esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
### Main Aim of this Work  
The article discusses the possibility of using the commercial software Excel for solving MFLP and describes the problems associated with it. Plane coordinates and calculation of distances in Euclidean space are used in the calculations. The calculated data must therefore be considered approximate, serving mainly as a basis for further decisions.  
In order to verify its applicability, we solve five MFLP benchmark tasks with different levels of difficulty. The benchmark tasks are designed so that it is possible to calculate the optimal solution (with the exception of the last task, which is created based on a real-world problem). We then compare the optimal solution with the result obtained by calculation in the Excel application environment and calculation by the metaheuristic method (Simulated Annealing). The tasks are solved on two computers with different performance parameters in order to verify the influence of the hardware configuration on the accuracy of the calculation. Both methods are also used to solve a practical example based on real data.  
Excel software and its Solver add-in are commercial products. This work does not examine the algorithms they use. From the point of view of this research, it is a \"black box\" for which the inputs, settings of basic parameters and outputs are known.  
The proposed procedure uses available commercial software (Excel) for solving Multi-facility location problem, as it is described in Section 1.3. This solution has not yet been described in the scientific literature. The use of the proposed procedure will allow the required calculations to be performed on a personal computer with commonly available office software. No special competencies are required; user-level knowledge is sufficient.  
The aim of this work is to verify the proposed procedure on test examples and to find the problems that this simplified approach brings.
## 3 Tests and Methodology
### Laboratory Physical Clogging Procedure
#### 3.2.1 Statistics of Clogging Materials  
The particle size and coverage density of clogging materials are the two main key factors affecting the physical clogging procedure of permeable pavement. Thus, the clogging materials at different times, different regions, and different pavement types were collected in Siping, China. The collection locations are summarized in Table 6.  
Figure 3: Anti-slip sand thrown on permeable pavement.  
The clogging materials collection procedure is detailed as follows. First, a 1 \(\times\) 1 m measurement area was drawn with a steel ruler at the collection point. Secondly, the clogging materials were collected from the periphery to the center with a brush, and branches, scraps, and cigarettes were removed with a twig before the residual clogging materials were placed in a sealed bag for further study. The clogging materials collection process was carried out separately during the spring season and summer season. Clogging materials include pavement surface dust, clay particles, rubber particles produced by wheel wear, and fine aggregate produced by the crushing of the pavement aggregate. For grit, dust, and rubber particles that have no expansibility, the particle size remains unchanged in the natural environment, so the particle size of those clogging materials is the key factor affecting the clogging behavior of permeable pavement. For clay particles with hydrophilicity and moisture swelling, the clay particles swell when encountering rainfall, which will seriously affect the effective void and reduce the permeability of permeable pavement. Moreover, the clogging behavior is also related to the shape and surface characteristics of the clogging material. The clogging material with a surface close to a spherical shape or a smooth surface flows more easily out from the connected pores, and the clogging material with a sharp edge or rough surface is more likely to remain in the pores and cannot easily flow. Considering the actual size of the clogging material, the size of the clogging material can be determined through a laboratory sieving method. The particle size score table of clogging materials is shown in Table 7.
# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
## 5 Conclusions  
In this study, we considered the problem of maximizing the sum rate of the secondary users in a NOMA based CR system. The constraints of power budget, interference temperature, QoS requirement of each user and NOMA power gap were taken into account. For the solution, we first transformed the non-convex optimization problem into a convex form. We proposed a dual decomposition based solution for power allocation. An efficient algorithm for user pairing and channel allocation was also designed. The simulations compared the performance of the proposed scheme with two scenarios. In the first case, the power allocated to each user pair was optimized for random user pairing and channel allocation (Random-\(x_{n,i,c}\)). In the second scenario, fix amount of power was allocated to each pair where the user pairing and channel allocation was carried out as in the proposed Opt scheme. In both cases, the allocated power to a pair was distributed among the user as in Opt. The results show that at small values of available power or high interference threshold the advantages of intelligent user pairing and channel allocation surpass optimal power allocation to the pairs. However, at low interference threshold or high power budget the optimal power allocation to each pair provides better performance. In addition, as the rate requirement of users in the system increases the gap in the rates offered by Fix-\(Q_{n,i,c}\) and Random-\(x_{n,i,c}\) also increases. At higher values of \(R_{T}\), Fix-\(Q_{n,i,c}\) provides much better performance as compared to Random-\(x_{n,i,c}\). The results show that the proposed Opt scheme offers more rate as compared to the other cases. In future works, we will incorporate some intelligent algorithms [33; 34] into the considered system, in order to enhance the system performance.  
