esa-sceva/satcom-chunk-collection huggingface dataset:
# Microwave-Supported Modulation of Functional Characteristics of Gluten-Free Breads
## 3 Results  
The bread dough consistency and its ability to maintain water inside during the baking process are reflected by the bake loss, while the gas retention ability and elasticity of the dough impact the resulting specific volume (Table 1). The highest bake loss was observed for the rice control bread, showing its poor ability to maintain water inside the matrix. Similar results were observed by [PERSON] et al., 2019, for rice bread; however, the authors used HPMC as an additive, which helped to maintain water inside the bread [12]. That behavior shows the importance of specific agent additions in gluten-free bread made from native gluten-free flours. The clean label trend forces the removal of all additives, which challenges the bread baking industry [6]. The addition of buckwheat native flours reduced the bake loss by 15% and 19% for Polish and Spanish samples, respectively. Unexpected characteristics were shown by the brown teff sample where the 50% addition of native flour increased the bake loss by 11% compared to the control rice bread. This behavior is most probably related to fiber content, which was higher in buckwheat flours, while teff flours, being more abundant in starch, exhibited the same characteristics as rice, being prone to fast water loss. Meanwhile, the white teff blends lowered the bake loss by 10%. For each MW-HMT flour, the bake loss of breads was significantly reduced, whereas for buckwheat flour, the values obtained for rice bread were reached with HPMC addition [12]. Also, white teff flour after MW-HMT showed a much improved bake loss of 22.5% compared to 100% in rice bread and 14% in the native flour blend bread. Also, the MW-HMT brown teff-based bread revealed improvement in water maintenance, reaching values similar to the native white teff. The 100% rice bread revealed the lowest decrease in specific volume, mainly due to the huge initial water loss after baking. Both buckwheat native flour-based breads presented two- or three-times higher specific volume losses of 20% and 28% for Polish and Spanish buckwheat, respectively. MW-HMT buckwheat flours had a stronger capacity to maintain water inside as their specific volume decreases were only 15% and 9% for Polish and Spanish buckwheat, respectively. Both teff native flour-based breads presented two- or three-times higher specific volume losses of 22% and 20% for white and brown teff, respectively. MW-HMT teff flours also showed improved maintenance of water inside the loaf as their specific volume decreases were only 8% and 5% for white and brown teff, respectively. The volume of the bread was created as a result of leavening; the elasticity of the dough matrix became fixed during baking. The air cells built from mainly starch can better resist the shrinkage and collapse of the foamy, fixed structure compared to the doughs that are richer in fiber, where water loss is slowed down but the air cell walls, being elastic, tend to collapse. The internal structure will also be reflected in texture profile (Tables 2 and 3).
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit
#### 2.1.3 High-Pass Negative Group Delay Circuits with Different \(R_{1}\) Values  
When the values of \(C_{1}\), \(L_{1}\), \(L_{2}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged from Table 1, we change the values of \(R_{1}\) to \(10\)\(\Omega\), \(50\)\(\Omega\), and \(100\)\(\Omega\) in sequence and observe the changes in the circuit transfer function and group delay. The circuit results in these three cases are shown in Table 4 and Figures 9 and 10. Through observation, it is found that, as the value of resistance \(R_{1}\) decreases, the negative group delay value of the circuit is no longer flat. However, as \(R_{1}\) decreases, the minimum negative group delay of the high-pass circuit decreases, and the circuit insertion loss also decreases with the increase in resistance \(R_{1}\).
## 2 UWB Patch Antenna Design
### The Flow Chart of the Proposed Approach  
Figure 2 shows the flow chart of the proposed mutual coupling reduction for the UWB MIMO antenna. Based on the geometrical structure in Figure 1, the antenna parameters are obtained using a set of design equations. Patches are beveled at both corners of the antenna to enhance the impedance bandwidth. In addition, a small cut is provided on the ground plane of the antenna behind the patch to achieve impedance matching. Thereafter, two MEBGs are placed at the back of the patch in symmetrical order to optimize the performance of the antenna. The antenna array elements are separated by a far-field distance \(d\). The value of \(d\) was varied, and corresponding mutual coupling values were observed for each distance. The far-field distance is varied until a satisfactory (lowest) MC value was obtained.
