esa-sceva/satcom-chunk-collection huggingface dataset:
# A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time
###### Abstract  
Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmo city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.  
Keywords:railway traffic; disturbance management; real-time re-scheduling; job-shop scheduling; optimization; alternative graph +
Footnote †: journal:
## 4 Validation
### Validation Results
#### 4.3.6 Validation Summary  
The above validation indicates that the proposed simulation approach works in principle. Regarding the number of ship arrivals per port, the simulation agrees well with the data. Regarding the number of instances of IB assistance and IB waiting times, the overall standard deviations between the averages of 35 independent simulation runs and the data are 13% and 18%, respectively. For each of the simulated metrics, the data fall within an approximated 95% confidence interval.  
On average, the simulation appears to somewhat overestimate both the number of instances of assistance (+11%) and the IB waiting times (+7%). The overestimated number of assistances may indicate that, in the real world, ships are occasionally assisted over larger distances than the fixed IB operating areas assumed in the simulation. The overestimated mean IB waiting time, in turn, may in part be explained by the large individual peak values observed in Figure 7c. In the real world, such peak values can perhaps be avoided by factors not considered in the present simulation model, such as convoy operations including more than two ships and various active measures by the masters of the involved ships.  
It should also be noted that the available maritime data, or the interpretation thereof, may include some errors. For instance, in individual cases, it is reported that a ship has received multiple instances of IB assistance during its inward voyage, but none during its outward voyage even though both voyages would have occurred in similar ice conditions. In addition, occasionally it appears like the data, instead of including all instances of IB assistance received by a ship on its way to or from a port, only include the last or the first instance of IB assistance before port arrival or after departure. Further studies are needed to identify and address such possible inconsistencies.
## Materials and Methods
### Clean-Up, Fractioning, and Pre-Oxidation of Samples  
For clean-up and fractionation of the samples solid-phase extraction (SPE) was applied, using C18 (500 mg/3 mL, Finisterre, Teknokroma, Barcelona, Spain) and COOH (500 mg/3 mL, Speed\({}^{\text{TM}}\) Applied Separations, Allentown, PA, USA) cartridges, respectively. For mussels, the adopted procedures of SPE-C18 and SPE-COOH were the same described in the AOAC Official Method 2005.06. After SPE-C18, all samples from mussels were passed by PVDF filters 0.45 \(\upmu\)m.  
For samples from _G. catenatum_ cultures, the SPE-C18 procedure applied was adapted from the official method, with the adaptations highlighted as follows. After conditioning, 2.5 mL of each sample or blank were added to each cartridge. An amount of 2.0 mL of ultrapure water was added to wash the cartridge, and the final volume was adjusted to 5.0 mL (after pH adjustment to 6.5) [23]. The SPE-COOH procedure was the same as in the official method. In all these procedures, ultrapure water (Simplicity\({}^{\circledR}\) Water Purification System, Merck KGaA, Darmstadt, Germany, 18.2 M\(\Omega\).cm) and analytical or HPLC grade solvents were used. After SPE, the samples were stored at -18 \({}^{\circ}\)C until oxidation for subsequent analysis.  
Before analysing the PST from the different matrices, an additional pre-oxidation step was necessary to allow their determination by HPLC-FLD. The samples from SPE-C18 were subjected to oxidation with peroxide (specific to detect non-\(N\)-hydroxylated toxins) and periodate (able to detect \(N\)-hydroxylated and non-\(N\)- hydroxylated toxins). The samples from SPE-COOH were only submitted to periodate oxidation.  
The peroxide and the periodate oxidation procedures for bivalve samples were those described in the official method [30; 31]. Matrix modifier was prepared from oysters _Crassostrea gigas_ harvested in Ria Formosa, Algarve (southern Portugal), which were previously depurated and submitted to the same procedures of preparation, extraction and SPE-C18 clean-up described above for mussels. Oysters' extracts were stored in a freezer (\(-18\)\({}^{\circ}\)C). In turn, the periodate oxidation procedure for samples from culture had minor modifications. Instead of the matrix modifier, ultrapure water was used, as suggested by other authors [32]. More details about this procedure and the conditions of preparation of the oxidizing agents may be found in our previous publication [23]. In both procedures, the periodate oxidant was freshly prepared on each day of analysis.