[PERSON], and [PERSON], contributed to the conception and development of the analytical model of the study. [PERSON], [PERSON], [PERSON] and [PERSON], contributed to the acquisition of simulation results. All authors read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
###### Abstract  
The use of deep learning methods to extract buildings from remote sensing images is a key contemporary research focus, and traditional deep convolutional networks continue to exhibit limitations in this regard. This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various sources. MFFNet improves feature capture for building targets by integrating deep semantic information from various attention mechanisms with multi-scale spatial information from a spatial pyramid module, significantly enhancing the results of building extraction. The performance of MFFNet was tested on three datasets: the self-constructed Jilin-1 building dataset, the Massachusetts building dataset, and the WHU building dataset. Notably, experimental results from the Jilin-1 building dataset demonstrated that MFFNet achieved an average intersection over union (MIoU) of 89.69%, an accuracy of 97.05%, a recall rate of 94.25%, a precision of 94.66%, and an F1 score of 94.82%. Comparisons with the other two public datasets also showed MFFNet's significant advantages over traditional deep convolutional networks. These results confirm the superiority of MFFNet in extracting buildings from different high-resolution remote sensing data compared to other network models.  
high-resolution; multi-feature fusion network; building extraction; deep learning 2023  
[PERSON][PERSON][PERSON], [PERSON], [PERSON], Y. MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data. _Appl. Sci._**2023**, 13067. [[https://doi.org/10.3390/appl32413067](https://doi.org/10.3390/appl32413067)]([https://doi.org/10.3390/appl32413067](https://doi.org/10.3390/appl32413067))
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## Appendix A Function Decomposition for Main Effects and Interactions  
[PERSON] [33] proves that any quadratically integrable function \(f(\cdot)\) can be decomposed in terms of its main effects and interactions as follows:  
\[y=f(\mathbf{x})=z_{0}+\Sigma_{i=1}^{d}z_{i}(x_{i})+\Sigma_{i<j}z_{i,j}(\mathbf{ x}_{i,j})+\ldots+z_{1,2,\ldots,d}(\mathbf{x}) \tag{1}\]  
where the relationship between \(y\) and \(\mathbf{x}\) is formulated via \(y=f(\mathbf{x})\), \(f(\cdot)\) is a function of uncertain quantities \(\mathbf{x}\), and its expected value is denoted by \(z_{0}=E[f(\mathbf{X})]\). The function \(z_{i}(x_{i})\) appeared in Equation (1) is the main effect of the \(i\)th variable, \(x_{i}\), and defined as:  
\[z_{i}(x_{i})=E[f(\mathbf{X})\mid x_{i}]-E[f(\mathbf{X})] \tag{2}\]  
The main effect, \(z_{i}(x_{i})\) is the function of \(x_{i}\) only that best approximates \(f(\cdot)\) in the sense of minimizing the variance (calculated over the other variables) [36; 37].  
The function \(z_{ij}(\mathbf{x}_{i,j})\) given in Equation (1), and defined in the Equation (3), describes the first-order _interaction_ between \(x_{i}\) and \(x_{j}\), and similarly \(z_{i,j,k}(\mathbf{x}_{i,j,k})\) is the second-order interaction, and so on.  
\[z_{i,j}(\mathbf{x}_{i,j})=E[f(\mathbf{X})\mid\mathbf{x}_{i,j}]-z_{i}(x_{i})- z_{j}(x_{j})-E[f(\mathbf{X})] \tag{3}\]  
The main effect has a straightforward interpretation for the context of present study; it is the expected change to the scenario outcome that would be obtained if we were to know that parameter \(i\) has value \(x_{i}\), taking into account the residual uncertainty in the other parameters. Additionally the decomposition of variance formula described in Equation (4) implies that the variance of the main effect for parameter \(i\) is the expected amount by which the variance of \(f\) would be reduced if the value of parameter \(i\) was known.  
\[var(Y)=E(var(Y|X)+var(E(Y|X)) \tag{4}\]  
From Equation (1), it is visible that the functions in the Sobol decomposition are pairwise orthogonal [45]. The used prior distribution of the input parameters (denoted by \(G\)), affects the definition of the main and interaction effects terms given in Equation (1) (more details are discussed in Section 4.2).
