esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design for Dual Polarization
These results are even better for smaller incidence angles, which will allow obtaining low levels of cross-polar radiation, as can be checked in other reflectarray antennas designed with similar cells based on orthogonal dipoles [20; 28]. To achieve simultaneous operation at Tx and Rx frequencies (20/30 GHz), a more complex version of the cell with two layers of stacked dipoles can be used [29]. In that case, the upper dipoles will control the phase response at the higher frequencies, while the lower dipoles will perform the same task at the lower frequencies. The design of the DRAA to produce the beams in dual-CP presents higher complexity than in the dual-LP case. A possible solution would be to place a linear-to-circular transmission polarizer in front of the main-RA (designed with the previous dual-LP cells based on orthogonal dipoles), as proposed in [30]. In this configuration, the incident field in dual-CP will be converted into dual-LP, and then each LP will be reflected by the main-RA with a different phase shift and converted again into CP. Another alternative would be to use dual-CP feeds and apply the variable rotation technique (VRT) to the reflectarray elements for generating adjacent beams in dual-CP [18] together with variable-size dipoles to provide the required phase-shift, as demonstrated in [31].
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 3 Results and Discussions
### Diversity Performance Analysis  
To verify the performance of the designed antenna further, the ECC and DG of the proposed antenna are given in Figure 20. The ECC of the proposed antenna is <0.0001, while the DG of the proposed antenna \(\geq\) 9.999 dB, as is clear from Figure 20.  
Figure 17: Radiation patterns of presented MIMO antenna at (**a**) 30 GHz and (**b**) 40 GHz.  
Figure 18: Radiation patterns of element 1 in proposed antenna.  
Figure 19: Gain and radiation efficiency of the presented antenna.
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
###### Abstract  
Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol.  
Keywords:5G; liquid detection; radio propagation; dielectric constant; WCI +
Footnote †: journal: Sensors
## 3 Results
### Findings from the Case Study Comparison  
5.1 Who Should the Latrobe Valley/Gippsland Be Comparing Itself to Regarding Emergency DES Responses?  
The high-level screening based on the Global Innovation index, found 130 countries that could act as potential case studies. The initially large pool of potential countries has been further narrowed down to six regions applying additional factors including the occurrence of recent extreme events and the risk type of these events. Six regions were shortlisted including the United States, Europe, Japan, India, Indonesia, and New Zealand (Table 11) discusses some of the factors that have been considered for the high-level analysis.  
Four case studies (California/USA, Christchurch/NZ, Odisha/India and Aegean Islands/Greece) were selected based on their proneness to environmental disasters, evidence of DES response and other relevant aspects, such as community energy and sustainable development (Table 5).
## 3 Results
### Monomer Composition of PHAs Produced by P. chloroformis PA23 and It Derivative Mutants  
In RMM medium with glucose, _P. chloroformis_ PA23 synthesized PHAs with 3-hydroxydecanoate as a major component (app. 60 mol%; Figure 3). The other monomers included 3-hydroxyhexanoate (2.6 mol%), 3-hydroxyoctanoate (14.2 mol%), 3-hydroxydodecanoate (18.7 mol%), and 3-hydroxytetradecanoate (5.0 mol%). The PHZ and PRN defective mutants synthesized PHA polymers with subunit compositions similar to those of the Wt. For the three regulatory mutants (_gacS_, _rpoS_, SR), the PHAs contained higher amounts of 3-hydroxyhexanoate and/or 3-hydroxyoctanoate compared to the parent. The monomer composition of PHAs synthesized by _P. chloroformis_ PA23 in RMM octanoate medium was different from that of RMM glucose medium.  
The major subunit components of PHAs synthesized in RMM octanoate medium were 3-hydroxyoctanoate (86 mol%), followed by 3-hydroxyhexanoate (11 mol%). Other monomers like 3-hydroxydecanoate, 3-hydroxydodecanoate, and 3-hydroxytetradecanoate were also present in small quantities. PHAs produced by PHZ and PRN mutants had greater 3-hydroxyoctanoate content and lower 3-hydroxyhexanoate content than the Wt _P. chloroformis_ PA23 strain. In this carbon source, the _gacS_ mutant showed a different monomer composition of all five PHA monomers compared to PA23.  
Figure 2: PHA accumulation by Wt _P. chloroformis_ PA23 and its mutant derivatives in RMM media with glucose versus octanoic acid as the carbon source. Bars with same letter are non-significantly different at the 5% level.  
