esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Eight Characteristic Aroma Substances of the 'Panguxiang' Compared to the 'Korla'  
To explore the characteristic aroma substances of the 'Panguxiang' compared to the 'Korla' pear, we further measured the concentrations of soluble sugars, soluble acids and amino acids of the 'Korla' pear at stages Z1 to Z5 and the metabolome of 'Korla' pear at stage Z5. As shown in Figure 4A, sorbitol content was dramatically reduced from stage Z3 to Z5, whereas other soluble sugars and acids were not affected by pear storage. Notably, the contents of nine amino acids in total, namely Glu, Ile, Ala, Phe, Lys, Met, Leu, His and Pro, gradually increased from stage Z1 to Z5 (Figure 4B). The concentrations of Thr and Asp strikingly decreased at stage Z2 and then rapidly increased at stage Z3. These data indicate that amino acids rather than soluble sugars and acids play key roles in the aroma biosynthesis of the 'Korla' pear.  
To clarify the differences of aroma components between the 'Panguxiang' and the 'Korla', we measured the metabolites of the 'Korla' at stage Z5 (ZK) and found that the all samples were clearly clustered into two groups based on pear varieties in PCA (Figure 4C). At stage Z5, 100 differentially expressed metabolites (DEMs), including 76 up-regulated metabolites and 24 down-regulated metabolites, were found in the 'Panguxiang' compared to the 'Korla' (Figure 4D; Table S4). To confirm the characteristic aroma substances of the 'Panguxiang', we compared the key metabolites in Table S3 and the up-regulated DEMs in the 'Panguxiang' compared to the 'Korla'. Results showed that only eight metabolites were shared between these two groups, including three esters, two aldehydes, one alcohol, one acid and one ketone, indicating that only a few metabolites belong to the characteristic aroma substances (Figure 4E,F, Table 2). Remarkably, these metabolites, except for NMW0070, were not enriched in KEGG pathways, indicating that the aroma biosynthesis pathway was not clear. Hence, these eight metabolites are the typical aroma ingredients of the 'Panguxiang' pear.  
Figure 4: Eight metabolites screened out as dominant components in the ‘Panguxiang’ pear aroma. (**A**) Concentrations of soluble sugars and acids from stage Z1 to Z5. (**B**) Concentrations of amino acids from stage Z1 to Z5. (**C**) PCA of the ‘Panguxiang’ and the ‘Korla’ at stage Z5. ZP, the ‘Panguxiang’ at stage Z5; ZK, the ‘Korla’ at stage Z5. (**D**) DEMs of the ‘Panguxiang’ compared to the ‘Korla’ at stage Z5. (**E**) Veen diagram between 65 key metabolites and up-regulated DEMs. (**F**) Class of eight characteristic aroma substances of the ‘Panguxiang’ pear.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 3 Methodology
### Data Visualization with t-SNE Analysis  
The t-distributed stochastic neighbor embedding (t-SNE) is an unsupervised, non-linear technique mainly used for high-dimensional data visualization [46]. The t-SNE algorithm optimizes a similarity measure between instances in the high and low dimensional space. Our input data is high dimensional data having sixteen dimensions. To visualize how the data samples are clustered in the custom dataset, we applied t-SNE to our dataset. To generate this plot, we selected two components for 2-dimensional plotting, the perplexity of 50 and a learning rate of 100.  
Figure 5 refers to the t-SNE plot using parameter settings mentioned above. Five different clusters can be seen in the plot. These clusters refer to the five classes of our dataset. The classes are labeled as'scream' as zero, 'glass breaking' as one, 'background' as two, 'alarms' as three, and 'conversation' as four. The clusters are separated from each other, apart from the edge samples. For instance, some of the 'glass-breaking' samples overlap with'scream' samples shown in red color. In high-noise or low SNR conditions, high levels of noise make the scream and glass breaking signals sound somewhat similar. Similar observations can be made for'scream' and 'alarm' audio samples. Interestingly, the'scream' audio samples do not overlap with conversation and 'background' samples.
## 2 Materials and Method
### Simulated Periods  
The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model [29] was used to calculate 72 h back trajectories arriving at the receptor site of Hyytiala. Six air parcels arriving at 3:00, 7:00, 11:00, 15:00, 19:00 and 23:00 local time (UTC + 2) in the ground forested site were simulated for 8 days (April 28, May 7, 11, 15 and 26 and June 9, 10 and 11). The air masses from the beginning of the campaign until the middle of May originated mostly from the Atlantic and arrived at Hyytiala either directly from the west over Scandinavia or from the south, passing over the Baltic Sea. After mid-May, air masses started often from the east or northeast, either from the Arctic Ocean or from western Russia. During the last weeks, the situation changed again with air masses arriving from the Atlantic passing over Scandinavia. The current meteorological analysis is consistent with the conclusions of previous studies [30]. We selected days for which the trajectories at the different altitudes originated all from the same region. For consistency, we used the same WRF meteorological data as input to HYSPLIT to calculate the back trajectories. Following [PERSON] et al. [18], the ensemble average of 20 trajectories with varied heights from 60 m up to 3 km is used, similarly to the application of PMCAMx-Trj in the Po Valley in Italy [20].  
