esa-sceva/satcom-chunk-collection huggingface dataset:
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS
### Experiment 3  
This experiment evaluated the scenario in which each client possesses a non-overlapping and disjoint set of attacks, assessing FL-IDS performance when participants are targeted by distinct attack types. The scenario is shown in Figure 16.  
The obtained results are comparable to those of Experiment 1, with negligible differences (\(\pm\)0.5%) achieved after 2200 and 1000 rounds for network and sensor data, respectively.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 3 Directivity Measurements
### Beam Steering Function
The patterns for the cases of 8 and 16 divisions are shown in Figure 18c,d, respectively. Figure 18c utilizes the two beams shown in Figure 17a,b, whereas Figure 18c employs the three beams shown in Figure 17.  
Figure 19 shows the radiation gain for the angle of the incoming wave by combining the measured directivities shown in Figure 17. As shown in Figure 19, the radiation gain increases with increasing number of division areas. In particular, a gain of 4 dBd or more can be maintained when all directions are divided into 22.5\({}^{\circ}\) intervals. These results reveal that the proposed antenna system can provide a high receiving power utilizing a beam steering function with a full-azimuth angle when compared to a conventional dipole antenna. \[REF=\frac{E\Big{[}\big{|}S_{21}\big{|}^{2}\Big{]}}{G} \tag{11}\]0 Division patterns of the beamforming area: (**a**) 90\({}^{\circ}\) intervals from 0\({}^{\circ}\); (**b**) 90\({}^{\circ}\) intervals from 45\({}^{\circ}\); (**c**) 45\({}^{\circ}\) intervals; and (**d**) 22.5\({}^{\circ}\) intervals.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Source Localization
#### 4.2.1 One-Dimensional Beam  
In this section, the 1D beam is analysed and the results of the source localization are presented. During the forward process, an excitation source located at point \(x_{s}\) and three receivers, equidistantly distributed along the medium at \(L/3,2L/3\) and at the end of the free edge, collect the data.  
For the calculations of the academic example presented here, a one-dimensional beam with a length \(L=30\) m, mass density \(\rho=1\) kg/m\({}^{3}\), modulus of elasticity \(E=1\)\(Pa\) and Poisson's ratio \(\
u=0.25\). Moreover, the total experiment's duration was \(T=t_{o}+\frac{2L}{\xi_{1}}=63\) s, where the initial time is \(t_{o}=3\) s, the number of discrete time steps is chosen as a power of two (\(2^{12}\)) and the mesh is the 1950 finite elements which means a distance between nodes \(dx=0.015\) m and a time step \(dt=0.015\) s.  
During the forward process, we assume three receivers, along the axis of the beam, collecting data for displacements and/or velocities for every DOF of nodes. The backward step problem is numerically solved again for each receiver. The receiver now acts as a source point that emits the recorded signal reversed in time as loading. Refocusing the wave at the source point \(x_{s}\) takes place at time \(T-t_{0}=60\) s. Plots of Figure 4 depict the Euclidean norm of displacements at the specific discrete time step \(N_{t}-\frac{t_{0}}{dt}=3900\). It is important to mention that secondary peaks are presented because of the existence of the boundaries. These peaks are also responsible for the noise created because the domain is bounded and waves are reflected back. The exact location of these secondary peaks depends on the relative position of receivers. This fact acts beneficially on the SNR improvement usingmultiple receivers distributed on the body of some structure, as it will be shown later in a following section.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 5 Experimental Evaluation and Results  
To assess the proposed LEO/SOP-based INS alignment method, two different experiments were designed and performed. The goal of both experiments was to estimate the trajectory of an aerial vehicle. The first experiment was implemented by a ground vehicle, while the second one was performed using a light aircraft. This section includes two body parts. First, we describe the hardware and software configuration and specifications of each experiment. Second, the observed satellite and outputs of the receiver are investigated for each experiment. Finally, the experimental results of the proposed method are discussed in detail.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 3 Request Expectation Index Based Cache Replacement Algorithm  
