esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Analysis of TTEG Performance under Varying External Loads
An analysis of Figure 11a-h reveals that the Tubular thermoelectric generators (TTEGs) consistently provide a more significant amount of power when the cooling water input temperature is set at 80 \({}^{\circ}\)C, regardless of the exhaust temperature variations. The improved performance of this output is mainly due to the substantial temperature difference between the high and low ends of the TTEGs. Moreover, the TTEGs exhibit a significant rise in power output when the external resistance increases. The power output of the TTEGs reaches its maximum when the external resistance matches the internal resistance. After reaching this point, any further rise in external load leads to a progressive decrease in power production, which follows a linear pattern. Figure 11: _Cont._  
Additionally, an examination of Figure 12a-h reveals a similar linear relationship between the thermal efficiency of the TTEGs and the external load, mirroring the trend observed in the power output versus the external load graph. Furthermore, it is noted that, when the temperature of the engine exhaust rises, both the highest possible efficiency of thermoelectric conversion and the external load at which the TTEGs generate the most power increase. Furthermore, when the cooling water temperature is lower at a consistent exhaust temperature, there is a higher temperature difference across the thermocouple, which leads to an enhanced thermoelectric conversion efficiency in the TTEGs. Figure 11: These diagrams (**a**–**h**) depict the relationship between the external load and output power of thermoelectric tubular modules under various temperature conditions. Figure 12: _Cont._
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 4 Methodology
### Static CFG Construction  
The main function of the component is to construct the iCFG for target program using static analyses. First, we construct CG and the function's CFGs. The CG of a program consists of the nodes representing functions and edges representing function calls. For each function, we construct a CFG in which nodes represent basic blocks and edges represent jumps among basic blocks. Some static analysis tools can provide the function to obtain CG and CFGs, such as LLVM's built-in APIs for source code and IDA Pro for binary code.  
Based on the CG and CFG, we can construct iCFG by connecting all call-sites with the first basic block of the called functions. Figure 3 shows an example of the static iCFG construction. The gray basic blocks represent the basic blocks ending with call instructions. For call-site \(a\), we can resolve the called function \(f_{3}\). Then, the edge from call-site to the first basic block of called function, \(a\to b\), is added to connect the CFGs of \(f_{1}\) and \(f_{2}\). Similarly, we can connect the CFGs of all functions according to the call relations to get the iCFG. It should be noted that the indirect jump edges are not included in the constructed iCFG because static tools cannot resolve them. In the following steps, we adopt dynamic analysis to resolve indirect jumps and augment the iCFG.
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 6 Conclusions  
This study proposed a condition monitoring method for classifying bearing defects by constituting the damage-sensitive feature vector using EEMD in conjunction with optimized weights of the vector by a PSO algorithm. The bearing defect classifying process included both numerical simulation and validation with experimental data. Here, we suggested an optimization technique that is key to enhancing damage sensitivity. The PSO determines the best-fit parameter vectors from IMFs of EEMD, so that inner-race surface defect and rolling element defect are well separated through the dimensional reduction method of PCA and Isomap. All the bearing data were divided by training and the testing sets, to perform cross-validation of the optimized feature vector weights. For the case of experimental data collected from a real bearing system, a small size dent-type defect (D15) on the inner-race bearing showed little difference in feature vectors compared to the healthy bearing condition. However, the study found that it was possible to enhance the separation of D15 cases from  
Figure 16: Isomap representation of testing characteristic vectors from a bearing testbed: conditions of Healthy (), D15 (), D30 (), and D45 (), (a) before, and (b) after PSO optimization.  
Figure 15: Isomap representation of the training characteristic vectors from a bearing testbed: conditions of Healthy (), D15 (), D30 (), and D45 (), (a) before, and (b) after PSO optimization.  
the healthy condition group by optimizing the parameter weightings through PSO. Both simulation and experimental results revealed that the performance of the bearing condition classification can be improved through proper tuning of the defect-sensitive feature-vectors. Future work will include adaptation of advanced classification methods and intelligent prediction platform through modular augmented visual representation of bearing data.  
This work was supported by a research program (2015R1D1A1A01059701) of the Basic Science Research Program, through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology.  
[PERSON] provided the idea of algorithms and carried out data analysis. [PERSON] contributed to the setup of data acquisition and experiments. [PERSON] supervised the work and provided major direction of the research.  