## 3 Materials and Methods
### MI Algorithms  
As we have introduced, the origins of the CFD systems date back to around the late 1990s. Initially, these systems were almost always based on experts' rules, and with the passage of time until now, ML systems have been developed to enhance the accuracy [2; 4; 6; 41]. In this respect, linear classifiers based on ML are very useful in CFD because they can be seen as a transformation from the space of the input features to weightings to each of these features in the decision process [13]. From this point of view, those weights summarize the contribution of each feature in the decision process, and they can be used to interpret the models. A detailed analysis of learning methods has been proposed with the efforts presented in the companion paper [1]. In the following lines, we first introduce the notation used throughout the paper. Let \(\mathbf{X}\in\mathbb{R}^{N\times L}\) be the input data matrix, containing the input set of vectors in rows, with \(N\) observations of \(L\) features, where \(\mathbf{x}_{n}\) is a vector with \(L\) features for \(n=1,\ldots,N\). We consider a classification problem with a binary output variable \(\mathbf{y}\in\mathbb{R}^{N}\), grouped in the observations in a vector such that \(\mathbf{y}_{n}\in\{-1,+1\}\) for \(n=1,\ldots,N\). Second, we present the summary of new nonlinear ML algorithms, such as the autoencoders, which we use in the rest of the article.  
An autoencoder [42] is a specific type of neural network, which is designed to encode the input into a compressed and meaningful representation, and then to decode it back such that the reconstructed input is as similar as possible to the original one. The potential of autoencoders is to compress high-dimensional data into latent representations, that is why they are defined as two parts: an encoder and a decoder, where the encoder learns to map the high-dimensional input space to a latent vector space, and the decoder maps the latent vector space to the original uncompressed input space. Overall, the output data matrix \(\mathbf{\hat{X}}\) is the result of reconstructing the original input data matrix \(\mathbf{X}\). We can see the architecture of a basic autoencoder in Figure 1.  
The problem, as formally defined in [43], consists of the transformation from an \(L\) dimensional domain or \(R^{L}\) toward a lower dimensional space, \(R^{P}\), recalled as encoder, followed by a second transformation from the latent space \(R^{P}\) to the reconstructed space \(R^{L}\), recalled as decoder. This problem is defined to minimize the reconstruction error after the encoding-decoding procedure.  
Figure 1: Architecture of an undercomplete autoencoder with a single encoding layer and a single decoding layer.
# Maximizing the Capacity of Edge Networks with Multicasting
## 8 Simulation Results and Performance Evaluation
### Simulation Results
Generally, network capacity is improved because the distribution intensity of SBS becomes larger and the distribution is relatively dense, the distance between SBSs shortens and the capacity expands. In addition, the advantages of multicast communication are strongly apparent, and it is always superior to unicast communication. This is because multicast communication can serve multi-user requests simultaneously. Although the unicast transmission adopted in [25] does not consider the influence of SBS distribution, as the SBS distribution intensity increases, the distance between SBSs changes, therefore, capacity will also fluctuate, but generally, shows no significant improvement. The capacity change effected by the scheme proposed in this paper is always higher than that in the study [25], because as it expands, more SBSs and users serve the multicast tree, thus more rapidly improving its capacity. The capacity in [19] relies on the pair of source and destinations, therefore its optimization effect would not significantly change as the number of SBSs increases. Figure 4: The fork number of the multicast tree vs. capacity. Figure 5: Content copy number vs. capacity. The parameter \(\lambda_{u}\) determines the distribution density of users. A similar result is achieved in Figure 7, which verifies that the schemes proposed in this study can significantly improve the capacity with an increase in the distribution intensity of users, but there is no significant change in the papers [19; 25]. This is because the proposed multicast tree structure can quickly serve more users with the increasing in \(\lambda_{u}\), which is also conducive to shortening the transmission distance and further improving the capacity. The more times the request is sent, the more energy the system consumes, so the simulation results on energy efficiency are shown in Figure 8. The scheme proposed in this paper greatly reduces energy efficiency, and it can be seen from the figure that multicast reduces by 80% on average. With an increase in the \(K\) value, the energy efficiency of the system increases. However, increasing in energy efficiency within multicasting is far slower than those within the unicast. This is because the multicast transmission method can quickly respond to user requests and reduce the transmission times. Figure 6: The distribution density of users vs. capacity [19; 25]. Figure 7: The distribution density of users vs. capacity [19; 25].
