esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Impact on Specific Methane Yield and Methane Percentage
#### 3.2.4 Effects of the Digestion System  
The inoculum I-LRD was tested in digestion system D-HBT1 and D-BFT (Figure 4). The CV for the digestion systems differed for the specific methane yield between 1.3% and 6.6% and for the methane percentage between 0.0% and 2.9%.  
Figure 4: Specific methane yield (**A**) and methane percentage (**C**) of various substrates digested with inoculum I-LRD in the digestion systems D-HBT1 and D-BFT (error bars indicate standard deviation, different letters indicate significant differences within substrates at \(p\leq 0.05\)); rel. specific methane yield (**B**) and rel. methane percentage (**D**) of the different inocula compared to digestion system D- HBT1 (boxplots show divergence of the average values of the digestion system/substrate- combination); for code of digestion systems, inocula and substrates see Tables 2, 3 and 6, respectively.  
However, differences between D-HBT1 and D-BFT were only significant for substrates S-TGF and S-MCC. By using I-LRD in D-BFT, the methane yield of S-MCC is 101% of the theoretical methane yield.  
The inocula I-BGP was used in three different digestion systems (D-HBT1, D-AMP, and D-EUD) (Figure 5). The CV for the specific methane yield was between 2.4% and 12.8% and for the methane percentage between 1.7% and 7.3%.  
The specific methane yields of the digestion systems were significantly different for all substrates beside S-HAY Figure 5A.  
The digestion system D-EUD showed in most cases the lowest methane yield (e.g., for S-MCC only 90% of the theoretical value). This could be explained by the fact that D-EUD has long pipes and could therefore be vulnerable to gas leaks.  
D-AMP showed a higher methane yield than D-HBT1 and D-EUD for most substrates. The D-AMP measures automatically and consequently avoids manual mistakes. Another reason for random differences could be, that the D-EUD and D-AMP have a headspace, which can result in a deviation of up to 15% to 25% as described in [PERSON] et al. (2018).  
The methane percentage could only be presented for D-HBT1 and D-EUD because it is not possible to measure the methane percentage in D-AMP Figure 5C.  
D-HBT1 and D-EUD showed statistically significant differences for all substrates, even when the difference was small.  
Figure 5: Specific methane yield (**A**) and methane percentage (**C**) of various substrates digested with inoculum I-BGP in the digestion systems D-HBT1, D-AMP, and D-EUD (**A**) respectively D-HBT1 and D-EUD (**B**) (error bars indicate standard deviation, different letters indicate significant differences within substrates at \(p\leq 0.05\)); rel. specific methane yield (B) and rel. methane percentage (**D**) of the different inocula compared to digestion system D-HBT1 (boxplots show divergence of the average values of the digestion system/substrate-combination); for code of digestion systems, inocula, and substrates see Tables 2, 3 and 6, respectively.
## 4 Experiment Results
### Different Skin Color  
One of the challenges for an RGB-based HR/RR monitoring system is that different skin colors could affect the signal quality because of strong melanin absorption in the visible wavelength range [18]. In contrast, the absorption of melanin is much lower at near-infrared, which allows the ToF-based system to perform more uniformly among demographics of different skin colors. Figure 14 shows the error rate of the HR measurements among participants with darker skin color and lighter skin color. Participants with Fitzpatrick skin type I-III (17/29 participants) are considered as light skin color, while participants with skin type IV-VI (12/29 participants) are considered as darker skin color. Among the lighter skin color group, the percentage of success rate measurement is 74% with the mean deviation of the measurements being \(-\)6.1 BPM. As for the darker skin color group, the same success rate of 74% is achieved with the mean deviation from the reference at \(-\)5.8 BPM.  
Figure 13: Percentage of correct HR measurement within each motion score category.  
Similar performance among two skin color groups is also evident when we examine the Bland-Altman plot in Figure 15. Both the mean deviation (\(-6.1\) BPM for the lighter skin color versus \(-5.8\) BPM for darker skin color) and the \(95\%\) percentile range (\(+/-35\) BPM for the lighter skin color versus \(+/-33\) BPM for darker skin color) are very similar between the two groups. Moreover, deviation distribution is also similar among the two groups, indicating that the ToF system used in this study performs equally well among participants of different skin colors. Thus, using near-infrared illumination, we are able to achieve performance independent of skin color.
