esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Feature Extraction
#### 3.2.2 Complexity of Data Boundaries  
The thermal bridges in thermal images show simple trends similar to straight lines without complex boundaries in addition to linear shapes. This study proposes the ratio  
Figure 4: Thermal anomaly clustering based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN).  
of actual data relative to the area surrounding individual clusters to extract features with uncomplicated boundary surfaces. The features of the trend with complex data boundaries proposed in this study are calculated as follows. First, the convex hull algorithm is applied to calculate the area surrounding the individual clusters. The convex hull algorithm is defined as a convex polygon that includes all the points in a binarized image [26]. The convex hull algorithm is widely used to extract specific shapes within an image. Figure 5 below shows an example of applying convex hull in a 2D image. The line surrounding the cluster represents the convex hull of that cluster, and the black shading represents the points constituting the cluster.  
A convex polygon that contains the points constituting each cluster can be obtained by calculating the convex hull. Based on the convex polygons, the area occupied by the convex polygons constituting each cluster is calculated. Among the areas occupied by the convex polygons, the proportion of the actual points depends on the distribution of the points in the cluster. As shown in Figure 5a, in the case of thermal bridges, the actual points account for a large proportion of the area of the convex polygon. However, in the case of clusters that are not thermal bridges, the actual points account for only a small proportion of this area, as shown in Figure 5b. Since each pixel has an area of 1 by 1, this study calculated feature \(F_{2}\), which determines the complexity of data boundaries, as the number of pixels relative to the area enclosed by a convex hull. The second feature is calculated as follows:  
\[F_{2}=\ \frac{N_{p}}{A_{c}} \tag{3}\]  
where \(A_{c}\) is the area of the convex polygon, and \(N_{p}\) is the number of points in the cluster.
## 2 Experimental Setups  
The terahertz photoconductive antenna has a strip lines configuration in our experiment, as shown in Figure 1. The photoconductive substrate material is SI-GaAs, which is a direct gap semiconductor with bandgap energy of 1.424 eV. The doping concentration is about \(1.5\times 10^{7}\) cm\({}^{-3}\), and the thickness is 0.6 mm. When there is no laser trigger, the average dark resistivity of SI-GaAs is much higher than \(5\times 10^{7}\)\(\Omega\cdot\)cm, the electron mobility of \(\Gamma\) valley is 6000 \(\sim\) 8000 cm\({}^{2}/\)V\(\cdot\)s, and the electron mobility of satellite valley (L valley) is 920 cm\({}^{2}/\)V\(\cdot\)s. The ohmic-contact electrodes (Au/Ge/Ni alloy) are located on both sides of the substrate surface. The coplanar photoconductive strip lines are 30 \(\upmu\)m wide with a 150 \(\upmu\)m gap. A 900 nm Si\({}_{3}\)N\({}_{4}\) material is plated on the surface of GaAs material, which mainly can be used as an insulating protective layer [16]. The PCA was placed onto an FR-4 glass-fiber woven reinforcing epoxy-laminate substrate with planar transmission lines. The photocurrent is then transmitted to the oscilloscope through the coaxial transmission lines.  
This setup is used to detect the THz time-domain waveform and the photocurrent of the SI-GaAs-based PCA--see Figure 2. The THz-TDS system mainly consists of a femtosecond laser, a time delay system, and a THz emitter source (SI-GaAs PCA), two groups of off-axis parabolic mirrors, a THz detector, a phase-locked amplifier, a pulsed power supply (Zemega Terahertz Crop. HVM-500 USB High-Voltage Modulator) and an oscilloscope (Teledyne LeCroy HD4096) [17]. The laser is Ti:sapphire laser (Mai-Tai, Spectra-Physics, Santa Clara, USA), the laser pulse duration is 70 fs, the center wavelength is 800 nm, the average power is 900 mW, and the repetition rate is 80 MHz. The system was used to generate and detect THz time-domain waveform (the average power of pump laser and probe laser is 220 mW and 30 mW, respectively), the diameter of the pump laser spot is around 225 \(\upmu\)m in the experiment). Once the SI-GaAs PCA is triggered by the femtosecond laser, the photocurrent pulse signal is reduced by a 20 dB attenuator, and finally detected and stored by an oscilloscope with a bandwidth of 1 GHz [18].  
