esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter X**} & \multirow{2}{*}{**Entered Values**} & \multicolumn{3}{c}{**Deviations of Approximated Functions**} \\ \cline{3-5}  & & **Least Squares** & **Coordinate** & **Polynomial** \\  & & **Method** & **Method** & **Method** \\ \hline
1 & 4–6 & \(-\)0.9–4.2 & \(-\)1.0–4.4 & 2.5–9.5 \\
2 & 6–8 & 4.5–10.8 & 4.4–19.9 & 17.0–26.0 \\
3 & 8–15 & 10.8–18.4 & 10.9–18.6 & 17.0–26.0 \\
4 & 16–25 & 18.4–27.5 & 18.6–28.0 & 26.0–36.5 \\
5 & 26–40 & 27.5–38.9 & 28.0–39.5 & 36.5–50.2 \\
6 & 41–60 & 38.9–53.3 & 39.5–54.2 & 50.2–70.0 \\
7 & 61–83 & 53.3–72.1 & 54.2–73.7 & 70.0–100.0 \\
8 & 84–105 & 72.1–97.9 & 73.7–99.6 & 100.0–150.5 \\
9 & 106–135 & 97.9–135.2 & 99.6–137.5 & 150.5–212.0 \\
10 & 136–245 & 135.2–194.2 & 137.5–197.3 & 212.0–308.0 \\
11 & 246–330 & 194.2–301.3 & 197.3–305.5 & 308.0–387.5 \\
12 & 331–400 & 301.3–555.9 & 305.5–560.0 & 387.5–409.3 \\
13 & 401 and next & 555.9–1982 & 560.0–1906 & 409.3–423.2 \\  & Dispersion & 2.33 \(\times\) 10\({}^{3}\) & 2.41 \(\times\) 10\({}^{3}\) & 3.22 \(\times\) 10\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of approximation of basic level of maintenance labor intensity. Best modelling results achieved:  
* for the sum of points, the model using the polynomial;
* for the basic level of operational maintenance, the model using a rational fractional function;
* for coefficients, the model using the least squares method. These results confirm the possibility of using the three above mentioned functions for the mathematical model of calculating the basic level of maintenance, directly from the design and operational parameters of the machine [14, 25].
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
### Notations  
A matrix \(\mathbf{A}\) is denoted by boldface capital letters and the column vector \(\mathbf{a}\) is denoted by boldface lowercase letters. The operator \(\left(.\right)^{T}\) denotes the transpose of a matrix. The functions \(\mathrm{corr}(\mathbf{a},\mathbf{b})\), \(\mathrm{sort}_{desc}(\mathbf{a})\), and \(\mathrm{rem}(c,N)\) represent the correlation between \(\mathbf{a}\) and \(\mathbf{b}\), the sort the vector \(\mathbf{a}\) in descending order, and the remainder after division of \(c\) by \(N\), \(c,N\in\mathbb{Z}\), respectively. The vector \(\mathbf{a}=\left[\mathbf{a}_{q}\right]_{1\leq q\leq Q_{1}}\in\mathbb{C}^{Q_{1} Q_{2}}\) is the concatenation of vector \(\mathbf{a}_{q}\in\mathbb{C}^{Q_{2}}\). Finally, the indices, \(t,k,l\), \(n\), and \(q\) represent the time domain, subcarrier in the frequency domain, the OFDM symbol, the subchannel, and sample, respectively.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Uncertainty-Guided Regression Within Random Patches  
In this section, we extend the random patch-based global structure modeling by introducing an uncertainty-guided regression strategy. This method aims to dynamically adjust the learning intensity within each patch based on its uncertainty, improving the model's ability to restore details in high-noise or hard-to-recover regions while maintaining global structural consistency [28].  
Random patch selection and hierarchical clustering. Non-overlapping regions of varying sizes are randomly selected from the input image \(X_{0}\). Each patch is vectorized into \(\{x_{0}^{(i)}\}_{i=1}^{6}\), where \(x_{0}^{(i)}\in\mathbb{R}^{m_{i}}\). These patches are grouped using hierarchical clustering, capturing multi-scale relationships. The clustered patches are stacked into matrices \(M_{0}^{n_{j}}\in\mathbb{R}^{n\times n_{j}}\) to represent their structural features.  