The authors declare no conflict of interest.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Improved Spatial Pyramid Matching (SPM) Model  
The traditional SPM (Spatial Pyramid Matching) model [17] has developed from the BOF (Bag of Features) method [16]. To overcome the inherent demerits of BOF, SPM introduces the spatial location information between local feature points by calculating the distribution of image feature points at a different resolution [17], as shown in Figure 4. That is, the image is divided into progressively refiner spatial sub-regions and then local feature histograms are computed from each region. The diagram of SPM is shown in Figure 5.  
Although SPM exhibits excellent performance in many image recognition and classification tasks, it only extracts a single feature of an image leading to the obtained feature information is insufficient to represent the rail corrugation image adequately. Moreover, the vector quantization (VQ) encoding method used in the SPM has a large reconstruction error and will result in low image classification accuracy. Therefore, taking the characteristics of rail corrugation image into account, an improved  
Figure 4: Sketch map of SPM model.  
Figure 5: The general structure diagram of Spatial Pyramid Matching (SPM).  
SPM model based on integrated multi-features and locality-constrained linear coding (IMFLLC) for rail corrugation identification is proposed, which is reviewed briefly as follows.
## 3 RHRM Framework
### Phase 3: Recommendation Generator  
To evaluate the performance of the proposed recommendation framework, we predict preference ratings by applying the UBCF, SVD, and NCF models, which are typically used in personalized recommendation services-related studies.  
The first is the UBCF model. UBCF approach is the standard approach that is based on neighborhood models in recommender systems. The most common UBCF measures similarity between users, where \(sim(u,v)\) represents user \(u\) and user \(v\) similarity [58; 59]. The goal of this technique is to predict the user \(u\) preference rating \(\hat{r}_{ui}\) for item \(i\). Using the similarity measure, we identify the items rated by user \(u\), most similar to \(i\). The predicted rating is taken as a weighted sum of the ratings for neighborhood users, defined as follows:  
\[\hat{r}_{ui}=\overline{r}_{u}+\frac{\sum\limits_{v\in N_{i}^{u}(u)}sim(u,v) \cdot(r_{ui}-\overline{r}_{v})}{\sum\limits_{v\in N_{i}^{u}(u)}sim(u,v)} \tag{10}\]  
The second is the SVD model. The latent factor approach has gained its popularity due to its high accuracy and scalability. This study focuses on methods that SVD of the user-item interaction matrix induces. The most common approach to estimating interaction components is the matrix factorization framework [1; 12]. A common approach widely used in research relates each latent factor vector of a user to a latent factor vector for the item. Typically, this approach is applied to explicit feedback datasets while addressing overfitting issues through a regularized model. The SVD model is defined as follows:  
\[\min_{U,V}\lVert Y-M\odot(UV)\rVert_{F}^{2}+\lambda(\lVert U\rVert_{F}^{2}+ \lVert V\rVert_{F}^{2}), \tag{11}\]  
where \(U\) and \(V\) indicates the number of latent factor users and items, respectively, and \(\lambda\) is used for regularizing the model. \(Y\) is the available ratings set, and \(M\) is the binary mask.  
The third is the NCF model. The traditional latent factor model utilized a simple vector dot item to estimate the relationship latent vector. Therefore, such an approach cannot produce excellent results. To solve the latent factor technique's limitations, the NCF model captures the interaction between the user's latent vector and the item's latent vector through a multi-layer perceptron [60; 61]. The user's latent vector and the item's latent vector are inputs to multi-layer perceptron to predict user preferences. The output layer is used to predict user preference, and the model performs learning by minimizing the loss between the prediction and actual ratings. The NCF predictive model is defined as follows:  
\[\hat{r}_{ui}=f(U^{T}\cdot s_{u}^{user},V^{T}\cdot s_{i}^{item}\lvert U,V,\theta \rangle\,, \tag{12}\]  
where \(s_{u}^{user}\) and \(s_{i}^{item}\) denote that the input layer consists of two feature vectors. \(U\) and \(V\) denote the latent factors for the user and item, respectively, and \(\theta\) denotes the model's parameter.