Figure 1: The structure schematic diagram of a SI-GaAs-based PCA in which the gap of the electrodes is 150 \(\upmu\)m. (**a**) Top view of SI-GaAs PCA. (**b**) Side view of SI-GaAs PCA.  
The bias voltage of the photoconductive antenna is 30, 50, 75, 100, 125, 150, 175 and 200 V, respectively, and the corresponding bias electric fields is 2.00, 3.33, 5.00, 6.67, 8.33, 10, 11.67 and 13.33 kV/cm.  
In experiment, the average pump laser power remains constant and the spot is irradiated at the center of the SI-GaAs PCA. For each bias voltage, the conversion efficiency between photocurrent and THz time-domain waveform is measured. At the same time the occupancy for photogenerated carriers for the different energy valleys is obtained.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 6 Methods to Study Biofilms in Diabetic Foot Ulcers
### In Vitro Methods  
#### 6.1.1 2-Dimensional Cell Culture Model  
Fibroblast and keratin-producing cells are obtained from the patient and cultured in a hyperglycemic environment to mimic the high glucose concentration characteristic of hyperglycemic patients. This method studies various markers of diabetic foot ulcers (DFU), such as the glycosylation of collagen fibers in the skin [34]. It is a straightforward approach to evaluate the mechanisms of wound formation and healing, as well as to test different therapies for DFU.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Discussion
#### 3.4.3 Model Complexity Analysis  
In water body extraction from remote sensing images, computational efficiency and model size are critical for practical deployment. To assess the structural complexity of the proposed DMLU-Net, we compare it with six mainstream segmentation models by analyzing their parameter counts (in millions, M), computational costs (in billions of floating-point operations, FLOPs), and reasoning speed (in frames per second, FPS). The comparison results are shown in Figure 8.  
As shown in the results, TransUNet, despite its strong global modeling capability, requires 105.9 M parameters and 168.7 G FLOPs, making it a computation-intensive model unsuitable for resource-constrained platforms (e.g., drones or satellite terminals). In contrast, DMLU-Net achieves a competitive segmentation accuracy, with only 37.1 M parameters and 218.4 G FLOPs, striking a better balance between efficiency and performance among the models of similar precision.  
Notably, while the computational cost of DMLU-Net (218.4 G FLOPs) exceeds that of Swin-Unet (46.4 G) and MU-Net (172.7 G), its parameter count remains efficiently constrained at 37.1 M--significantly lower than that of Transformer-based models such as TransUNet (105.9 M). The reasoning speed of DMLU-Net (68.8 FPS) is not overly sacrificed with the improvement in accuracy, achieving a good balance between high accuracy and computing efficiency. This validates the effectiveness of our designed modules (DMLKA, SSAM, and DySample) in balancing feature representation and computational efficiency.  
In conclusion, if high-precision segmentation is required, then DMLU-Net leads overall in terms of performance and is suitable for scenarios requiring high-quality results. DMLU-Net achieves a trade-off between a lightweight architecture and high performance while maintaining segmentation accuracy. If efficient real-time reasoning is required, then Deeplabv3+ can be chosen. Although it has a large number of parameters, its FPS advantage is obvious. If the computational load and parameter count of SwinUnet are low due to hardware resource limitations, then it can be employed when resources are tight.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 2 Prediction of Tool Wear Based on Simulation
### Constitutive Model of Tool Wear  
There are complex friction relations between the cutter, workpiece and chips. To ensure the accuracy of the simulation results of tool wear, it is necessary to select the appropriate constitutive model of tool wear.  