The twenty 72 h HYSPLIT back trajectories arriving at Hyytiala at 15:00 LT on 7 May 2013, are shown in Figure 1 as an example. They all originated from the eastern Atlantic Ocean, passed a day over the ocean and then over Ireland and the United Kingdom. During the next day they passed over the North Sea and Denmark. The air masses continued over Sweden and the Baltic Sea, and a few hours later arrived in the receptor site of Hyytiala. The HYSPLIT clustering analysis utility was used to estimate the average trajectory that was used in the simulations (Figure 1b).
# Models of HF Interference over Cyprus
## 2 Materials and Methods
To investigate the effect of congestion on such NVIS links, we also collected a high-elevation dataset obtained by the turnstile component of the HE016 antenna. We then calculated the correlation coefficient for all 1 kHz channels plotted as a function of frequency across the entire HF spectrum in the range of 1.606-30.000 MHz for 280 days from our dataset, for which we collected the same number of measurements for each of the 1 kHz channels. The corresponding plot is shown in Figure 7. From this plot, we can observe that the correlation coefficient exhibits a notable variation across the HF spectrum between the low and high antenna components of the HE016 array, which indicates there is a considerable difference in the signals received with the two antenna components. As a result, only the low-elevation dataset received by the monopole antenna component was incorporated in the present modeling study.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Link  
The dynamic behaviors, especially the time-varying connection relationship and the dynamic link characteristic, are the main features of SN and also pose challenges to emulate the SN environment.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 5 Conclusions  
This paper investigated the method of geolocation and tracking by TDOA measurements based on SAGI networks. The CRLB for target localization and tracking accuracy was derived in different coordinate systems. Then, the effects of different system errors were analyzed, such as clock synchronization error, position bias of observers, elevation bias of target and non-horizontal velocity of target. Further, a ML estimator for the target's position and velocity was proposed to compare with theoretical performance. Finally, extensive numerical and experimental results were presented to validate the theoretical analyses.  
Conceptualization, J.L.; methodology, S.L. ([PERSON]); software, C.W.; validation, Y.L. and S.L. ([PERSON]); writing--original draft preparation, Y.J.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Defense Science Foundation for Distinguished Young Scholars] grant number [2020-JCJQ-ZQ-014] and [Young Elite Scientists Sponsorship Program by CAST] grant number [18-JCJQ-QT-015].  
The locations of these observers are generated by computer simulation. It is easy to generate simulation with the method in the manuscript.
## 4 Software Defined Networks
### Software Defined Vehicular Ad Hoc Networks  
Software Defined Vehicular Ad Hoc Networks (SD-VANET) is a new paradigm resulting from the integration of Software Defined Networking (SDN) technology into VANET. This architecture enhances the flexibility and programmability of networks by separating the data and control planes while enabling more efficient resource management. SD-VANET comprises controllers, OpenFlow-compatible transmission devices (such as Road Side Units and Access Points), and vehicles under the control of the controller [30].  
The SD-VANET architecture is divided into three fundamental planes: the data plane, the control plane, and the application plane (Figure 3). The data plane includes data transmission devices such as Road Side Units, Access Points, and switches. These devices connect to the controller via the OpenFlow protocol and have a fallback mechanism that  
Figure 2: Software Defined Network Architecture.  
allows them to revert to traditional VANET functions if necessary. The control plane acts as the brain of the network, directing traffic, minimizing security threats, and optimizing network performance [6]. The use of multiple controllers enhances security and increases data transmission speed. The application plane includes network services and applications, such as routing, security, load balancing, and virtualization. The Northbound interface allows applications to interact with the controller, facilitating dynamic network management.  
This architecture combines the advantages of centralized control and programmability provided by SDN with the flexibility of VANET, offering innovative solutions, particularly in autonomous vehicles and intelligent transportation systems [9].  
The utilization of multi-controller architectures in mobile network structures such as SD-VANETs enhances real-time response rates, providing an effective counter to attacks. This architecture enables the network to operate efficiently at larger scales by distributing tasks and resources among controllers, thereby balancing the load of network management. Furthermore, the multi-controller setup allows for seamless failover; if one controller fails, others take over its responsibilities, ensuring uninterrupted network availability [31]. By enabling different sections of the network to be managed by specific controllers, this design facilitates the application of tailored solutions across various network segments, thereby enhancing flexibility. In Software-Defined wireless networks, hierarchical multi-controller configurations and strategic controller placement optimize resource usage and reduce latency by strengthening control over data plane elements [28].