Growth and PHA Production by P. chlororaphis PA23 in Glucose and Octanoic Acid Medium at Different Time Points  
P. chlororaphis PA23 was grown in RMM glucose or RMM octanoic acid media for 96 h and samples were analyzed for cell dry mass, PHA accumulation, residual ammonium-N, and residual glucose or octanoic acid (Figure 4). Cell dry mass in RMM glucose medium increased up to 48 h pi and then stabilized with no further increase in cell dry mass. In RMM octanoic acid medium, cell dry mass increased up to 24 h pi, with no further increase in cell dry mass thereafter. PHA production in RMM glucose medium increased up to 96 h pi, but in RMM octanoic acid medium, maximum PHA accumulated at 24 h pi with no further increase up to 72 h pi. In RMM glucose and RMM octanoic media, maximum ammonium-N was consumed within 24 h pi and no detectable ammonium nitrogen was present in the medium after 36 h pi. Samples for qPCR were taken at 24 h pi in RMM glucose medium and at 16 h pi in RMM octanoic acid medium, respectively. At these time points, both cultures had started to accumulate PHA polymers (Figure 4B).  
Figure 3: Monomer composition of PHAs synthesized by _P. chlororaphis_ PA23 and its mutant derivatives in (**A**) RMM glucose medium and (**B**) RMM octanoate medium as the carbon source.
## 3 Proposed Method
### Deep Residual Network Layers  
In our steganalysis model, the deep network layer consists of multiple fully connected layers. The purpose of this layer is to capture higher-order feature interactions between the codewords implicitly. Additionally, this paper adopts a residual structure [40] within the deep network layer, which offers several advantages. First, the residual structure  
Figure 3: Example of bilinear interaction.  
enables the model to preserve lower-order codeword interaction feature information. This is achieved by incorporating identity mappings, which allow the model to learn residual connections that capture additional information beyond lower-order interactions. By preserving this information, the model can benefit from both lower-order and higher-order interactions, leading to improved performance. Second, the residual structure enhances the correlation between the gradients and the loss function. This correlation is vital for effective learning. By using residual connections, the gradients can directly flow through the network without facing significant impediments. Consequently, the model's learning ability is enhanced, and it becomes more capable of capturing complex patterns and relationships within the data. Third, the residual structure mitigates the issue of vanishing gradients. In deep networks, as the gradients propagate through multiple layers, they can become exponentially small, leading to difficulties in training deeper models. With residual connections, the gradients have multiple paths to flow through, allowing them to bypass several layers if needed. This alleviates the problem of vanishing gradients and enables more stable and effective training of deep steganalysis models.  
As shown in Figure 1, the input of the deep residual network layer is the output of the bilinear interaction layer. The output of the bilinear interaction layer \(\mathbf{R}\) is denoted as \(R_{0}\), which is passed to the deep neural network. The feedforward process is as follows:  
\[\mathbf{R}_{g}=\sigma(\mathbf{W}_{g}\mathbf{R}_{g-1}+\mathbf{b}_{g})+\mathbf{R}_{g-1} \tag{16}\]  
where \(g\) represents the \(g\)-th fully connected layer and \(\sigma\) is the activation function. \(\mathbf{R}_{g}\) denotes the output of the \(g\)-th fully connected layer. \(W_{g}\) and \(b_{g}\) are the weight parameter and bias of the \(g\)-th layer. The output of the deep network is denoted as \(\mathbf{O}\).  
In summary, the adoption of a residual structure [40] within the deep network layer provides benefits such as preserving lower-order interaction features, enhancing the correlation between the gradients and loss function, and alleviating the vanishing gradient problem. These advantages contribute to the overall performance and effectiveness of the steganalysis model.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Comparison  
The aforementioned privacy mechanisms have been widely utilized in FRSs to offer stronger privacy protection. Table 2 details the comparison between these mechanisms. Similarly, Table 3 overviews privacy-preserving techniques, including their descriptions, domains, platforms, and environments. Firstly, the main objects of protection vary between the mechanisms. The tokenization mechanism secures user interaction behaviors, while the remaining mechanisms protect user ratings. Moreover, homomorphic encryption can also integrate data from other participants in a secure manner. Secondly, homomorphic encryption and secure multi-party computation are both encryption-based mechanisms, which protect privacy while maintaining accuracy, but the high computational cost of homomorphic encryption restricts its application in large-scale industrial settings. Secret multi-party computation reduces the computation cost, but increases the communication costs. On the other hand, differential privacy and local differential mechanisms protect privacy by adding random noise, which has low computational costs and does not add any communication costs. Nonetheless, adding random noise will unavoidably influence model performance to a certain degree.
## 2 Results
### Linear Discriminant Analysis (LDA)  
To perform the LDA, statistically significant compounds (\(p<0.05\)) were selected. The LDA is a method that relates quantitative independent variables (relative areas of compounds) with qualitative dependent variables (varieties under study) through discriminant functions to explain differences between varieties [44].  