The constitutive model of tool wear describes the relationship between cutter volume loss rate and cutting temperature, relative sliding velocity, contact pressure and cutting parameters. Archard and Usui models are provided in DEFORM 3D to calculate the tool wear rate in the cutting process. Usui model can not only study the influence of bond wear on the tool and simulate the crater wear form on the rake face of the cutter but also simulate the flank wear of the cutter [16; 17; 18]. Usui model is shown in Equation (5):  
\[\frac{dW}{dt}=a\sigma_{n}v_{s}e^{-b/\tau_{c}} \tag{5}\]  
where, \(dW/dt\) represents the wear volume of the cutter on a unit area in a unit time; \(\sigma_{n}\) represents normal stress; \(v_{s}\) represents the sliding speed of the workpiece material relative to the cutter; \(T_{c}\) represents the absolute temperature of cutter surface; model constants a and b depend on the material and cutting parameters. The simulation set a to \(1\times 10^{-7}\) and b to 850 [19].
###### Abstract
Plant information extracted from remote-sensed imagery primarily relies on interpreting differences and changes in the green foliage and canopy spectral characteristics. Therefore, hyperspatial imagery obtained through UAVs enables the quantification of plant cover, composition, and structure across various spatial scales. There are three types of platforms, including satellite-based remote-sensing, airborne, and, more recently, UAV platforms, to collect remote-sensed information. Compared with remote-sensing and airborne platforms, UAVs are relatively affordable and easier to carry and use [30]. In the past, UAVs have been widely applied in natural resource management, with precision agriculture and rangeland monitoring emerging as the most common applications [31]. Moreover, multispectral remote-sensing data and geographic information systems (GISs) have been leveraged to assess greenness and health condition in both forest and urban ecosystems [32]. European forest research studies have highlighted that the majority of UAV studies have been focused on the estimation of dendrometry parameters, followed by forest health monitoring and disease mapping, tree-species composition classification, post-fire recovery monitoring, and the estimation of post-harvest soil displacement [33]. More recently, the NDVI from UAV waveband data were derived to detect health and disease in vegetation in an urban green space in Bulgaria [34]. Additionally, feasibility studies comparing ground-based and UAV methods of monitoring biomass and primary production in two bioswale cells at an urban stormwater facility have been conducted [35]. Despite the wide-range of applications of UAVs in recent decades, their utilization in GSIs has been surprisingly limited. This study aimed to apply UAVs in the collection of images and to develop an integrated framework for evaluating plant density and health within GSIs. It is crucial to inspect and validate plant density and health for optimizing GSI performance and maintaining their functionality.
###### Abstract  
Blockchains are gaining traction as secure and reliable platforms for data sharing in fields such as banking, supply chain management, food production, energy, the Internet, and medical services. Furthermore, when decentralized, a blockchain can be regarded as an immutable ledger storing data entries. Moreover, this modern technology was designed to disrupt various data-driven industries, including the healthcare industry. While electronic healthcare services have enabled more straightforward and accessible treatment, patient privacy has become vulnerable to external and internal attacks by healthcare personnel. Therefore, we aimed to design a framework to control patient health records that ensures the patient can provide the necessary permissions to those who access his/her health records. This framework will record all activities via blockchain and usage control. Through this framework, we aim to create a user-centric and privacy-aware experience. A literature review and experiments have been performed to select an optimized and placable blockchain operating system. In addition, performance analysis showed that the OS and smart contracts work at an acceptable speed.  
health record; blockchain; Hyperledger Fabric; usage control; privacy +
Footnote †: journal: Medical  
the owner (patient) so he/she can grant permission to the specific health personnel for a specific period of time.  
The blockchain provides a trusted mechanism with the help of a consensus algorithm [3,4]. Some consensus algorithms work on truly anonymous data, such as proof-of-work (PoW). However, the problem with PoW is that it is an order-executing architecture. That means the transaction is generated and then waits for the specific hash to be generated by minors. This process takes plenty of time. On average, it takes five to ten minutes. Due to this problem, we searched for permissioned blockchain systems which efficiently execute the transactions compared to public blockchains. We selected the Hyperledger Fabric blockchain operating system after the analysis phase of our research. Further details of the blockchain terminology, such as decentralization, immutability, and transparency, are explained in Section 3.  
