esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Zero-Shot Detector Training
#### 3.4.1 Detector Backbone Training  
We employ a two-stage object detector (Faster R-CNN with ResNet-101 backbone) as our base architecture. The detector is first trained on the set of seen hazard classes \(\mathcal{Y}_{s}\) using standard detection losses:  
\[\mathcal{L}_{det}=\mathcal{L}_{cls}+\mathcal{L}_{reg}+\mathcal{L}_{rpn} \tag{17}\]where \(\mathcal{L}_{cls}\) is the classification loss, \(\mathcal{L}_{reg}\) is the bounding box regression loss, and \(\mathcal{L}_{rpn}\) is the region proposal network loss.  
**Negative Sampling Strategy:** For the Region Proposal Network, we maintain a 1:3 positive-to-negative ratio, sampling negative anchors with IoU \(<\) 0.3 with any ground truth box. We prioritize hard negatives by selecting 70% from anchors with the highest objectness scores. For the second-stage classifier, negative proposals have 0.1 \(<\) IoU \(<\) 0.5 with ground truth boxes, focusing on challenging near-miss cases.  
To address class imbalance, we implement importance sampling where negative samples from rare hazard classes are upweighted by their inverse frequency:  
\[w_{i}=\frac{N_{total}}{N_{i}\cdot C} \tag{18}\]  
where \(N_{total}\) is the total number of training samples, \(N_{i}\) is the number of samples in class \(i\), and \(C\) is the number of classes.
# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
###### Abstract  
Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain ensures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners' running status. By timely monitoring of a miner's footprint that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA)-based detection achieves the identification of individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect dishonest miners that collude to control a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of the proposed MinT scheme under a distributed byzantine network environment.  
digital twin; blockchain; Proof-of-Work; microservices; Singular Spectrum Analysis (SSA); byzantine fault tolerance
## 4 Data and Research Method
### Modeling the Parameters Affecting Population Distribution  
In order to model the parameters affecting population movement, first, the identification of effective parameters, such as pick-up and drop-off locations, area, number of types of land use, number of migrations, and the population of the region, was performed. Then, modeling was conducted using Poisson regression. One of the most important general linear models is the Poisson model, which uses the Poisson distribution for the response variable and assumes that the mean and variance of the response variable are equal [35]. In a Poisson model, the probability of the \(i\)th neighborhood, which has \(y_{i}\) pick-up and drop-off locations, is obtained by Equation (4). The relationship between the independent variables and the number of pick-up and drop-off locations for the expected study area is a logarithmic, linear model derived from Equation (5). The general form of the model is depicted in Equation (6) [44].  
\[P(y_{i})=\frac{\exp(-\lambda_{i})\lambda_{i}{}^{-y_{i}}}{y_{i}!} \tag{4}\]  
\[\ln(\lambda_{i})\ =\ \beta X_{i}\ \ \ \ \ \ or\ \ \ \ \lambda_{i}\ =\ \exp(\beta X_{i}) \tag{5}\]  
\[\lambda_{i}=e^{(\beta_{0}+\beta_{1}x_{1}+\beta_{2 predict the number of pick-up and drop-off points to determine its efficiency and adequacy in modeling the number of pick-up and drop-off points.
# Simplified Calculation Model for Contact Resistance Based on Fractal Rough Surfaces Method
## 4 Conclusions  
Based on fractal theory and the Monte Carlo method, a computational model taking into account the contact characteristics and contact resistance between rough contact surfaces is proposed in this paper. The conclusions are as follows:  
(1) The rough surface follows certain rules: as the fractal dimension \(D\) increases, the surface becomes finer. Additionally, when the characteristic parameter \(G\) increases, the height difference of the surface profile also increases. This observation aligns with the law proposed by the traditional W-M function.  
(2) When the fractal dimension \(D\) equals 1.5, and under a given contact load, the rough surface exhibits the largest effective contact area and the smallest contact resistance, demonstrating optimal contact characteristics. The relationship between contact load and the effective contact area is approximately linear. In both experimental and finite element verification, the contact model presented in this paper demonstrates relatively high accuracy when \(F^{*}\) is less than 4 \(\times\) 10\({}^{-3}\). However, under heavy load conditions, there is a certain deviation due to a lack of consideration of the multiscale coupling of asperities.  