###### Abstract  
The rock deformation and failure characteristics and mechanisms are very important for stability evaluation and hazard control in rock engineering. The process of rock deformation and failure is often accompanied by temperature changes. It is of great significance to study the characteristics and mechanism of temperature variation in rock under deformation and fracturing for a better understanding of rock failure and to obtain some probable precursor information for guiding the prediction of the mechanical behavior of rock. However, most of the studies are based on observations in the field and laboratory tests, while it is still required to develop an effective method for modeling and calculating the temperature variation of rock during the deformation and failure processes. In this paper, a particle flow modeling method based on energy analyses is proposed for simulating the temperature variation of rocks, considering four temperature effects, including the thermoelastic effect, friction effect, damping effect, and heat conduction effect. The four effects are analyzed, and the theoretical equations have been provided. On this basis, the numerical model is built and calibrated according to the laboratory uniaxial compressive experiment on a marble specimen, and a comparison study has been conducted between the laboratory and numerical experiment results. It is found that the numerical model can well simulate the average value and distribution of the temperature variation of rock specimens, so this method can be applied for studying the mechanism of temperature variation more comprehensively during the whole process of rock deformation and fracturing compared with the continuous modeling methods. With this method, it is shown that the temperature change has three different stages with different characteristics during the uniaxial compression experiments. In the different stages, the different effects play different roles in temperature variation, and stress distribution and crack propagation have obvious influences on the local distribution of temperature. Further investigations have also been conducted in a series of sensitive analyses on the influences of four factors, including the thermal conductivity, friction coefficient, thermal expansion coefficient, and particle size ratio. The results show that they have different influences on the thermal and mechanical behaviors of the rock specimens during the deformation and failure process, while the thermal expansion coefficient and the particle size ratio have more significant impacts than the other two factors. These findings increase our knowledge on the characteristics and mechanism of temperature variation in rock during the deformation and fracturing process, and the proposed modeling method can be used in more studies for deformation and fracturing analyses in rock experiments and engineering.  
Keywords:failure mechanism; temperature variation; energy analyses; rock deformation and fracturing; temperature effect; particle flow model +
Footnote †: journal: Applied Sciences
## 5 Results
### Practical Evaluation  
To evaluate the influence of graph clustering on industrial diversification recommendations, we conducted interviews with industrial professionals. For anonymization and confidentiality regarding strategic details, we refrained from disclosing the names of the companies involved. The focus of these companies predominantly revolves around the production of two specific product codes:  
* 9031: Measuring or checking instruments, appliances, and machines
* 8465: Machine tools, incl. machines for nailing, stapling, gluing or otherwise assembling, for working wood, cork, bone, hard rubber, hard plastics, or similar hard materials.  
From these product codes and Hidalgo's Product Space, the diversification recommendations follow the edges of the graph (as in Figure 1). In our work, we made the choice to also use neighboring products at the second level (neighbor of neighbor), so we had many possible recommendations for the products 9031 and 8465, respectively.  
Thus, the use of graph clustering methods in the Product Space makes it possible to refine the number of recommendations to select only the neighbors that belong to the same clusters as the products 9031 and 8465.  
This filtering of industrial diversification recommendations by Product Space clustering reduces the possibilities by offering more relevant recommendations. The relevance of the recommendations was verified through interviews with industrial companies that manufacture products 9031 and 8465. The results of these interviews show that the recommendations filtered in the GraphSAGE algorithms are the most relevant and possible for manufacturers without much modification of their production equipment. In addition, for a manufacturer, GraphSAGE's recommendation for the 8465 product provided the 8480 product, which is a product that the manufacturer already produced several years ago, which is a molding box for a metal foundry.  
These field results show that the filtering of recommendations by GraphSAGE provides more relevant industrial diversification recommendations than with the Louvain and I-Louvain methods. Moreover, the filtering of these modularity-based methods is less important because they maintain many more irrelevant recommendations than GraphSAGE.
## 5 Summary  
In conclusion, the theoretical foundations and experimental approaches to generate vector beams with longitudinal distribution of polarization in free space have been reviewed. The longitudinal variation of polarization originates from the variable difference between the complex amplitudes of two orthogonally polarized beams with propagation. Thus, there are two theoretical strategies to achieve this special vector beam, which are constructing the longitudinally varying phase difference and amplitude difference. In experiments, the generation methods can be summarized as the modulation of complex amplitude in real space and the filtering of the spatial spectrum. By tailoring the spatial phase distribution in the initial plane, a \(z\)-dependent phase difference between two orthogonally polarized beams can be achieved, and the VBB with varying polarization during propagation can be generated based on modulation in real space. In addition, based on spatial spectrum engineering, the axial intensity of two orthogonal polarization components can be modulated to realize the Bessel beam with uniform axial intensity and longitudinally varying polarization. The successful generation of vector beams with controllable variation of polarization along the propagation direction in free space enriches the structured light with a new degree of freedom and will open new paths in light's interaction with matter. The customization of the longitudinal distribution of polarization and corresponding optimized generation methods with high efficiency and flexibility are anticipated to be achieved by further applied research into this special beam.  