### Request Expectation Index \(Rxi(i,j)\)  
\(Rxi(i,j)\) is the request expectation index of chunk \(C_{i,j}\), which estimates the expectation of chunk \(C_{i,j}\) being requested in the future. \(Rxi(i,j)\) is calculated according to two levels of the request probability and the recent request status, as shown in Equation (1):  
\[RXI(i,j)=P_{f}(i)\times IRXI_{f_{i}}(j). \tag{1}\]  
\(P_{f}(i)\) is the real-time file-level request probability of file \(f_{i}\) among different cached files in the ICN router, and \(\sum_{i=1}^{N}P_{f}(i)=1\). \(IRXI_{f_{i}}(j)\) indicates the internal request expectation index of chunk \(C_{i,j}\), which estimates the expectation of chunk \(C_{i,j}\) being requested in the future among the chunks within the file \(f_{i}\). The difference between \(RXI(i,j)\) and \(IRXI_{f_{i}}(j)\) is that \(RXI(i,j)\) denotes the request expectation of chunk \(C_{i,j}\) among all cached chunks and \(IRXI_{f_{i}}(j)\) only reflects the request expectation among the chunks within file \(f_{i}\). For instance, assume there are two streaming files \(f_{1}\) and \(f_{2}\) and each file has two chunks, i.e., \(f_{1}=\{C_{1,1},C_{1,2}\}\), \(f_{2}=\{C_{2,1},C_{2,2}\}\). In addition, the file-level request probability of each file is given by \(P_{f}(1)=0.3\) and \(P_{f}(2)=0.7\), while, within file \(f_{1}\), the request expectation of each chunk is given by \(IRXI_{f_{1}}(1)=0.8\) and \(IRXI_{f_{1}}(2)=0.2\). Then, in this ICN router, the request expectation index of chunk \(C_{1,1}\) is \(RXI(1,1)=P_{f}(1)\times IRXI_{f_{1}}(1)=0.3\times 0.8=0.24\).
## 4 Experiment Evaluation
### Evaluation Indicators  
To evaluate the prediction accuracy of the proposed methods, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) [3; 47; 48], which are predictive accuracy metrics widely used in the research field of recommender systems, were used as measurements of the rating prediction methods. MAE measures the average of the absolute difference between the predicted ratings and the true ratings. RMSE squares the error before summing it, and its result emphasizes large errors. The smaller the MAE and RMSE values, the more accurate the prediction, and vice versa. These two metrics are simple and easy to understand [49]. The MAE and RMSE are calculated as follows:  
\[\text{MAE}=\frac{\sum_{r\in R}\left|TRS_{r}-RS_{r}\right|}{\left|R\right|} \tag{19}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{r\in R}\left(TRS_{r}-RS_{r}\right)^{2}}{\left| R\right|}} \tag{20}\]  
where \(r\) is a review in the review set \(R\), \(TRS_{r}\) is a true rating score of review \(r\), and \(RS_{r}\) is a predicted rating of review \(r\).
## 3 A Short Introduction to the PPP Method  
The Precise Point Positioning (PPP) method [21] is based on the availability of accurate GNSS satellite coordinates and clocks. It uses models with centimetre precision to remove systematic effects on estimated parameters. For accurate GNSS satellite coordinates and clocks, the products from IGS (International GNSS Service--[[https://www.igs.org/data-products-overview/](https://www.igs.org/data-products-overview/)]([https://www.igs.org/data-products-overview/](https://www.igs.org/data-products-overview/)) [Accessed at 17 January 2021]) [22] were used. The most precise final orbit and clock products available two weeks after the last observation of the GPS week (a week from Sunday to Saturday) were used. Although the accuracy of the GNSS orbits and clocks are vital to achieving the highest level of PPP accuracy, the quality and type of processed GNSS observation is also critical. The highest positioning accuracy is achieved with geodetic quality, dual-frequency GNSS receivers operating in a low multipath environment where carrier phase cycle slips are infrequent.
## 4 Discussion  
### The Segmentation of the Trees  
The high vegetation points, which are not segmented, are assigned to the corresponding tree point group with the shortest distance to the central axis of the tree, and the final tree crown segmentation is complete. The segmentation results are closely related to the grid size and the elevation threshold of the template calculation. If the values are too low, the calculation amount will increase. Otherwise, if the values are set too high, the trees will be missed. The optimal value is related to the point cloud density, tree crown size, and shapes.  