(3) The existence of oxide film in the rough surface will increase the contact resistance by several orders of magnitude.  
Since contact resistance plays a key role in the thermal performance and expected in-service time of electrical contacts, the model serves as a valuable reference for the reliability verification and optimal design of electrical equipment with multiple contacts. The surface simulation and contact analysis described in this study can be extended to other transport properties of rough surfaces, including frictional wear, heat transfer, and radiation.  
Conceptualization, C.Z. and B.Y.; methodology, C.Z. and B.Y.; software, B.Y.; validation, C.Z. and Y.L.; formal analysis, B.Y.; data curation, B.Y.; writing-original draft, B.Y.; visualization, B.Y.; writing-review and editing, C.Z.; supervision, C.Z., Y.L. and Q.Y.; project administration, C.Z.; funding acquisition, C.Z. and Q.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China, (No. 52277008, 92066206), and the Funds for Creative Research Groups of Hebei Province, (No. E2020202142). Supported by State Key Laboratory of Reliability and Intelligence of Electrical Equipment (No. EERI_OY2020008).  
Not applicable.  
The author declares no conflict of interest.  
Figure 16: Loss of morphological features in reverse modeling.
## 3 Materials
### IoT Communication Protocols  
In every IoT implementation, especially in rural environments, selecting an appropriate communication protocol is a crucial design decision [31]. These systems often face constraints such as limited bandwidth, limited energy resources, and poor connectivity. Therefore, the communication layer must be efficient and robust.  
Several protocols are particularly relevant for such conditions, each offering a different balance between energy consumption, data reliability, latency, and security features. Among the long-range options, LoRaWAN stands out for its low-power operation and star topology, where end devices transmit to central gateways [27; 32]. It supports both public and private infrastructures, incorporates AES-128 encryption [18], and offers flexible communication modes through device classes A, B, and C [33].  
For short-range communication, MQTT (Message Queue Telemetry Transport) is a publish-subscribe protocol that allows multiple devices to communicate asynchronously through an MQTT broker, optimizing communication in systems with multiple sensors. However, it lacks native security and depends on external solutions like TLS (Transport Layer Security) for encryption and access control [34; 35; 36; 37; 38]. Another protocol widely used in IoT implementations is HTTP. It is used in IoT due to its broad compatibility and ease of integration with web services [39]. However, its request-response architecture requires a stable and permanent connection to a server for data transmission, making it less suitable for battery powered or intermittently connected devices [40]. The User Datagram Protocol (UDP) is often chosen for use cases that prioritize speed and minimal latency. This protocol is useful in applications with limited response times or where occasional data loss is acceptable [24; 25]. However, UDP lacks native security and delivery guarantees, so it requires the implementation of complementary security measures [40]. Lastly, the Constrained Application Protocol (CoAP) offers an attractive option for resource-constrained devices. It mirrors HTTP's model but is optimized for constrained devices over UDP, reducing overhead and supporting DTLS (Datagram Transport Layer Security) for secure transmissions [39; 40; 41].  
Table 2 summarizes the main characteristics of IoT communication protocols used in this work: MQTT, CoAP, HTTP, UDP, and LoRaWAN. Each protocol is analyzed across several key dimensions relevant to rural IoT deployments, such as communication model and security support.
# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 3 Pneumonet Framework  
In order to detect pneumonia, the PneumoNet framework examines chest X-ray images and produces medical reports. As already mentioned, this is performed by combining the GPT-Neo LLM [24] with a customised AlexNet convolutional neural network, initially trained with ImageNet. Figure 1 illustrates the architecture of the framework, which is divided into three main modules: (a) the pneumonia classification module (PCM), the pneumonia detection process via X-ray analysis using the adapted AlexNet (top left in the figure); (b) the XAI Module to visualise the model's focused areas during predictions through a heat map and presenting the achieved performance results (top right in the figure); and (c) the Medical Report Module (MRM), an LLM model, is used to generate medical reports and answer the physician's questions about the X-ray (bottom component of the figure).  
Going in more detail, the PneumoNet framework adopts a modified AlexNet architecture as the backbone for its PCM, chosen over modern architectures such as EfficientNet or Vision Transformers (ViTs) due to its balance of simplicity, computational efficiency, and high accuracy for X-ray image classification. AlexNet's relatively shallow architecture and lower parameter count (approximately 60 million, compared to EfficientNet-B7's 66 million or ViT's over 100 million) make it computationally efficient, enabling faster training and inference on resource-constrained medical imaging systems, which is critical for deployment in diverse clinical settings.  
