esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Assessment
### Scenario  
The considered scenario, similar to the one presented in [32], is shown in Figure 3 and consists of several nodes, i.e., Light Detection and Ranging (LiDAR), ultrasonic sensors, radars and cameras all connected to four peripheral Electronic Control Units (ECUs) that send raw and aggregated sample data to a centralized controller.  
As shown in Table 2, each ECU transmits to the controller an ED data flow, here called ADAS sensors, consisting of bursts of frames generated following a random distribution. For example, such data could consist of maps for obstacle detection performed by radars to be included in the Advanced Driver Assistance System (ADAS). All the peripheral ECUs transmit the same flows. Each flow in Table 2 consists of periodic messages or ED messages with a specific size, e.g., 43 KB for the Video flow. The simulated network operates at 1 Gbps. The deadlines of LiDAR and Ultrasonic flows are set equal to their periods, while the deadlines of the ADAS sensors flows and the video flow are set to 1 ms and 10 ms, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Flow**} & **Size** & **Workload** & \multicolumn{2}{c}{**Relative**} & \\  & **(KB)** & \(L_{SW,Ctr}\) & **Period (ms)** & **Deadline** & **Type** \\  & **(MBPs)** & & **(ms)** & \\ \hline LiDAR & 0.25 & 0.93 & 10 & 10 & Periodic \\ Ultrasonic & 0.10 & 0.23 & 20 & 20 & Periodic \\ ADAS sensors & 10 & 34 & uniform (10–100) & 1 & Event-driven \\ Video & 43 & 100 & 16 & 10 & Periodic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flow parameters.  
Figure 3: The simulated scenario.  
This section presents the results of two comparative performance assessments. The first comparison is between D-ST and an approach that applies some of the TSN protocols enrolled in [27], i.e., the Credit-Based Shaping, the enhancements for ST (i.e., the transmission gate mechanism for the ST class), and the Strict Priority selection algorithm. Here, such a configuration is called Time-Sensitive Networking with Scheduled Traffic (TSN-ST). The second comparison is between D-ST and D-TSN.
# Implementing Circular Economy Strategies in Buildings--From Theory to Practice
## 6 Data Relevance for CE in Buildings  
With the technological advances, data are becoming an essential asset for the construction industry to manage buildings' performances from the design stages until the end-of-life. Specific information is required to ensure the maintenance of a building and the technical life of all in-use materials. Tracking resource flows will allow building stakeholders to get deeper insights into the resource input-output. For the time being, the most significant challenge on the urban level is the generation of knowledge and data on the material composition of buildings [75]. Digitizing the built environment is regarded as a fast-track approach to implementing CE in the building sector. Keeping track of materials through specific datasets will support materials reuse once they reach their end-of-life, which will smoothen value retention. For materials and components, related information can be stored in a \"Material passport\" that will be shared with all building's stakeholders [76]. The Material Passport (MP) was developed during the EU-funded project \"BAMB\" to support circular building design, smoothen circular building materials selection, and extend their lives while keeping their value [77].  
Comprehensive and accurate data can enhance decision-making, manage financial risk, and monitor environmental impacts. The MP provides qualitative and quantitative information regarding physical, chemical, and biological properties of materials composition, allowing practitioners to select building products that are safe to humans and ecosystems whilst taking into account CE principles. MP comprises different hierarchy levels, from materials, components, products, systems, and buildings, to describe specific features for value recovery and reversibility [78].  
The Building Information Management (BIM) is a computational methodology that digitally models buildings to provide the construction stakeholders with valuable information to aid decision-making throughout the building's life cycle [79]. Understanding the purpose of BIM will support applying the planned strategy throughout the asset's life cycle [80; 81]. In the context of CE, BIM can be considered as an approach to minimize waste throughout the building's life cycle [82; 83]. However, BIM is seldomly used for managing buildings when they reach their end-of-life [82]. Recent studies have attempted to link MP to BIM to compile data on building materials characteristics and assess their recyclability and environmental impacts [75; 76] or reuse waste as a novel construction material [84]. Nonetheless, research regarding MPs' integration into BIM is still in its early stage and requires more profound commitment from the construction industry. Digitizing the building industry will still face several obstacles such as the amount of data that must be generated, their quality, and the reliability of the used data, which will require standardization, collaboration, and transparency from the building stakeholders along the value chain [35].
