esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation and Optimization of the Airflow Field of a Forage Drum Mower
## 3 Material and Method  
### Numerical Simulation  
#### 3.1.1 Simulation Scheme  
To simulate the airflow field around the double-drum cutter, the perturbation of the fluid by rotating components is required. In the present study, the airflow field is numerically simulated through the immersed solid calculation performed using the CFX software [15]. Figure 3 shows the flow scheme of the numerical simulation. Firstly, the geometric modeling of the double-drum cutter was performed using Solidworks software. Secondly, it was imported into ICEM software for meshing. Thirdly, the physical model was defined by the CFX-Pre module, such as setting the boundary conditions, initial conditions, motion parameters, and solving parameters of each solid and fluid domain. Finally, the numerical solution was obtained through the CFX-Solver module and the CFX-Post module was used to visualize the simulation results and extract data.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
### Write  
The Proxy can reject a request without forwarding it to the Client if one of the following conditions is met: (i) the specified content format is not supported; (ii) the value of the incoming resource does not match the expected format; (iii) the value of the incoming resource is not in the range specified in the Object definition; (iv) the incoming resource is an Objink and its value is not valid; (v) the deletion or allocation of an instance of a multiple-instance resource is not allowed (see Figure 4, Step 2).  
Moreover, the Proxy checks all the requests to reject them if they are not allowed, or if they are incorrect, i.e., if the required parameters are not present in the uri-path/uri-query of the message; or if the specified Object ID, Object Instance ID, or Resource does not exist or it is not supported by the Client, or if the specified format is not supported (see Figure 4, Step 2).  
In addition to this, the Core function enables the support of QoS-aware services by orchestrating the extended functions. These functions are optional: the Proxy can choose not to implement them, so, in this case its only component is the Core; or it can choose to implement one, two, or all of them. The Proxy takes this decision according to the Server requirements. Indeed, before the Server starts sending messages to the Client, there is a configuration phase where the Server and the Proxy establishes a service level agreement: during this phase the Server specifies its QoS requirements in terms of service latency, data freshness, or both.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments
### Performance Under Extreme Conditions  
To validate the robustness of the proposed method under extreme conditions where input image pairs consist of completely sharp and completely blurred images, we conducted a challenging experiment using images from the MFI-WHU dataset [36], as illustrated in Figure 9. For each image, we synthesized a corresponding completely blurred version using Gaussian blur kernels.  
The paired images, comprising the original sharp image and its completely blurred counterpart, were then fed into the proposed model to generate the decision maps and fusion results shown in the figure. Remarkably, the proposed model maintains excellent performance even under these extreme conditions, successfully distinguishing between defocused and focused regions while producing accurate decision maps for effective image fusion. This demonstrates the model's robustness and reliability across diverse imaging conditions.  
Figure 9: Performance evaluation under extreme conditions. The proposed method demonstrates robust performance when processing image pairs consisting of completely sharp and completely blurred images. From left to right: original sharp image, synthetically blurred counterpart, generated decision map, and final fusion result. The model successfully distinguishes between focused and defocused regions even in these challenging scenarios.