###### Abstract  
The purpose of this article is to evaluate and compare the mechanical and electrochemical properties of four new materials, composed of a B\({}_{4}\)C ceramic matrix doped with 0.5%, 1%, 2% and 3% volumes of CoCrFeNiMo HEA with monolithic B\({}_{4}\)C. The studied samples were obtained using the spark plasma sintering technique. The structure and hardness of the samples were analyzed via scanning electron microscopy (SEM) and a Vickers microhardness test. After immersion in artificial sea water to simulate a corrosive marine environment, corrosion potential, corrosion rate and electrochemical impedance spectroscopy tests were carried out to determine the samples' electrochemical behavior. Tafel slopes and the equivalent circuit that fit the EIS experimental data were obtained. A denser microstructure and smaller grain size was achieved as the HEA content increase. According to the Vickers measurements, every sample showed a normal distribution. All studied samples exhibit great corrosion resistance in a two-step chemical interaction, influenced by the presence of the Warburg element. The research demonstrates that increasing the HEA content implies better performance of corrosion resistance and mechanical properties, confirming the materials' potential use in corrosive environments and harsh mechanical applications.  
boron carbide; ceramic; HEA; EIS; corrosion behavior; microhardness; SEM Article
## 4 Methodologies and Implementation
### Research on the Application of DDPM in Biomedical Image Segmentation
#### 4.1.2 Segmentation Algorithms Incorporating DDPM
[72] introduces a novel approach to medical image segmentation by combining diffusion-based models with transformer architectures. This innovative framework addresses the need for accurate and consistent segmentation in medical imaging, crucial for applications such as diagnosis and surgical planning. The authors propose an anchor condition to ensure model stability and introduce the Spectrum-Space Transformer (SS-Former) to enhance the interaction between noise and semantic features. Through comparative experiments on 18 organs and 4 medical image segmentation datasets with different modalities, MedSegDiff-V2 outperforms previous state-of-the-art methods, demonstrating its effectiveness and generalizability. The paper highlights the model's success in tasks such as optic-cup segmentation, brain tumor segmentation, and abdominal organs segmentation. Visual comparisons with state-of-the-art segmentation models showcase MedSegDiff-V2's ability to generate precise and accurate segmentation maps, even in challenging areas. Overall, MedSegDiff-V2 sets a new benchmark for medical image segmentation and paves the way for future research in this domain. Ref. [73] presents a novel approach to histopathological image analysis using self-supervised learning and generative diffusion models, addressing the challenges of data annotation and model performance through the innovative use of self-supervised learning and generative diffusion models. Ref. [74] presents a compelling approach to address the scarcity of expert annotations in medical image analysis. Leveraging semi-supervised learning and diffusion models, the study demonstrates the potential for extracting visual representations from multi-modal medical images in an unsupervised setting. The authors emphasize the practical relevance of this approach in the medical domain, where limited annotated samples are available compared to the vast amount of unlabeled data. By fine-tuning the noise predictor network for semantic segmentation, the proposed method showcases promising performance in brain tumor segmentation, even with a small number of training samples. The experimental results, based on the Brain Tumor Segmentation (BraTS) 2021 challenge dataset, highlight the effectiveness of the approach in accurately delineating tumor regions. Overall, the paper provides valuable insights into the application of diffusion models for semi-supervised learning in medical image analysis, offering a potential pathway to address the challenges associated with limited expert annotations and facilitating more efficient and accurate brain tumor segmentation. All of the research discussed in this section is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 4 Results  