###### Abstract  
It is well known that mechanical systems require supervision and maintenance procedures. There are a lot of condition monitoring techniques that are commonly used, and in the era of IoT and predictive maintenance one may find plenty of solutions for various applications. Unfortunately in the case of belt conveyors used in underground mining a list of possible solutions shrinks quickly. The reason is that they are specific mechanical systems--the typical conveyor is located in the mining tunnel and its length may vary between 100 and 1000 m. According to mining regulations, visual inspection of the conveyor route should be done before it will start the operation. On the other hand, since environmental conditions in mining tunnels are extremely harsh and the risk of accidents is high, there is a tendency to minimize human presence in the tunnels. In this paper, we propose a prototype of an inspection robot based on a UGV platform that could support maintenance staff during the inspection. At present, the robot is controlled by an operator using radio however, we plan to make it autonomous. Moreover, its support could be significant--the robot can \"see\" elements of the conveyor route (RGB camera) and can identify hot spots using infrared thermography. Moreover, the detected hot spots could be localized and its position can be stored together with both types of images. In parallel, it is possible to preview images in a real-time and stored data allow analysing state of conveyor system after the inspection mission. It is also important that due to radio control systems, an operator can stay in a safe place. Such a robot can be classified as a mobile monitoring system for spatially distributed underground infrastructure.  
Keywords:inspection robotics; underground mining; belt conveyor; damaged idler; infrared thermography +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
Focusing on the fatigue failure of the fillet weld of the tie rod seat of a locomotive bogie during its design life, the causes of failure and methods of improvement were studied according to UIC615-4 standard load conditions and the Goodman fatigue limit diagram. The main results can be summarized as follows:  
1. When there are the maximum clearance at weld roots, maximum incomplete fusion of sidewalls, maximum incomplete fusion at weld roots, and maximum porosity allowable in ISO 5817, the stress amplitude separately increases by 70-97%, 53-55%, 40-46%, and 19-34%;
2. When various types of defects with the maximum size allowable in the ISO 5817 standard are present in the weld alone, the static and fatigue strengths of fillet welds with a throat depth of 6 mm of the traction rod bracket can still meet the requirement codified in the UIC615-4 standard. The reasons for fatigue failure of fillet welds of the tie rod seat during the design life are complex; analysis shows that in actual fillet welds, defects such as root gap, a lack of fusion, and porosity may occur at the same time, which is one of the possible reasons for fatigue failure of fillet welds during their design life;
3. According to the size of the frame and the traction rod brackets, a strengthening scheme for increasing the throat depth of fillet welds of traction rod brackets to 8  
Figure 21: (**a**) Goodman diagram of stress at the weld root of the type A traction rod bracket under UIC conditions with residual stress induced by welding; (**b**) Goodman diagram of stress at the weld root of the type B traction rod bracket under UIC conditions with residual stress induced by welding.  
mm was presented. Calculations show that for new structures subjected to overall post-veld stress-relief thermal treatment, the maximum stress amplitude decreases by 5-29% when increasing the throat depth of fillet welds from 6 to 8 mm;
4. For structures in service, when the throat depth of fillet welds increases from 6 to 8 mm through repair welding, the peak residual stress at the weld root after repair welding can reach 383 MPa; because overall stress-relief thermal treatments cannot be performed on repair-welded structures, the fatigue strength of repair-welded fillet welds cannot meet the requirements codified in the UIC615-4 standard, so local stress-relief treatments must be applied in the welded zone.  