In this paper, the laser point cloud data is first segmented into point groups, and each point group is taken as a whole; the structural features embodied in the whole point set are extracted to achieve the overall classification. To obtain the tree groups of all the samples in the test area, the threshold plays a significant role during the processing of tree locations, including the grid size \(d\) and the elevation threshold \(H_{T}\). Generally, the size \(d\) is related to the crown size, and setting of the elevation threshold \(H_{T}\) refers to the shape of the treetop. In the test site, the minimum diameter of the tree crown is about 3 m, so the grid size is set to \(d=1.5\) m to ensure that all trees can be detected. The elevation threshold depends on the shape of the treetop. If the shape of the treetop is sharp, such as pine trees, the elevation threshold is larger. If the shape of the treetop is relatively flat, such as shrubs, the elevation threshold is smaller. However, in natural forest land, a variety of tree species are mixed. In order to ensure that all the samples are sorted out, the threshold is often chosen to be small, and the value here is \(H_{T}=1\) m.  
For this paper, the quality of the crown segmentation results mainly depends on the point cloud density and the growth mode of forest vegetation, whether it is loose distribution or interlaced branches and vines. The survey area in this paper belongs to the northeast forest region, and the vegetation distribution is relatively sparse. Therefore, the result of tree crown segmentation essentially depends on the point cloud density of forest land data and whether it is covered by trees. Some small trees or trees close to each other will inevitably be ignored, resulting in multiple trees combined into one tree. On the contrary, when a tree is too big or broad, it will be easily divided into two trees. The uniformity of tree growth in the forest area is also particularly important. Compared with the traditional watershed method, this method can avoid missing or excessive segmentation of the tree crown by moving the profile in multiple directions. In this paper, when \(d=1.5\) and \(H_{T}=1\), the crown segmentation accuracy can reach 95.7%.  
Figure 19: The tree species classification results of the 10 plots.
## 3 Algorithm Description
### Aerosol Type Selection
The region with positive UVAI and negative visible AI enclosed by the red box is identified as fine absorbing HAF particles. Most other aerosol plumes (yellow boxes) have positive UVAI and visible AI and thus were identified as dust particles in this study. In cloud pixels, both UV and visible AI were negative. To avoid cloud contamination in the retrieval, a cloud mask is applied based on radiance and its spatial standard deviation. Additionally, most pixels with weak aerosol signal have positive UVAI and negative visible AI and thus HAF aerosols were frequently detected in this study. However, visible range measurements are strongly dependent on the land surface type, which can introduce noise into the visible AI calculation. Furthermore, detection of absorbing aerosol near the surface is difficult because the UVAI is sensitive to elevated aerosol [14]. Based on the positive relationship between concentrations of CO and carbonaceous aerosol, [PERSON] et al. [14] introduced the CO index (COI) using AIRS CO products to distinguish smoke and dust particles, to avoid errors induced by visible-AI noise. The COI was used to detect high concentrations of smoke particles in the boundary layer, which otherwise could have been mistaken as cloud contamination. However, the combined use of observations from various satellite instruments is possible when the measurements are performed within a specified time window. The OMI algorithm also takes advantage of A-train observations. In this study, the visible AI is used instead of the COI to identify dust layers in high-UVAI pixels. Figure 4: Spectral difference in normalized radiance (N) for pristine (AOD = 0) and polluted (AOD = 1) conditions with respect to aerosol type: (**a**) HAF, (**b**) dust and (**c**) NA. Colors indicate SSA and solid lines show the average SSA obtained from integrated AERONET inversion data for each type. Figure 5: RGB images observed from MODIS (Aqua), UVAI, visible AI and aerosol type calculated from OMI measurement on 10 March 2006, 8 April 2006, 10 October 2006, 29 March 2007 and 27 April 2012. Yellow and red boxes indicate dust and HAF scenarios, respectively.
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 2 Methods
### Integration of Detection and Embedding  
In computational tasks in which concurrent object detection and state recognition is a requisite, conventional methodologies typically necessitate a resampling of the original image after the detection phase, followed by the application of specialized algorithms for  
Figure 1: The reduction of feature maps across YOLOv7. The dashed boxes in the figures signify changes introduced in our study, elaborated in subsequent sections.  