Uncertainty estimation and regression. For each chosen patch, the model \(\mu_{\theta_{1}}(\cdot)\) estimates pixel-level uncertainty, creating an uncertainty map \(P_{t}^{(i)}\) that depicts the uncertainty of individual pixels and informs the learning process:  
\[P_{t}^{(i)}:=\mu_{\theta_{1}}(Y,X_{t}^{(i)},\tilde{\alpha}_{t}) \tag{11}\]  
where \(P_{t}^{(i)}\in\mathbb{R}^{m_{i}\times n_{j}}\) represents the uncertainty weight for patch \(i\) at time step \(t\). \(X_{t}^{(i)}\) denotes the intermediate patch during the diffusion process.  
Uncertainty-Guided loss function.We design a weighted L1 loss function that assigns higher weights to pixels with higher uncertainty. The loss function for each patch is:  
\[L_{\mu}^{(i)}=\|e^{-P_{t}^{(i)}}\odot(\epsilon^{(i)}-\epsilon_{\theta}(Y,X_{t }^{(i)},\tilde{\alpha}_{t}))\|_{1}+2\sum_{j}P_{t}^{(i)}[j] \tag{12}\]  
where \(\epsilon^{(i)}\) is the true noise, \(\epsilon_{\theta}\) is the predicted noise, and \(\odot\) denotes element-wise multiplication. The second term, \(2\sum_{j}P_{t}^{(i)}[j]\), serves as a penalty to prevent the uncertainty weights from growing too large.  
Multi-Scale regularization and patch consistency. We impose multi-scale consistency by regularizing the patch matrices of the generated sample and the original image. Specifically, we constrain the singular values of the patch matrices:  
\[L_{s}(M_{t-1}^{n_{j}},M_{0}^{n_{j}})=\|\text{diag}(S_{t-1}^{j})-\text{diag}(S_ {0}^{j})\|_{1} \tag{13}\]  
where \(S_{t}^{j}\) is the singular value matrix of the \(j\)-th cluster.
## 4 Application of CRISPR/Cas9-Based Genome Editing Technology in _A. orgyaeae_ and _A. niger_
### CRISPR/Cas9-Based Genome Editing Technology for A. oryzae  
To achieve accurate and efficient genome editing in _A. oryzae_ and better meet the demands of industrial production, the CRISPR-Cas9 system has been rapidly adopted in this filamentous fungus recently [144]. In 2016, [PERSON] et al. successfully established a CRISPR/Cas9-mediated genome editing method in _A. oryzae_, marking the first application of targeted mutagenesis in this filamentous fungus [146]. Using the CRISPR/Cas9 system, they knocked out the uracil synthesis gene _pyrG_ (encoding orotidine 5\({}^{\prime}\)-phosphate decarboxylase) to construct a stable uridine/uracil auxotrophic _A. oryzae_ strain, achieving a 10% mutagenesis efficiency with a characteristic 1 bp deletion at the target site 3-4 bp upstream of the PAM sequence. On this basis, the same group further applied this system in 2017 to delete the _ligD_ gene--a DNA ligase gene participating in non-homologous end joining (NHE)) in _A. oryzae_[158]. This study demonstrated that introducing the _ligD_ mutation by genome editing is an effective strategy to improve gene targeting efficiency in _A. oryzae_ industrial strains. Furthermore, [PERSON] et al. established an optimized CRISPR/Cas9 method using AMA1-based autonomous replicating genome editing plasmids harboring a drug resistance marker (_ptrA_) [159]. Notably, this system enabled highly efficient marker-free multiple gene modifications, with mutation efficiency significantly improved to 50-100% in both wild-type and industrial _A. oryzae_ strains. Additionally, [PERSON] et al. developed a CRISPR/Cas9 genome editing method by optimizing protoplasst preparation conditions, which further enhanced the transformation and multiplexed genome editing efficiency in _A. oryzae_ (37.6% single-gene and 19.8% dual-gene editing efficiency) [69]. In this study, the morphological gene _yA_ was identified as a promising selection marker for the rapid and precise isolation of positive transformants during genetic manipulation in _A. oryzae_. Very recently, a novel modular synthetic biology toolkit for _A. oryzae_ was developed, featuring a recyclable ribonucleoprotein (RNP)-based CRISPR-Cas9 method [160]. Unlike traditional plasmid-based systems, this approach achieved gene editing through direct transformation of pre-assembled CRISPR-Cas9 RNP complexes, eliminating the need for plasmid-based encoding of Cas9 and sgRNAs. Notably, this RNP-based method demonstrates comparable efficiency and versatility to plasmid-based CRISPR-Cas9 systems for genetic engineering in _A. oryzae_[159].