Therefore, we aimed to design a framework for controlling patient health records that enables patients to provide the necessary permission to those who will access their health records. This framework will record all activities via blockchain and usage control (UCON). Through this framework, we aim to create an experience that is user-centric, private, and secure.  
The significant contribution of this new healthcare record framework is to provide a user-centric, privacy-preserving health record system with a blockchain, which also includes efficient solution selection and its criteria, and the complete process discussed in this paper. Any health record access is continuously monitored by the usage control model, which is the novelty in this work. Patients can establish parameters around the time frame in which healthcare records are available to concerned health personnel.
# Local Community Detection Based on Small Cliques
## 4 Experiments
### Scoring  
We compare the detected communities to the ground truth communities generated in the synthetic benchmark graphs and the communities induced by the attribute \"dormitory\" in the Facebook 100 dataset. For simplicity we will call them also \"ground truth\" communities in the following. For the comparison, we match each detected community with a ground truth community. We use two variants of this: one where we compare the detected community to the best-matching ground truth community that contains the seed node and one where we choose the best-matching ground truth community irrespective of the seed. As score we use the standard \(F_{1}\) score. The \(F_{1}\) score is between 0 and 1, where 1 is the best score, it indicates a perfect match of ground truth and detected community.  
The \(F_{1}\)-score and the similar Jaccard-Index are often-used measures in the area of community detection and graph clustering [4; 17; 21; 36]. Both work well when comparing a single community that an algorithm finds to one or several ground truth communities by taking into account nodes correctly and incorrectly identified. Many other comparison measure typically used for comparing detected communities to ground truth communities like NMI assume a whole clustering consisting of many communities covering the whole graph. They are thus not suitable for our evaluation where we explicitly want to compare individual detected communities to (possibly only some) ground truth communities.  
In order to give an idea how good a given \(F_{1}\) score is, we implemented a simple baseline algorithm. The simplest idea would be to return a random set of nodes. However, any connected subgraph containing the seed node could easily outperform this. To get a more realistic, yet basically random community we instead perform a breadth-first search starting at the seed node. As input it takes not only the seed node but also the size \(k\) of a random ground truth community the seed node is part of. It returns the first \(k\) nodes it finds during the breadth-first search. Therefore, the size of the detectedcommunity always perfectly matches the size of one of the ground truth communities the seed node is part of. In the experiments, we denote this algorithm by \"BFS\".
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 5 Theoretical Framework and Methodology
### Theoretical Framework
In contrast, Figure 10c shows a situation where the two distributions (testing and validation sets) are very close to each other; i.e., the jammer is far from the receiver, and its impact cannot be clearly distinguished from the background noise. As a result, the threshold computed with the criteria mentioned above cannot distinguish between anomalous and regular data.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Multi-Agent Modeling Approach
#### 3.1.1 Sensor Agent  
Wireless sensor nodes serve as network nodes with ad-hoc network functions and as communication relays. The ad-hoc network function depends on the routing protocol processed by the computing component, while the communication relay function relies on the communication component that receives and sends radio signals. Additionally, the power supply component is essential for these components to function. Figure 1 illustrates the structure of a wireless sensor node.  
A sensor node typically comprises four main components, namely the sensing component, computing component, communication component, and battery component, as depicted in Figure 1.  
As this paper focuses on the impact of jamming on WSN reliability, it is important to abstract sensor nodes into agents by considering their activities and state transitions in the network layer.  
The primary function of sensors is to collect environmental data, which are then uploaded and concentrated to the base station via the ad hoc network. To achieve the self-organizing network function, routing protocols must be deployed to help sensor nodes select successor nodes closer to the base station. In the context of jamming, routing protocols should also enable sensor nodes to avoid affected areas, select an alternative successor node when the current node is interfered with, and maintain normal data transmission. Section 3.2 details the design of the routing protocol.  
Under strong jamming, the signal-to-noise ratio of sensor nodes decreases, causing communication failure, while weak jamming still allows nodes to act as information relay nodes. In both cases, a sensor node can broadcast information about its jammed state and notify other unaffected nodes to consider other successor nodes, reducing the probability of relay node disconnection from the base station.