## 2 Materials and Methods
### The Construction Project Model
#### 2.3.2 The Optimization Model
The objective function assumes minimization of the total cost of the construction project. It consists of:  
* direct costs \(U_{dir}\), which are the sum of the costs of all activities in the project,
* indirect costs \(U_{indir}\) (all costs that do not become a final part of the installation but are crucial for the operation of the contractor) depending on the duration of the project,
* penalty costs (optional) \(U_{pen}\) charged in case of delays (failure to meet the directive deadlines for the completion of construction works),
* cost bonuses (optional) \(U_{inc}\) awarded in case of finishing the works early (earlier than directive deadlines). Such bonuses reduce the overall cost of the project. More precisely, the optimization task focuses on finding the minimum total cost of the project for the permutation \(\pi^{*}\) and for the decision variable \(R^{*}\), for which:  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]4  
where  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]5  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]6  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]7  
* \(c^{ind}\)--daily indirect construction costs,
* \(F_{n,m}\)--deadline for completion of works in the project,  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]8  
* \(F_{i}^{d}\)--deadline for completion of works on \(i\) unit,
* \(F_{i,m}\)--the finish time for construction works on \(i\) unit,
* \(c^{pen}\)--daily cost of the penalty for exceeding the directive deadlines for finishing works on units,  
\[B=\{B_{1},\,B_{2},\,B_{3},\,...,\,B_{j},\,...,\,B_{m}\}.\]9  
* \(c^{inc}\)--the contractor's daily bonus for not exceeding the directive deadlines for completion of works in the units,
* \(\Pi\)--a set of all possible permutations in a given project,
* \(n\)--number of all units (objects),
* \(m\)--number of works (activities) to be completed on each of the units. The presented problem of discrete optimization is a variant of the flow shop permutation problem. This problem is often considered in the theory of job scheduling. Due to the NP-difficulty of the presented problem and the existence of two different decision variables, it was solved using a dedicated algorithm using the metaheuristic simulated annealing algorithm.
# Identification of Critical Road Links Based on Static and Dynamic Features [PERSON]
###### Abstract  
Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road links, and ensuring the smooth operation of urban transportation systems. However, the existing road link importance evaluation metrics mostly rely on complex network metrics and traffic metrics, which may lead to biased results. In this paper, we propose a critical road link identification framework based on the fusion of dynamic and static features. First, we propose a directed dual topological traffic network model that considers the subjectivity of road links, traffic circulation characteristics, and time-varying characteristics, which addresses the limitations of existing traffic network topology construction. Subsequently, we employ a novel graph representation learning network to learn the road link node low-dimensional embeddings. Finally, we utilize clustering algorithms to cluster each road link node and evaluate critical road links using the average importance evaluation indicator of different categories. The results of comparison experiments using real-world data demonstrate the clear superiority and effectiveness of our proposed method. Specifically, our method is able to achieve a reduction in traffic network efficiency of 70-75% when less than 25% of the road links are removed. In contrast, the other baseline methods only achieve a reduction of 50-70% when removing the same proportion of road links. These findings highlight the significant advantages of our approach in identifying the critical links.  
urban traffic network; critical links; network representation learning; intelligent transportation system; data mining +
Footnote †: journal: Applied
## 3 Literature Review
### Convolutional Neural Network Models
#### 3.1.5 ShuffleNet  
ShuffleNet proposes channel shuffling and group convolution layers to reduce the computational cost and complexity [27]. The purpose of channel shuffling is to shuffle and cross the features from channels so that the feature weights will be the combination of the channels. In the proposed framework, we shuffle every four-channel features in the channel-shuffling layers. This operation leads to a superior structure in the CNN architecture due to multiple feature weights existing in the convolutional layers [27]. Furthermore, the channel shuffling layers are combined with the group convolution layers to achieve a more powerful CNN model. Group convolution layers were introduced in AlexNet [28] and used with different approaches by [29; 30] and certainly in MobileNetV1 [31], which is the fundamental architecture of ShuffleNet. Figure 7 shows the basic architecture of ShuffleNet. Figure 7a is the ShuffleNet unit with stride 1 for dimension reduction, while Figure 7b is the ShuffleNet unit with stride 2.  
Figure 6: The basic architecture of MobileNetV2: (**a**) MobileNetV2 units with stride = 1, (**b**) MobileNetV2 units with stride = 2.  
Figure 7: The basic architecture of ShuffleNet: (**a**) ShuffleNet units with stride = 1, (**b**) ShuffleNet units with stride = 2.