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 1 Introduction
Section 2 provides a detailed overview of the NSSC iGNSS-R altimeter payload, the iGNSS-R dual-frequency interferometric waveforms, the spatial resolution of iGNSS-R SSH, and the retrieval and validation methods. Section 3 validates the iGNSS-R SSH retrievals, using high-precision radar altimeter data from Jason-3 and Sentinel-6 as reference benchmarks. Section 4 analyzes and discusses the preliminary performance of iGNSS-R altimetry. Section 5 concludes the paper with key findings and future research directions.
# Enhancing the SRAM PUF with an XOR Gate
## 3 Materials and Methods  
### Design  
The SRAM PUF design incorporates two separate SRAM devices and a Boolean XOR gate. In this design, each SRAM device generates its response, and these responses are combined using the XOR gate to generate a single bit. This approach substantially increases the number of available CRPs from the original count of \(2n\) (representing the number of SRAM cells in each device) to \(n^{2}\). Consequently, this significant increase in the number of CRPs greatly enhances the entropy of the PUF. The SRAM XOR gate was added through software. The responses of both SRAMs are read and then XORed through software to generate the final response. The SRAM PUF XOR design is visually represented in Figure 3.
## 4 Experiments and Results
Figure 7: (a) Color images; Reconstructed images of (**b**) traditional PCG, (**c**) C-PCG, (**d**) RS-PCG, and (**e**) TR-PCG when the diffraction distance is 24.85 cm and the resolution is 2048 \(\times\) 2048. As can be seen from Figure 7, the reconstructed image quality of TR-PCG is a little different from that of traditional PCG, C-PCG, and RS-PCG. Compared with the case of 1024 \(\times\) 1024 resolution, the image quality is improved. Considering the speed and quality, it is acceptable. The PSNRs of traditional PCG method are shown in Figure 7b; the PSNRs are 17.38, 18.18, 17.42, 16.78, and 17.44 dB, respectively. The PSNRs of C-PCG method are shown in Figure 7c; the PSNRs are 17.14, 17.42, 17.87, 16.03, and 17.10 dB, respectively. The PSNRs of RS-PCG method are shown in Figure 7d; the PSNRs are 17.56, 17.84, 18.01, 16.05, and 16.98 dB, respectively. As shown in Figure 7e, the PSNRs of the reconstructed images using the proposed TR-PCG method are 17.50, 17.56, 17.65, 16.14, and 17.13 dB, respectively. Currently, there are no accurate evaluation criteria for 3D images, so we adopted PSNR in  
rate of this method is over 90% higher than WRP, about 40% higher than the traditional PCG, about 20% higher than C-PCG, and about 30% higher than the RS-PCG. The feasibility and effectiveness of the proposed method are verified by simulation experiments. This will promote the development of full-color holographic reconstruction technology and reduce the consumption of hardware resources. Conceptualization, Q.Y. and Y.Z. ; methodology, Q.Y. and Y.Z. ; software, Q.Y. ; validation, Q.Y. and Y.Z. ; formal analysis, Q.Y. ; investigation, J.B. and J.J.; resources, Q.Y. and Y.Z. ; data curation, Q.Y. ; writing--original draft preparation, Q.Y. and Y.Z. ; writing--review and editing, W.L. ; visualization, Q.Y. ; supervision, Y.Z. ; project administration, Y.Z. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number No.62205283. This research was funded by Natural Science Foundation of Jiangsu Province, grant number No. BK20200921. This research was funded by China Postdoctoral Science Foundation, grant number No.2022M712697. This research was funded by Natural Science Research of the Jiangsu Higher Education Institutions of China, grant number No. 20 KJB510024. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. Authors thank all those who provided help for us for their great support with the experiments. The authors declare no conflict of interest.
