esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 3 Design of Experiment Data  
The data used in this research were retrieved from the experiment reported in [16]. We applied the machine learning algorithms to predict the energy consumption and compared it with the actual experimental results reported by the authors in [16]. The experiment was conducted using a Prusa i3 MK35 3D printer. Electrical energy consumption was assessed for printing 68 different isovolumetric mechanical components with multiple orientations (total of 184 model files); these components were selected from Mechanical Components Benchmark (MCB) [28]. The experimental setup included four Adafruit INA260 sensors to measure the current consumption of the 3D printer, Arduino MKR WiFi 1010 to collect the electric current data from the current sensors, and a Raspberry Pi board to read the serial data coming from the Arduino board.  
Many parameters affect the energy consumption in FDM 3D printing, including printing orientation of the model, stl surface area, number of facets, extrusion speed, extrusion temperature, sliced X', sliced Y', sliced Z', sliced volume, sliced volume including support, total filament, percentage infill (20%), layer thickness, expected print time, and number of layers.  
Extrusion speed and temperature are among the most important parameters affecting FDM printing; they have been used as recommended by the manufacturer [16]. Since extrusion speed, extrusion temperature, percentage infill, and layer thickness are constant parameters, they do not affect the learning phase, so we will not take these as input data.  
The range of input parameters used is shown in Table 2[28].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameters** & **Intervals** \\ \hline Orientation & [0–180] \\ \hline stl surface area & [1533.85–10,850.92] \\ \hline Number of facets & [40–9368] \\ \hline Sliced X & [3.23–153.87] \\ \hline Sliced Y & [8.45–67.27] \\ \hline Sliced Z & [3.23–153.87] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input parameters interval values.  
The experimental data are used in this research to train machine learning algorithms to predict energy consumption in the FDM process.
## 4 Experiments
### Results
#### 4.2.1 Evaluation of Optimization Algorithms

Table 2: Performance of baseline models trained with different optimizers on CIFAR-10 dataset. Figure 3: Loss learning curves for all optimizers on baseline models. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Model 1**} & \multicolumn{8}{c}{**Model 2**} & \multicolumn{8}{c}{**Model 3**} \\ \hline \multirow{2}{*}{**Optimizer**} & \multicolumn{2}{c}{**Loss**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Loss**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Loss**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline SGD & \(1.25\times 10^{-1}\) & **0.353** & 95.43 & 89.64 & \(1.34\times 10^{-5}\) & 0.833 & 100.00 & 92.43 & \(1.13\times 10^{-5}\) & 0.954 & 100.00 & 91.88 \\ Momentum & \(1.36\times 10^{-6}\) & 0. 3. RMSProp optimization algorithm, in all six cases, has considerably larger validation loss than other optimizers that consistently keeps growing. Interestingly, despite great discrepancy between RMSProp and others optimizers losses, its validation, and finally test set accuracy remains reasonable well and comparable with others. 4. In terms of test set accuracy, the ranking of classical optimizers stays consistent across all six models; Nesterov ranked as best, followed by Momentum, and SGD at last. Ranking of adaptive optimizers places Adagrad optimizer on last place, closely followed by RMSProp optimizer. 5. Most of the optimizers in 350 epochs succeeded in reaching the \(\approx\)0 loss and \(\approx\)100% accuracy on the training data in all six models. Exceptions can be found in SGD and RMSProp optimizers, with the overall worst performance obtained by SGD with 95.43% training accuracy. 6. In the early stages, especially on _Model 2_ and _Model 3_ (_Fashion-MNIST_), all optimizers beside SGD on _Model 1_ show signs of overfitting. A large gap between accuracies on the training and new data is noticeable during the whole training process. In the rest of the article, we examine how incorporating different regularization methods and Batch Normalization technique affect generalization performance of a given model. For further investigation, we used one optimizer and model architecture per each dataset. Namely, on CIFAR-10 data, we used Nesterov optimizer with first model architecture, Adam with the second and again Nesterov with the third one and refer to them as _baseline_ model architectures. Analogously, for further research on Fashion-MNIST data, as _baseline_ model architectures we used _Model 1_ and _Model 2_ with Adam optimizer and _Model 3_ architecture with AdaMax optimization algorithm.
# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 3 Methodology
### Improvement of the Model's Generalization Ability
#### 3.3.2 Improvement Based on Mixup  
In the field of target detection, the data enhancement technique will frequently be used. When the dataset does not contain particularly sufficient data, the deep model is unable to learn good features. In addition, in most datasets, various types of samples are not balanced in number, which will result in poor ability of the model in predicting samples with small numbers. The above problem can be alleviated through data enhancement, which can efficiently improve the network's performance and generalization ability. Data enhancement refers to utilizing certain image conversion methods (such as random cropping, random translation, angle rotation, mirror image flipping, etc.) based on existing datasets to generate some \"new data\" for the neural network.  