The choice of AlexNet is further justified by its proven effectiveness in medical imaging (see also Section 2) tasks and compatibility with the characteristics of chest X-ray datasets [8; 9; 10]. Unlike EfficientNet, which relies on compound scaling and deeper architectures to optimise performance, or ViTs, which require large-scale pretraining and substantial computational resources to process image patches via self-attention, AlexNet's straightforward convolutional design excels at capturing local features (e.g., consolidations, opacities) in greyscale-derived RGB X-rays with minimal preprocessing. The code demonstrates preprocessing steps (e.g., resizing to 227 \(\times\) 227, colour constancy, and augmentation with \(\pm\)5\({}^{\circ}\) rotations; see Section 3 for more details) tailored to AlexNet's input requirements, ensuring robust feature extraction. Additionally, AlexNet's transfer learning from ImageNet, combined with fine-tuning (Adam optimiser, 10\({}^{-4}\) learning rate, 10 epochs; Section 3.1 for more details), enhances its adaptability to medical images, outperforming modern architectures in this context. For instance, PCM's AUC of 99.70% on Dataset #2 [9] exceeds reported ViT-based PneuNet's 94.96% [22], highlighting AlexNet's superior accuracy and practical advantages for pneumonia detection in resource-limited environments.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 6 Conclusions  
This paper presents a 3D point cloud completion method based on a building-contour-constrained diffusion probability model. It constructs contour shape latent codes using building contour information, which consists of both walls and roofs, to constrain point cloud generation using the diffusion probability model. This ensures that the generated point cloud has the overall shape with geometric details of building edges. Our method effectively addresses the issue of incorrect geometric distribution in completing point clouds for missing areas in buildings. We tested our method's performance in building point cloud completion on the BuildingNet dataset and reached the following conclusions:  
1. By grouping building surface polygons, we simplified the process of constructing building-contour-constrained polygons. This eliminates the need for training complex point cloud semantic segmentation models to obtain the required building-contour-constrained regions.
2. In the comparison experiments presented in Tables 1 and 2, our method demonstrates superior building point cloud completion accuracy compared to other point cloud completion methods, such as PCN, PF-Net, and VRC-Net. This is because we incorporated the geometric contour information of the target building point cloud during the building point cloud completion process, resulting in a point cloud distribution that closely matches the geometric shape of the target building.  
Conceptualization, B.Y. and H.W.; methodology, B.Y., H.W. and J.L.; software, J.J.; validation, B.Y. and J.L.; formal analysis, B.Y. and J.J.; investigation, Y.L.; resources, E.G.; data curation, H.W.; writing--original draft preparation, B.Y. and H.W.; writing--review and editing, B.Y. and J.L.; visualization, J.J.; supervision, T.Y.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, (No. 41961063); Guilin City Technology Application and Promotion Project in 2022 (20220138-2); Key R&D Projects in Guilin City in 2022 (20220109).
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 2 NCV-CPM Methodology
### Morphological Reconstruction Based on Curvature Propagation Method  
The core idea of the wing leading-edge morphological reconstruction method based on the curvature propagation method is as follows. Firstly, following the segmentation scheme for the arc segments and leveraging principles of differential geometry, calculate the coordinate increments between the endpoints and starting points of all the arc segments. Secondly, starting from the initial segment, cumulatively add the coordinate increments of all arc segments. This process yields the coordinate positions after the deformation of the wing leading edge [29].  
Assuming the existence of the arc segment \(AB\), as depicted in Figure 5, the principal coordinate system \(XY\) is established at the location of the arc segment, and the tangent line at starting point \(A\) is defined as the \(X^{\prime}\) coordinate axis to establish the auxiliary coordinate system \(X^{\prime}Y^{\prime}\). The projections of point \(B\) on the principal coordinate system \(XY\) are denoted as \(x_{BA}\) and \(y_{BA}\), while the projections of point \(B\) on the auxiliary coordinate system \(X^{\prime}Y^{\prime}\) are denoted as \(x_{B1}\) and \(y_{B1}\). Furthermore, \(x_{BC}\) and \(y_{BC}\) represent the projection of \(x_{B1}\) onto the principal coordinate system \(XY\).  