For this analysis, we employed four discriminant functions, which were evaluated based on their significance and Wilks lambda factor. The Wilks lambda factor allows for the testing of the significance of the discriminant functions for a significance level of 0.05. The Wilks lambda range varies from 0 to 1 and the closer to zero the Wilks lambda value is, the more distinct the varieties are [45]. In the case of varieties (Figure 4a), it was found that all functions had significance levels <0.001, which suggests that the discriminant functions are significantly different. For the 2021 (Figure 4b) and 2022 (Figure 4c) varieties, the Wilks lambda values obtained showed significance levels <0.001 for functions 1, 2, and 3. Function 4 exhibited significance levels of 0.027 and 0.005 for the varieties in the years 2021 and 2022, respectively, indicating significant differences among the discriminant functions once more.  
The eigenvalues indicate how different the varieties are according to the discriminant functions; that is, for the results of Figure 4a, function 1 explains 73.1%, function 2 explains 19.3%, function 3 explains 5.5%, and function 4 explains 2.1%. The Wilks lambda value is 0 for the first 2 functions and 0.002 and 0.068 for function 3 and 4, respectively. Thus, the first 3 functions have good discriminatory power. As for the results of the 2021 data set, function 1 explains 58.9%, function 2 explains 34.4%, function 3 explains 5.5%, and function 4 explains 1.2%. The Wilks lambda value is 0 for the first two functions, while for functions 3 and 4, it is 0.002 and 0.089, respectively, indicating that only three functions possess high discriminatory power. As for the results for the 2022 data set, function 1 explains 79.8%, function 2 explains 16.1%, function 3 explains 3.3%, and function 4 explains 0.9%. The Wilks lambda value is 0 for the first 2 functions and 0.001 and 0.043 for function 3 and 4, respectively. Therefore, all functions have good discriminatory power. The application of the LDA demonstrated statistical differences among the five varieties studied, both in 2021 and 2022.
# Volatilome and Bioaccessible Phenolics Profiles in Lab-Scale Fermented Bee Pollen
## 3 Results and Discussion
### In Vitro Gastrointestinal Batch Digestion of BCP and Phenolics Bioaccessibility Assay
Furthermore, during plant fermentations, phenolics are converted to biologically active metabolites through glycosyl hydrolase, phenolic acid decarboxylase and reductase, and esterase activities, which have been previously described also in lactic acid bacteria and yeasts [47; 72; 73; 74]. The higher content of epicatechin after gastrointestinal digestion might be due to the partial hydrolysis of proanthocyanidins [75]. Quercetin may be released by glycosides like rutin, through microbial glycosyl hydrolases. Microbial esterases acids likely underwent the release of caffeic acid, through the hydrolys of esters of phenolic acids like chlorogenic acid [19]. Other than the increased nutrient bioaccessibility, the beneficial effect of fermentation might also be related to the microbial catabolites resulting from phenolics metabolism [19]. Hydroxycinnamic acids (for example, caffeic and ferulic acids) may be decarboxylated to the corresponding vinyl derivatives (for example, dihydrocafie and dihydroferulic acids) by lactic acid bacteria. These latter also display phenolic acid reductase activities, able to hydrogenate the double bond of hydroxylcium acids and their vinyl derivatives [19; 47]. Hydroxycinnamate decarboxylase and vinyl phenol reductase activities have been also described in some yeasts [73; 74]. Certain of these metabolites have improved biological and antioxidant activities compared to their precursors [19]. Dihydrocaffeic acid, the reduced derivative of caffeic acid, has well-known and higher antioxidant activity than its precursor [76; 77]. The reduction of vinylcatechol led to ethyl catechol, which is able to interfere with the induction of NF-kB and MAPKs signaling pathways in mammalian cells [19]. Although both spontaneous and controlled fermentation affected the profile of bioaccessible phenolic, the selected microbial starters led to more substantial results.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 4 System Model  
### Network Model  
Consider a set of RRHs distributed over a geographical area, and each RRH is collocated with a fog node \(n_{i}\), \(n_{i}\in\) N, where N is the total number of fog nodes in the network. A fog node acts as the processing unit that delivers various NF without relaying to the cloud core that acts as the BBU. Functions include control, communication, storage, and management, etc. The processing of various NFs at the edge of the network alleviates latency in the backhaul of the network, as opposed to traversing traffic to the core, which incurs aggregated delays. The local processing also saves in resources that can be used instead to enhance the network capacity, i.e., radio resources and processing units at the cloud core. Further, each Fog node \(n_{i}\) in the F-RAN possesses processing capacity and memory expressed by \(q_{prc}(n_{i})\) and \(q_{ee}(n_{i})\), respectively. Note that \(q_{prc}(n_{i})\leq Q_{prc}(n_{i})\) and  
Figure 1: F-RAN Architecture for mmWave NR Operations.  