## 2 Related Work
### Dynamic Adaptation and Real-Time Planning  
Real-time responsiveness is necessary for applications such as the tracking of wild-fires. HPO-RRT* [20] and AP-GWO [21] improve the convergence rate by hierarchical sampling and adaptive leadership mechanisms. Reinforcement-learning-based methods, including HAS-DQN [22] and action selection in DQN [23], facilitate independent decision-making but are daunted by continuous action spaces. The multi-scale discrete layered grid (MS-DLG) model [13] improves conflict detection, and the adaptive path planner (APP) [24] replans trajectories for pollution sampling in response to environmental dynamics. Connectivity-aware methods like dueling DDQN [25] maintain UAV-ground connections, and the improved pied kingfisher optimizer (IPKO) [26] optimizes maritime rescue navigation. A recent work also explores language-model-based command generation. A real-time decision system using LLMs for UAV was proposed in [27], achieving high BLEU scores and low latency in dynamic IoD missions. Despite these improvements, most frameworks achieve the non-seamless integration of global exploration and local exploitation.
# Mid-Infrared Multispectral Gaseous Stimulated Raman Scattering Laser
###### Abstract  
We demonstrated mid-infrared gaseous stimulated Raman scattering lasers in free space. Mixed gases of hydrogen and deuterium were used as Raman gain media in one Raman cell. Pumped by laser pulses at 1064 nm, the first Stokes Raman components at 1560 nm and 1907 nm were generated. A four-wave mixing process with the pump laser at 1064 nm and Raman lasers at 1560 nm and 1907 nm contributed to dramatically reducing the threshold of mid-IR laser generation at 4432 nm. The maximum output peak power of a mid-IR laser at 4432 nm reached 121 kW. Furthermore, by scattering on the rotational transition of deuterium, multispectral mid-IR Raman lasers at wavelengths of 2071 nm, 2266 nm, 2604 nm, 2920 nm, 3322 nm, 3743 nm, 4432 nm, and 5431 nm were also generated. Our results show that this is a convenient method to reduce the threshold and achieve a high power output with mid-IR Raman lasers.  
mid-infrared laser; stimulated Raman scattering; four-wave mixing; multispectral Raman lasers +
Footnote †: journal: Applied Sciences
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 2 Related Work  
In the context of temporal alignment between sequences, there are several studies that have addressed this issue. Moreover, there are a number of real-world problems where these algorithms found application, e.g., image and video alignment, physiological signals (electonencephalogram (EEG) and EMG signals), video classification, etc. In the following paragraphs, we provide a review of previous works on the topic of sequence temporal alignment.  
The most closely related work to our method is that of [PERSON] et al. [23], in which an algorithm called deep canonical time warping (DCTW), which combines canonical correlation analysis (CCA) [24] with DTW, is proposed. The key differences in our approach with respect to DCTW can be summarized as follows. First, we evaluate different similarity metrics, not only CCA, to optimize the parameters of the DNNs used to map the feature vectors extracted from the multiple views into their common, latent subspace. Also, we introduce an autoencoder-based loss function, which helps to regularize the training and avoids naive solutions. Finally, inspired by the work in [25], we also propose the introduction of private latent variables for each view which aim at modeling the specific peculiarities within each view. Our technique also shares similarities with the generalized canonical time warping (GCTW) technique described in [26]. However, in contrast to this technique, TRANSIENCE solves the optimal alignment problem with DTW rather than approximating the temporal warping with a set of predefined monotonic bases and optimizing the weights of these bases with a Gauss-Newton algorithm. Also, being based on CCA, GCTW computes the latent variables by applying a linear transformation to the feature vectors from the different views, while our method uses powerful autoencoders to nonlinearly transform the data, which could be expected to yield better alignments. Another technique similar to our proposal is the one presented in [27], which proposes an extension of CCA, an alignment-agnostic CCA (AA-CCA). AA-CCA models the uncertainty of alignments using a new data correlation term, and this allows to use not only decently aligned data (if available) when learning CCA, but also the unaligned ones. It is based on the optimization of a constrained objective function that combines two terms: a correlation criterion and a context-based regularizer.  
Contrary to now popular sequence-to-sequence (seq2 seq) models (e.g., [28; 29]), our method has different advantages. First, our aim is to align sequences of different lengths to obtain the necessary parallel data to train a machine learning technique, not to model a full-fledged mapping between sequences. In other words, our technique is only used to align the training data, while in test time the articulatory data is directly mapped to speech. Also, it is relatively straightforward to modify our method to process multiple views (more than 2), while the adaptation of seq2 seq models is more involved.