### Effect of Database Size and Latent Space Dimension on the Quality of AE Approximation  
We first tested the effect of the latent space dimension and the number of database entries on the training of the autoencoder network. The accuracy of the trained network was evaluated by varying the amount of training data and the dimensionality of the latent space (\(L\)). Database defined by Equation (8) was used for this purpose. We trained \(M=10\) (determined empirically) networks for each tested pair of training data and dimensionality of the latent space. For the training data, an appropriately sized random subset of database given by Equation (8) was chosen for each of the \(M\) networks. Additionally, each of the \(M\) networks was trained with a different random initialization of the parameters and tested using Equations (14)-(16) with \(K=40\) randomly selected throwing trajectories from the database specified in Equation (8), which were not included in the training dataset. Thus, we obtained \(M\) results describing the error of each autoencoder network: \(\text{MSE}_{\text{pos},m},\text{MSE}_{\varphi,m},\text{MSE}_{\theta,m},m=1, \ldots,M\), all calculated according to Equations (14), (15), and (16), respectively. The final evaluation results were then obtained by averaging  
\[\overline{\text{MSE}}_{\text{pos}} = \frac{1}{M}\sum_{m=1}^{M}\text{MSE}_{\text{pos},m}, \tag{18}\] \[\overline{\text{MSE}}_{\varphi} = \frac{1}{M}\sum_{m=1}^{M}\text{MSE}_{\varphi,m},\] (19) \[\overline{\text{MSE}}_{\mathbf{\theta}} = \frac{1}{M}\sum_{m=1}^{M}\text{MSE}_{\mathbf{\theta},m}. \tag{20}\]  
Figure 5 shows the error of autoencoder trajectory encoding. The left plot shows the difference in policy parameters, while the right-two plots show the errors for position and orientation, as defined in Equations (20), (18), and (19), respectively. By comparing the three graphs, we can notice that the differences in quality of approximation are most pronounced in the left-most graph, that is, in the approximation of CDMP parameters. A significant decrease in error with both increasing database size, i.e., training samples, and latent space dimension can be observed. A sort of plateau is roughly reached with \(L=4\) and \(300\) training samples, and the error only marginal decreases afterwards. Therefore, we used these values in our next experiments. Note also that the error in the estimation of the  
position trajectory, shown in the center plot of Figure 5, is slightly larger than 1 cm, whereas the error in the orientation trajectory (right plot) is about 3 degrees.
## 1 Introduction  
Elements for laser optics are typically coated with multilayer interference coatings for antireflection, high reflectance, or wavelength, as well as polarization separation [1, 2]. In most cases, interference coatings consist of two materials with a high and a low refractive index, respectively. In the case of high-end laser applications, a widely used high index material is hafnia--in this work, HfO\({}_{2}\) is deposited by means of ion beam sputtering (IBS). It exhibits low absorption in the entire spectral range, from the UV to NIR [3, 4], and a high laser damage threshold (LIDT) [4, 5]. It is also interesting for electronic applications as a gate oxide to replace SiO\({}_{2}\) due to its higher dielectric constant [6, 7].  
During layer deposition, an assisting ion beam is often directed to the substrate to tune the quality of the deposited layers. If the primary deposition method is evaporation, the technique is called ion beam assisted deposition (IAD), while in the case of IBS it is called dual ion beam sputtering (DIBS). The assisting source has several advantages. An ion beam can be applied for surface pre-cleaning; it can also be used to provide excited oxygen for better film oxidation and additional high energetic excited oxygen atoms for densifying the films [8].  
For several coating applications, thin-film stress plays an important role. The stress effects from assisted deposition has been researched since the 1990s [9, 10]. Stress is especially important in the case of thick single- as well as multi-layer stacks (i.e., high reflectance coatings) [4, 11]. Stress can lead to (partial) crystallization [12, 13], with suboptimal optical performance or, in the catastrophic case, delamination of the film [14]. The kinetic energy of the impinging atoms influences the thin-film stress. E-beam-deposited thin films exhibit low stress due to the low kinetic energy of the film-building atoms. With increasing energy, the film stress becomes compressive (e.g., magnetron sputtering). This compressive stress increases further with increasing kinetic energy (e.g., IBS, high-power impulse magnetron sputtering (HiPIMS)). In the case of IBS, there are several studies on other materials where the properties are improved by using the second ion beam source. In the case of Ta\({}_{2}\)O\({}_{5}\), stress is reduced and the visible and NIR transmittance is increased at an intermediate level (350-450 V) of the assisting ion beam voltage [15].  