## 3 Remote Sensing Image Scene Understanding  
Derived from the traditional machine learning field, deep learning has established a multi-layer processing learning model that can hierarchically describe the characteristics of raw data. Consequently, deep learning has made great progress in solving many problems of pattern recognition and the traditional machine learning algorithm by nonlinear processing. One of the reasons why deep learning can achieve high accuracy and efficiency is because a complicated deep learning architecture can be fitted with a large number of training data with diversity and variability to form a better deep network structure. According to the level of scene classification, it can be divided into low-level feature description and middle-level feature description. The middle-level features are aggregation and integration of low-level features, including BoVW [13], SVM [14] based on the structural risk minimization principle, a random forest [15] classifier based on bootstrap resampling [16], and the Markov random field (MRF) [17]. In addition, with the rapid development of artificial intelligence (AI), sparse coding and deep learning algorithms are widely used in scene classification because of their high classification accuracy.  
As an important application, scene classification needs a big volume of data, and RS datasets that are frequently used for scene classification are introduced in Section 3.1. Furthermore, methods of supervision based on deep learning have their own strengths and weaknesses, which are identified in Section 3.2.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 3 Incorporation of By-Products
### Culinary Quality
#### 3.2.2 Swelling, Water Absorption, and Weight Loss Index
However, if the incorporated by-product has a high water retention capacity, it may compensate for or even reverse this trend. As a result, water absorption outcomes will largely depend on the water absorption capacity of the by-product. For example, [PERSON] et al. [93] observed that when a tomato processing by-product was included, water absorption during cooking decreased. However, this could be increased by incorporating hydrocolloids with a high water absorption capacity, such as xanthan or guar gum. Nevertheless, this incorporation did not reduce cooking losses, which are more related to gluten network deterioration, and increased the hardness of the pasta. It is also necessary to consider dough hydration since higher dough hydration results in lower water absorption during cooking. Studies that adjust dough hydration based on the water absorption capacity of the incorporated by-product observe lower water absorption during cooking compared to those that do not adjust hydration. The main issue is that most studies that modify dough hydration do not specify the exact criterion for the changes they make, and there is no clear measure to indicate the basis for these modifications beyond the experience or observations of those making the pasta. This is an area that should be improved in such studies. This is particularly important in fresh pasta, although dried pasta undergoes dehydration before cooking. In these cases, the problem is that most studies do not report the final moisture content of the pasta. A drying time and temperature are typically provided, along with a maximum moisture content, usually between 12% and 12.5%. However, it is unlikely that all types of pasta will reach the same final moisture content with the same treatment, as it tends to be higher in pasta containing ingredients that retain water better or when hydration is increased. For example, while [PERSON] et al. [95] increased the water content of the dough based on the water absorption capacity of the incorporated by-product and observed a reduction in water absorption, [PERSON] et al. [71], who did not adjust the hydration, observed higher water absorption. However, this is not always the case, and [PERSON] et al. [52], who did increase dough hydration when incorporating by-products, observed an increased final absorption in those cases. In general, explanations for these effects are not provided, and in most cases, the results are compared to other studies with similar outcomes, without addressing contradictions with those reporting opposite results.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
###### Abstract
In general, most of the techniques published to date on EM MWD signal processing are either data-intensive, time-intensive, focused on signal modulations other than the (binary) phase shift key, or ineffective with noisy recovered or highly noisy recorded signals. According to the above challenges, we need a suitable workflow for electromagnetic telemetry (EMT) signal recovery and an ANN model for the best demodulation of EMT signals, with very low signal-to-noise ratio. Therefore, with the aim of increasing the effective operational depth of EM telemetry, we introduce fuzzy wavelet neural network (FWNN) techniques as a highly effective tool to develop an ANN model that can be used for the best prediction in EMT signal demodulation. In the proposed workflow, first, the regulated multi-channel adaptive noise canceling technique is applied to the EM MWD noise problem. Based on the regularized variable step size least mean square adaptive correlation detection algorithm (RSVSLMS or RVSSLMS), with the improvement of in-band noise processing ability, the retrieved signal SNR is increased [12]. Then, the demodulation systems based on the backpropagation neural network and the fuzzy wavelet neural network is introduced. The remainder of this paper is as follows: Section 2 includes an overview of ANN architecture. Section 3 explains the structure of fuzzy wavelet neural networks. It also explains the materials and methodology. Section 4 includes the examples and results, and Section 5 concludes this research.