\(q_{me}(n_{i})\leq Q_{me}(n_{i})\), where \(Q_{prc}(n_{i})\) and \(Q_{me}(n_{i})\) are the maximum capacity and memory, respectively. Additionally, incurred processing delay at node n\({}_{i}\) is denoted by \(D_{prc}(n_{i})\). Further, consider that the fog nodes are interconnected via wireless links, thus forming a set E = {e} of links. The instantaneous available link bandwidth is denoted by b(e), b(e) \(\leq\) B(e), where B(e) represents the maximum available bandwidth. Here, \(D_{prp}(e)\) accounts for the propagation delay at the link.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 5 Conclusions  
This paper presents a technique for implementing the path tracking of manipulators using a deep-reinforcement-learning algorithm. The target path is generated using a conventional path-planning method, while the control signal for manipulator control and target path tracking is generated using a deep-reinforcement-learning approach. In this study, simulation experiments were conducted on a six-degree-of-freedom robotic arm, which is the most widely used form in practical applications and research. The experimental results show that the method has good results in manipulator-path tracking, which not only avoids the process of finding the inverse kinematic solution, but also does not require a dynamics model and can ensure the control of the tracking of a manipulator in continuous space. In addition, through further verification experiments on the tracking of the path of the redundant manipulator, the generalization and stability of our method were reflected. Therefore, the method used in this paper is important for the study of deep reinforcement learning in conjunction with manipulator-path tracking. In response to the issues with the presented algorithm that were noted in the Discussion session, the network will next be trained using inputs that consist of randomly generated 3D pathways. In this method, the problem of the inability to track in real time is resolved, since the trained network can control the manipulator to execute path tracking inside a specific working region in the face of randomly generated desired paths.  
Conceptualization, P.Z. and J.Z.; methodology, P.Z. and J.K.; software, P.Z. and J.Z.; resources, J.K.; data curation, J.Z.; writing--original draft preparation, P.Z.; writing--review and editing, P.Z.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province, grant number no. 2019B020223003; and Guangdong Basic and Applied Basic Research Foundation, grant no. 2022A1515140013.
## 4 Materials and Methods
### Analysis of Primary Metabolites by Using GC-MS  
The seeds, stones and peels were first removed, and then the fruit flesh was cubed. Primary metabolites in the cubes of Korla pear fruit were detected by GC-MS as described in previous studies [29; 30] with minor modifications. The relative concentration of compounds was calculated by comparison with the internal standard. The cubes were quickly ground into powder with liquid nitrogen. Then, 0.30 g powder was thoroughly mixed with 3 mL methanol containing 0.02 mg/mL ribitol as the internal standard. After shaking and ultrasonic treatment for 30 min, the mixture was put into a thermostated water bath at 70 \({}^{\circ}\)C for 15 min and centrifuged at 5000 \(g\) for 15 min. Then, 100 \(\upmu\) supernatant were collected and vacuum-concentrated. Metabolites were trimethylsilylation (TMS) derivatized before GC-MS analysis. Samples were dissolved in 80 \(\upmu\)L methoxamine hydrochloride (20 mg/mL in pyridine), incubated for 90 min at 37 \({}^{\circ}\)C, and then reacted with 80 \(\upmu\)L of MSTFA for 30 min at 37 \({}^{\circ}\)C. After that, 1 \(\upmu\)L of each sample was injected into the gas chromatography (Thermo Fisher Scientific, Waltham, MA, USA) onto a fused-silica capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m DB-5 MS stationary phase). The temperature program of GC was as follows: 100 \({}^{\circ}\)C for 1 min, heated to 184 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, heated to 190 \({}^{\circ}\)C at 0.5 \({}^{\circ}\)C/min and kept for 1 min, heated to 280 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min and kept for 5 min, with the pulsed split injector temperature held at 230 \({}^{\circ}\)C as the split ratio was 10, and the carrier gas was set at a flow rate of 1.2 mL/min. The conditions for mass spectrometer were: electron ionization source, electron energy of 70 eV; ionization temperature 260 \({}^{\circ}\)C, transfer line temperature 280 \({}^{\circ}\)C and scanning range of 40 to 650 amu.  
The compounds detected by using GC-MS was identified by comparing their mass spectra with the NIST MS Search 2.3.  
Semiquantitative determinations were conducted using ribitol as an internal standard. The contents of volatiles were calculated from the GC TIC peak areas related to the GC TIC peak area of the internal standard.