# A Study on the Channel Expansion VAE for Content-Based Image Retrieval
## 3 Method
### Channel Expansion  
As we see the importance of channel or dimension, studies of instance retrieval have been focusing on weighting channel and spatial information. In this work, we aimed to expand the area, channels, and search for more features for the better representation of an image. The more channels a network has, the more various features it can search. Since kernels have different patterns in training, more channels have the chance to obtain key features. Thus, our observation was that more channel exploration would improve feature extraction performance. However, it causes an increase in complexity; therefore, we temporarily expanded the number of channels. Hence, the network had a bottleneck structure to repeat the expansion in the middle of the block and a reduction between blocks, which can explore and select more kernels. Figure 3 depicts the encoder structure with the expansion in comparison to normal VAE. The structure of the decoder is symmetric to the encoder.  
In Figure 3, when the encoder splits into blocks; (b) base VAE and (c) channel expansion have different elements in the block. Each block consists of convolution and batch normalization and Relu in (b) base VAE. On the other hand, (c) channel expansion VAE (CEVAE) has depth-wise convolution and \(1\times 1\) convolutions at the front and the rear, respectively. Convolution supersedes depth-wise convolution in order to reduce the computation derived from expansion.  
Surprisingly, we found a similar block module in the MNASNet [24] when searching network architecture based on reinforcement learning. That is, our intuition was proved correct in terms of accuracy with inference. However, we did not apply a varied expansion in accordance with the layer of MNASNet because this is not automatic network searching. In addition, our model was an auto-encoder structure, unlike the reference. The expansion coefficient in our proposed model was fixed as 6; details of the model are shown in Table 1. In consequence, blocks of this model are replaced with an expansion module as in Figure 3c.  
The size of the network was based on a 128 \(\times\) 128 image dataset, and layer out was an output layer consisting of a fully convolutional layer and global max pooling. The output of the encoder was a 512-dimensional latent vector.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 4 Simulation Results  
In this section, we performed simulations to compare the performance of the proposed GB-FERMA with that of FERMA-QL [21], FERMA [17], and GEAR [16]. These schemes are implemented in MATLAB for simulations. The number of sensor nodes is increased from 200 to 1200, and the number of sensor nodes is increased by 200 each time. The sensor nodes are randomly deployed with the initial power of 0.25 J and 0.5 J in a 200 m \(\times\) 200 m network area. The number of cells is 10 m \(\times\) 10 m. We assume that the sink is located at (0, 0) with an unlimited power supply. Each packet size is 2000 bits.  
In the simulations, the energy model adapts the First Order Radio Model [25]. The energy consumption of the transmitting node is determined by Equation (3) and the energy consumption of the receiving node is determined by Equation (4). These two equations define the energy consumption of \(k\)-bit packets to transmit data over distance \(d\). \(E_{\mathit{elec}}\) is the energy consumed by a sensor node used in a transmitter or a receiver circuit. \(E_{\mathit{amp}}\) is the energy consumed by the amplifier used to send data. In addition, the transmitted data have a \(d^{2}\) energy loss over distance \(d\).  
\[E_{Tx}(k,d)=E_{\mathit{elec}}\times k+E_{\mathit{amp}}\times k\times d^{2} \tag{3}\]  
\[E_{Rx}(k)=E_{\mathit{elec}}\times k \tag{4}\]
## 1 Introduction  
Biochar can be produced from a wide variety of organic materials [1]. This carbon rich substance can be used in various applications such as carbon sequestration, soil conditioning, and filtration of pollutants from aqueous and gas media [2; 3; 4; 5]. Initiatives to investigate new ways to produce biochar have been implemented in many parts of the globe [1; 6; 7; 8]. However, to date, slow pyrolysis is still the most known method for biochar production that is characterized by the thermal conversion of biomass in an oxygen-free atmosphere [9; 10]. This process can take place at temperatures between 350 \({}^{\circ}\)C and 700 \({}^{\circ}\)C [1]. The operational parameters and biomass type/properties play an important role in the surface chemistry of biochar. Extensive work has been carried out in the evaluation of pyrolysis systems for the production of biochar with specific properties [1; 11; 12; 13]. For example, [PERSON] [14] characterized biochar from sewage sludge. It was reported that chemical modification induced by the variation of temperature caused modification to the chemical composition of the biochar that was initially identified by an increase in pH. Similarly, [PERSON] [15] observed that the properties of biochar can drastically vary when comparing freshly produced biochar with aged biochar.  
