esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Biochemical Methane Potential Test  
The biochemical methane potential test (BMP) is a commonly used method to determine the anaerobic biodegradability of different organic materials and their potential to produce biogas [30]. In this study, 150-mL serum glass vials with a working volume of 70 mL were utilised across three batches of experiments with an incubation period of 30 d for each experiment at mesophilic conditions (\(42\pm 1\)\({}^{\circ}\)C). During the first experiment, inorganic nitrogen (NH\({}_{4}\)Cl) was added in the system to adjust the C/N ratio to 30 (_vw_) for both feedstocks, alongside bottles with no added nitrogen (controls), at an inoculum to substrate ratio (I/S) of 3.5 (_w/w_). The second experiment comprised two different I/S ratios (3.5 and 1.75 (_w/w_)) across separate WS, SE, DDGS and RM substrates. Finally, the two different straw samples (e.g., untreated and steam-exploded straw) were mixed on ratios of 50:50, 70:30 and 90:10 (_w/w_) with DDGS and RM, respectively. Based on the results of the second experiment, the I/S ratio for the third batch trial was also set at 1.75 (_w/w_). The BMP outcome from the mono-digestion experiment wasassumed to represent the average potential for the two agri-industrial by-products. It was also assumed that the microorganisms would selectively catabolise the more easily degradable available feedstock (either DDGS or RM) and then switch to WS or SE, which are less digestible. As such, the produced gas from WS or SE was presented as the remaining amount from the total yield minus the theoretical expected yield from DDGS or RM. For all three experiments, methane production was measured daily by the liquid displacement method, where NaOH (aq) was used for scrubbing the CO\({}_{2}\) from the produced biogas. All BMP variations were performed in triplicate, alongside blank and control trials. The blanks were used for measuring the endogenous methane production, while the obtained values were subtracted from those acquired from the vials with the substrate. In addition to the blank vials, during all experimental setups, control samples with microcrystalline cellulose (Avicel) along with the inoculum were used as a positive control, as suggested in previous publications [31]. All BMP vials were manually shaken every 12 h to ensure the homogenisation of the samples. Finally, all methane measurements were reported and presented after correction to normal conditions based on Equation (1):  
\[CH_{4}\;n=\frac{CH_{4}\;e\;\times\;K}{(K+T)} \tag{1}\]  
where \(CH_{4}\;n\), total methane production at normal conditions; \(CH_{4}\;e\), experimentally measured methane values; \(T\), room temperature at the point of the measurement; and \(K\) equals 273.15 and is used to express the temperature in Kelvin.
###### Abstract  
The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image's spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6 Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods.  
Keywords:polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification
## 2 Materials and Methods
### Methods
#### 2.2.3 Reinforcement Characterization  
A balanced, coated, bidirectional fabric made out of basalt fibers was selected as reinforcement. ASTM D3039 was used to investigate the mechanical properties of the fabric. Tensile tests were carried out on six specimens having a length of 500 mm and a width of 50 mm. Aluminum smoothed tabs with a thickness of 3 mm, bonded to the edge of each specimen for a length of 70 mm, provided a uniform load distribution.  
Tests were performed in displacement control at a rate of 1.25 mm/min and strain was measured using an extensometer having 50 mm gage length, installed in the center of the specimen. Stresses were then calculated using the recorded load \(F\), divided by cross-section area of the fabric reinforcement \(A_{f}\) as per Equation (2):  
\[\sigma=\mathrm{F}/\mathrm{A}_{\mathrm{f}}\] with \[\mathrm{A}_{\mathrm{f}}=\mathrm{t}_{\mathrm{eq}}\times\mathrm{b}_{\mathrm{ eff}}\], \[\mathrm{b}_{\mathrm{eff}}=\mathrm{b}_{\mathrm{y}}\times\mathrm{n}_{\mathrm{y}} \tag{2}\]  
where:  
\(\mathrm{t}_{\mathrm{eq}}\) is the equivalent design thickness of the fabric,  
\(\mathrm{b}_{\mathrm{eff}}\) is the nominal specimen width,  
\(\mathrm{b}_{\mathrm{y}}\) is the mid-yarn spacing (normal to the load application direction)  
\(\mathrm{n}_{\mathrm{y}}\) is the number of longitudinal yarns.  
Textile tensile tests were carried out by the authors on six specimens and the obtained stress-strain response curves are shown in Figure 2.  
The main physical and measured mechanical properties of the textile are summarized in Table 2. Nearly simultaneous rupture of the fiber bundles was observed as failure mode of the tested samples.
