esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Genological Characterization of Predominant Native _S. cerevisiae_ Isolated Strains
#### 3.3.3 Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Volatile Compounds** & **Biotype I** & **Biotype II** & **OKAY** & **EC1118** & **VIN13** \\ \hline
**Alcohols** & & & & & \\ Hexanol (\(\upmu\)g/L) & 20.0 \(\pm\) 0.0 \({}^{\text{a}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{a}}\) & 20.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{a}}\) & 20.0 \(\pm\) 0.1 \({}^{\text{a}}\) \\ \(\beta\)-Phenyl ethanol (mg/L) & 30.7 \(\pm\) 3.1 \({}^{\text{c}}\) & 12.9 \(\pm\) 2.9 \({}^{\text{e}}\) & 35.2 \(\pm\) 0.7 \({}^{\text{a}}\) & 32.7 \(\pm\) 2.9 \({}^{\text{b}}\) & 16.3 \(\pm\) 2.1 \({}^{\text{d}}\) \\
**Esters** & & & & \\ Isoamyl acetate (mg/L) & 3.8 \(\pm\) 0.3 \({}^{\text{a}}\) & 1.8 \(\pm\) 0.1 \({}^{\text{b}}\) & 1.3 \(\pm\) 0.0 \({}^{\text{c}}\) & 1.1 \(\pm\) 0.1 \({}^{\text{c}}\) & 1.5 \(\pm\) 0.0 \({}^{\text{b}}\) \\ Phenylethyl acetate (mg/L) & 0.2 \(\pm\) 0.0 \({}^{\text{b}}\) & 0.1 \(\pm\) 0.0 \({}^{\text{c}}\) & 0.1 \(\pm\) 0.0 \({}^{\text{b}}\) & 0.4 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.1 \(\pm\) 0.0 \({}^{\text{c}}\) \\ Ethyl hexanoate (mg/L) & 0.2 \(\pm\) 0.1 \({}^{\text{c}}\) & 0.4 \(\pm\) 0.0 \({}^{\text{c}}\) & 1.3 \(\pm\) 0.2 \({}^{\text{a}}\) & 0.5 \(\pm\) 0.1 \({}^{\text{b}}\) & 0.5 \(\pm\) 0.0 \({}^{\text{b}}\) \\ Ethyl octanoate (\(\upmu\)g/L) & 40.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 40.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 40.0 \(\pm\) 0.2 \({}^{\text{a}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) & 30.0 \(\pm\) 0.1 \({}^{\text{a}}\) \\ Diethyl succinate \(\upmu\)g/L) & 20.0 \(\pm\) 0.0 \({}^{\text{b}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{c}}\) & 60.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{c}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{c}}\) \\
**Terpenes** & & & & & \\ Linalool (\(\upmu\)g/L) & 40.0 \(\pm\) 0.01 \({}^{\text{b}}\) & 30.0 \(\pm\) 0.1 \({}^{\text{b}}\) & 60.0 \(\pm\) 0.2 \({}^{\text{a}}\) & 40.0 \(\pm\) 0.1 \({}^{\text{b}}\) & 20.0 \(\pm\) 0.1 \({}^{\text{b}}\) \\ Nerol (\(\upmu\)g/L) & 10.0 \(\pm\) 0.01 \({}^{\text{b}}\) & 30.0 \(\pm\) 0.0 \({}^{\text{a}}\) & 30.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 20.0 \(\pm\) 0.1 \({}^{\text{b}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) \\
**Enones** & & & & & \\ \(\beta\)-Damascenone (\(\upmu\)g/L) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) & 20.0 \(\pm\) 0.1 \({}^{\text{a}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) & 10.0 \(\pm\) 0.0 \({}^{\text{b}}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Main volatile compounds of wines carried out at 22 \({}^{\text{c}}\)C. Data are means \(\pm\) standard deviations, and those with different superscript letters (\({}^{\text{a}-\text{d}}\)) within each column are significantly different, according to [PERSON]’s tests (\(p\)-value < 0.05).
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 6 Technical/Critical Analysis and Recommendations for Deployment
### Critical Analysis  
A critical assessment of our approach reveals both strengths and limitations. For example, as previously mentioned, the simplification of the Q-table construction mitigated scalability concerns while maintaining the essential functionality of the Q-learning algorithm within the RPL protocol. However, this approach comes at the cost of rebuilding the Q-table whenever nodes are deployed in new scenarios or locations, thus requiring a repeat of the learning process. Consequently, this method sacrifices the potential benefits of transfer learning, where pre-trained Q-tables could serve as a starting point for further training in varied environments.  
