer Nanocomposites Membranes for Gas Separation
Furthermore, there was a close connection amid the microstructures of polymer mixture precursors and carbon molecular sieve membranes. Their study established that the microstructure and gas separation performance of carbon molecular sieve membranes could be modified via the thermal blending of two stable polymers. [PERSON] et al. [88] used a poly [2, 2'-(p-oxyldiphenylene)-5, 5'-bibenzimidazole] for pyrolysis under an inert Argon atmosphere for the production of alumina-supported carbon molecular sieve membranes utilized for CO\({}_{2}\)/CH\({}_{4}\) gas separation [88]. There was an establishment of a structural performance relationship and it was deduced that high pyrolysis temperature could result in making a dense and well-organized graphite carbon structure and subsequently result in outstanding amalgamations of separation factor and gas penetrability of carbon molecular sieve membranes. Mechanisms of Gas Separation in Nanocomposite Membranes  
The principal mechanism of the transportation of gas in some composite membranes is the Knudsen diffusion, while other composite membranes are principally modelled by sorption-diffusion or solution-diffusion mechanisms. The Knudsen diffusion mechanism is usually used to govern porous membranes. This mechanism makes use of viscous flow in narrow pores. The mean free path length of a gas molecule is strongly contingent on the type of the gas, the pressure, and the temperature [89]. Example of Knudsen diffusion mechanism occurs in Polyethersulfone incorporation with inorganic fillers of different shapes used for the separation of CO\({}_{2}\)/CH\({}_{4}\). The study shows that the permeability of the membrane composites increased for both gases. The principal membrane mechanism responsible for gas transport is Knudsen diffusion; hence, CO\({}_{2}\) and CH\({}_{4}\) cannot really be separated [90]. Gas transport of dense solid materials is attained through the mechanism of solution-diffusion. For a considerably higher selectivity, this mechanism of transportation is subject to the solution and diffusion of the several components contained in the membrane phase. The mass transport in a solution-diffusion membrane comprises of three important steps [89]:  
1. Diverse components sorption from a feed in accordance with their partition coefficient amid the gas and polymer phase. 2. The separate components diffusion contained in the membrane phase in accordance with their activity gradients. 3. Components desorption from the membrane in the permeate gas phase. Example of the solution-diffusion mechanism is the transportation of gas via carbon molecular sieve membranes. Gas molecules is specifically sorbed into the membrane at the upstream, which will then diffuse on the account of the influence of a chemical potential gradient, and lastly, desorb from the membrane at the downstream. Penetrability and selectivity are the two intrinsic properties, used in evaluating the performance of such membrane materials [91].
## 3 Proposed Fault Diagnosis Method
### Procedure of Fault Diagnosis  
In this subsection, a novel fault diagnosis method is proposed for centrifugal pumps with few failure samples, which is mainly composed of the CEEMD-based signal processing, TfEn-based feature extraction and RFE-based feature selection. The procedure of our propose fault diagnosis method is shown in Figure 1.  
As shown in Figure 1, the main steps of our proposed method can be represented as follows:  
1. Decompose the non-stationary signal into several sub-signals using CEEMD to obtain the multiple-scale components;
2. Using our proposed TfEn, transform each non-stationary or approximately stationary component into the 2D time-frequency matrix, and construct their fault representation by taking into account nonlinear behaviors from both the time and frequency domain;
3. Select the optimal TfEn feature subset from the high-dimensional feature set using the RFE, where the redundant components are eliminated automatically;
4. Choose classifier models to implement the fault diagnosis for centrifugal pumps, based on the optimal TFIE feature subset.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 2 Materials and Methods
### Instrument Design
#### 2.1.3 Flexible Shaft  
The flexible shaft is an essential part of the instrument, which connects the control handle to the distal tip. Its length is critical to the instrument's performance as it determines the reach and accessibility to the target operating region. A highly flexible shaft enables easy navigation around anatomical landmarks, while sufficient stiffness is necessary for effective tissue manipulation and object handling. The flexible shaft was developed to achieve a balance between these two properties.  
The shaft's torsional rigidity should also be strong enough to overcome friction and allow for the smooth rotation of the distal tip in both directions. Additionally, the flexible shaft facilitates the uninterrupted transfer of movement from the handle to the distal tip to ensure efficient instrument control.  
Figure 1: Image of the instrument’s distal tip at the maximal bending angle with annotated dimensions.  
The flexible shaft of the instrument is produced from a flat steel spiral coated with a layer of biocompatible polyvinyl chloride (PVC). The diameter of the shaft is 5 mm, and the length can be varied between 30 and 200 cm, depending on the intended use. A set of wires is routed through the flexible shaft to facilitate the steering of the bending section and opening of the grasping jaw. This design ensures that the instrument can be easily maneuvered, and the physician can accurately control the movements of the distal tip, even in the narrow regions of GI tract.