## 5 Conclusions  
In this study, we used a satellite-based light use efficiency model (VPM) to analyze the spatial and temporal patterns of cropland productivity and their influencing factors. The results showed that the inter-annual trend of gross primary productivity in China cropland markedly increased in northern China (i.e., NE, IM, HHH, and LP) and significantly decreased in southern China (i.e., MLY, SW, and SC). The increase in cropping intensity, irrigation area, fertilizer consumption, agricultural machinery, and precipitation have jointly contributed to the positive trend of cropland productivity in northern China, while decreasing cropping intensity directly contributed to the negative trend of GPP in southern China, explaining more than 50% of the observed change. Despite the decline in cropping intensity, the increasing potential cropping intensity can provide support for China's future food security. Additionally, the Cang-liang-yu-di and Cang-liang-yu-ji policies can help to achieve the sustainable development of agriculture.  
The following are available online at [[http://www.mdpi.com/2071-1050/12/23/10070/s1](http://www.mdpi.com/2071-1050/12/23/10070/s1)]([http://www.mdpi.com/2071-1050/12/23/10070/s1](http://www.mdpi.com/2071-1050/12/23/10070/s1)): Table S1: Several major agricultural policies implemented in 2000-2015; Table S2: Interannual trends of socioeconomic and climatic factors for each region in 2000-2015; Table S3: Determination coefficient between GPP and influencing factors.  
Conceptualization, Z.N. and H.Y.; investigation, Z.N.; methodology, Z.N.; resources, H.Y.; supervision, H.Y.; writing--original draft, Z.N.; writing--review and editing, H.Y. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040301) and Establishment of National Ecological Environmental Accounting System (2110105).  
We thank the National Ecosystem Science Data Center for providing climatic data, the National Bureau of Statistics of China for providing agricultural statistics data, and the Distributed Active Archive Center of the Oak Ridge National Laboratory and the Earth Observing System Data for providing MODIS products.  
The authors declare no conflict of interest.
## 4 FPGA-Based Hardware and Software Architecture Design
### Storage Module  
The memory module consists of three IP cores, wr_fifo, rd_fifo, and u_mig_7 series_0, and the FIFO-to-mig_axi module. The FIFO is in between the computation module and the FIFO to mig_axi because the clock and transfer rate are different between the modules, so the data is cached in the FIFO, and the data is read and written in the DDR through the FIFO interface to the AXI interface. It can realize the function of reading and writing data in DDR through the FIFO interface to the AXI interface.  
The wr_fifo and rd_fifo write and read data are input video streams. wr_fifo uses the development board operating clock of 50 MHz, with the write width set to 16 bits and the write depth set to 512 bits. rd_fifo uses the ui_clk clock of 200 MHz output from the MIG IP, with the read width set to 128 bits and the read depth set to 64 bits.  
The memory controller is based on Xilinx's MIG IP cores to meet the high-capacity, high-speed DDR3 SDRAM memory, where the AXI bus under the Xilinx platform is suitable for the development of various IP cores.  
The AXI interface bus has a total of five channels, namely, read address channel (RD_ADDR), read data channel (RD_DATA), write address channel (WR_ADDR), write data channel (WR_DATA), and write response channel (WR_RESP), and each AXI transmission channel is a unidirectional channel.  
AXI like is more suitable for communication between the FPGA main chip and peripherals than AXI full. In this paper, the arbitration module of the FIFO interface to the AXI interface is designed based on mig's AXI interface. The initial state after power-on is IDLE, and after waiting for init_calib_complete to go high after DDR initialization is completed, it enters the read/write arbitration state ARB, after which the conversion between the FIFO interface and the AXI interface is carried out in accordance with the operating state transition diagram of the read/write transaction, as seen in Figure 7. The state transfer conditions are shown in Table 2.
## 2 System Architecture and Communication
### Clock Synchronization Technology  
The terminal's clock synchronization uses the IEEE 1588 timing method. IEEE 1588 is an Ethernet synchronous clock that provides sub-microsecond timing accuracy. IEEE 1588 uses the Best Master Clock algorithm to determine the most accurate clock in the network to be the Master. All the remaining clocks are used as slaves, synchronized with the master clock. In the adaptive differential protection, since the sampling information and the trip information need to be transmitted, and the timing accuracy is required to be very high, the IEEE 1588 dedicated timing chip is deployed inside the terminal, that is, the timing information is processed at the MAC layer to ensure the required timing accuracy. In the application, the international common time format code is used to combine the timing of the timing pulse with the time data of the serial message to transmit time information. After CPE receives the time information, it converts to IRIG-B code with timing pulse. DTU then uses on-time edge of the timing pulse to synchronize the sampling of the currents data and perform current differential protection calculation. The accuracy of the time synchronization was tested in the factory, and the test results are shown in Table 1.
