esa-sceva/satcom-chunk-collection huggingface dataset:
# The Volatile Compounds Change during Fermentation of _Saccharina japonica_ Seedling
## 3 Results
### OAV Analysis of Volatile Compounds of S. japonica Seedling before and after Fermentation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Volatile Compounds** & **Odor Description \({}^{\text{a}}\)** & **Threshold Value \({}^{\text{b}}\)** & \multicolumn{2}{c}{**OAV Value**} \\ \cline{2-5}
1 & 1-Octen-3-ol & Mushroom, fatty, fishy & 1.5 & 41,603 \(\pm\) 1.995 & - \\
2 & 2-Octen-1-ol & Mushroom, fatty & 840 & 5 \(\pm\) 1.538 & - \\
3 & 1-Octanol & Lemon, floral & 0.13 & 20 \(\pm\) 3.204 & - \\
4 & 1-Nonanol & Earthy, fatty & 280 & 2 \(\pm\) 0.355 & - \\
5 & Hexanal & Herbal, fishy & 5 & 4895 \(\pm\) 0.104 & - \\
6 & 1-Nonanal & Herbal, paint, fatty, fishy & 1.1 & 43,093 \(\pm\) 1.628 \({}^{\text{c}}\) & 10,541 \(\pm\) 1.355 \({}^{\text{d}}\) \\
7 & 2-Nonenal & Fishy, herbal & 0.4 & 3253 \(\pm\) 1.751 & - \\
8 & 2,4-Dimethylbenzaldehyde & Bitter Almond & 200 & 4 \(\pm\) 0.500 & - \\
9 & Decyl aldehyde & Ocean, cucumber, herbal & 1.97 & 1.11 \(\pm\) 0.30 \({}^{\text{c}}\) & 2.615 \(\pm\) 0.138 \({}^{\text{d}}\) \\
10 & \(\beta\)-Cycloctral & Liquorice, fruity, fresh & 3 & 1274 \(\pm\) 0.103 \({}^{\text{c}}\) & 6831 \(\pm\) 0.974 \({}^{\text{d}}\) \\
11 & Undecanal & Fruity & 12.5 & 72 \(\pm\) 0.004 & - \\
12 & Trans-2,4-decadional & Fishy, fatty & 10 & 340 \(\pm\) 30.002 & - \\
13 & Dodecyl aldehyde & Herbal, fatty, soap & 2 & 1150 \(\pm\) 0.295 \({}^{\text{c}}\) & 2050 \(\pm\) 0.201 \({}^{\text{d}}\) \\
14 & 3,5-Octalation-2-one & Woody, sweet & 150 & 54 \(\pm\) 0.006 & - \\
15 & \(\beta\)-Ionone & Floral, raspberry & 2 & 21,431 \(\pm\) 0.441 \({}^{\text{c}}\) & 54,243 \(\pm\) 0.803 \({}^{\text{d}}\) \\
16 & D-Limonene & Lemon, citrusy & 200 & 42 \(\pm\) 0.015 \\
17 & Methyl palmitate & Fatty & 2000 & 33 \(\pm\) 0.006 \({}^{\text{c}}\) & 22 \(\pm\) 0.013 \({}^{\text{d}}\) \\
18 & 2-Pentyfuran & Beany, spicy & 5.8 & 3204 \(\pm\) 0.521 & - \\ \hline \hline \end{tabular} Note: SJS refers to unfermented _S. japonica_ seedling; FSJS refers to fermented _S. japonica_ seedling. \({}^{\text{a}}\) Odor description refers to the Odor Database ([[http://www.odour.org.uk/](http://www.odour.org.uk/)]([http://www.odour.org.uk/](http://www.odour.org.uk/))). \({}^{\text{b}}\) Threshold values were according to the reported compilations of odor threshold values in air, water, and other media [25]\({}^{\text{d}}\) Signal intensities were shown in mean \(\pm\) standard deviation (\(n=3\)). Significant differences were performed using [PERSON]’s test (\(p<0.05\)) in SPSS 19.0.  
\end{table} 
Table 2: OAVs of the volatile compounds of the _S. japonica_ seedling before and after fermentation. Figure 3: OAVs of the volatile compounds of the _S. japonica_ seedling before and after _S. cerevisiae_ fermentation. SJS means unfermented _S. japonica_ seedling; FSJS means fermented _S. japonica_ seedling (A refers to the OAV value of volatile compounds relate with sensory properties; B refers to the OAV value of alcohols, aldehydes, ketones, alkenes and esters).