Another important consideration in Q-RPL is the balance between exploration and exploitation. Maintaining a degree of exploration is essential for discovering potentially better routing paths. However, excessive exploration might lead to instability and inefficiency, particularly in critical applications where consistent and reliable performance is paramount. For such applications or critical devices within the AMI network, it may be necessary to differentiate the exploration and exploitation policies. Integrating a QoS-aware strategy into Q-RPL could prevent critical data losses due to exploratory decisions, aligning network performance with operational priorities.
## 2 Materials and Methods  
### Materials  
Three commercial PHA materials were used as a matrix. These were Mirel PHA1005 and Mirel PHA3002 (food contact P3 HB-co-P4 HB, purchased from Metabolix, Cambridge, MA, USA), and Biomer PHB P226 (homopolymer P3 HB grade purchased from Biomer, Krailling, Germany).  
Three different modified and unmodified organoclays were kindly provided by TOLSA (Madrid, Spain). A natural sepiolite (T2), a modified Sepiolite with aminosilane groups on its surface (T1) and a sodium montmorillonite modified with a quaternary ammonium salt (T3). The three candidates selected present different behaviours from polar (T1) and neutral (T2) to non-polar (T3) feature which directly affects their affinity for the PHAs polyesters (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Formulation** & **Commercial Matrix Used** & **Nature of the PHA** & **Type of Reinforcement (3 wt \%)** \\ \hline PHA1005 & PHA1005 (Metabolix) & P3 HB-co-P4 HB & (3 HB-co-17 mol \% 4 HB) \& Talc \\ PHA1005\_T1 & PHA1005 (Metabolix) & P3 HB-co-P4 HB & T1: Aminosilane Sepiolite \\ PHA1005\_T2 & PHA1005 (Metabolix) & P3 HB-co-P4 HB & T2: Natural Sepiolite \\  & & & T3: Sodium \\ PHA1005\_T3 & PHA1005 (Metabolix) & P3 HB-co-P4 HB & Montmorillonite- Quaternary \\  & & & ammonium salt \\ PHA3002 & PHA3002 (Metabolix) & P3 HB-co-P4 HB & (3 HB-co-23.5 mol \% 4 HB) \& Talc \\ PHA3002\_T1 & PHA3002 (Metabolix) & P3 HB-co-P4 HB & T1: Aminosilane Sepiolite \\ PHA3002\_T2 & PHA3002 (Metabolix) & P3 HB-co-P4 HB & T2: Natural Sepiolite \\  & & & T3: Sodium \\ PHA3002\_T3 & PHA3002 (Metabolix) & P3 HB-co-P4 HB & Montmorillonite- Quaternary \\  & & & ammonium salt \\ PHB226 & PHB226 (Biomer) & P3 HB & Traces of PBA and plasticizer \\  & & & found, \& Talc \\ PHB226\_T1 & PHB226 (Biomer) & P3 HB & T1: Aminosilane Sepiolite \\ PHB226\_T2 & PHB226 (Biomer) & P3 HB & T2: Natural Sepiolite \\  & & & T3: Sodium \\  & & & T3: Sodium \\  & & & \\ PHA3002\_T3 & PHA3002 (Metabolix) & P3 HB-co-P4 HB & Montmorillonite- Quaternary \\  & & & ammonium salt \\  & & & \\ \end{tabular}
\end{table}
Table 1: Summary of material formulations. Table reproduced with permission of [PERSON] et al. [30].  
Chemical patterns for volatile quantification (considered as main responsible for the odour), were purchased from Sigma-Aldrich. These are 1-hexanol, heptanal, octanal, decanal, \(\alpha\)-Methylstyrene, 4-methylstyrene and Benzaldehyde.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### Performance of Input Sample Length  
When other parameters of the improved RNN are not changed, the samples with the length of input samples ranging from 32 to 8192 are trained. The performance of the model is in the range of \(-10\) dB to 10 dB. The Figure 8 indicates the influence curve of input sample length on performance.  