## 7 Results
### Scenario 2: Variable Irradiance and Constant Temperature  
According to Table 15, the ANN clearly outperformed the remaining methods, with the lowest absolute and integral errors, as well as the highest efficiency (95.20%). The DQN  
Figure 16: Error evolution up to 0.5 s for all algorithms in Scenario 1.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 2.2740 & 23.6469 & 8.4456 & 96.7815 & 93.6013 & 0.2029 \\ InC & 2.2681 & 23.6453 & 8.4224 & 96.7687 & 93.6261 & 0.2029 \\ Fuzzy & 1.3028 & 13.5664 & 3.4857 & 45.5630 & 95.3713 & 0.2385 \\ InC + P\&O & 2.2755 & 23.6486 & 8.4512 & 96.7889 & 93.5962 & 0.2030 \\ InC + GA & 1.3144 & 13.6370 & 2.7629 & 33.8812 & 95.3102 & 0.3783 \\ P\&O + GA & 1.3124 & 13.6443 & 2.7561 & 33.8601 & 95.3165 & 0.3787 \\ ANN & 1.2931 & 13.4424 & 3.3865 & 45.5965 & 95.4995 & 0.1763 \\ DQN & 1.3396 & 14.2425 & 2.7681 & 43.0119 & 95.2981 & 0.2118 \\ \hline \end{tabular}
\end{table}
Table 14: Performance metrics for Scenario 1 (uniform irradiance at 1000 W/m2).  
Figure 15: Comparison of convergence behavior between ANN and DQN in Scenario 1.  
maintained solid performance but showed higher error variance. Hybrid methods like InC + GA and P&O + GA improved upon their base algorithms, although they still lagged behind the AI-based models in accuracy and responsiveness.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Use Cases and Metrics  
Exhaustive testing of routing protocols for fat-trees should comprise the following use cases: (i) Bootstrap, (ii) Normal Operation, (iii) Node Failure, (iv) Node Recovery, (v) Link Failure, and (vi) Link Recovery.  
For each use case, the typical operations include (i) starting the emulation, (ii) capturing exchanged control plane messages, (iii) waiting for convergence, (iv) stopping the emulation, and (v) analyzing the captures. A sound metric for comparing different routing protocols is the amount of control plane traffic exchanged until convergence (i.e., the \"protocol churn\"). Note that measuring convergence time is not a valid metric in an emulated environment since each execution depends not only on the emulation but also on background processes (e.g., operating system, hypervisor), which may alter the total elapsed time.  
In the rest of the paper, we will only consider the Bootstrap process since our aim in this work is to compare environments, not a thorough assessment of routing protocols.  
Moreover, we will also consider the \"layer 2 overlay\" use case to show one possible application of GNS3, a GUI-based environment.
## 8 Formal Security Evaluation  
Evaluating the security of cryptographic protocols is not an easy task and flaws can readily occur in protocols. During the past decades, several tools [3, 5, 28] have been developed to automatically verify cryptographic protocols. We use Scyther [5] because it is one of the fastest as has been shown in [29] and also one of the most user friendly.  
[PERSON] has developed an automatic tool called Scyther [5]. It is a free tool available on all operating systems (Linux, Mac and Windows). This tool can automatically find attacks on cryptographic protocols and prove their security for bounded and unbounded numbers of sessions. One main advantage of Scyther is that it provides an easy way to model security properties like secrecy and authentication.  
Scyther uses the Dolev-Yao intruder model [6]. In this model, the intruder controls the network and all communications pass through it, which means that all packets can be captured by the intruder. This is why we do not consider multiple forwarder nodes in our analysis, since it does not provide more security. Moreover, the intruder has its own public and secret pairs of keys, enabling it to play the role of any participant in the protocol. It can also encrypt messages with all public or symmetric keys that it knows and it decrypts cipher texts only if it knows the decryption key. We verified all our protocols using Scyther without any intermediate nodes. When Scyther proves a security property, the tool only outputs \"OK\", meaning that the property is satisfied. However, if there is a flaw, then a graphical description of the discovered attack is given and the attack is automatically generated. More precisely, we proved the secrecy of all sensitive data exchanged (keys and nonces) and also the authenticity of the communication. Our Scyther codes are available at [9]. The secrecy of the keys and the nonces means that an intruder cannot learn these data. The authentication property of the communication means that each node does indeed communicate with the expected node. This property protects our protocols from replay or man-in-the-middle attacks. Our protocols might appear simple, but they are the result of an optimization process using Scyther. It leads us to minimal protocol in the sense that removing any piece of information in any message will enable an attack.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 4 INNC-GA  
### INNC  
The minimum cost Nearest Neighbor Algorithm (NNC) is utilized in vehicle routing problems to produce high-quality feasible solutions. This paper introduces an enhanced version of NNC, called Improved NNC (INNC), specifically tailored to the Vehicle Routing Problem with Time Windows (VRPTW). The key steps for enhancing NNC are outlined as follows:  
Step 1: Determining the distance from each customer point to the distribution center and selecting the point with the shortest distance as the initial customer on the vehicle route.  