## 3 Results and Discussion
### Rheological Measurements
#### 3.2.2 Young's Modulus of Elasticity and Elongational Viscosity
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resting Time**} & \multicolumn{3}{c}{**45 min**} & \multicolumn{3}{c}{**90 min**} & \multicolumn{3}{c}{**135 min**} \\ \hline \multirow{2}{*}{**Samples**} & **R50** & **A** & **Ex** & **R50** & **A** & **Ex** & **R50** & **A** & **Ex** \\  & **(BU)** & **(cm2)** & **(mm)** & **(BU)** & **(cm2)** & **(mm)** & **(BU)** & **(cm2)** & **(mm)** \\ \hline Control dough (0\%) & 305 & 84.4 & 158 & 330 & 98.2 & 161.5 & 335 & 99.3 & 163.3 \\ Dough enriched with 5\% hemp powder & 318 & 69.9 & 133.8 & 335.5 & 69.8 & 130.6 & 357 & 64.8 & 125.2 \\ Dough enriched with 10\% hemp powder & 359 & 63.2 & 115.3 & 350 & 59.5 & 113.8 & 350.5 & 64.6 & 123.1 \\ Dough enriched with 15\% hemp powder & 357.5 & 56.1 & 106 & 346 & 52.5 & 104.6 & 321.5 & 51.3 & 108.5 \\ \hline \hline \end{tabular} RT: Resting Times of 45, 90, and 135 min. R50: resistance to extension up to 50 mm. A: energy. Ex: extensibility.  
\end{table} 
Table 3: Extensiograph parameters (R50, resistance to extension up to 50 mm; A, energy; Ex, extensibility) of wheat flour dough (control, 0% hemp protein) and doughs enriched with 5, 10, and 15% hemp protein. Figure 1: [PERSON]’s modulus of elasticity of wheat flour dough (control, 0% of hemp protein) and of doughs with 5, 10, and 15% hemp protein. Vertical bars show the 95% confidence intervals as determined by the error mean square of the ANOVA. Groups that do not share a common letter are significantly different based on [PERSON]’s pairwise comparison of means (\(p<0.05\)). Figure 2: Force–distance curves of wheat flour dough (control, 0% of hemp powder) and of doughs containing 5, 10, and 15% hemp protein, under constant strain rate (nominal) values of 1.0, 4.0, 7.0, 10.0%. Figure 4: Elongational viscosity as a function of biaxial strain rate of wheat flour dough (control, 0% of hemp protein) and of doughs containing 5, 10, and 15% hemp protein, measured under controlled strain rate. Figure 3: Elongational viscosity flow curves as a function of biaxial strain rate of wheat flour dough (control, 0% of hemp protein) and of doughs containing 5, 10, and 15% hemp protein, under constant strain rate (nominal) values of 1.0, 4.0, 7.0, 10.0%.
The main contributions of this study are:  
* Firstly, the relationship between retroreflectivity and other factors such as the CIE color coordinates, sign age, its class, GPS position, and direction are investigated. Linear and logarithmic regression models were conducted to identify the significant factors that affect retroreflectivity. * Secondly, the study tries to find a connection between the performance and deterioration of retroreflectivity and the other factors mentioned above. * Finally, the study proposes a way to analyze and calculate the performance and deterioration of retroreflectivity using factors that can be collected without using expensive instruments. Such factors include the traffic sign's color, age, class, GPS position, and direction. The rest of the paper is organized as follows. Section 2 discusses the prior literature, while Section 3 presents the description of the datasets used in this study and reviews the statistical methods applied. The results and discussion are provided in Section 4. Finally, the limitations, suggestions for further research, and conclusions are presented in Sections 5 and 6.
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
## 2 Materials and Methods
### Preparation of Core-Shell Iron Microparticles  
The core-shell microparticles were prepared by linking PLACA-g-CS to the surface of the ZV microparticles through amide bonding between the free CS amino groups and the ZV carboxyl groups by carbodiimide chemistry [12]. In brief, 4.6 mg of \(N\)-hydroxysuccinimide (NHS), 3.8 mg N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) and 20 mg of iron microparticles were added to 20 mL of distilled water and stirred mechanically for 6 h. The obtained suspension was added to 30 mL of chloroform solution containing PLACA-g-CS (200 mg). The mixture was stirred for 48 h at 7500 rpm. Afterward, the particles were recovered by a magnet, accurately washed by distilled water to remove all the unreacted components and then freeze-dried.  