## 4 Establishment of a Prediction Model for Dual-Mode Shield Tunneling Parameters
### Data Preprocessing
#### 4.2.4 Data Normalization  
When data features have significantly different magnitudes and exhibit a wide range of values, normalization methods are commonly employed to balance the importance of different features in prediction, thereby enhancing prediction accuracy. Two commonly used normalization methods are min-max normalization and Z-score normalization. The computation formulas for these two methods are as follows:  
\[x^{*}=\frac{x-x_{\min}}{x_{\max}-x_{\min}}, \tag{13}\]  
\[x^{*}=\frac{x-x_{mean}}{\delta}, \tag{14}\]  
As shown in Formula (13), the max-min normalization method linearly transforms the original data to the range [0, 1], where \(x_{max}\) and \(x_{min}\) represent the maximum and minimum values of the column data, respectively. This method is suitable for data distributions with clear boundaries, mainly when scaling the data to a fixed range is necessary. Formula (14) demonstrated that the z-score normalization method normalizes the original data set to have a mean of 0 and a standard deviation of 1. Here, \(x_{mean}\) represents the mean of all sample data, and \(\delta\) represents the standard deviation of all sample data. This method is applicable when data distribution lacks clear boundaries and is suitable for cases where comparing data from different features on the same scale is necessary. In the subsequent sections of this paper, different normalization methods will be applied to the training set, and the same method will then be used to normalize the validation set. The most suitable normalization method will be determined by comparing the model's predictive accuracy on the test set. It is important to note that considering the potential differences in data distribution between the training and validation sets, separate normalization processes for the training and validation sets are necessary to achieve optimal predictive performance.
## 3 Results
### Tracking Results with Sea-Trial Data  
Following those simulation tests, we employ real sea-trial data to evaluate and validate the proposed approach. The sea trial is conducted by the State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences on the South China Sea. The test ship moves in variant distances and directions, and its signal receiver is a horizontal array composed of 128 array elements. The sound velocity profile is provided in Figure 12a to characterize the circumstances surrounding the sea region of experiment.  
Figure 12d shows that five acoustic targets were collected and labeled with a number. The first and second targets appear to have higher normalized power levels than the other three targets.  
The proposed approach is next tested against the LTM in a series of comparison trials. To begin, consider Targets 1 and 2 to be the interested targets, while the remainder are jamming targets. The noise power of the trajectories to track is larger in this case, making the work easier. As predicted, Figure 12c,f indicate that the proposed approach can successfully track the interested targets. Figure 12b,e reveal that the LTM can follow the targets as well, although its accuracy appears to be worse than the former. We can observe that the LTM's tracking constantly deviates. The SIRs of Targets 4 and 5 are \(-19.1\) dB and \(-17.8\) dB, respectively, with noise powers lower than the jamming; Figure 13d,e show that the proposed approach can still track the target successfully, but Target 3, whose SIR is \(-22.3\) dB, fails, as shown in Figure 13a-c. Figure 13f, on the other hand, displays an unexpected tracking result. The tracking trajectory deviates under interference from the first jamming target. Figure 14 zooms in Figure 13f for a further analysis, and it shows that the interested target is completely covered by the two jamming targets, whose trajectories are extremely close around this part. From the perspective of the image, the interested target cannot be detected in the next dozens of snapshots, making tracking impossible using  
Figure 11: Evaluation of Accuracy Performance.  