Methodology, L.-X.L.; Software, Q.-K.X.; Resources, Y.-M.H.; Writing--original draft, Y.-Q.W.; Project administration, M.-X.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shaanxi Province, China Grant (No. 2020 JM-484) and Shanxi Province Qinchuangyuan \"Scientists + Engineers\" Team Construction Grant (No. 2022 KJ032).  
Not applicable.  
The authors declare no conflict of interest.
Figure A2. Design drawing of the type A traction rod bracket.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 6 Model Test Result  
The test results are divided into the accuracy evaluation of EMI signal classification and threat level prediction. According to the evaluation indicators in Section 5, the output performance of the MIM-CNN with different input channels is compared. For the model structure with relatively good performance, the confusion matrix is used to analyze the classification accuracy of each EMI signal category. Besides, the boxplot is drawn to evaluate the model prediction accuracy under different EMI threat levels.
## 2 Two Degrees of Freedom (2-DOF) Model
### Soil Parameters  
The dependency of period elongation and damping increase have been shown to depend on the coefficient \(\beta\) and consequently on the soil mass that is relatively complex to calculate. In this regard, following the approach in [1], soil frequency can be calculated as:  
\[\omega_{g}=\frac{\pi V_{s}}{2H} \tag{29}\]  
Therefore, considering the soil as an equivalent 1-DOF system:  
\[\omega_{g}=\frac{\pi V_{s}}{2H}=\sqrt{\frac{k_{g}}{m_{g}}} \tag{30}\]  
where vs. is the shear wave velocity and H is the depth of a uniform layer of isotropic, linear elastic soil underlying rigid bedrock [1], it is possible to calculate the soil mass as:  
\[m_{g}=\frac{4k_{g}H^{2}}{\pi^{2}V_{g}{}^{2}} \tag{31}\]Finally, the stiffness of the soil can be defined [1] as:  
\[k_{g}=\frac{8 Ga}{2-\
u} \tag{32}\]  
It is worth noting that previous publications, such as [1], proposed that period elongation and damping increase depend on the ratio between the translational and rotational stiffness of the structure and of the system. Here instead \(\alpha\) and \(\beta\) are defined by considering the physical parameters of both the soil (see Equations (31) and (32)) and the structure and thus avoiding to consider empirical definitions of the stiffness. In addition, Equations (20) and (28) confirmed that (1) SSI is relatively significant for cases of flexible structures on stiff soil deposits, as shown in [1]; (2) SSI may be significant for stiff structures founded on soft soils; (3) the fundamental period of soil-structure system is longer than that of fixed-base structure (period elongation); (4) damping of soil-structure system is higher than the damping of the structure alone.
# Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
## 2 Materials and Methods  
### Database  
The development of a classification system is intimately linked to the database used [8; 9]. In this work, a public dataset (provided by AIDA: AutoImmunite, Diagnostic Assiste par ordinateur project) was used [5]. At the time, this dataset is the only public dataset with positive and negative wells, while the other two main HEp-2 image public datasets [10; 11] contain only positive and weak positive images, but not negative cases. This database consists of two parts--one part is public, the other is private.  
The AIDA HEp-2 public Database is a subset of the full AIDA database, where three physician experts have expressed, independently, unanimous opinion on reporting. This database is available to the scientific community, and to our knowledge, until today, it is the biggest HEp-2 image public database, with a total of 2080 images; 1498 images show positive fluorescence intensity, 582 show negative. These images correspond to the routine IIF technique performed in different hospitals for autoimmune diseases diagnosis, and were thus reported by senior immunologists. The database contains fluorescence positive sera with a variety of more than twenty staining patterns. They carried out serial dilutions and considered the dilution of 1/80 as positive. HEp-2 images have been acquired by means of a unit consisting of a fluorescence microscope (40-fold magnification). The images have 24 bits color depth and are stored in common image file formats.  