The formation of the amide bond was detected by ATR-FTIR (Nicolet iS5 FTIR Spectrometer equipped with an iD5 ATR accessory and Ge crystal; resolution 4 cm\({}^{-1}\); 64 scans). Microparticle morphology and size in solution were investigated by transmission electronic microscopy (TEM- JEOL JEM 2100) and by laser light scattering (Mastersize 3000, Malven Panalytic). Particle dispersions were measured under the following specifications: data acquisition rate of 10 kHz; measurement time 10 s; temperature 25 \({}^{\circ}\)C. Data were processed by software according to Fraunhofer diffraction and Mie scattering theory.  
The influence of the surface modification and the presence of the polymer coat on the magnetic properties of the microparticles was measured by vibrating-sample magnetometer (VSM 7407 LakeShore Cryotronics) at room temperature.Applied field strength 2.17 Tesla (T).
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 2 Time Reversal Approaches for Structures
### Time Reversal Refocusing as Outcome of Reciprocity  
The TR is considered as a two-stage physical process. During the first step, the forward problem takes place when an initial disturbance is such that excites higher frequency motion in the structure which might be seen as axial and/or bending wave propagation phenomena. The second step is the backward one where some quantity of the forward process is reversed in time and re-emitted in the structure. It has been experimentally observed that at the end of this backward process, energy has again been localized at the area where the disturbance originally begun during the forward step [30].  
Here, we make an effort to investigate this two-stage procedure through reciprocal statement of linear elastodynamics. We also discuss some different possible ways in order to fire the backward step.
## 2 System Definition
### The Converter  
According to Section 2.1, a two-stage converter is considered to interface with the VRLA or the Li-ion 50 kW batteries of Figure 1 (blue dashed box) with the four-wire microgrid. It has been decided to use a common two-level three-leg power stack to maintain homogeneity for both power stages (see Figure 2).  
In consonance with the available storage technologies, a DC voltage \(u_{bat}\) range from 150 to 500 VDC is considered. With this configuration, a DC-DC converter interfaces with this wide DC voltage range by means of a three-phase interleaved topology. By the use of an interleaved topology, the output inductive filter size is split and reduced, so it makes for easier operation and maintenance tasks. At the same time, high power converters could be designed with lower current modules, reducing the voltage and current ripple in the DC-link and decreasing the power capacity of the inductors [46]. Thus, as cited in [47], the reliability of an interleaved DC-DC converters increases compared with conventional one leg devices.  
In order to manage AC unbalanced loads, an inverter topology able to control any current sequence is required. The inverter stage is constituted by two three-leg bridges: one dedicated for the active phases and the other one for the neutral wire. This configuration allows for controlling each line current independently using optimized modulation techniques such as Space Vector Pulse Width Modulation (SVPWM) [48]. An LCL-type coupling filter completes the inverter where an isolation  
Figure 1: Diagram of the ATENEA microgrid at The National Renewable Energy Centre (CENER). A blue dashed box indicates the two possible direct current–alternating current (DC–AC) converters that can implement the proposed strategies.  
Figure 2: Scheme of the full direct current–direct current (DC–DC) and DC–AC proposed converter.  
free-flux YNyn transformer bank is assumed as part of the LCL-type filter providing galvanic isolation and offering different possible neutral schemes [49].
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 2 Methodologies
### Prediction Modeling Phase
#### 2.2.2 Attention Mechanism  
The main idea of the attention mechanism comes from the process of human visual attention. Human vision can quickly find key areas and add focus to them to obtain more detailed information. Based on the PM2.5 concentration prediction, it can selectively pay attention to some more important information related to PM2.5 concentration, assigns the weight for those, and ignores the irrelevant information [26].  