## 4 Semantic Understanding of Object Layer
### Spatial Structure Semantics
#### 4.3.4 Global Organization Method Based on Local Structure  
In this method, firstly, the local structural features, such as feature points, feature lines and feature surfaces, are obtained. Then, according to the spatial structure of the objects, the global structure model of the objects is constructed by using certain organization rules and mathematical models [90]. The process is mainly based on the geometric structure of the object itself, spatial relationship information and prior knowledge of the object structure [91]. For instance, when extracting building targets in HSR remote sensing images, we can make full use of the feature that the building roof is a rectangular structure. Firstly, local structure features such as corners, lines and ridges are extracted. Then, the method of perceptual organization is used to organize it into a complete roof contour of the building [92, 93]. This method accords with the cognition rule of people to things, but it has a higher request for the construction of mathematical models and the realization of calculation methods [94].
## 4 Discussion  
### The Significance of Real-Time Reporting of Nonconformities via Edge Devices  
Modern agricultural organizations can benefit from integrating advanced technologies like edge devices for real-time defect reporting. The continuous evolution of SA solutions offers high availability, constant data accessibility, and remote services, allowing organizations to enhance their business capabilities while minimizing costs. SA technologies enable customizable, flexible, collaborative, and reconfigurable services, facilitating a unified human-machine manufacturing system.  
Our case study, implemented within an agricultural company, demonstrates how small and medium-sized enterprises can expand their in-house, production-oriented applications to include efficiency-enhancing SA solutions. By using YOLO models trained on cloud-stored image data and deploying them on local edge devices, our defect detection solution promotes continuous improvement in quality management. The edge devices identify and report defects throughout manufacturing, emphasizing evidence-based decision-making and improvement.  
A Dahua 2 MP network bullet camera with full-color technology was employed for this application. This camera operates effectively in low light conditions and provides a maximum resolution of 1920 \(\times\) 1080. It features a fixed 2.8 mm lens, H.265+/H.264+ compression, and an IP67 protection rating, making it suitable for various environments. The camera's video analytics capabilities, such as Tripwire and intrusion detection, enhance its functionality. The local server used for processing is a Lenovo W541 with an Intel i7-4810 MQ processor, 32 GB DDR3 RAM, and a 512 GB SSD. This configuration supports the computational demands of the system, facilitating real-time decision-making based on captured data.
# Decoupled MPC Power Balancing Strategy for Coupled Inductor Flying Capacitor DC-DC Converter
## 4 Experimental Verification
### Performance Discussion  
**Efficiency analysis:** The converter efficiency is mainly determined by the switching loss. According to Figures 2 and 3, it can be seen that the coupled inductor FCDC studied in this paper achieves Buck operation by chopping the upper bridge arm and keeping the lower bridge arm switch off. In Boost mode, the lower bridge arm switch operates under the chopping state, and the upper bridge switches are turned off. Therefore, its basic modulation method is the same as the flying capacitor topology controlled by a traditional PI strategy [20]. The MPC control strategy proposed in the paper is fixed frequency control, and the switching frequency is not affected by the control algorithm, so there will be no additional losses due to frequency changes. In addition, the control variable generated by the MPC strategy is the duty ratio, and the switch does not operate in soft switching mode. Therefore, we can say that the loss of the proposed MPC strategy is basically the same as that of the traditional PI control strategy.  
**Comparison with other strategies:** The MPC strategy has better dynamic performance compared to traditional PI control. The proposed MPC strategy in the paper mainly solves the power imbalance issue under coupled inductance and the control of flying capacitor voltage. Compared with the coupled inductance balancing strategy proposed in [16], according to the experimental results shown in Figures 7-10, it can be seen that the proposed MPC strategy has favorable balancing performance, with a two-phase current difference close to 0 under a steady state and dynamic load changes. Meanwhile, according to the voltage response curve of the flying capacitor proposed in [18], it can be seen that under similar operating conditions, the response time of the strategy is longer than 500 ms,  
Figure 10: Abrupt load changing results under Boost mode. (**a**) Abrupt loading. (**b**) Abrupt unloading.  
Figure 9: Abrupt load changing results under Buck mode. (**a**) Abrupt loading. (**b**) Abrupt unloading.  
while the response time of the MPC strategy proposed in this paper is about 10 ms and the capacitor voltage response time is about 5 ms. In comparison, the proposed strategy has obvious advantages in both balancing performance and dynamic response performance.