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
Therefore, we propose a binary PUF construction based on stochastically assembled CNTs produced by the printing processes as depicted in Figure 1. In combination with the subsequently deposited regular arrays of source (\(S\)) and drain (\(D\)) electrode pairs, separated by a small electrode gap, a certain proportion of those will be electrically connected by at least one CNT. Figure 1: Conducting cells contain CNTs that bridge the source and the drain electrodes, resulting in a high source–drain conductance when applying a high negative or positive gate voltage such as \(\pm 2.5\,\mathrm{V}\). In contrast, non-conducting cells exhibit very low source–drain conductance, independent of the applied gate voltage. This figure was adapted and modified from our previous work [19], which this work extends. The conducting cells in Figure 1, specifically Cells (3) and (4), are treated as logical 1, whereas the non-conductive cells (Cells (1) and (2)) are treated as logical 0. Since those distinct states are clearly distinguishable in terms of electrical conductance, the PUF architecture is expected to be highly robust in contrast to many previously reported silicon PUFs, where errors have to be compensated by computationally intensive error-correction algorithms. Based on our construction, it is also expected that a high degree of robustness can be maintained even under varying environmental conditions, such as varying the ambient temperature. At the same time, several well-established PUF types suffer from insufficient temperature robustness, for example Latch or D-FlipFlop PUFs [20], along with DRAM retention [21] or ring-oscillator PUFs [22], limiting their field of application. Furthermore, in contrast to many other CNT-based PUFs, a passivation layer serves as an additional protection barrier, allowing for deployment in harsh environments. Consequently, this allows a robust key generation, for example in the automotive or industrial sector. Furthermore, a PUF implementation based on CNTs opens the door for various types of further hybrid nano-electronic devices, with additional cost-efficient intrinsic and highly robust CNT-based security anchors.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
###### Abstract  
To implement minimum power consumption of the link, cluster heads adopt the multi-hop manner for inter-cluster communication so as to forward the aggregation data to the relay nodes. This paper proposes a collaborative data aggregation in emerging sensor networks using a bio-level Voronoi diagram, which is an energy-efficient data aggregation protocol that integrates topology control, Multiple Access Control (MAC) and routing. The sensor nodes situated in the lower level of the diagram are responsible for listening and gathering data, and should be organized by optimal clustering node. In the inter-cluster communication stage, a particle swarm optimization algorithm is addressed to seek optimal transmission path which could simultaneously achieve the minimization of the maximum next hop distance between two nodes in the routing path and the minimization of the maximum hop count, so the minimization of whole network energy consumption is realized. The results of theoretical analysis and simulation results show that energy efficiency and synchronization accuracy of the proposed algorithm can be much better than with traditional routing protocols, and the energy consumption of nodes in the whole network can be more balanced.  
emerging sensor networks; collaborative data aggregation; Voronoi diagram; multi-hop wireless network; time synchronization +
Footnote †: journal: Sensors
## 1 Introduction
### Background  
Real-time monitoring through Digital Twin systems is crucial for tackling operational inefficiencies, enabling predictive maintenance, and dynamically optimizing manufacturing processes [2]. This reinforces the necessity of advanced monitoring solutions in modern production environments [3].  
Our study explores how Digital Twin (DT) technology can be applied to CNC machines, with specific emphasis on printing industrial machines, to create a virtual reproduction of physical production processes. This enables real-time monitoring, data synchronization, and informed decision-making. These applications significantly enhance operational efficiency [4], reduce downtime, and improve overall equipment performance by providing continuous insights into the status and performance of machines and processes. By establishing a virtual representation of equipment and production workflows, the system allows real-time tracking of key parameters, analysis of data, and the identification of potential issues or inefficiencies [2] before they affect production.  
The primary objective of developing production monitoring applications using Digital Twin concepts is to improve the efficiency, productivity, and responsiveness of manufacturing environments. Integrating SQL databases and.NET interfaces, these systems allow for seamless synchronization of data, averaging 1 s for each of 150 resources running on a Microsoft Windows Server 2022. This ensures that production managers can make data-driven decisions and reduce inefficiencies by optimizing machine utilization and maintenance processes. Furthermore, this approach supports preventive maintenance strategies that minimize equipment downtime [5], ensuring smoother operations and significant cost reductions [6].
## 3 Use-Case-Based Categorisation of Technologies  
Aboveground wireless communication technologies are often installed as general infrastructure that can be used by anyone, sometimes connected to provider-based fees. This infrastructure not only exists as public infrastructure but also as local (campus) networks at companies, sites or institutions. This infrastructure is often set up as a multi-layer communication, especially in urban zones:  
* LTE to 5G from voice transmission towards high-bandwidth, real-time transmission capabilities
* Public Wi-Fi as a free service, even city-wide
* LPWAN for low-power devices and low data rate applications (e.g., [24; 25; 26])  
These listed categories provide connectivity for different use cases and/or devices and vary in their properties. There are various ways to structure wireless communication technologies by properties or application cases. We consider a mapping onto the data rate and range space, which can be seen in Figure 4, as introduced in [27]. Wireless Technologies appear in clusters \"short range\" (short range & low data rate), \"cellular\" (medium range & high data rate) and \"long range\" (long range & low data rate).