By observing the Figure 8, under the circumstance of low signal-noise, the recognition accuracy is low regardless of the size of the sample length value and it is difficult to meet the requirements of codeword recognition. With the increase of SNR, the accuracy of network recognition performs significantly, which can meet the requirement of increasing the average accuracy by about 4% when the input length is increasing. As the sample length reaches L = 4096 or L = 8192, there is little difference in the accuracy and the overall accuracy is stable at 0.99. As the Figure 8 shows, when L is less than 2048, the recognition accuracy is significantly improved with each increase in the length level. However, when L length exceeds 4096, the performance of the model is not significantly improved. In addition, the longer the input length is, the more memory, training time and data sets are needed and the corresponding recognition efficiency will decrease.  
Figure 8: Recognition performance of the improved RNN in different lengths of input sample (L).  
Figure 7: Confusion matrix for improved RNN trained at \(-5\) dB SNR.
## 3 Application to Synthetic Data
### Model 2  
The second example model illustrates the detection of a resistive target located above a resistive basement. The model is shown in Figure 16. One resistive body (100 ohm-m) with 100 m of thickness, at 900 m below the sea bed, is embedded in a layered model with 1, 2, and 3 ohm-m, over a 250 ohm-m basement. All layers are 1 km thick. The survey is run at the same frequencies and the same transmitter and receiver positions as in the previous example.  
Inversion Results 2  
The same inversion grid of the first example was used, but cropped in the \(y\) direction between \(-4500\) m to 4500 m. Again, the resistivity start model was a homogenous layer of 1 ohm-m from the sea bed to 4500 m and the last layer with 250 ohm-m.  
Figure 17 shows a vertical section on the \(xz\) plane at \(y=0\), and Figure 18 shows a vertical slice in the \(yz\) plane on \(x=0\) m, with the resistivity values recovered by the inversion process. The body appears in the final section well positioned in depth. The presence of the resistive basement is apparent, with its top interface located close to the true depth by the sharp vertical variation in the resistivity. The region below the body is detected with less sensitivity by the data and appears in the final model with lower resistivities than the true values.  
A horizontal cut at a depth of 2500 m is shown in Figure 19.  
Figure 14: Depth slice at \(Z=2450\) m from the constrained CMP inversion with absolute equality constraint from well log data. The results show the best fit with shape of the real body in white.  
Figure 15: Depth slice at \(Z=3450\) m from the constrained CMP inversion with absolute equality constraint from well log data. The results show the best fit with the shape of the real body in white.  
Figure 16: Model 2: 1.5 km water layer over layered sediments and a resistive target with 100 \(\Omega\)-m.  
Figure 17: \(XZ\) slice on \(Y=0\) m from the CMP inversion. The real body has 100 m of thickness, starting on 2400 m of depth.
# The Role of Fucoidan in Controlling _Listeria monocytogenes_ Biofilms on Seafood-Contact Surfaces
## 4 Discussion
The observed downregulation suggests that fucoidan interferes with these molecular pathways, preventing biofilm formation and destabilizing pre-existing biofilms [36]. These findings align withprevious research suggesting that natural polysaccharides, like fucoidan, can act as potent antibofilm agents by targeting QS, stress response, and pathogenicity mechanisms [37; 38; 39]. The seafood sector is facing substantial issues, which offer significant food safety and global health hazards. It reveals fucoidan's potential as an environmental therapy for various difficulties. Fucoidan has various advantages over typical compound sanitizers, including the fact that it is naturally sourced, low in toxicity, and environmentally benign [30; 40]. Furthermore, its efficacy at sub-MIC levels makes it more industrially applicable by lowering costs and minimizing the possibility of antibiotic resistance [41]. In comparison, fucoidan (_Fucus vesicolus_) has recently garnered attention for its wide-ranging antimicrobial and antibiofilm actions. Fucoidan has shown efficacy hostile to both Gram-positive and Gram-negative bacteria, involving _Salmonella_ spp., with MICs ranging between 125 and 150 \(\upmu\)g/mL [8; 22]. Like quercetin, fucoidan can inhibit biofilm formation on food-contact surfaces such as SS, HG, and SR, and it also modulates bacterial gene expression related to quorum sensing, stress tolerance, and virulence [8]. A sensory assessment was performed on fucoidan-treated groups, incorporating various concentrations of fucoidan over multiple storage intervals [42]. Findings indicated butter samples with 0.5% fucoidan showed a marked decrease in acid and peroxide values, thereby enhancing shelf life, as confirmed through sensory testing [43]. On the first day, no differences in taste were detected across all samples at fucoidan applications, there were no negative impacts on flavor. As fucoidan levels increased, the butter's color became marginally lighter, and textural improvements such as greater softness and spreadability were observed. By the 40 th day of storage, a slight development of a rancid taste was present in all samples, with the control exhibiting the most intense off-flavor. In contrast, samples treated with higher levels of fucoidan retained acceptable sensory attributes and did not develop undesirable organoleptic qualities [44]. Overall, the shelf life--the time frame throughout food product remains safe, nutritious, and sensorially acceptable--was significantly extended in the fucoidan-treated butter. Given their different molecular structures and bioactivities--polyphenolic versus polysaccharide--comparative research into natural compounds like quercetin and fucoidan is critical to identify effective, safe, and sustainable biofilm control strategies in food systems. This study builds on this growing body of knowledge by further exploring fucoidan's antibiofilm effects, with particular attention to its mechanism of action and practical implications in seafood processing environments.