The authors all contributed to the preparation of the paper. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (NSFC) (62005076, 61805067) and the Natural Science Foundation of Hebei Province (F2020202035).  
Figure 10: The variation of polarization along (**a**) the equator (**b**) a meridian and (**c**) an arbitrary trajectory on the Poincaré sphere.
# Exploring an Integrated Manure-Sewater System for Sustainable Cyanobacterial Biomass Production
## 4 Discussion
### Growth Response of Cyanobacterial Strains HSaC and LC to Manure and Seawater Media
The results of comparing natural and artificial seawater with cow manure media did not show a significant (\(p<0.05\)) difference between the 10N and 10A treatments for cyanobacterial strain HSaC biomass production, chlorophyll a and carotenoid content (Figure 6, Figure 7 and Figure S2). However, as compared to other treatments, like SN, SA and DW, a significant difference was observed among the treatments. Therefore, in both natural and artificial seawaters with cow manure media, the highest biomass yield, chlorophyll a, and carotenoid content were recorded in the cow manure with diluted (10 dS/m) seawater medium (Figure 6, Figure 7 and Figure S2). This result suggested that both artificial and natural seawater at similar diluted salt concentrations can be used interchangeably as a potential source of cyanobacterial growth media in combination with cow manure. The biomass yield and photosynthetic pigment contents showed a strong correlation in the current experiment, but we found a weak correlation in treatment SN, that is, the biomass yield showed a higher decrement as compared to photosynthetic pigment (Figures 6 and 7). This weak correlation between biomass yield and photosynthetic pigment content could be because high salt concentrations (76.0 dS/m) in natural seawater influenced cyanobacterial cell division more than the photosynthetic pigment content. Unlike in artificial seawater, which has a low salt concentration (42.3 dS/cm), in natural seawater, cyanobacterial cells likely invested more energy in cell protection (maintaining the health of cells and photosynthetic pigments) than in cell division, which resulted in a decreased biomass yield and increased photosynthetic pigment content (Figures 6 and 7). In addition, the high heavy metal concentration in our natural seawater [32] might have influenced cyanobacterial cell division more than it influenced photosynthetic pigments. To identify the optimum growth conditions for the salt-sensitive cyanobacterial strain LC, experiments were conducted on media with a low salinity concentration of natural seawater and cow manure, and the highest values of biomass production and chlorophyll a content were recorded using 0.5 dS/m diluted seawater with cow manure medium (Figure 8).
## 2 Materials and Methods
### Analyzed Methods
#### 2.1.4 Autoencoder (AE)  
Autoencoders, introduced in [34], are neural networks designed for dimensionality reduction that consists of an encoder network \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{l}\) and a decoder network \(g:\mathbb{R}^{l}\rightarrow\mathbb{R}^{n}\), where \(p,n\in\mathbb{N}\) and \(l<n\). These networks are trained to reconstruct their input by learning a latent representation. The autoencoder problem can be formalized according to [35] as:  
\[\operatorname*{arg\,min}_{f,g}\mathbb{E}[\Delta(x,g(f(x))]\]  
with \(x\in\mathcal{X}\) being the input data, \(\Delta\) the reconstruction loss, i.e., usually the \(L_{2}^{2}\) error function and \(\mathbb{E}[\cdot]\) the expectation over its argument [35; 36]. In the context of unsupervised anomaly detection in time series data, the autoencoder learns a normal profile of the time series \(\mathcal{T}\) and detects anomalous input sequences \(\tilde{S}\) with a high reconstruction error. Figure 1 illustrates this approach.  
In our experiments, we used a dense autoencoder with two hidden layers in the encoder and decoder, which have each a doubled number of neurons of the latent space and ReLU activations as non-linearities. We included the autoencoder as a basic deep-learning model in this study.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Remote Sensing Interpretation of Paddy Fields  
Four types of feature locations (paddy fields, other vegetation, buildings and water) were collected as remote sensing interpretation marker samples by field survey in July 2019 (Figure 1). The number of samples were 120, 280, 69 and 51, respectively, with a sample size of 20 \(\times\) 20 m. The sample locations were located using the GNSS system for the four to points. Seventy-five percent of the samples of each type were randomly selected for model training, and the remaining 25% of samples were used for accuracy verification. The remote sensing interpretation of Sentinel-2 data was performed in QGIS 3.22.1 software using the random forest classification method. The accuracy of the remote sensing interpretation was verified using a confusion matrix. The classification accuracy of rice in this study reached 98.3%.