## 5 Analysis of the Solution and Simulation
### Analytical and Validation Study
Such assumptions can be described as follows: (i) nodes send messages at a relatively slow rate (unsaturated traffic regime); (ii) message collisions are very unlikely to occur (the backoff of the CSMA algorithm is not taken into consideration) and (iii) the transmission and acknowledgement of packets take place in a very short period of time (therefore the _transmitting_ and _waiting_ states are not modeled).
## 6 Evaluation
### Learning Performance and Computational Cost
#### 6.1.3 Accuracy Comparison for Non-Independent and Non-Identically Distributed Data (Non-IID)  
In the real world, the data held by each customer vary due to differences in environment and habits, resulting in differences in the type or amount of data held by customers. To simulate this real-world situation, we conducted non-IID experiments. Specifically, we simulated non-IID quantity skew, which refers to the variation in the amount of data owned by each client. In the non-IID experiment, we first shuffle the dataset and then randomly assign different amounts of data to each client. Each client uses the assigned data to start local training.  
As with the IID experiment, we used two datasets, MNIST [29] and Fashion-MNIST [30], and set the number of servers for FedAvg [2] to one, and the number of servers for Fedshare [8] and our method to five. We also conducted four experiments with 2, 5, 10, and 20 clients for comparison. Figure 7(a) shows the experimental results for the MNIST [29] dataset, and Figure 7(b) shows the experimental results for the Fashion-MNIST [30] dataset. The results show that the model has lower accuracy when trained with non-IID data than with IID data, due to the uneven distribution of the former. Additionally, Figure 7(a),b clearly demonstrate that because we incorporate the same weighted mechanism as FedAvg [2] and Fedshare [8] in the non-IID experiment, the accuracy of the three methods is very similar.  
Figure 6: Comparison of the accuracy of our proposed method with FedAvg [2] and FedShare [8] using the IID MNIST [29] dataset with 5 and 10 servers and varying numbers of clients.  
Figure 7: Comparison of the accuracy of our proposed method with FedAvg [2] and FedShare [8] using the IID Fashion-MNIST [30] dataset with 5 and 10 servers and varying numbers of clients.
## 4 Machine Learning Pipeline for Passive Inspection of TLS-Encrypted Traffic
### Collecting Flow Records  
Once the TLS-encrypted traffic is sniffed, collection of flow records should be conducted, as various machine-learning based methods assume that the raw input is a unidirectional/bidirectional TLS flow. A flow record may have various raw data for the corresponding flow and packets.  
In practice, conventional flow records can be collected from the middleboxes (e.g., routers, switches), or software-based traffic sensors (e.g., nProbe), which can export NetFlow/IPFIX. While conventional NetFlow records have minuscule information for TLS, [PERSON], and [PERSON] [83] proposed enhanced TLS flow records, which contain the sequence of packet lengths and (interarrival) times (SPLT), the byte distribution (BD) in the TLS flow data (an array keeping a count for each byte value in the packet payloads for the TLS flow), and TLS handshake metadata (features that can be collected in the TLS handshaking procedure described in Section 2.1). The enhanced flow records can be collected using Cisco joy [84], which is an open-source prototype of Cisco's encrypted traffic analytics (ETA) [11] and a precursor of Cisco mercury, while the current version of Cisco mercury does not support to collect some fields in the enhanced flow records.  
Note, that while multiple TLS-encrypted NTA techniques utilize a subset of the enhanced TLS flow records in [83], there are several approaches to utilize high-level connection logs as flow records in bot detection [85, 86] and malware family classification [87]. These approaches can be effective solutions for SOCs, because such information can be readily collected using conventional network security monitoring systems, such as Zeek (formerly known as Bro [88]).  
Figure 4: Machine learning pipeline for passive inspection of TLS-encrypted traffic.  
However, it is unclear whether these researches [85; 86; 87] are applicable in TLS-encrypted traffic, as there has been no performance evaluation for TLS-encrypted traffic dataset.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 2 Refocusing Model Conforms to Central Projection
### Problems of the Traditional Refocusing Algorithm
#### 2.1.2 Diverse Theoretical Imaging Positions of Same Object among Focal Stack  
For a raw image with \(N_{u}\times N_{v}\) pixels, the size of the refocused image can be determined as \((dN_{u},dN_{v})\) with \(d\) representing the scale factor. Given an arbitrary imaging point \(Q\) with coordinates \((Q_{u},Q_{v})\) in \(ouv\), let \(G\) denote the center of the imaging circle of \(Q\) in certain refocus plane, as shown in Figure 2a. The pixel coordinates of \(G\) in \(o^{\prime}st\) is  
\[(G_{s},G_{t})=d\frac{G_{z}}{Q_{z}}(Q_{u},Q_{v}), \tag{2}\]  
with \((G_{x},G_{y},G_{z})\) and \((Q_{x},Q_{y},Q_{z})\) standing for the coordinates of \(G\) and \(Q\) in \(OXYZ\), respectively. When \(d\) is fixed, \((G_{s},G_{t})\) changes with \(G_{z}\). This makes the theoretical imaging positions of the same point differ in the focal stack, which makes the feature matching among traditional focal stack more difficult.