## 3 Data and Methods
### Rst-Flood
With regards to cloud shadows, after a visual inspection of images within the used dataset, a simple 2 km (i.e., five-and six-pixel for MODIS and VIIRS, respectively) buffer around the detected clouds has been applied, thus ensuring a good trade-off between the possibility to produce false positives and omission errors.
## 3 Results and Discussion
### Titractable Acidity Determination  
Titrtable acidity is an important parameter in determining the puree quality, which also denotes the degree of ripening of the apples and vegetables used. Previous studies have shown that the trend in producing fruit pures is that organic acids increase during the heat treatment process and during storage [37].  
The acidity determined for the fresh puree was similar for all ten varieties of pureses, with values ranging between 0.30% and 0.45%. Following the application of heat treatment, the acidity values decreased, reaching values of a maximum of 0.40% (Table 3).  
Our findings were consistent with those reported by [PERSON] and [PERSON] in 2020 [38], who studied 24 fruit-and-vegetable-based baby food pures, for which values between 0.23-0.54% were determined.  
\begin{table}
\begin{tabular}{r c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Moisture**} & \multicolumn{2}{c}{**Ash Content\%**} & \multicolumn{2}{c}{**Acidity Determination**} \\  & \multicolumn{2}{c}{**Determination\%**} & & & & **(mg/100 g)** \\ \hline
**Puree** & **Unsterilized** & **Sterilized** & **Unsterilized** & **Sterilized** & **Unsterilized** & **Sterilized** \\
**Sample** & **Product** & **Product** & **Product** & **Product** & **Product** & **Product** \\ \hline
1 & 84.0 \(\pm\) 0.2 & 81.1 \(\pm\) 0.1 & 2.9 \(\pm\) 0.1 & 3.3 \(\pm\) 0.1 & 0.34 \(\pm\) 0.01 & 0.29 \(\pm\) 0.01 \\
2 & 84.4 \(\pm\) 0.1 & 82.3 \(\pm\) 0.1 & 1.8 \(\pm\) 0.1 & 2.7 \(\pm\) 0.2 & 0.30 \(\pm\) 0.02 & 0.26 \(\pm\) 0.01 \\
3 & 87.2 \(\pm\) 0.2 & 84.5 \(\pm\) 0.2 & 1.9 \(\pm\) 0.2 & 3.0 \(\pm\) 0.1 & 0.42 \(\pm\) 0.01 & 0.27 \(\pm\) 0.02 \\
4 & 83.4 \(\pm\) 0.4 & 82.6 \(\pm\) 0.3 & 1.6 \(\pm\) 0.3 & 1.1 \(\pm\) 0.1 & 0.26 \(\pm\) 0.01 & 0.19 \(\pm\) 0.01 \\
5 & 86.7 \(\pm\) 0.2 & 83.7 \(\pm\) 0.2 & 2.4 \(\pm\) 0.1 & 2.7 \(\pm\) 0.3 & 0.45 \(\pm\) 0.02 & 0.4 \(\pm\) 0.02 \\
6 & 82.6 \(\pm\) 0.1 & 81.1 \(\pm\) 0.1 & 2.6 \(\pm\) 0.3 & 2.9 \(\pm\) 0.2 & 0.38 \(\pm\) 0.01 & 0.37 \(\pm\) 0.01 \\
7 & 86.2 \(\pm\) 0.1 & 84.7 \(\pm\) 0.1 & 3.7 \(\pm\) 0.2 & 2.6 \(\pm\) 0.3 & 0.4 \(\pm\) 0.02 & 0.21 \(\pm\) 0.02 \\
8 & 80.7 \(\pm\) 0.3 & 78.1 \(\pm\) 0.1 & 2.3 \(\pm\) 0.1 & 3.9 \(\pm\) 0.1 & 0.48 \(\pm\) 0.01 & 0.32 \(\pm\) 0.01 \\
9 & 84.5 \(\pm\) 0.3 & 81.0 \(\pm\) 0.2 & 1.6 \(\pm\) 0.2 & 3.4 \(\pm\) 0.2 & 0.29 \(\pm\) 0.02 & 0.24 \(\pm\) 0.02 \\
10 & 82.0 \(\pm\) 0.2 & 76.5 \(\pm\) 0.2 & 4.7 \(\pm\) 0.1 & 4.0 \(\pm\) 0.3 & 0.41 \(\pm\) 0.01 & 0.34 \(\pm\) 0.01 \\ \hline \hline \end{tabular}  
* Puree sample column represents the apple variety used in order to obtain the puree: 1—Champion, 2—Elstar, 3—Baujade 4—Grimnes Golden, 5—Granny Smith, 6—Fatul, 7—[PERSON], 8—Juliana, 9—Dommesec, 10—Parmen Clone.  
\end{table}
Table 3: Moisture, ash content and tittralable acidity determination.