Figure 10: Example of Evaluation Experiments.  
the template matching approach. On the other hand, the power levels of this interested target are quite weak compared with these two jamming ones, which means the SNR is too low to track.  
Figure 14: Observation Window of jamming area.  
Figure 12: Experiment results with weak jamming targets.  
Figure 13: Experiment results with strong jamming targets.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 2 Technical Background
### Security vs. Wireless Communication  
Beyond the safety, the overall system dependability relies on security: there are many subtle interactions and interdependencies among safety, QoS and security as introduced in previous sections. Often, security is conflicting with safety and performances, thus requiring to evaluate a quantifiable trade-off between the three. Security is a composite of several attributes including availability, confidentiality and integrity [28]. The introduction of security requirements into systems tends to modify the priorities of some other non-functional requirements. In addition, resource constraints may make it infeasible to guarantee absolute security in all circumstances. One of the key issues that allows the trade-off evaluation is the definition of a metric [35; 36]. Metrics are also suitable for security assessment of services, applications, as well as users and communication channels. Several strategieshave been proposed in the area of communication channels security to secure protocols and messaging schema. The most widely used mechanism over TCP/IP networks is currently the Secure Socket Layer (SSL), a cryptographic protocol that provides data authentication, encryption and integrity. A SSL connection is established, between two pair nodes, exchanging identification parameters in the form of digital certificates. Defense mechanisms against possible threats, either malicious or due to environment, are also defined. In particular, wireless communications are subject to physical layer attacks, like, e.g., the well-known family of wireless jamming attacks (a noise burst that may result in a Denial of Service (DoS) attack on a wireless channel), or, the most common eavesdropping attacks. These require specific measures known as _physical layer security mechanisms_ aimed at increasing the robustness and secrecy capacity of the wireless channel [37]. The main issues are related to the enhanced flexibility and scalability of the networks, especially in the case of Co-CPSs, where different systems could participate to a cooperative group with different roles and the group dimension can vary over time, like in vehicular use cases, where a platoon is a high dynamic set of cars continuously entering and exiting the platoon itself. Vision and details on these items are available in, e.g., [38; 39; 40]. While the security approaches proposed in the literature mainly focus on the trustiness of the information flowing through the network, enforcing the network access and utilization (e.g., user authentication, message integrity, etc.), the evolutionary scenarios for Co-CPSs require that the trustiness of services and users must also be guaranteed, extending the authentication mechanisms.
###### Abstract  
**Motivation:** Precise tracking of individual cells--especially tracking the family lineage, for example in a developing embryo--has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation \"model\" of any scene that we observe. Because we understand simple truths about the world--for example cells can move and divide, but they cannot instantaneously move vast distances--this model \"in our heads\" helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. **Results:** Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby \"Universes\" and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presence of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.  
**Keywords:** ensemble simulation; data assimilation; cell tracking; image analysis; cell lineage trees; stochastic simulation _\"What I cannot create, I do not understand.\"_  
[PERSON] (Nobel Prize in Physics, 1965)
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 5 Sensitivity Analysis
#### 5.1.1 Varying Number of Processors Available to Rent  
In the case of having four processors available for rent to be used in the batch process, the results were the same as the case of five available processors since the same number of processors were actually rented as in the illustrative example results shown earlier. However, having three available processors to rent resulted in a longer batch process time and higher total cost in addition to that, the SLA was exceeded by more time units. Having two processors available for rent resulted in more delay in batch process time, increased the total cost, and the SLA was exceeded by 6 time units. The batch process had the highest delay and total cost in the last case of having only one available processor to rent. The previous results shows that the algorithm is renting additional processors only when needed in order to perform the batch process with minimum execution time and total cost.