## 2 Materials and Methods
### YOLOv11-OAM Architecture Overview
#### 2.4.3 Multi-Point Distance Intersection over Union (MPDIoU) Loss Function  
Accurate bounding box regression is critical for reliable object detection, particularly in UAV orchard imagery where tree crowns are densely packed, overlapping, and frequently occluded. Traditional IoU-based losses--such as GIoU, DIoU, and CIoU--primarily focus on overlap and center alignment but often lack geometric sensitivity for small or crowded targets.  
To address this limitation, we employ Multi-Point Distance IoU (MPDIoU) [37] as the regression loss in the detection head. MPDIoU extends standard IoU by incorporating spatial misalignment penalties, computed via Euclidean distances between multiple reference points on predicted and ground-truth boxes. Nine key points are considered: the box center, four edge midpoints, and four corners.  
Formally, let \(B_{p}\) and \(B_{g}\) denote the predicted and ground-truth bounding boxes, respectively, and \(P_{k}^{(p)}\), \(P_{k}^{(g)}\) represent their \(k-th\) reference points. The MPDIoU loss is defined as  
\[\text{MPDIoU}=1-\text{IoU}(B_{p},B_{g})+\frac{1}{K}\sum_{k=1}^{K}\frac{\|P_{k} ^{(p)}-P_{k}^{(g)}\|_{2}^{2}}{c^{2}} \tag{5}\]  
where \(K=9\) is the number of reference points, \(c\) is the diagonal length of the smallest enclosing box containing both \(B_{p}\) and \(B_{g}\) and \(\|\cdot\|\) is the Euclidean distance.  
This loss function provides more comprehensive geometric supervision by penalizing spatial misalignment across multiple points, which improves regression precision and convergence speed. Its normalization by the enclosing box diagonal length renders the loss scale-invariant, enhancing robustness to varying object sizes. Figure 7 illustrates the MPDIoU bounding boxes, key reference points, and IoU overlap, clarifying the geometric relationships captured by the loss.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 1 Introduction
### Contributions  
The paper considers an underwater sensor network where there is a sink node and the sensor nodes transmit the information to it directly. Suppose that the sink node knows the location of each sensor node. Combining Bayesian theory and convex optimization theory, we investigated an optimized sensor node selection scheme based on compressed sensing in underwater sensor networks. The proposed scheme can be obtained by solving an optimization problem. Both the network lifetime and reconstruction accuracy performance are considered in the optimization problem. In this paper, our main contributions can be summarized as follows:  
* The paper focuses on the improvement of performance of a compressed sensing scheme. First, a Bayesian estimation theory is provided for the signal reconstruction. Then, based on Bayesian estimation theory, the closed-form expression of posterior density function and error covariance matrix are given. By a maximum posteriori estimation, the noise variance can be obtained by updating iteratively.  
* The sensor node selection scheme is transformed as a sensing matrix design problem. By using the error covariance matrix of Bayesian estimation and regarding the sensing matrix as a variable, the sensing matrix design problem can be treated as an optimization problem of minimizing the MSE. Because the proposed optimization problem is non-convex, the optimal solution is difficult to obtain. By relaxing the integer constraint as a continuous constraint, the proposed optimization problem becomes a convex semidefinite programming problem that can be solved efficiently.
* The sustainability of networks is considered in the proposed optimization problem. To prolong the network lifetime, the scheme aims at selecting the sensor nodes holding more residual energy. This idea is transformed into a constraint in the optimization problem.  
The proposed sensor node selection scheme is evaluated by simulation results. Simulation results demonstrate that the optimized node selection scheme has superior performance compared to the traditional one.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 1 Introduction
### Contribution of This Paper  
In this paper, we present a novel routing protocol we call adapted diffusion (AD). In this protocol, the source and sink are always discovered, and the requested data retrieved. Secure communication is achieved with significant energy saving. The network is made up of nodes. All nodes can forward information to their immediate neighbors and receive information from their immediate neighbors.  
The network has a minimum of two special nodes. These are the source (where the information comes from) and the sink (the node that creates an interest, collects the information and forwards data to an external server.) We set up the sink at the edge of the network, and we randomly choose one of the other nodes to be the source.  
* The source has sensors that collect information about either their own condition, or their surroundings.
* The sink can request information that is available at the source.
* Each node can select one of its neighbors for transmitting or receiving data based on the conditions we introduce.