In this work, we investigated the influence of the assisting ion beam source on the properties of HfO\({}_{2}\) single layers deposited by IBS.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 4 Evaluation
### Comparison of Experimental Results
Additionally, MP-QGRD considers remaining energy of a node, allowing only nodes with an energy level exceeding the threshold to participate in communication. Therefore, MP-QGRD has the lowest energy consumption level. Figure 3(d) demonstrates that as the number of nodes increases, the routing overheads of MP-QGRD, QMR, QGeo, and GPSR protocols all steadily increase. With the increase in node density, the GPSR routing protocol experiences an increase in control message quantity. The rapid movement of nodes also leads to frequent changes in the network topology, resulting in more routing retransmissions and thus increased routing overhead. QGeo and QMR, by introducing Q-learning technology, can perceive changes in the network environment and reduce some routing overhead during routing decisions. MP-QGRD, through mobility prediction, can avoid invalid node connections before link disconnection, reducing routing overhead. Therefore, MP-QGRD exhibits the lowest routing overhead. Based on the above analysis, MP-QGRD improves the stability of wireless links through location prediction. Using dynamic time slots to send Hello messages effectively reduces congestion on wireless links, thereby improving the success rate of data transmission. Meanwhile, by integrating reinforcement learning methods and considering multiple factors, the energy consumption of network communication has been further optimized.
# Privacy-Preserving Federated Singular Value Decomposition
## 4 Enhancing the Utility of FedPower
```
0: Datasets \(\{\mathbb{M}_{i}\}_{i=1}^{n}\), target rank \(k\), iteration rank \(r\), number of iteration \(T\), synchronous trigger \(p\), the variance of noise \(\sigma\), and key pair \((sk_{hm},pk_{hm})\)
0: Approximated eigenspace \(\overline{\mathbb{Z}}_{T}\)
1: initialise \(\mathbb{Z}_{0}^{(i)}=\mathbb{Z}_{0}\in\mathbb{R}^{d\times r}\sim\mathsf{N}(0, 1)^{d\times r}\) with orthonormal columns and generate an \(r\times r\) zero matrix \(\mathbb{P}\) and another all-ones matrix \(\mathbb{P}^{\prime}\) of the same size
2:for\(t=1\) to \(T\)do
3: each node device \(i\) computes \(\mathbb{Y}_{t}^{(i)}=\mathbb{M}_{i}^{r}\mathbb{Z}_{t-1}^{(i)}\), where \(\mathbb{M}_{i}^{\prime}=\frac{1}{s_{i}}\mathbb{M}_{i}^{T}\mathbb{M}_{i}\)
4:if\(t\equiv 0\pmod{p}\)then
5: each node device \(i\) computes \(\mathbb{Y}_{t}^{(i)}=\mathbb{Y}_{t}^{(i)}\mathbb{D}_{t}^{(i)}\) (orthogonal transformation)
6: each node device \(i\) adds Gaussian noise: \(\mathbb{Y}_{t}^{\prime(i)}=\mathbb{Y}_{t}^{(i)}+\mathbb{N}^{(i)}\sim\mathsf{N }(0,\sigma)^{d\times r}\)
7: SA protocol is executed among the server and all node devices, with inputs \(\mathbb{Y}_{t}^{\prime(i)}\) and output \(\mathbb{Y}_{t}\)
8: the server chooses one random index \(j\in[1,n]\) and encrypts \(\mathbb{P}^{\prime}\) and \(\mathbb{P}^{\prime}\): \(\mathbb{C}^{(j)}=\mathsf{Enc}_{pk_{k}}(\mathbb{P})\) and \(\mathbb{C}^{(j^{\prime})}=\mathsf{Enc}_{pk_{k}}(\mathbb{P}^{\prime})\) for \(j^{\prime}\in[1,n]\setminus\{j\}\)
9: the server sends value \(\mathbb{C}^{(j)}\) and \(\mathbb{C}^{(j^{\prime})}\) to the appropriate node devices
10: each node device \(i\) computes \(\mathbb{C}^{\prime(i)}=\mathbb{N}^{(i)}\cdot\mathbb{C}_{(i)}\) which is \(\mathsf{Enc}_{pk_{k}}(\mathbb{N}^{(i)}\cdot\mathbb{P}^{\prime})\) if \(i=j\) and \(\mathsf{Enc}_{pk_{k}}(\mathbb{N}^{(i)}\cdot\mathbb{P})\) otherwise
11: each node device \(i\) sends \(\mathbb{C}^{\prime(i)}\) back to the server
12: for all \(i\in[1,n]\setminus\{j\}\), the server decrypts the receiving messages \(\mathbb{C}^{\prime(i)}\) to obtain \(\mathbb{N}^{(i)}\equiv\mathbb{N}^{(i)}\cdot\mathbb{P}^{\prime}=\mathsf{Dec}_ {sk_{k}}(\mathbb{C}^{\prime(i)})\)