## 3 System Model
### Substrate Node Importance Metric  
It is essential to assess the availability of physical nodes so as to select them as hosts for deploying virtual nodes. We model the substrate mode importance metric with topological characteristics of substrate nodes (including degree and betweenness centrality) and network features (including node capacity and current forwarded rate). First, the substrate node resource is typically measured with network features [44] as follows:  
\[NF_{i}=C_{i}\sum_{ij\in L^{s}}B_{ij}, \tag{1}\]  
where \(C_{i}\) denotes the current capacity of node \(i\) and \(B_{ij}\) denotes the current bandwidth of link \(ij\) which connects the node \(i\). With combining the topological characteristics of substrate nodes to measure node importance, we weight the parameters in terms of degree and betweenness centrality based on nodes' normalized metrics [41] and define the node important metric as:  
\[NI_{i}=NF_{i}\cdot\frac{(d_{i}+b_{i})}{2}, \tag{2}\]  
where \(d_{i}\) denotes normalization of the degree of node \(i\), which measures the number of edges that connect to the node. \(b_{i}\) denotes normalization of the betweenness centrality of node \(i\), which quantifies the possibility a node is found between the path linking other pair of nodes.
## 4 Conclusions  
Torrefaction has been proposed as an effective thermal pretreatment process for the thermochemical conversion of biomass. To optimize this process and predict the quality of biochar, a dual kinetic model based on Arrhenius law was developed to describe the torrefaction of wheat straw at different temperatures. The schemes using global and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**HHV (MJ\(\cdot\)kg\({}^{-1}\))**} & \multicolumn{2}{c}{**Relative Errors (\%)**} \\ \hline
**Temp (\({}^{\circ}\)C)** & **Ref** & **Global** & **Individual** & **Global** & **Individual** \\ \hline raw & 18.535 & 18.905 & 19.724 & 1.995 & 6.417 \\
250 & 19.689 & 19.552 & 19.884 & 0.695 & 0.989 \\
275 & 21.373 & 20.732 & 19.794 & 3.001 & 7.389 \\
300 & 22.239 & 22.331 & 21.694 & 0.415 & 2.449 \\
325 & 23.341 & 23.191 & 23.615 & 0.642 & 1.172 \\ \hline \hline \end{tabular}
\end{table}
Table 6: HHV analysis prediction results.  
Figure 11: Study of HHV prediction curves for wheat straw at different torrefaction temperatures: (**a**) global kinetic parameters and (**b**) individual parameters.  
individual parameters were compared and used to predict the evolution of the elemental composition (CHNO) and the higher heating value (HHV) of the resulting biochar.  
The results demonstrate that the proposed model accurately reproduces the experimental profiles, showing strong agreement between the predicted and observed values of elemental composition and HHV. During the torrefaction process, the O/C and H/C atomic ratios decreased as the temperature increased, followed by a dehydration process, causing an increase in energy densification when the temperature is higher.  
Furthermore, it was found that by determining the kinetic parameters at a torrefaction temperature higher than the maximum peak observed in the derivative thermogravimetric (DTG) curve (i.e., 275 \({}^{\circ}\)C), it is possible to predict the behavior of the process within the 250-325 \({}^{\circ}\)C range with an R-squared value corresponding to an error lower than 3%. This approach significantly reduces the number of required experiments--from twelve to only four--by relying on a single isothermal condition for parameter estimation. Such a strategy enhances experimental efficiency and reinforces the model's robustness for process design and optimization.  
Biomass torrefaction can have environmental benefits in relation to carbon sequestration. Furthermore, the reduction in experiments to obtain the kinetic model, as was developed in the present study, drives economic benefits because the experimentation costs could be up to 75% less.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 5 Evaluation
### Mobility Handover Duration  
We compared our mobility support method with rendezvous NDN mobility support proposed in [15] and Mobile-IP [14]. We consider two levels of network topology to evaluate. One topology is for a single region and the other one is for cross regions. The scenario is group communication between two responders connecting to two border edge nodes in the same district (Gangnam in our scenario). Then, one responder will move to another district. The topologies and scenarios are shown in Figure 15.  
For the rendezvous NDN mobility method, we deploy the rendezvous server at the center of the topology for each region. Rendezvous servers between regions are interconnected. The rendezvous server functionality is kept the same as [15]. When receiving mobility handling request (routes advertisement request from a user device when it connects to a new edge node), this server will update intermediate nodes between it and the device with new routes. Additionally, any interest that does not has the route to the newly moved device will be forwarded to the rendezvous server. After that, the interest will be forwarded to the device using new updated routes.  
We measured mobility handover duration based on the amount of network hops that the mobility handling request from the device needs to go through. Figure 16 shows the comparison between our mobility method, rendezvous NDN method, and Mobile-IP.  