Step 2: Calculating the distance between the remaining customer points and the last customer point in the current path, arranging them in ascending order, and assessing whether the vehicle load constraints and customer time window constraints are met after insertion, then selecting the customer point with the most advanced order that satisfies the constraints to be inserted into the current path.  
Step 3: Repeating Step 2; if no customer point meets the conditions, an additional vehicle is dispatched. If all customer points have been visited, the calculation process concludes.
## 3 Example Analysis  
### Data Sources  
This paper is based on the British Grid's residential electricity consumption data set for London in 2014. The dataset contains partial consumption readings from November 2011 to February 2014 for a sample of 5567 London households, with readings taken every half hour in kilowatt-hours. In this paper, the electricity consumption data of households with relatively complete data from January to December 2012 are selected for research. Each household has 1753 load data pieces, and there are 3533 households, with a total of 6,193,349 load data pieces.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
###### Abstract
In neural networks, CNNs automatically extract features from input images through operations in convolutional and pooling layers [4]. Bidirectional Long Short-Term Memory (BLSTM) networks are a special type of Recurrent Neural Network (RNN) that address the issue of gradient vanishing or explosion when processing long sequences in an RNN. By incorporating bidirectional LSTM modules, BLSTM networks can simultaneously consider past and future contextual information while handling long-term dependencies in sequential data, generating more comprehensive feature representations [5]. The attention mechanism allows neural networks to focus more on relevant information within the input while reducing attention to irrelevant data. This mechanism enables the network to better adapt to different input scenarios, enhancing the model's expressiveness and performance [6]. In summary, the main contributions of this study are as follows:  
1. Comprehensive data generation and integration: Utilizing the established digital twin model, we generate operational data under various conditions and integrate multidimensional production data. This provides the deep learning model with more comprehensive and accurate training samples. 2. Innovative integrated model: Based on the obtained feature data, we propose an innovative integrated model that combines CNN, BLSTM, and attention mechanisms. This model achieves the goals of feature learning, capturing temporal information, and providing interpretability. 3. Effective fault diagnosis for electric screw presseses: Using the proposed diagnostic model, we effectively address the fault diagnosis problem of electric screw presses. The model has been validated to significantly improve prediction accuracy and reduce noise sensitivity. The organization of this paper is as follows: Section 2 reviews some existing fault diagnosis methods; Section 3 introduces the proposed DCBA-integrated fault diagnosis method; Section 4 details the implementation of the proposed method in fault diagnosis for electric screw presses; and Section 5 summarizes the research conclusions and discusses potential future research directions.
## 3 Results and Discussion
### Optimization of MAE  
Effect of the Extraction Solvent on Recoveries  
The selection of the extraction solvent when using MAE is based on the solvent's dielectric constant, which is a solvent's ability to absorb microwave energy. This constant, also known as the relative permittivity, measures a solvent's ability to store electric charges and is directly propositional to polarity [24]. One solvent is often immiscible while the other one is miscible with water; the miscible solvent helps the mixed solvent to penetrate the layer of water on the surface of the solid in order to successfully facilitate extraction [20]. Therefore, the effectiveness of the extraction solvent was determined as shown in Figure 5 using (i) n-hexane + acetone, (ii) n-hexane + acetonitrile and (iii) n-hexane + ethyl acetate. The n-hexane and acetone combination gave the highest recoveries, ranging between 79.6% and 120%, while nhexane + acetonitrile gave comparable recoveries that ranged from 71.8% to 101%, with no significant difference (\(p=0.15\)) observed between the mixture's recoveries; therefore, the solvent with higher recoveries was used as the optimum. Thehexane + ethyl acetate mixture gave low recoveries (12.8-92.6%) and the \(p\) value of the mixture for nH + ACN (0.03) and nH + ACT (0.007) suggested a significant difference in their recoveries (Table S4). The recoveries obtained using acetone and acetonitrile were high compared to ethyl acetate, which could be a result of their high dielectric constants compared to that of ethyl acetate, which allows the penetration of the surface layer of the solid, leading to a successful extraction as the microwave directly binds and polarise the molecules that are available in the mixture [27], The results obtained above are in agreement with results obtained by [PERSON] et al. where nhexane/acetone as an extracting solvent was compared to acetone alone for the same purpose [28].