## 2 The Basis of the Analytical Model  
Figure 1 shows a simplified physical structure of Vertical Double-diffused MOSFET (VDMOSFET) considering the discrete package. There is an internal resistance due to the gate contact. The pad parasitic inductances and the internal bonding between the carbon silicon die and the package pads, which are relevant to the package technology, contribute to the total parasitic inductance in the power device. The only viable alternative to further lower the parasitic inductances is to concurrently address  
the underlying package problem, using clip lead or sandwich package assembly methods to eliminate bond wires while maximizing the ratio of die area to package footprint [25]. As a unipolar device, the dynamic characteristics of SiC MOSFET are mainly determined by the charging and discharging process of the junction capacitances which are composed of oxide capacitances and depletion edge capacitances [26]. The width of depletion edge is related to the applied voltage between drain terminal and source terminal, so that the value of the junction capacitances is nonlinear. When a positive voltage applied between the gate terminal and source terminal, the depletion region gets wider towards the body, and it begins to drag the free electrons to the interface. As the density of the free holes of the body and the density of the free electrons of the interface becomes equal, the free electron layer is called the inversion layer (N channel).  
Based on the above knowledge, the equivalent circuit of the MOSFET is obtained, as shown in Figure 2. The junction capacitances are represented by three nonlinear capacitances connected in parallel with each internal node. The parasitic inductances in series in each terminal of the device (the gate terminal also includes the internal resistance). The channel is the key element of modeling, which determines the accuracy of the model and should be carefully considered. The equivalent of the channel in the switching transients will be thoroughly discussed in the next section.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 4 Merging Pollution Data over a Grid on the City Map  
The street network within a city can be seen as a bidirectional graph, where nodes correspond to intersections and edges represent streets, paths, or road segments. In addition, since the AQ sampling remains constrained to the specific locations where AQ monitoring nodes are installed, a spatial interpolation technique becomes necessary. This technique will ensure precise pollution measurements at the grid over the city map, enabling the analysis of different pathways for routes.
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 2 Related Works
The generator used has a U-Net based architecture and the discriminator is a markovian discriminator [17; 25]. PatchGAN does not signify the whole image as fake or real instead of evaluating the local patches from the images. The patch size can be adjusted by changing the size of the kernel in convolution layers or the number of layers in the discriminator. In [17], an input image of \(256\times 256\) size is concatenated with the generated \(256\times 256\) image and provided to the generator \(G\). The patch size used in this paper is \(70\times 70\). The generator provides a feature map of \(30\times 30\times 1\) which means that each pixel of this map corresponds to the \(70\times 70\) patch of the input image. All the values of the \(30\times 30\times 1\) feature maps are averaged to estimate the probability of the patch being real or fake. This makes patchGAN more attentive to the local features of the images. PatchGAN is now being widely used in many applications. In PGGAN [26], the patchGAN is combined with the globalGAN for the task of image inpainting. The discriminator of PGGAN, first uses the shared layers between the patchGAN and the global GAN to learn the basic low-level features which is later split to generate two separate adversarial losses to preserve both the local and the global features in images. In [27], the author proposes an image text deblurring method using two GAN networks which are used to convert the blurred images to deblurred images and the deblurred images to blurred images which helps in putting the constraints on the generated samples. The discriminator used in this model is patchGAN discriminator. PatchGAN is also used in multilevel feature fusion for underwater image color correction [28]. In this model, the multi-scale features are extracted and then global features are fused together with low-level features at each scale.
## 5 Discussion
Furthermore, by integrating existing standards such as OPC-UA and FIWARE, we eliminate the need to develop new interfaces from scratch, leading to a more cost-effective solution. This component has the ability to serve as a communication bridge between FIWARE and standards such as OPC UA or OpenAPI. However, to translate into different vocabularies, adaptive algorithms should be developed to allow translating one data model into another, or translation fields should be manually defined for the mapping. Further work will be oriented towards testing and validating the solution in real shop floor scenarios and increasing the number of supported standards.