The comparison shows that our proposed mobility support method has the lowest mobility handover duration. In our method, because the mobility handling process is conducted right at the edge node that the moving device connects to, the number of hops needed in both topologies is only 1. In the rendezvous NDN method, the mobility handling request needs to be forwarded from the edge node to the rendezvous server. Hence, the number of hops is 2 for single region topology and 3 for cross-region topology (one more hop between two rendezvous servers at two regions). In the Mobile-IP method, an announcement packet will be sent from the newly connected edge node to the previously connected one so that the number of hops is 4 and 7 for single region topology and cross-region topology respectively.  
Figure 15: Network topologies and scenario for mobility handover measurement (a) Single region topology; (b) Cross-region topology.
# Few-Cycle, \(\mu\)J-Class, Deep-UV Source from Gas Media
Despite the importance of nonlinear dynamics, however, the cell position dependence, which has been intensively studied for HHG, has not been reported in studies on THG thus far. Previous studies on THG have discussed the underlying optical mechanisms when the gas cell is placed near/at the laser focus [4; 14; 15; 16]. In this work, we measured the deep-UV (DUV) yields and the spectra in various conditions--the gas type, gas pressure, and gas cell location--clarifying not only the optimum conditions for the highest DUV yield, but also the role of highly nonlinear optical processes on the DUV generation efficiencies and spectra. In particular, depending on the cell position with respect to the laser focus, different energy yields and spectra were observed, which was not reported in the previous studies. In our experimental conditions, the highest DUV yield of 1 \(\upmu\)J was observed from Ar gas at a low pressure of 0.8 bar. The underlying physical mechanisms are explained by numerical simulations.
## 2 HSM for Base Station PA
### Linear Amplifier Design
#### 2.3.1 LA Power Efficiency  
In HSM applications, the LA provides wide bandwidth but suffers from low power efficiency. The efficiency decreases when the LA operates under a single high-voltage supply. The proposed LA improves power efficiency through a dual supply voltage domain  
Figure 4: Proposed LA schematic.  
Figure 3: HSM operation waveform.  
architecture. The reference current source and first stage operate in a 5 V LV domain. The second and third stages, which drive large output swings, utilize a 30 V HV domain.
## 5 Innovative Control System for Personalized Thermal Comfort Prediction
### Real-Time Skin-Measurement for Smart and Personalized Control
#### 5.1.1 Data Server and Data Model  
According to [PERSON] et al. [15; 153; 155] the data server serves as abstraction layer for multi-modal, real-time data communication and acts as proxy between hardware and software components. Signals can be sent and demanded simultaneously, and the received data can be saved in an external database. The used data model for the communication between software and hardware is key-value-based. The key and value of the model correspond to the names and values of the sent signals, respectively. Corresponding to this scheme, the received information is saved in a hash map and also in a database in case it is reasonable.  
The data server developed by [PERSON] et al. [15; 153; 155] is implemented in Java and contains a HTTP-interface for the connection with various independent modules via TCP/IP applying an XML schema. A dedicated software which transforms the serial data into the XML schema and transfers the information towards the data server allows the integration of serial devices, too. Several independent software components are connected with the data server and enable the processing of conventional sensor data about the air temperature, relative humidity, air velocity and average radiant temperature; the recognition of thermal and segmental data, and their visualization. The fundamental system of [PERSON] et al. [15; 153; 155] is highly expandable and numerous calculation models for statistical evaluation can be integrated via TCP/IP. All the connected modules are allowed to request and send data points. For instance, the system is suited to control several personal local thermal actuators and in this way pursues the automated control of personal climatization systems regarding thermal comfort and energy consumption.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions
### Efficiency of Polar-LDPC Secrecy Coding Scheme  
The energy-efficiency of secrecy coding schemes mainly depends on the additional amount of energy required to jam all eavesdroppers. Typically, the transmission power of a jamming signal is equal to the transmission power of the information-bearing signal [48], which means that an additional radio unit is required to transmit the jamming signal. As a result, the energy consumption of the transmitter employing secrecy-coding is doubled. It is thus reasonable to expect that secrecy-coding schemes will be energy-efficient in systems where transmission powers are relatively low, for example, short-range communication systems and local area networks.  
Typical power consumptions of various WLAN cards and access points are presented in [49]. Since generating a jamming signal does not involve any processing for incoming or outgoing data packets, the power consumption of the network interface card is a valid figure of merit.  
The computational complexity of the proposed secrecy coding scheme will be studied in Section 6.1.