## 3 Results
### Kinetics of Adsorption  
The adsorption kinetic is an essential tool in the study of the adsorption mechanism. Therefore, pseudo-first order, pseudo-second order, and intra-particle diffusion models were used to describe the adsorption process. The constants obtained from these three kinetic models are given in Table 3. The results show that the pseudo-second-order model is better adapted because of the high correlation coefficient (R\({}^{2}\)\(>\) 0.99), good agreement between equilibrium uptakes calculated by the model (q\({}_{2}\)) and the experimentally obtained (q\({}_{e}\)), and the weak average relative errors. Similar results have been observed in the adsorption of MC on activated carbon [40] and factory-rejected tea [18]. Adsorption of MC onto the adsorbent may comprise several steps, but in this study, kinetic models of pseudo-first order and pseudo-second order could not determine the adsorption mechanism. Hence, intra-particle scattering was used to clarify the adsorption mechanism. The results of this study also showed that adsorption of MC takes place in three stages. The first step is attributed to the transfer of MC in the solution to the liquid phase located near the surface of the adsorbent. The second is the diffusion of the adsorbate through the liquid film surrounding the adsorbent grain while the third corresponds to the diffusion of the MC in the pores of the adsorbent. This last step is the limiting step; it is the slowest step of the process [9; 41].  
Figure 6: Effect of temperature on the adsorption efficiency (**a**) and uptake (**b**) of MC onto SSS (conditions: initial MC concentration 60 mg/L, adsorbent dose: 2.5 g/L, pH: 6, flask shaking at 300 rpm at 25 \({}^{\circ}\)C).
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 2 Materials and Methods
### Model Construction
#### 2.2.3 Sperm Head Classification Network  
As mentioned earlier, the symmetry of pyriform and amorphous sperm heads is a useful characteristic for describing their morphology. To make full use of this property, we adapted the flip feature fusion module, originally developed for lane feature extraction [28], and applied it to the task of sperm head morphology classification. As illustrated in Figure 3, the feature maps are combined with their horizontally and vertically flipped versions; these feature maps are processed separately through convolutional and normalization layers, then fused by element-wise addition and activated using the ReLU function. We expect this will enable the model to learn the inherent symmetry of the sperm head.  
To address slight misalignments, such as rotational or positional shifts during sperm head orientation correction, we apply deformable convolutions with a 3 \(\times\) 3 kernel. These convolutions adjust the flipped features by learning offsets from the original feature map, ensuring proper alignment and improving the accuracy of sperm morphology classification. The fused features are then passed into a classification network composed of residual modules and fully connected layers to classify sperm head morphology. All convolutional layers within the flip feature fusion module have output dimensions of 256. The residual modules used in the classification part are taken from layers 3 and 4 of ResNet, while the fully connected layers have a hidden layer with 1024 neurons.
# A Visual Analysis Method for Predicting Material Properties Based on Uncertainty
## 3 Performance-Prediction Model's Design
### Building a Performance Prediction Model
#### 3.2.2 Random Forest (RF) Model  
The random forest algorithm is essentially a combination of K samples that are automatically sampled and repeated in the original sample data to form a new sample. Then, N new features are randomly selected, and the best ones from these features are chosen as samples. Each sample is individually constructed into a decision tree, and finally, these decision trees are randomly voted on, as shown in Figure 2.  
The random forest algorithm is the result of the optimization of decision tree algorithms and was first proposed by [PERSON] in 2001. Its essence is to take K samples by automatically sampling the original sample data with put-back repetitions and combining them into a new sample. Then N new features are randomly selected, and the best ones from these features are chosen as samples, and each sample is constructed as a separate decision tree. Finally, these decision trees are voted on by a random method, as shown in Equation (8). The one with the most votes is taken as the final decision result (Figure 2).  
\[D(x)=\arg\max_{T}\sum_{j=1}^{K}L(d_{j}(x)=T) \tag{8}\]where \(D(x)\) represents the final decision result of the random forest model, \(d_{j}(x)\) represents the \(j\)th decision tree, \(T\) is the predicted outcome of each decision tree, and \(L()\) is the indicative function.  
The random forest algorithm uses the Bootstrap sampling method compared to other prediction algorithms. Hence, its subset of features is randomized, allows comparison of the importance of each feature value, generalizes well, is more tolerant of noise values, does not require specialized feature selection, and therefore, has the advantage of being used on many current datasets.  
However, in the process of fluorinated-material performance prediction, this paper also found some defects of the random forest. When faced with highly unbalanced data, inaccurate classification often occurs, and at this time, if an inappropriate evaluation index is chosen, it will affect the predictiveness of the model. When the redundant features in the material dataset are complex, there are problems such as too much noise and easy overfitting after model training.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Apply Orbit File  
Orbit state vectors, contained within the metadata information of SAR products, are generally not accurate. The precise orbits of satellites are determined after several days and are available days-to-weeks after the generation of the product. The operation of applying a precise orbit available in SNAP allows the automatic download and update of the orbit state vectors for each SAR scene in its product metadata, providing an accurate satellite position and velocity information.