[PERSON] et al. proposed the mixup method, which can be used to effectively improve the performance and generalization ability of the object classification model [20]. The mixup method is a data enhancement method irrelevant to data, which is also simple. It is used to generate new samples with the method shown in Formulas (9) and (10).  
\[x =\lambda x_{i}+(1-\lambda)x_{j} \tag{9}\] \[y =\lambda y_{i}+(1-\lambda)y_{j} \tag{10}\]  
The original author applied mixup in the classification task, in which \(x_{i}\) and \(x_{j}\) are two random image samples, while \(y_{i}\) and \(y_{j}\) are their corresponding class labels. \(\lambda\in\) (0,1), and \(\lambda\) follow a Beta distribution: \(\lambda\in\) Beta (\(\alpha\),\(\alpha\)), \(\alpha\in\) (0,\(\infty\)). The mixup method linearly integrates existing samples and generates new samples, which makes the model more stable.  
In this paper, the data enhancement technique of mixup is introduced into vehicle target detection. In the dataset used in this paper, all images have \(540\times 960\) pixels. In image integration, similar to the above Formulas (9) and (10), a new sample \(x\) can be obtained through superposition of the corresponding pixels of randomly selected images \(x_{i}\) and \(x_{j}\) according to linear coefficients \(\lambda\) and \((1-\lambda)\). Tags \(y_{i}\) and \(y_{j}\) of images \(x_{i}\) and \(x_{j}\) are kept, and tag \(y\) is obtained corresponding to a new sample \(x\). A schematic diagram of using mixup to realize data enhancement in the vehicle target detection problem is shown in Figure 6.  
Figure 5: Schematic diagram of Label Smoothing Regularization.
# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
## 3 Reverse Positioning of Panoramic Imaging Probe  
The eccentricity of panoramic imaging probes within the borehole is the main cause of brightness variation issues in borehole images. Conversely, the brightness characteristics of borehole images indirectly reflect the positional relationship of panoramic imaging probes within the borehole. Therefore, this study focused on the brightness characteristics of borehole images, studying the grayscale variation patterns, establishing a borehole image grayscale feature model, and proposing a method for three-dimensional positioning analysis of panoramic imaging probes.
# Eulerian-Eularian Model for Agglomeration Behavior of Combusted Iron Particles
###### Abstract  
Direct reduction of iron (DRI) technology in fluidized beds has been identified as a promising approach due to its environmental benefits over other methods. Nevertheless, the process of iron particle sintering in the DRI approach poses a significant obstacle to its advancement. The present work investigated the phenomenon of agglomeration in fine iron particles across various temperatures and with multiple sintering force models of different intensities of solid bridge force. The study utilized a simple but comprehensive and cost-effective CFD model developed using the Eularian-Eularian two-fluid model. The model was explicitly incorporated with user-defined subroutines for the solid phase, while the gas phase was modeled with AVL Fire advance simulation software. The solid bridge force between solid particles was modeled as the inter-particle cohesive force. The model was validated with the experimental results and results from another CFD-DEM model for the same experiment. High temperatures with increased sintering forces were observed to have the most impact on the iron particle agglomeration, while the gas's superficial velocity had a minimal effect on it. The predictions of this model closely align with the CFD-DEM model results, providing sufficient reliability to implement this model on a large scale.  
particle agglomeration; solid bridge force; fluidized bed; two-fluid method; DRI 2024 14 7829 10.3390/app14177829
## 3 Results
### Biocompatibility
#### 3.2.3 Cell Proliferation Assay  
Figure 7 shows the course of proliferation during the ten days of the experiment. In the cell culture plate with the remaining cells (C + R), hardly any proliferation can be seen in all three cell types at any time. Consequently, all cells adhere to the Thermanox membrane (C+). The number of cells remaining in the cell culture plate with the scaffolds (Scaff + R) increases over the experimental period of 10 days. The proliferation rates of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**MG-63 Cells**} \\ \hline
**Analysis Time [d]** & **Total [Cells/mm\({}^{2}\)]** & **Dead [Cells/mm\({}^{2}\)]** & **Percentage Dead Cells [\%]** \\ \hline
3 & \(263\pm 219\) & \(44\pm 29\) & 17 \\
7 & \(881\pm 118\) & \(63\pm 31\) & 7 \\
10 & \(1518\pm 258\) & \(90\pm 35\) & 6 \\ \hline \multicolumn{4}{c}{**MLO-Y4 Cells**} \\ \hline
3 & \(354\pm 169\) & \(37\pm 10\) & 10 \\
7 & \(1174\pm 754\) & \(192\pm 146\) & 16 \\
10 & \(984\pm 546\) & \(223\pm 189\) & 23 \\ \hline \multicolumn{4}{c}{**MSC**} \\ \hline
3 & \(398\pm 140\) & \(27\pm 6\) & 7 \\
7 & \(744\pm 89\) & \(58\pm 18\) & 8 \\
10 & \(630\pm 80\) & \(61\pm 7\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of all cells or dead cells, and percentage of dead cells for three different cell lines (MG-63, MLO-Y4, MSC).  