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
## 2 Polycyclic Aromatic Hydrocarbons
### Regulatons of PAHs in the EU and US
While no specific limits are set for PAHs in milk and dairy products, the FDA regularly monitors contamination levels and emphasizes minimizing PAHs wherever possible. The FDA constantly monitors their food content to ensure that they do not appear in alarming amounts. The Environmental Protection Agency has selected 16 PAHs that are frequently found in environmental monitoring samples, namely acenaphthene, acenaphthene, anthracene, fluoranthene, fluorene naphthalene, phenanthrene, pyrene, benz[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[ghi]perylene, benzo[a]pyrene, chrysene, dibenzo[a,h]anthracene and indeno[1,2,3-cd]pyrene. These compounds are regulated in drinking water (0.2 ppb) and workplace air (0.2 mg/m\({}^{3}\)) [59]. Other countries, such as Canada, Australia, and China, have also implemented regulations to manage PAHs. For instance, Canada's Environmental Protection Act (CEPA) includes measures to control PAH emissions and their presence in soil and water [33; 58]. While approaches may vary, these nations align with the U.S. focus on reducing environmental PAHs rather than strictly targeting food products [40; 63].
# Design and Performance Analysis of BDS-3 Integrity Concept
## 3 Integrity Risk Probability Distribution
### ModelBottom Event Probability
#### 3.2.2 Bottom Event Probability for Ground Segment  
The failure probabilities of the seven bottom events of the ground segment are estimated as following:  
1. Satellite orbit calculation anomaly probability and satellite clock calculation anomaly probability  
The IFMEA result shows that only one satellite orbit calculation anomaly event occurred since 27 December 2018, which was detected in time by the ground segment and did not affect the satellite SIS integrity. No satellite clock calculation anomaly event occurred during the assessment period.  
Due to the similarities in the causes and mechanisms of these two types of bottom events, we assume that the average probability of each event is 0.5 times per year. Moreover, since the anomaly event can only affect the satellite SIS integrity when the system fails to detect it, we further assume that the missing alarm (MA) rate of the BDS-3 ground segment is 0.01. Thus, for the 24 MEOs and 3 IGSOs of BDS-3 (that is, the BDS-3 nominal constellation defined in ICAO SAPRs), the probabilities of these two anomalies can be determined as following:  
\[\mathrm{P}_{\mathrm{event}_{1,2}}=\frac{0.5}{365\times 24\;\mathrm{h}\times 27} \times 0.01=2.1\times 10^{-8}/\mathrm{h} \tag{12}\]  
where indices 1, 2 refer to the bottom events 1, 2 in the integrity risk tree.  
2. Bottom events 3, 4, 5, and 6 anomaly probability  
The IFMEA result shows that none of these four anomaly events occurred since 27 December 2018. According to the internal system design requirements of BDS-3, the probabilities of these four anomalies are all 1 \(\times\)\(10^{-9}/\mathrm{h}\), that is:  
\[\mathrm{P}_{\mathrm{event}_{3,4,5,6}}=1\times 10^{-9}/\mathrm{h} \tag{13}\]  
where indices 3, 4, 5, 6 refer to the bottom events 3, 4, 5, 6 in the integrity risk tree.  
3. Message upload anomaly  
The IFMEA result shows that only one message upload anomaly event occurred since 27 December 2018, which was detected in time by the ground segment and did not affect the satellite SIS integrity. Therefore, the probability of occurrence of this anomaly can be assumed to be 1 time per year.  
Still assuming that the MA rate of the BDS-3 ground segment is 0.01, the probability of such anomaly can be determined as following:  
\[\mathrm{P}_{\mathrm{events}_{9}}=\frac{1}{365\times 24\;\mathrm{h}\times 27} \times 0.01=4.2\times 10^{-8}/\mathrm{h} \tag{14}\]  
where index 9 refers to the bottom event 9 in Figure 1.