The public database can be downloaded, after registration, from the download section of the site ([[http://www.aidaproject.net/downloads](http://www.aidaproject.net/downloads)]([http://www.aidaproject.net/downloads](http://www.aidaproject.net/downloads))). The private part of the AIDA Database is structured in the same way as the public part, and it has about 20,000 images. However, among all these images, only a number of about 3000 have triple concordance of reports. This part of the AIDA database is only accessible to the partners who participated in the project.  
The classification system here proposed has been trained using the private part of the database and tested on the public part.
## 5 Performance Analysis
### Numerical Simulation
#### 5.2.1 Multiplexing Efficiency  
Multiplexing efficiency represents the power proportion of useful signal components in the whole constant-envelope signal, which is not only influenced by the IM power in the ACE-FHBOC but also depends on the receiving strategy. If the whole ACE-FHBOC signal can be reproduced for a local replica and processed with a wideband receiver, the multiplexing efficiency can reach 100%. Another strategy is to receive each signal component independently with a local replica of the corresponding subcarrier in the decomposed form of ACE-FHBOC. For the second strategy, the multiplexing efficiency is defined as  
\[\eta=\frac{\sum\limits_{i}R_{i}^{2}(0)}{A^{2}},\ i\in\{\text{UI,LI,UQ,LQ}\} \tag{27}\]  
where \(A\) is the amplitude of the multiplexed signal. \(R_{i}(t)\) is the autocorrelation for the \(i\)th signal component. Figure 12 shows the multiplexing efficiency versus power ratio \(\beta^{2}\). It is evident that Type-I, Type-II, and Type-III ACE-FHBOC and ACE-BOC have the same multiplexing efficiency. A larger power ratio \(\beta^{2}\) means higher multiplexing efficiency.
## 2 Related Work  
### Definition of FMCG Supply Chain and Sales Forecasting  
The FMCG supply chain encompasses the end-to-end processes involved in delivering fast-moving consumer goods from manufacturers to consumers [5; 6]. This includes procurement of raw materials, manufacturing, inventory management, warehousing, transportation, and retail distribution. An efficient FMCG supply chain ensures that products are available at the right time, in the right quantity, and at the right place to meet consumer demand.  
Sales forecasting for FMCG refers to the process of predicting future demand for FMCG products based on historical sales data, market trends, seasonal patterns, promotional activities, and external factors such as economic conditions or weather fluctuations. Accurate sales forecasting plays a critical role in ensuring demand-supply alignment, optimizing inventory levels, and minimizing waste [7]. The key elements of FMCG forecasting are:  
* Dynamic Demand Patterns: FMCG products are characterized by high demand volatility driven by seasonality, promotions, and shifts in consumer preferences.
* Short Product Life Cycles: Many FMCG products, such as food and beverages, have limited shelf lives, necessitating precise and timely forecasting.
* Big Data Integration: Modern forecasting incorporates large-scale data from diverse sources, including sales history, social media trends, and real-time consumer feedback.
* Advanced Forecasting Techniques: To improve prediction accuracy, methods such as time-series analysis (e.g., ARIMA, LSTM) and machine learning models (e.g., Informer) are increasingly adopted.  
Sales forecasting is a critical foundation of FMCG supply-chain operations, enabling efficient planning and execution by aligning production schedules with market demand, optimizing inventory levels to minimize costs and waste, improving logistics and distributionefficiency through timely deliveries, and enhancing customer satisfaction with consistent product availability [8]. The integration of accurate sales forecasting with a responsive FMCG supply chain is essential for achieving operational excellence and maintaining a competitive edge in the fast-moving consumer goods industry.