Figure 6: Cytotoxicity of the collagen-coated PCL scaffolds at defined times for the 3 different cell lines; neg. control = cells only; pos. control = Triton X.  
cells on the scaffolds (Scaff) differ. In MG-63 (Figure 7a), proliferation increases steadily over the days, whereas in MLO-Y4 (Figure 7b) it hardly increases from day 7 onwards and even decreases in MSC (Figure 7c). With regard to proliferation on the scaffolds, it can be summarized that proliferation increases for all three cell types in both wells (Scaff and Scaff + R) during the course of the experiment. However, a higher number of cells seem to migrate through the scaffolds in MG-63 and MLO-Y4 than in MSC. Cell proliferation on the scaffolds is highest in the MSC, followed by the MLO-Y4 and the MG-63.
## 3 Results and Discussion
### Proposed Structure of Ionic Hybrid Systems  
A schematic representation of the proposed network structure of ionic hybrid systems is given in Figure 5. The rubber (black) and the PF network (green) are generated simultaneously during vulcanization. This results in the formation of an interpenetrating network, where the two networks are interlaced with each other [10]. However, the addition of ionic coagents contributes to a multiple network structure governed by covalent and ionic cross-links. It has been reported that ZD(M)A is introduced in the rubber matrix in several ways, namely as (i) homo-polymer, (ii) grafted-polymer, or (iii) residual ZD(M)A monomer [40]. However, highly saturated rubbers have a low amount of grafted poly-ZD(M)A due to the low number of residual double bonds [41; 42]. The applied EPDM matrix is highly saturated. Therefore, a high amount of poly-ZD(M)A is expected, which contributes to one side as reinforcing particles and introduces ionic cross-links. These ionic bonds are expected to contribute to the superior mechanical performance of the ionic hybrid systems [43]. Though this study focused on an EPDM grade development for industrial V- belts, this in situ filler loading and supportive ionic coagents seemingly have many other applications, especially in many elastomeric components, which require sophisticated material properties, for example, as sealing applications in high-pressure gas conditions. With these developments, materials can be developed, having stiffer material combinations with higher ultimate strength and elongation at break to obtain tougher materials, which are very useful for those specific applications to avoid permeation and rapid gas decompression [44].  
Figure 4: SEM images at 2000\(\times\) magnification of fracture surfaces of 24 h postcured hybrid reinforced compounds.  
Figure 5: The proposed network structure of an ionic hybrid filler system.
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
###### Abstract  
In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this paper introduces HSM4 SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4 SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4 SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4 SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted comparing HSM4 SSL with a benchmark tool for cryptographic operations. The results indicate that HSM4 SSL achieved 33% higher requests per second (RPS) compared to OpenSSL, along with a 13% lower latency rate. Additionally, HSM4 SSL efficiently utilizes CPU and network resources, outperforming OpenSSL in various aspects. These findings highlight the effectiveness and reliability of HSM4 SSL in providing secure communication within intra-domain environments, thus addressing the pressing need for enhanced security mechanisms.  
Keywords:intra-domain; secure socket layer; transport layer security; public-key certificates; hardware security modules +
Footnote †: This paper is an extended version of our paper published in the International Symposium on Networks, Computers and Communications (ISNCC), Still Computers Networking is Less Secure Than It should be, Causes and Solution, Doha, Qatar, 23-26 October 2023.