Biomass gasification has also been demonstrated as an alternative method to produce biochar [16]. However, common gasification processes produce little biochar compared to the amount of gases generated [8]. Consequently, there is insufficient literature on the characterization of the surface chemistry of this biochar. Top-lit updraft (TLUD) gasification is believed to have potential for biochar production because of its relatively high yield of biochar (up to 39%) with relatively low energy input [17; 18]. Despite all of this, there is not sufficient literature focused on the surface chemistry and the effect of the operational parameters and physical properties of the biomass in TLUD gasification produced biochars. The characterization of biochars from TLUD gasification can be a major indicator of the possible applications for the produced biochar [2].  
The objective of this work was to investigate the effect of airflow rate, moisture content, particle size, and biomass compactness on the surface properties of pine wood biochar produced in a TLUD gasifier. The basic and acid functional groups were measured and correlated with the reaction temperature to identify variations of biochar surface chemistry related to operational conditions. The elemental composition of the biochar was evaluated as an indicator of the overall level of oxidation as a result of the incomplete combustion of biomass. The surface charge of biochar at varying airflow rates was also investigated.
## 5 Conclusions  
In this paper, we investigate the critical challenge of energy-efficient routing in edge-AI-driven satellite optical networks (SONs), where intensive on-orbit computation and laser inter-satellite communications exacerbate battery degradation through frequent depth-of-discharge (DoD) events. We proposed GreenRP, a task-aware routing framework that strategically optimizes network topology through payload dormancy and discharge-resilient path selection, explicitly addressing the spatial-temporal imbalances in AI workloads. Addressing the challenge posed by uneven global traffic distribution, which leads to idle energy consumption in some satellites, the algorithm optimizes the original network topology based on satellite traffic demands. It strategically places less critical communication payloads in dormancy, thereby reducing unnecessary lifetime consumption and enhancing the overall efficiency of the network. The simulation results show that, compared with the conventional methods, GreenRP not only extends the network's lifetime by up to 176% but also ensures that the increase in operational duration does not come at the cost of reduced performance. These attributes make GreenRP a promising solution for future satellite network operations, offering a balanced approach between energy efficiency and reliable service delivery. With the development of edge AI and the increasing complexity and scale of satellite networks, the principles of GreenRP will play an invaluable role in guiding the development of sustainable, efficient, and high-performance network architectures.  
However, GreenRP could be further optimized by edge AI and integrating real-time traffic prediction models. This would enable the framework to more accurately anticipate traffic changes, adjust network topology and payload dormancy status in advance, and further improve energy efficiency. Regarding practical deployment issues, it is necessary to consider the ability of satellite hardware to support frequent dormancy and wake-up operations, as well as the impact of differences in communication environments of satellites in different orbits on path selection. Subsequent research can carry out adaptability optimization for these practical problems.  
Conceptualization, H.Z. and Y.Z.; methodology, H.Z.; software, K.Z.; validation, H.Z., D.D. and K.Z.; formal analysis, H.Z.; investigation, D.D.; resources, J.Z.; data curation, L.Z.; writing--original draft preparation, H.Z.; writing--review and editing, Y.Z.; visualization, Y.C.; supervision, Y.Z.; project administration, H.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSFC project, grant number 62425105, 62350001, 62021005, and 6201276; the Shenzhen Science and Technology Program, grant number KJZD20240903100009013; and the Fundamental Research Funds for the Central Universities, grant number 530424001.  
The data are contained within the article.  
The authors declare no conflicts of interest.
# Trepenoid Metabolic Engineering in Photosynthetic Microorganisms
## 2 Pathway Modules in Terpenoid Biosynthesis
### Module II: Prenyl Phosphate Metabolism  
Different classes of terpenoids are produced from preenyl phosphates with different chain-length carbon skeletons (Figure 1). Hemiterpenes are produced from the C5 preenyl phosphates with the best known example being isoprene, which is derived from the DMAPP. The sequential addition of IPP units to DMAPP generates the longer preenyl phosphate skeletons--namely the precursors GPP (C10 terpenoids), FPP (C15 and C30 terpenoids), and GGPP (C20 and C40 terpenoids) (Figure 1).  