# LoRa Resource Allocation Algorithm for Higher Data Rates
###### Abstract  
LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (\(T_{tx}\)) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa's Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting \(T_{tx}\) to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces \(T_{tx}\) by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above \(-5\) dB in line-of-sight communication scenarios.  
Keywords:image transmission; LoRa PHY; LoRa MAC layer; LoRa modulation; SF selection
## 3 Wireless Sensor Networks  
Wireless Sensor Networks are a pervasive technology that targets the connectivity between sensor nodes in multiple environments. Its infrastructure is usually composed of a large number of sensor nodes, with small physical size, which runs upon relatively inexpensive computational processes. Sensor nodes measure local environmental conditions and forward sensed values to a set of central points, referred as sink nodes, for appropriate processing. Sensor nodes can sense the environment, communicate with neighbor nodes, and perform basic computations on collected data. Installation flexibility and easy configuration enable better usability and maintenance than traditional communication technologies [1]. These characteristics allow the use of WSN over a wide range of useful applications [3; 27; 28; 29].  
Currently, WSN solutions are based on standardized or proprietary protocols. There are many different protocols for the upper layers, but the IEEE 802.15.4 [30] is a _de facto_ standard for the lower layers. Recently the IEEE 802.15.5 standard [31] has been released to provide multi-hop mesh functions. Both standards are compatible while maintaining simplicity. On the other hand, Zigbee (2004) and Zigbee Pro (2007) were the first standards to implement the upper layers. Both standards do not have the support for channel hopping and are still not scalable enough to support large topologies [32]. Channel hopping is an important feature when industrial applications are considered, due to its robustness against external interferences and persistent multi-path fading. A new standard, IEEE 802.15.4e, is being developed to support additional industrial requirements and it is expected to be approved by the end of 2011. Currently, only the WirelessHART and ISA 100.11a standards are suitable to be used in industrial applications.  
The methodology proposed in this paper can be easily implemented to evaluate the reliability and availability of Zigbee, WirelessHART and ISA 100.11a networks. However, as the application focus is for industrial environments, only the WirelessHart and ISA 100.11a standards will be described in the following sections.
## 3 Integration of the Dual-Mode Converter and the Conical Horn Antenna
It radiates the \(TE_{11}{}^{cir}\) and \(TE_{01}{}^{cir}\) modes which are excited by the DMC, to generate two independent diagrams: the sum (\(\Sigma\)) and difference (\(\Delta\)) radiation patterns, respectively. They are shown in Figure 8.  
Figure 7: E-field simulation of the Dual-Mode Converter (DMC) network. (**a**) Excitation by the \(TE_{10}{}^{rec}\) mode at the rectangular waveguide dedicated to the sum pattern (_port_ 1, \(\Sigma\)), generating the \(TE_{11}{}^{cir}\) mode at the circular waveguide. (**b**) Excitation by the \(TE_{10}{}^{rec}\) mode at the rectangular waveguide dedicated to the difference pattern (_port_ 2, \(\Delta\)), generating the \(TE_{01}{}^{cir}\) mode at the circular waveguide.