Figure 4: Curvature and strain field reconstruction process based on node curvature vectors.  
According to geometric relationships, \(x_{BA}\) and \(y_{BA}\) can be expressed as follows:  
\[\begin{cases}x_{BA}=x_{B1}\times\cos\beta-y_{B1}\times\sin\beta\\ y_{BA}=x_{B1}\times\sin\beta+y_{B1}\times\cos\beta\end{cases},\ \beta=\int\limits_{0}^{s}q(s) \mathrm{d}s \tag{12}\]  
where \(\beta\) represents the angle between the tangent line at point \(A\) and the horizontal axis.  
In the auxiliary coordinate system \(X^{\prime}Y^{\prime}\), the expressions for \(x_{B1}\) and \(y_{B1}\) are given as follows:  
\[\left\{\begin{array}{l}x_{B1}=r_{AB}\times\sin a\\ y_{B1}=r_{AB}\times(1-\cos a)\end{array}\right.,\quad a=\frac{S}{r_{AB}}\quad r _{AB}=\frac{q_{A}+q_{B}}{2q_{A}q_{B}} \tag{13}\]  
where \(\alpha\) represents the angle between the radii of points \(A\) and \(B\), \(r_{AB}\) denotes the average radius of the arc segment \(AB\), \(S\) represents the size of the arc segment length, and \(q_{A}\) and \(q_{B}\), respectively, represent the curvature at points \(A\) and \(B\).  
By combining Equations (12) and (13), the coordinate increments \(x_{BA}\) and \(y_{BA}\) relative to point \(A\) can be computed for point \(B\) on the arc segment. Cumulatively adding the coordinate increments of all arc segments allows for the reconstruction of the cross-sectional propagation of the wing leading edge after deformation.
# Fog vs. Cloud Computing: Should I Stay or Should I Go? +
## 5 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline
**Benchmark** & **Dataset** & \(t\) **(m)** & \(p\) **(\%)** & \(\upsilon\) & \(n\) & \(f\) **(Est)** & \(f\) **(Real)** & **Slope (Est)** & **Slope (Real)** & **Slope Error (\%)** \\ \hline \multirow{8}{*}{MinMax \([-5,15]\)} & HRelFummidity & 0.023170 & 0.000035 & 14.453 & 14.453 & 1.0000 & 1.0000 & \(-0.9778\) & \(-0.9778\) & \(-0.0000\) \\  & HVSibility & 0.021135 & 0.000032 & 15.845 & 0 & 0.0000 & 0.0000 & \(-48.3565\) & \(-48.3565\) & \(-0.0000\) \\  & HWBTempC & 0.021667 & 0.000032 & 15.456 & 3920 & 0.2536 & 0.2784 & \(-3.6387\) & \(-3.3376\) & \(+0.0198\) \\  & HWBTepC & 0.02129 & 0.000032 & 15.723 & 2131 & 0.1355 & 0.1393 & \(-6.3949\) & \(-6.2452\) & \(+2.3970\) \\  & Synthetic & 0.021848 & 0.000033 & 15.327 & 8567 & 0.5589 & 0.5536 & \(-1.7223\) & \(-1.7393\) & \(-0.9288\) \\ \hline \multirow{8}{*}{MinMax \([-5,5]\)} & HRelFummidity & 0.023174 & 0.000035 & 14.451 & 14.451 & 1.0000 & 1.0000 & \(-0.9778\) & \(-0.9778\) & \(-0.0000\) \\  & HVSibility & 0.022708 & 0.000034 & 14.747 & 12.184 & 0.8262 & 0.8336 & \(-1.1787\) & \(-1.1685\) & \(+0.8687\) \\  & HWBTempC & 0.022246 & 0.000035 & 15.054 & 11.189 & 0.7433 & 0.6911 & \(-1.3071\) & \(-1.4028\) & \(-6.8176\) \\  & HWMBepC & 0.022567 & 0.000034 & 14.839 & 11.701 & 0.7885 & 0.8013 & \(-1.2336\) & \(-1.2145\) & \(+1.5716\) \\  & Synthetic & 0.022150 & 0.000033 & 15.119 & 12.530 & 0.8288 & 0.8299 & \(-1.1759\) & \(-1.1743\) & \(+0.1301\) \\ \hline \multirow{8}{*}{Outlier 16} & HRelFummidity & 0.024622 & 0.000037 & 13.601 & 324 & 0.0238 & 0.0236 & \(-20.8708\) & \(-20.9521\) & \(-0.3880\) \\  & HVSibility & 0.022958 & 0.000034 & 14.587 & 14.07 & 10.008 & 0.0911 & \(-8.1144\) & \(-8.8072\) & \(-7.8668\) \\  & HVSIPempC & 0.024901 & 0.000037 & 13.448 & 297 & 0.0212 & 0.0222 & \(-21.5284\) & \(-21.4534\) & \(+0.3493\) \\  & HWMBepC & 0.024746 & 0.000037 & 13.532 & 409 & 0.0302 & 0.0267 & \(-13.3694\) & \(-19.6518\) & \(-6.5253\) \\  & Synthetic & 0.025497 & 0.000038 & 13.134 & 90 & 0.0069 & 0.0076 & \(-31.4547\) & \(-30.6958\) & \(+2.4432\) \\ \hline \multirow{8}{*}{Outlier 256} & HRelFummidity & 0.08120 & 0.000133 & 3757 & 47 & 0.0125 & 0.0051 & \(-10.0289\) & \(-10.8308\) & \(-7.4044\) \\  & HVSibility & 0.059418 & 0.000089 & 5630 & 531 & 0.0943 & 0.1112 & \(-6.5567\) & \(-5.9035\) & \(+11.0641\) \\ \cline{1-1}  & HWMBepC & 0.088124 & 0.000132 & 3800 & 15 & 0.0009 & 0.0124 & \(-11.0896\) & \(-10.1371\) & \(+9.3965\) \\ \cline{1-1}  & HWMBepC & 0.084443 & 0.000126 & 3965 & 52 & 0.0131 & 0.0160 & \(-10.4449\) & \(-10.1371\) & \(+3.0363\) \\ \cline{1-1}  & Synthetic & 0.090580 & 0.000135 & 3697 & 5 & 0.0014 & 0.0012 & \(-11.1133\) & \(-11.1277\) & \(-0.1295\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: NodeMCU energy consumption results in fog-prone cases.
## 2 Analysis of the Design Model of the Virtual Engine Sound Actuator for AVAS
### Electric Field of the Initial Model and Magnetic Force Analysis  
Electromagnetic Field of Actuator and Vibration and Response Analysis  
Electromagnetic field analysis used FEMM 4.2 software.  
The virtual engine sound actuator generates electromagnetic force by the current flowing through the coil and the magnetic field of the permanent magnet in accordance with [PERSON]'s left-hand rule, and the electromagnetic force is expressed as Equation (1) in accordance with the Lorentz force law.  
\[\text{F}=\oint\text{I}\,\text{d}\mathrm{l}\,\times\overset{\rightarrow}{ \text{B}}, \tag{1}\]  
where F is the electromagnetic force, I is current, I is coil length, and \(\overset{\rightarrow}{\text{B}}\) is magnetic field.  
The operating conditions of the virtual engine sound actuator are as follows: rated voltage = 12.65 V, impedance = 8 \(\Omega\), and current = 1.65 A. Table 1 outlines the properties of the virtual engine sound actuator.  
The basic design geometry of the magnetic circuit structure has cylindrical cyclic symmetry, and we performed an electromagnetic field analysis using a two-dimensional plane model. The electromagnetic field analysis model was constructed by applying the Robin boundary condition (mixed boundary condition), which can define both the Dirichlet boundary condition of zero tangential electric potential (\(V=0\)) and the Neumann boundary condition that constrains the magnetic flux passing in the normal direction as the boundary condition to maintain the magnetic flux, as illustrated in Figure 2. Electromagnetic field analysis was used FEMM 4.2 software.  
The electromagnetic field analysis enabled the calculation of the electromagnetic force generated by the coil current and the magnetic field formed by the yoke, magnetic force, and plate, as well as the magnetic flux density generated by the coil and the permanent magnet, as illustrated in Figure 3. The electromagnetic force was calculated at 19.80 N using Equation (1), which does not satisfy the design target of 20.00 N or higher. To meet the design target, a slimming optimization is necessary.