Figure 2: An autoencoder.  
state recognition, as shown in Figure 3. Our integrated approach seeks to streamline this process by encapsulating both detection and embedding functionalities within a singular forward pass of the network. This strategy leverages a low-dimensional embedding of the target image, which serves as a distilled representational form, facilitating the extraction of state information with significantly reduced computational demands. We opted for the YOLOv7 model as an object detection network. Among various object detection methods, the YOLO series is widely recognized for its detection speed. YOLOv7, as one of the newer iterations, incorporates many advantages from previous research, making it a comprehensive solution for real-time applications. Specifically, YOLOv7-tiny, a variant with significantly fewer parameters (only 6.2 M), has been proven effective. It strikes an excellent balance between detection speed and detection accuracy, making it highly suitable for edge computing environments in which resources are limited. Concurrently, we incorporated an autoencoder into the YOLOv7 training regimen to imbue it with the requisite capabilities for target embedding.  
To empower YOLOv7 with the functionality to output an embedding reflective of the target's state, we expanded the channel capacity of the output layer, which shown in Figure 2. This modification enables each grid cell to maintain its primary detection output while appending a state embedding vector. Consequently, each candidate target within the grid is represented by a comprehensive output ensemble that includes central coordinates, dimensions, a confidence score, class probabilities, and a state embedding vector. This state embedding vector, characterized by a predetermined length, encapsulates the salient attributes of the target's state.
## 3 Results and Discussion
### Energy Analysis
Furthermore, when the discharge pressure was higher than \(P_{\text{dis,opt}}\), both COP and \(\dot{Q}_{cooling}\) decreased significantly in the insufficient charge condition, whereas they declined more slightly in the sufficient charge  
Figure 4: The effect of normalized refrigerant charge (NRC) on COP\({}_{\text{opt}}\), cooling capacity (\(\dot{Q}_{cooling}\)), optimal discharge pressure (P\({}_{\text{dis,opt}}\)), outlet pressure of evaporator (P\({}_{\text{e,out}}\)) and optimal EEV opening degree (OD\({}_{\text{opt}}\)) at ambient temperatures of 35 \({}^{\circ}\)C, 38 \({}^{\circ}\)C and 40 \({}^{\circ}\)C.  
condition. However, in the excessive charge condition, as the COP started decreasing slightly, \(\hat{Q}_{cooling}\) was still increasing significantly. These phenomena were caused by the migration characteristics of the refrigerant. Figure 5b depicts the P-h diagrams of the investigated system at the aforementioned three conditions at different EEV opening degrees. As the value of OD decreased from 1 to 0.417, P\({}_{\text{dis}}\) increased slightly (from 9.25 MPa to 9.78 MPa) and P\({}_{\text{suc}}\) reduced from 4.58 MPa to 3.63 MPa under the insufficient charge condition. The variation of P\({}_{\text{suc}}\) was more marked than P\({}_{\text{dis}}\) with the rise in OD, leading to a significant increase in the superheating degree of the system and thus to a sharp reduction of the CO\({}_{2}\) mass flow rate. Although the enthalpy difference in indoor HXs increased, the cooling capacity was found to reduce. With the rise in refrigerant charge, the amount of refrigerant in the indoor HXs and outdoor HX increased. Additionally, the P\({}_{\text{suc}}\) decreased slightly and the P\({}_{\text{dis}}\) increased with the reduction in OD. Under the sufficient charge condition, P\({}_{\text{dis}}\) increased from 10.04 MPa to 11.17 MPa and the P\({}_{\text{suc}}\) reduced from 5.02 MPa to 4.53 MPa as the value of OD decreased from 1 to 0.417. In addition, as OD was lower than the optimal value, the refrigerant drawn by the compressor was superheated vapor and its superheating degree increased with decrease in EEV opening degree, resulting in a slight reduction in cooling capacity. Under the excessive charge condition, P\({}_{\text{dis}}\) sharply increased from 10.16 MPa to 11.81 MPa and the refrigerant drawn by the compressor was in the condition of a vapor-liquid mixture. Thus, the increase of enthalpy difference in the evaporators rather than the decrease of flow rate was the main factor affecting the system cooling capacity, causing an increase in cooling capacity. Therefore, it could be concluded that from the insufficient charge condition to the sufficient charge condition, the refrigerant mainly migrated to the three heat exchangers, whereas in the excessive charge condition the accumulation of refrigerant in the gas cooler was more marked than that in the evaporators. Figure 5: _Cont._