# Quantitative Optical Coherence Elastography for Robust Stiffness Assessment
## 5 Results  
### Measurement Capabilities of qOCE  
We first validated the force sensing function of the qOCE system. We exerted force through the qOCE probe to the sensing tip of a commercial force gauge. Using Doppler phase shift (\(\Delta\Phi_{\text{FP}}\)) obtained from the OCT signal \(I_{\text{FP}}\) and the force reading from the force gauge, we extracted the coefficient \(\alpha\) that converts a Doppler phase shift to a force value: \(\text{F}=\alpha\Delta\Phi_{\text{FP}}\). With the coefficient \(\alpha\), we exerted force through the qOCE probe in a loading (increasing force) and an unloading (decreasing force) process. The resultant force readings obtained from OCT data are shown in Figure 3, suggesting that the instrument allowed accurate force quantification in both the loading and unloading process.  
To demonstrate the capability of our qOCE system in tracking depth resolved displacement (\(d(z)\)), we prepared a thin (\(T\) = 1 mm) elastic phantom. We placed the phantom on a flat, rigid surface, and compressed the phantom using the qOCE probe that was attached to a high precision linear motor to perform axial translation. The magnitude OCT signal obtained from the phantom is shown in Figure 4 (black curve with vertical axis on the right). The signal peak (green arrow) corresponding to the probe-sample interface can be easily identified. Through Doppler analysis, we also obtained depth resolved sample displacement (blue and red curves with vertical axis on the left). The blue curve was obtained when we translated the qOCE probe by 0.1 mm (\(h\) = 0.1 mm), and the red curve was obtained when we translated the qOCE probe by 0.2 mm (\(h\) = 0.2 mm). As shown in Figure 4, the displacement is approximately 0 at the probe-sample interface. Although the qOCE probe was translated axially by the linear motor, the probe-sample interface corresponded to a fixed optical path length. Therefore, the displacement extracted directly from the Doppler phase shift of the OCT signal started from 0 at the surface of the sample. Figure 4 also shows that the displacement (\(d(z)\)) increases gradually from 0 to \(h\) as \(z\) increases from 0 to \(T\). It is worth mentioning that the magnitude of OCT  
Figure 2: Extraction of elastic modulus using qOCE data.  
signal and sample displacement were non-zero beyond the thickness of the sample (\(T\) = 1 mm) because photons experienced multiple scattering events. The ghost signal due to multiple scattering was not used in stiffness assessment. Although it is challenging to remove signals due to multiple scattering through digital filtering, multiple scattering artifacts do not compromise the signal quality in qOCE measurement when the thickness of the sample is sufficiently large.
## 3 Materials and Methods  
### Obsidian Geological Samples  
In the present work, 33 geological samples from the five main sources in the Mediterranean basin were considered, as indicated in Figure 1. Most of the samples are from the Italian islands, namely 10 from Pantelleria (PNT1-PNT10), 4 from Palmarola (PA1-PA4), 4 from Sardinia-Monte Arci (MN1-MN4), and 12 from Lipari (LP1-LP12). Four more samples are from the Aegean Sea, namely from Milos Island in Greece (PM1-PM4). Ongoing studies are enlarging this data set, which will later be used as a reference for the classification of archaeological samples. In fact, from the archaeological point of view, the Mediterranean islands of Lipari, Melos, Palmarola, Pantelleria, and Sardinia have always been known as obsidian sources, and the raw material from these different sources can be clearly distinguished as they show different chemical-physical characteristics [12]. For instance, Monte Arci obsidian is grayish and shiny and very rich in microlites and phenocryst, while Pantelleria samples are poor in microlites, gray-greenish in color and relatively transparent. Both Lipari and Palmarola have a vitreous aspect, the former black and shiny and the latter black and semi-opaque [4].  
The samples considered in the present work have been supplied during the last decades to the Laboratory for Fission Track Dating (FTD) of the University of Milan and underwent FTD age determination. Indeed, a possible alternative to the chemical characterization for discriminating obsidian sources and provenance is the fission track dating method, a radiometric technique [43]. In fact, the comparison of fission track parameters, such as age and track densities, has already been used as a different tool for correlating obsidian artifacts with their potential natural sources. This method was applied by various research groups in the field of fission track dating considering various regions [44; 45; 46], and outcomes can be matched to those obtained via chemical composition characterization, especially when the chemical fingerprint does not fully discriminate different sources.  
Please note that for this first study, we did not consider further information about sample sub-sources as it is not in the aim of the present work and as the number of samples considered is relatively small. It is worth also noting that materials from geological sources should not require a non-invasive protocol, but on the other hand, the final scope of this characterization is the future classification of archaeological samples, such as splinters and blades, clearly more prone to a non-destructive approach.