The attention mechanism, given in Figure 4, is divided into three stages. In the first stage, the similarity score between PM2.5 concentration and other variables' values are calculated, as shown in Equation (2). This is just one way to calculate the score, which is obtained from the current state of the neural unit itself, not the previous state. It is a highly accepted calculation method. In the second stage, the _softmax_ function is used to normalize the similarity score obtained in the first stage to get the weight coefficient \(\alpha_{t}\) of each BiLSTM unit output vector, which can be defined as Equation (3). In the third stage, it can be seen from Equation (4) that the attention mechanism performs a weighted summation on the vector output by each BiLSTM unit and the weight coefficient obtained above to get the final attention values \(c_{t}\) of each variable.  
\[S_{t}^{j}=tanh(W_{t}\Big{[}h_{t}^{PM2.5},h_{t}^{j}\Big{]}+b_{t}) \tag{2}\]  
\[\alpha_{t}^{j}=\frac{exp(s_{t}^{j})}{\sum_{j=1}^{n}exp(s_{t}^{j})} \tag{3}\]  
Figure 3: The steps of prediction modeling.  
\[c_{t}=\sum_{j=1}^{n}\alpha_{t}^{j}h_{t}^{j} \tag{4}\]  
where \(j\) indicates the serial number of variables, \(t\) denotes the current moment, \(s_{t}\) represents the similarity score of PM2.5 concentration to other variables, and \(W_{t}\), \(h_{t}\), and \(b_{t}\) stand for weight matrix, the output of BiLSTM layer, and bias unit, respectively.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### CNN Surrogate Model Results  
To evaluate the effectiveness of the CNN surrogate model, experiments are conducted on three initial models: Case 1, Case 2, and Case 3. The initial model designs vary from Case 1 to 3 in terms of the complexity of hydraulic conductivity fields. The complexity of hydraulic conductivity data increases from Case 1 to Case 3, as described in Section 2.1.2. Case 1 involves 10 randomly generated values for each hydraulic conductivity volume within a field. Case 2 divides the hydraulic conductivity field into 20 blocks, each with randomly generated values in the range of 10 to 100 m/month. In Case 3, Kriging interpolation is employed to obtain the hydraulic conductivity field from 40 sampling points, where the values is determined from available materials of the study area. Case 3 incorporates pumping rate data from two pumping wells that are not considered in Case 1 and Case 2. Consequently, the overall complexity of the initial model increases progressively from Case1 to Case 3, accompanied by a sequential rise in the volume of data requiring processing by the surrogate model.  
The training and testing sample sets are modified for different cases, while the network architecture and parameters remained constant. Figure 10 illustrates that the MSE value consistently decreases as the training period extends. Generally, the complexity of the models from Case 1 to 3 leads to a gradual increase in MSE during training. However, an intriguing phenomenon was observed at the 30 th iteration: the MSE value of model c_3 was lower than that of model c_2. This may be attributed to the more complex Case 3 scenario, where the surrogate model processes a larger number of input features and nonlinear relationships, leading it to focus on global data patterns rather than overfitting to local noise, thus achieving better prediction performance.  
To capture the temporal characteristics of the head data, an additional fully connected layer is incorporated into the network structure. In the test sample set, as depicted in Figure 11, the MSE and \(\text{R}^{2}\) values of the alternative model's predictions for Cases 1, 2, and 3 increase sequentially. This indicates that as the complexity of the initial model rises, the prediction accuracy of the same surrogate model declines, accompanied by an increase in training time. To further validate this finding, the experiment introduced the SSIM as a new metric to assess the structural similarity between the predicted image data and the original image data in the test set, as shown in Figure 11b. Notably, the SSIM will also decrease from 0.969 to 0.917 as the complexity of the initial model increases. Despite a gradual decrease in accuracy from Case 1 to Case 3, the CNN surrogate model still performs well in Case 3, with an average R\({}^{2}\) of 0.902 and SSIM of 0.917 when comparing predicted and original head field data.
## 3 Materials and Method
### Fault Tolerance
#### Migration Controller  
Reinsertion and replica policy, as in [12], was used to test DR-tree integration operations utilizing internal \(v\)-nodes. Once a non-leaf \(p\)-node crash has been created by each DR-tree, the cost of device reconstruction in terms of the number of messages and the stabilization period is determined depending on both reinsertion as well as replication policies.  