13: the server updates aggregation result as \(\mathbb{Y}_{t}^{\prime\prime}=\mathbb{Y}_{t}^{\prime}-\sum_{i\in[1,n] \setminus\{j\}}\mathbb{N}^{(i)}\)
14: the server performs orthogonalization \(\mathbb{Z}_{t}=\mathsf{orth}(\mathbb{Y}_{t}^{\prime\prime})\)
15: the server broadcasts \(\mathbb{Z}_{t}\) to all node devices
16: each node device \(i\) sets \(\mathbb{Z}_{t}^{(i)}=\mathbb{Z}_{t}\)
17:else
18: each node device \(i\) calculates the latest \(\mathbb{Z}_{t}^{(i)}=\mathsf{orth}(\mathbb{Y}_{t}^{(i)})\)
19:endif
20:endfor
21:return approximated eigenspace
```  
**Algorithm 2** Utility-enhanced FedPower. Compared with the original solution by [PERSON] et al. [18], we have improved the utility of the aggregation result by keeping the added noise from one single node device. As a side effect, the complexity has grown due to the SA protocol. This is a trade-off between result accuracy and solution efficiency.
# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 1 Introduction
Experimental results show that the accuracy of the approach can meet the requirements of the apparel industry since the average relative error is \(\sim\)2%. Tolerable error in the fashion industry is \(\sim\)2 cm. The authors in [20] present an idea and apps that allow measuring the lay-down of a garment placed on a marked board. The proposed app then shoots the garment using the top camera form above and automatically captures many of the garment's standard measurement points. The basic strategy is to first detect key points of interest in the clothing item and then use known measurements from demarcations on the backdrop to infer distances between those points. To measure lying-down clothes a fuzzy edge-detection algorithm can be used to detect the edge of garment image [21]. Then a corner-detection algorithm based on Freeman code is invoked to locate the corner points. The experiment results show that the proposed approach can measure t-shirts with the related error from 0.73% to 2.84% depending on the measured points. The smallest error has been obtained for the garment length (less than 1%). Measurement solutions with requirements for a fixed position of the garment may limit their use and application, although the accuracy of such solutions is quite high (up to 0.5 cm error) [22], because, under real-world conditions (non-laboratory or industrial-oriented), the position of different garments in each image can vary. This means that it is quite complicated to use pre-designed templates to extract the essential dimensions of a garment. Adherence to certain equipment or templates is more semi-automatic solutions that require additional calibration, widespread interruptions from distinct angles and specific positions (mobile apps). All this process takes a lot of time and therefore the essential goal of optimizing time by measuring the garment is lost. How to make automatic garment measurement as versatile and accurate as possible is also one of the most important issues for the autonomous retrieval of garments information. In this work, we focus on the challenge of automatically measuring the hanging garments (in this particular study, on the mannequin), without being restricted by space, background requirements, shooting distances or additional tags needed for measurements. The aim of this research is to create a solution by implementing an automatic clothing segmentation and measuring algorithm that would let us not only separate clothing into different groups but also measure their basic measurements such as distance between shoulders, length of a sleeve, etc. Identifying the main problems and limitations of both objectives--accurate segmentation and measurement--is also an essential task, as it can provide insights and avenues for further research.