Technological Aspects of a Reparation of the Leading Edge of Helicopter Main Rotor Blades in Field Conditions  
[PERSON]  
1 Air Force Institute of Technology, Airworthiness Division, 01-494 Warsaw, Poland; 1  
[PERSON]  
1 Air Force Institute of Technology, Airworthiness Division, 01-494 Warsaw, Poland; 1
During maintenance work on the main rotor blades, damage is detected in the form of a piece of the metal shield peeling off the leading edge (Figure 2). The detection of the damage makes it necessary to decide whether to repair the blade or to withdraw it from use.  
The lack of repair technology for this type of damage can lead to the interruption of operational activities during military missions. Therefore, research was undertaken to develop a repair technology to quickly return the blades to service.  
The high progressive velocity of the leading edge of the main rotor blade causes it to flap around at high speed. Dust, droplets of water, and ice in the air hit the leading edge at speeds of up to 200 m/s. Therefore, a metal shield protects the leading edge. The loss of the shield can cause damage to the blade. Local peeling quickly leads to the complete detachment of the shield. The leading edge unprotected by the metal cap is then rapidly eroded.  
The available scientific publications on leading edges mainly focus on composite structures. Repairs of main rotors concern damage in the form of delamination or puncture [1]. Bonded joints are common in aerospace technology [2]. The problem of the erosion of unprotected metal shield blades of helicopter and wind turbine rotors composed of composite material was well illustrated in [3]. Repairs of damage to this type of blade are presented in publications [4; 5]. The repair in both cases consists of the removal of the damaged composite layers and reconstructing the structure from new composite layers. Depending on the blade manufacturing technology, repair is performed with the use of wet or prepreg technology.  
No publications were found on damage formation and the repair of shields debonding from the leading edge. Therefore, during the research presented in this paper, use was made of publications on bonding ply, surface preparation for bonding metals and rubber, and thermo-physical research on polymeric materials.  
Figure 1: Structural baseline model of a Mi helicopter rotor blade.  
Figure 2: Damage to the leading edge—debonding shield from the leading edge: (**a**) Diagram; (**b**) Damage to a Mi-2 helicopter rotor blade (series 22-2700-3000).  
With respect to technological development, useful information was obtained from websites on helicopter blade repairs [6; 7], which presented repair techniques for other typical damage to metal blades.  
In addition, experience gained during other studies related to repairs to the metal blades of main rotors [8] was used. The phenomenon of the negative effects of temperature on polymeric materials was also addressed.  
The aim of the paper is to present the validation/qualification of a developed repair technology for the leading edge of main helicopter rotor blades in field conditions.