1. Stabilization time: the reinsertion mechanism stagnates the system in several cycles and also at the crashed \(p\)-node. As stability time is the longest reinsertion time, it is proportional to \(\log_{\text{m}}(N)\).
2. Message recovery cost: calculated as the percentage of messages required to stabilize a non-leaf \(p\)-node collapse. Costs range in size, but with the reinsertion policy, the amount of message propagation is much distorted, resulting in a large standard deviation.
# Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent
###### Abstract  
Sophisticated mechanisms for attacking computer networks are emerging, making it crucial to have equally advanced mechanisms in place to defend against these malicious attacks. Autonomous cyber operations (ACOs) are considered a potential solution to provide timely defense. In ACOs, an agent that attacks the network is called a red agent, while an agent that defends against the red agent is called a blue agent. In real-world scenarios, different types of red agents can attack a network, requiring blue agents to defend against a variety of red agents, each with unique attack strategies and goals. This requires the training of blue agents capable of responding effectively, regardless of the specific strategy employed RED. Additionally, a generic blue agent must also be adaptable to different network topologies. This paper presents a framework for the training of a generic blue agent capable of defending against various red agents. The framework combines reinforcement learning (RL) and supervised learning. RL is used to train a blue agent against a specific red agent in a specific networking environment, resulting in multiple RL-trained blue agents--one for each red agent. Supervised learning is then used to train a generic blue agent using these RL-trained blue agents. Our results demonstrate that the proposed framework successfully trains a generic blue agent that can defend against different types of red agents across various network topologies. The framework demonstrates consistently improved performance over a range of existing methods, as validated through extensive empirical evaluation. Detailed comparisons highlight its robustness and generalization capabilities. Additionally, to enable generalization across different adversarial strategies, the framework employs a variational autoencoder (VAE) that learns compact latent representations of observations, allowing the blue agent to focus on high-level behavioral features rather than raw inputs. Our results demonstrate that incorporating a VAE into the proposed framework further improves its overall performance.  
Keywords:autonomous cyber operations (ACOs); cyber security; blue agent; red agent; generic blue agent; machine learning
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 2 Materials and Methods  
### Study Area and Data  
Beijing is located in the northern part of the North China Plain with a total area of 16,410.54 km\({}^{2}\), corresponding to a bounding box from 115.7\({}^{\circ}\) to 117.4\({}^{\circ}\) E and from 39.4\({}^{\circ}\) to 41.6\({}^{\circ}\) N. The terrain DEM is higher in the northwest than that in the southeast. The elevation is 20-60 m with an average of 43 m; the area has a typical northern temperate semi-humid continental monsoon climate, with a high temperature and rain in summer, a cold and dry winter, and a short duration of spring and autumn.  
In this study, MODIS surface temperature images (MOD11A1 and MYD11A1) were mainly used from 2003 to 2020 with a spatial resolution of 1 km, and a total of 12 observational variables, including diurnal and nocturnal surface temperature data, quality assessment data, date of observation, and temperature products were used. The NDVI data were used with the MODIS (MYD13Q1) data, and the land use data with data derived from the China Land Cover Dataset (CLCD) (10.5194/essd-13-3907-2021).
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 4 Model Deployment of Edge Computing Based on Knowledge Distillation
### Model Deployment on the Edge Computing Side
In practical applications, model quantization has become an important technique for accelerating deep learning model inference. FP32 uses 32 bits to represent a floating-point number, with 1 bit for the sign, 8 bits for the exponent, and 23 bits for the mantissa. This representation provides high precision and a wide dynamic range but requires substantial memory and computational resources. Using FP32 for calculations in deep learning provides high precision but demands significant resources. Storing deep learning model parameters and computing gradients in FP32 typically requires a large amount of memory and computational resources. Furthermore, FP32 calculations take longer, resulting in slower model inference speeds. In this study, PyTorch models were converted to TensorRT models with three different precisions: FP32, FP16, and INT8. The results, as shown in Table 4, indicate that even at FP32 precision, the inference speed can reach 45 FPS, meeting real-time detection requirements. At FP16 and INT8 precision, the inference speeds reach approximately 58 FPS and 91 FPS, respectively, allowing the device to perform simultaneous inference on multiple video streams.