## 1 Introduction  
Poultry meat is the most consumed meat worldwide, and the second, after pork, in countries of the EU [1]. It gained a great popularity among consumers because of its low preparation time necessary, low cost, and a wide offer of easy-to-prepare poultry-based products [2]. Moreover, qualitative and quantitative nutritional characteristics of poultry meat have still increased its consumption on the global market. Chicken meat is an excellent source of highly-digestible proteins with essential amino acid content, lipids with high polyunsaturated fatty acid content, as well as B group vitamins and some minerals [3,4]. Nevertheless, the much higher degree of unsaturation of fatty acids of chicken meat in comparison to other types of meat accelerates the oxidative processes, leading to the fast decrease of meat quality [4,5]. Lipid oxidation causes deteriorations in meat flavor, color, physicochemical properties, nutritional value, and finally food safety [4,6]. Other factors affecting oxidative stability of meat are storage conditions and technological processes the meat is subjected to, like grinding which increases meat surface exposed to reactive oxygen species (ROS) [7]. Additionally, chicken meat is prone to the contamination of undesired microorganisms originating from animal, abattoir facilities, manufacturing and/or storage. Gram positive: Lactic acid bacteria (LAB) and Gram negative: _Pseudomonas_ and _Enterobacteriaceae_ are the most common bacteria in raw meat related with its spoilage [8].  
Herbs and spices have been used since ages not only for meat seasoning to give a specific flavor to the final products but also to delay the spoilage of meat during storage. At present, the positive effect of plants and plant extracts on meat quality has been ascribed to their high antioxidant and antimicrobial activities [5,9,10,11,12,13]. The trend of using natural agents as food preservatives has been still of interest since the consumers, especially in EU and US countries, demand for \"clean label\" in meat product.  
Hardly any study has been conducted to investigate the efficiency of black seed extract in muscle food system [11,13]. Black seed and spices like allspice, bay leaf, cardamom, caraway, cloves, and nutmeg are commonly used in Asian and European kitchen. Their antioxidant properties have been applied to protect meat products from oxidation [9,11,13]. Nevertheless, a direct comparison of their effectiveness in meat is difficult due to the large variety of methods employed to prepare the extracts and to measure their antioxidant activity in the meat matrix [14,15].  
Thus, the aim of this study was to investigate the effect of black seed and allspice, bay leaf, cardamom, caraway, clove, and nutmeg extracts on the oxidative quality of raw ground chicken legs stored at 4 \({}^{\circ}\)C. According to the best knowledge of authors the effect of black seed (_Nigella sativa_) addition to chicken meat and the comparison of its properties to other spices had been investigated for the first time.
###### Abstract  
To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, a mathematical morphological network layer is constructed by leveraging the advantages of a multi-scale enhanced top-hat morphological operator (MEAVGH) that can extract positive and negative pulses, which are then integrated into the deep learning network. Second, the input signal undergoes processing with different scale structural elements (SEs) to obtain multi-branch data. This is followed by channel attention and spatial attention mechanism-based weighting of the generated multi-branch data. Finally, the fused information is fed to the neural network to yield the final result. The experimental results demonstrate the efficacy of the proposed method in extracting fault feature information, achieving a fault classification accuracy of 98.56%. Furthermore, the algorithm exhibits robustness and high training efficiency. Comparative analysis reveals that the proposed method outperforms other approaches regarding cluster analysis, accuracy, recall rate, and computational efficiency. These findings further highlight the advantages of MMNet in acoustic signal-based fault diagnosis for rolling bearings.  
fault diagnosis; rolling bearings; mathematical morphological; deep learning +
Footnote †: journal: Applied sciences