## 4 Codeword-Independent Localization
### Feature Extractor  
The fingerprint graphs generated by the transformer are heterogeneous, and thereby we need a heterogeneous GNN-based model to encode them. Specifically, as shown in Figure 12, for each sub-graph, we first respectively assign a GraphSAGE [84] model using pooling aggregators by default to obtain the corresponding sub-graph-wise readout by the mean of latent features of all vertices. The reason for choosing the GraphSAGE to encode each sub-graph is we can manually set the aggregation depth for neural message passing [85] and randomly select several paths among all possible ones to reduce the complexity due to potential numerous vertices (BSs) and edges (communication links) in our fingerprint graphs [84]. Then, all sub-graph-wise readouts are concatenated to a vector, which will be fed into a dense layer for the fingerprint representations. We utilize leaky ReLU [86] as the activation functions for the feature extractors, for which the negative slope coefficient was \(-0.02\) for all the following experiments.
## 5 Conclusions  
Aiming at the domain shift problem in the case of insufficient source domain labels, a method of applying semi-supervised adversarial transfer networks for cross-domain fault detection was proposed in this literature. Initially, vibration signals were converted into images using the signal-to-image method. Furthermore, a semi-supervised generation  
Figure 14: Comparison of contrast test accuracy.  
Figure 15: Visualizations of the learned feature representations in the fully connected layer in the feature extractor: (**a**) DAN; (**b**) Deep CORAL; (**c**) DANN; (**d**) Proposed.  
module was designed to generate artificial labels for unlabeled images, so as to solve the dilemma of insufficient source domain labels. Eventually, the adversarial transfer networks were introduced to extract domain-invariant features in different domains and achieve fault detections. Two case studies were conducted on the public rolling bearing dataset to validate the performance of the proposed approach. The analysis results clarify that the proposed networks are able to diminish the distribution discrepancy and extract generalized features for domain adaptation detection. Meanwhile, the generalization and robustness of the proposed networks were demonstrated by carrying out a cross-domain transfer experiment and insufficient source-domain label experiment. Therefore, the finding of this research has great potential in practical applications, since comprehensive experimental data on health status labels are generally hard to acquire in the real industry.  
Although the proposed approach is effective for processing common cross-domain diagnosis problems, when the training data in each health state are unbalanced, diagnostic accuracy is dramatically degraded. To address this issue, future research will be considered to perform accurate and efficient fault detection in the presence of an unbalanced health state dataset.  
Methodology, validation, formal analysis, writing--original draft, W.W.; conceptualization, Y.L., J.W., and W.W.; investigation, J.W.; writing--review and editing, J.W. and Y.L.; funding acquisition, B.P. and J.W.; supervision, B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Projects of Zhejiang Provincial Natural Science Foundation of China (Grant No. LD22E050009), Shaoxing Municipality Science and Technology Projects of \"JieBangGuaShuai\" (Grant 2021B41006) and Postdoctoral Science Preferential Funding of Zhejiang Province, China. (Grant 273426).  
Not applicable.  
The authors declare no conflict of interest.
# Orthofacade-Based Assisted Inspection Method for Buildings
## 2 Materials and Methods
### Building Facade Survey  
The second phase of AIM is conducted on site, referring to the data acquisition carried out through a survey of the building facade and includes two activities: (i) image dataset acquisition and (ii) survey of control points.  
_Image dataset acquisition_ is performed in accordance with flight parameters defined in phase 1. Before and during the flight, safety procedures should be conducted [28]. Prior to the flight, the UAV operator should: check if the UAV system is working properly; check the battery condition; ensure that all UAV equipment is properly secured; gather all necessary information for the safe performance of the planned flight; confirm that meteorological and other conditions in the flight area ensure its safe and efficient performance. During the flight, the UAV operator should: ensure that the flight does not endanger the life, health and property of people and does not disturb public order; use the UAV in a way that ensures compliance with the flight rules prescribed by current legislation; ensure that the flight is performed completely within the permitted area; ensure that UAV keeps a safe distance from obstacles; not be under the influence of alcohol or psychoactive substances, nor in such a psychophysical condition that prevents him from safely operating the drone.  
_Survey of control points_ is performed in order to increase the accuracy of the final orthofacade image. The method of precise electronic tachymetry is applied for the survey. The process includes measuring the 3D coordinates of selected characteristic points on the facade of interest using a geodetic instrument--a total station. A minimum of three GCPs is required, while a minimum of five is recommended. Five to ten GCPs are usually enough, even for large projects [29], but increasing the number of GCPs will lead to higher accuracy of the final results [30].