## 4 Experiments
### Ablation Study
#### 4.6.4 Ablation on BPPNet for Dehazing Problem  
We compared the performance and computational load of the original BPPNet [24], the reduced BPPNet (as explained in Section 4.1) without LGA, and reduced BPPNet with LGA. The results are reported in Table 7. The reduced BPPNet without LGA comprised fewer than 10% of the parameters of the original and approximately 20% the number of FLOPs. This resulted in poorer performance in terms of both the structural similarity index (SSIM) and peak-signal-to-noise-ratio (PSNR). However, the inclusion of LGA improved both the SSIM and the PSNR, with almost no extra computational load.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MODEL**} & \multirow{2}{*}{**Variation**} & \multirow{2}{*}{**mIoU (\%)**} & **Parameters** & **FLOPS** \\  & & & **(\(\times 10^{6}\))** & **(\(\times 10^{9}\))** \\ \hline \multirow{4}{*}{Squeeze} & No LGA & 41.5 & 1.018 & 13.140 \\  & LGA with SC and \(L_{dip}\) & **45.8** & 1.150 & 13.280 \\ \cline{1-1}  & LGA with GC and \(L_{div}\) & **44.6** & 1.035 & 13.162 \\ \cline{1-1}  & LGA with SC, no \(L_{div}\) & 43.6 & & \\ \cline{1-1}  & LGA with GC, no \(L_{div}\) & 42.3 & & \\ \hline \multirow{4}{*}{Shuffle} & No LGA & 36.9 & 0.395 & 3.42 \\ \cline{1-1}  & LGA with SC and \(L_{div}\) & **44.6** & 0.506 & 3.69 \\ \cline{1-1}  & LGA with GC and \(L_{div}\) & **44.5** & 0.412 & 3.5 \\ \cline{1-1}  & LGA with SC, no \(L_{div}\) & 43.0 & & \\ \cline{1-1}  & LGA with GC, no \(L_{div}\) & 41.6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study 3: Comparison of mIOU value (%) of LGA using simple convolution (SC) or group convolution (GC) and with or without divergence loss. We also include the number of parameters and FLOPs in the first three rows to illustrate that the computational needs of incorporating LGA are marginal and even further reduced when employing group-transposed convolution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MODEL**} & \multicolumn{2}{c}{**Performance (\(\uparrow\))**} & \multicolumn{2}{c}{**Extra resources (\(\downarrow\))**} \\ \cline{2-5}  & **mIoU (\%)** & **Accuracy (\%)** & **Parameters (\(\times 10^{3}\))** & **FLOPS (\(\times 10^{6}\))** \\ \hline CCNet [8] & 42.8 & 79.6 & 2686 & 5652 \\ LGA & 45.8 & 81.8 & 132 & 140 \\ LGA small & 44.6 & 79.6 & 17 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study 2: Comparison of LGA (with simple and group convolutions) vs. CCNet with a SqueezeNet encoder. The original mIoU and accuracy without any LGA or CCNet module were 41.5 and 76.2, respectively.
Many of the works in this field emphasise the use of frequency-dependent boundary conditions to describe the acoustic characteristics of the interfaces present in the model [22; 23; 24; 25; 26; 27; 28; 29]. However, to the authors knowledge, no work has yet been conducted for applying metamaterial-based impedance boundary conditions into large-scale time-domain acoustic computational methods. In this work, we propose an acoustic scattering study where compact metamaterial-inspired acoustic diffusers, called metadiffusers [30; 31; 32], are considered too complex to simulate directly in a 3D Finite-Difference Time-Domain (FDTD) scheme, and thus decide to evaluate the computational and scattering impact of RLC IBCs on the diffuse field of a larger space in which they could be installed, such as an orchestra pit. Figure 1 illustrates this approach, where the scaled scheme of a \(N=5\) slits Quadratic Residue Metadiffuser (QRM) used here as a reference compact metamaterial is shown in Figure 1a. The local scattering generated by the metadifuser can be alternatively reproduced through a simplification of the metamaterial geometry into a set of IBCs. The resulting IBCs can thereafter be reproduced through an RLC circuit [28] integrated at the desired boundaries of the 3D FDTD volume, as illustrated in Figure 1b,c. The novelty of this approach lies in the optimised fitting of the RLC circuit IBCs based on zero crossings of admittance phase derivatives in order to ultimately replicate the scattering generated by metamaterial structures in large FDTD volumes. A time-domain scheme has been chosen for this work as not only it can yield broadband frequency information, but also enables time-domain signal processing which can later be used for further virtual audio operations for auralisation and spatialisation purposes.