Both _E. coli_ and _S. cerevisiae_ preferentially produce FPP, which is a key precursor for the primary isoprenoids produced by these organisms (e.g., sterols in yeast, dolichols in _E. coli_). As a consequence, sesquiterpene (C15) production in these organisms has yielded relatively high titers especially in yeast [14; 35]. The competition for the FPP by sterol catabolism must be alleviated and squalene synthase, which consumes FPP and is the first committed step of sterol biosynthesis, has been targeted for down-regulation for this reason [16; 36; 37; 38].  
Directing productivity towards the C10 and C20 branches faces different challenges. In _S. cerevisiae_ and _E. coli_, the primary preenyl transferase converts GPP immediately to FPP by the addition of a second IPP unit without releasing GPP from the active site under normal conditions [39]. This means that there is a very low in vivo pool of GPP for the production of monoterenes. In yeast, an FPP synthase (FPPS) engineered to exclude FPP from the active site has been used to increase GPP and monoterene production [40; 41]. An alternative approach is the use of a degradation tag on FPPS to minimize competition and limit flux towards FPP, which enhances monoterene production [41]. This approach had to be paired with the replacement of the FPPS promoter by the sterol-responsive promoter of squalene epoxidase to ensure sufficient activity of this essential enzyme [41].  
Accumulation of GGPP requires an uninhibited flux through GPP and FPP and usually a strong \"metabolic pull\" strategy is used by overexpressing GGPP synthase [15; 42; 43]. A more elaborate strategy was also used in _S. cerevisiae_ whereby the GPP/FPP synthase was engineered to expand its substrate binding site, which allows it to add an IPP unit to catalyze GGPP formation [44]. This allowed for the enhanced heterologous diterpenoid production.
## 1 Introduction
### Iteration Particle Swarm Optimization  
Iteration particle swarm optimization (IPSO) is a modification version of PSO method which proposed by [27]. Iteration particle swarm optimization offers the improvement of quality solution and computational time of the algorithm. In the IPSO, three best values were used to update the velocity and the position of the particles, which are known as \(P_{best}\), \(G_{best}\) and \(I_{best}\). The definition and the method to determine the \(P_{best}\) and \(G_{best}\) values in the IPSO are similar to the original PSO. Meanwhile, the new parameters \(I_{best}\) is defined as the best value of the fitness function that has been achieved by any particle in the present iteration. In other words, \(I_{best}\) value is the \(P_{best}\) value that is randomly selected among the existing particles from the current population. In addition, the authors also introduced a dynamic acceleration constant parameter, \(c_{3}\) value for \(I_{best}\) in IPSO. Therefore, the new velocity formula for the IPSO algorithm is shown in Equation (18) [27; 28].  
\[V_{i+1}=wv_{i}+c_{1}r_{1}(P_{best}-x_{i})+c_{2}r_{2}(G_{best}-x_{i})+c_{3}(I_{ best}-x_{i}), \tag{18}\]  
\[c_{3}=c_{1}\Big{(}1-e^{-c_{1}k}\Big{)}, \tag{19}\]  
where \(r_{1}\) and \(r_{2}\) are two random variables in the range zero to one, \(c_{1}\) and \(c_{2}\) are positive constants, \(c_{3}\) is the acceleration constant that pulls each particle towards \(I_{best}\). The value of \(c_{3}\) will change according to \(c_{1}\) and the number of iteration \(k\), \(\omega\) is the inertia weight. \(X_{i}=\big{(}x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{n}\big{)}\) and \(V_{i}=\big{(}v_{i}^{1},v_{i}^{2},\ldots,v_{i}^{n}\big{)}\) represent the position and velocity of the \(i\)th particle, respectively. \(P_{best}\) represents the personal best location of \(i\)th particle. \(G_{best}\) represents the global best location of the whole swarm. \(I_{best}\) represents one value taken from the personal best location of \(i\)th particle.