## 1 Introduction
Through pollution risk assessment and source apportionment studies, accurate and effective environmental management strategies can be formulated, and the potential sources of HMs can be elucidated. Serving as China's second-longest river, the Yellow River (YR) is a vital water source in northern China, supporting around 12% of the national population and irrigating approximately 15% of the agricultural land [26; 27]. Since the 1880s, it has been a primary water supply for industries and agriculture along its banks [12]. However, due to the dense anthropogenic activities along the riverbanks that have discharged a large amount of pollutants, the YR has been severely polluted at times, resulting in high concentrations of HMs in certain areas [26; 28; 29]. For instance, the concentrations of heavy metals in the Yellow River Basin are significantly higher in the upper and middle reaches than in the lower reaches [30]. Despite extensive research on heavy metal pollution in the sediments of the Yellow River Basin (YRB), the majority of existing studies have predominantly focused on analyzing heavy metals in sediments from individual rivers, regions, or lakes, with limited assessments of heavy metal pollution at the basin-wide scale, particularly in the areas of pollution source identification and integrated risk assessment of multiple heavy metals. This study took the YRB as the research object, collected 44 sediment samples, and measured the concentrations of chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb) in the sediments. The objectives of this study are to (1) investigate the characteristics of HM concentrations in sediments of the YRB, (2) evaluate the pollution status and HM risks of HMs in the sediments, and (3) elucidate the potential sources of HMs in the sediments. The findings of this study can provide a theoretical basis and data support for the ecological and environmental management of regional socio-economic sustainable development.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 9 Lessons Learned
### Privacy Considerations  
As we mentioned in the Introduction (Section 1), the Regulation 1501/2015 [7] defines only data minimization (minimal set of personal user data claims that each eID scheme shall release to online service) as a privacy requirement within the eIDAS framework. This would be the main reason why only two eID schemes (Gov.UK Verify, and German eID) consider additional privacy considerations within their solution's design even though there exist proposals for some authentication standards (e.g., SAML profiles [49]) to provide a privacy-preserving solution. The Gov.UK Verify eID scheme uses an intermediary HUB to prevent the eID scheme provider from knowing which application the user is using and hence exploiting this information for commercial gain, and the same goes for the application side. In the case of Germany, the key privacy enabler is supporting pseudonym transactions, which issues a unique user identifier for every application, thereby avoiding colluding applications to breach privacy by linking the transaction histories of any given users.  
In the following sections, we first elaborate on the implementation choices that should be considered during the development of all eID schemes to preserve privacy. After that, we provide the specific considerations for the eID schemes that are based on the OIDC.
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### Throughput and Delay  
The throughput and end-to-end delay are shown in Figure 6 and Figure 7, respectively. In Figure 6, since _FIFO_ does not distinguish _Event_ and _Non-Event_ packets, the capacities of \"Event FIFO\" and \"Non-Event FIFO\" are roughly proportional to the traffic generation rate. However, when the traffic generation rate exceeds the bound, which degrades the wireless link quality by introducing more collision and larger contention window in MAC protocol, the overall capacity of _FIFO_ decreases. _PCC_ provides _Event_ packets higher priority. Therefore, the throughput of \"Event PCC\" keeps increasing until it reaches the whole system capacity. On the other hand, more _Non-Event_ packets are dropped during congestion, and therefore the capacity of \"Non-Event PCC\" decreases with the increase of the basic packet generation rate. As we explained before, with the constraint of the system capacity, sinks are more interested in the _Event_ packets, so the results are consistent with our design objective.  
In Figure 7, it is obvious that the end-to-end delay of _Event_ or _Non-Event_ packets in _FIFO_ almost remains the same. Since _PCC_ preferentially accepts _Event_ packets, _Event_ packets experience longer queue delay on average. However, during congestion, only a few _Non-Event_ packets reach the sink (most are dropped in the intermediate nodes) and they experience low queueing delay. Therefore, the average delay of _Non-Event_ packets in _PCC_ is comparably low.
## 5 Results
### Dynamic Position Estimation  
In the final experiment, the receiver unit is guided along two different rectangular trajectories similar to the one in [4], albeit smaller. The first trajectory lies completely within the convex hull spanned by the beacons. The second trajectory lies outside of the convex hull. The results for the two trajectories are shown in Figures 18 and 19. For each of the trajectories, an EKF and a PF are used to fuse the RSS values for position estimation. The ground truths and the estimated positions of the receiver are shown in Figure 18a,b for the inner and in Figure 19a,b for the outer rectangle. The RSS measurements at the four distinct frequencies are illustrated in Figures 18c and 19c. As in the previous subsection, the results are less accurate than the ones reported in [4]. However, for most \(\mu\)AUV applications in confined test tanks, the results are sufficient, especially given the small size and cost of the system.  
The EKF and PF approaches show similar results for both trajectories. In general, PFs tend to outperform EKFs if nonlinearities are strong and noise variance is large. For these cases, the Gaussian assumption on noise of the EKF framework is not accurate enough anymore. The PF allows including uniform priors, which can be an advantage in certain applications. It is worth mentioning that coupling with other sensors such as IMUs can be more straight forward for EKF's framework than for PF's.  
The estimated positions are compared against the ground truth, and the RSS measurements are assigned to corresponding gantry positions. The RSS values in Figures 18c and 19c exhibit discontinuities at Time Steps 100 and 300, respectively. At this point, the gantry did not provide ground truth data, and the RSS measurements were rejected for several consecutive time steps. Nevertheless, the system is able to recover the position as soon as the RSS measurements become available again, which demonstrates the robustness of the system.  
Figure 17: Static receiver self-localization at ten different positions. A PF with 1000 particles merges the measurements.  
Figure 18: Self-localization results along a rectangular trajectory within the convex hull of the beacons. The receiver traverses the rectangle three times.  
Figure 19: Self-localization results along a rectangular trajectory outside the convex hull of the beacons. The receiver traverses the rectangle three times.