esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Valorization via Liquid Hot Water (Temperatures from 120 to 180 \({}^{\circ}\)C)
#### 3.2.3 Characterization of Solid Fractions  
Infrared Spectroscopy of LHW solid fractionsOn the other hand, the solid fraction was analyzed with FTIR spectroscopy. Figure 7 shows the comparison of the spectra acquired after 1 h of reaction time at 120 \({}^{\circ}\)C. At temperatures of 160 \({}^{\circ}\)C and 180 \({}^{\circ}\)C, the biomass remains unreacted. The absorption bands correspond to functional groups of O-H (3284-3308 cm\({}^{-1}\)), C-H stretching of aliphatic groups (2919-2923 and 2853 cm\({}^{-1}\)), C-=O (1705 cm\({}^{-1}\)), C=C of aromatic compounds (1608-1622 and 1516 cm\({}^{-1}\)), and C-O-C,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Time (h)**} & \multicolumn{6}{c}{**Concentration PC (g/L)**} & \multirow{2}{*}{**Yield (\%w)***} \\ \cline{3-3} \cline{5-8}  & & **Sugars** & & **Levulinic Acid** & & **Formic Acid** & **HMF** & **Furfural** \\ \hline
180 & 1 & 34.818 & 0.577 & & & & 70.001 \\
180 & 2 & 26.341 & 1.063 & 2.392 & 0.299 & & 62.284 \\
180 & 3 & 15.539 & 1.120 & 4.067 & 0.757 & 0.632 & 51.528 \\
180 & 4 & 1.250 & 1.244 & 8.566 & 1.032 & 1.249 & 42.838 \\ \hline \end{tabular}  
* Based on lignocellulosic structure.  
\end{table}
Table 8: Quantification and yields of platform chemicals via LHW (180 \({}^{\circ}\)C) for 1–4 h.  
Figure 6: Obtention of platform chemicals via LHW (180 \({}^{\circ}\)C) for 1–4 h.  
C-O-H, and C-O vibrations predominantly of groups present in cellulose, hemicellulose, and lignin (1500-1350 and 1300-1150 cm\({}^{-1}\)). The differences in band absorption are related to concentration.  
Moreover, the solid phase of the reaction at 180 \({}^{\circ}\)C at different reaction times was analyzed, and the results are shown in Figure 8. It shows that longer reaction times can decrease the transmittance of the band associated with cellulose, hemicellulose, and lignin (1000-1500 cm\({}^{-1}\)), confirming that at higher reaction times, those structures are broken in order to form sugars.  
Figure 8: Infrared spectrum of LHW at 180 \({}^{\circ}\)C for 1–3 h.  
Figure 7: Infrared spectrum of LHW for 1 h.  
In order to track the changes in the elemental composition of the biomass, the elemental analysis of the solid fractions obtained via LHW is presented in Table 9. It can be seen that there is not a significant change in the O/C and H/C ratios because the biochar obtained still has the characteristics of biomass (Figure 9); hence, it does not assimilate to the properties of coal. The biggest change takes place at 170 \({}^{\circ}\)C, where the amount of carbon increases, influencing the ratios of characterization.  
On the other hand, after the hydrothermal valorization processes, the C/N ratio varies towards a decrease in the decomposition rate via the use of microorganisms, making the biochar obtained not optimal for the processes of the microorganism diet for transformation or fermentation.
# Immobilization of Pectinolytic Enzymes on Nylon 6/6 Carriers
## 3 Results and Discussion
### Stability and Reusability of the Immobilized Pectinase  
Stability and reusability are some of the major features of immobilized enzymes to characterize their industrial applicability. We studied the inactivation of the immobilized enzymes during storage and throughout the active use of both the threads and pellets (Figure 4).  
The activity decreased in an exponential mode and the relative inactivation was not dependent on the shape of the carrier, indicating that the attachment of the enzymes on the surface of nylon 6/6 carriers was similar. The inactivation process was characterized with a 2-exponential decay model, as there is a stabilization phase after the first quick decay of the activity. The F test revealed that there was no statistically relevant difference in the rate of inactivation process of immobilized pectinase (\(p\) = 0.9791) on nylon 6/6 threads or pellets. The half-life of the enzyme fast phase inactivation in 0.2 M sodium acetate buffer (pH 4.8) at 4 \({}^{\circ}\)C was 12.8 days and after 5 weeks, the remaining pectinase activity of the unused threads was 63% of the initial activity. Pectinase immobilized on nylon 6/6 threads and pellets exhibits storage stability comparable to other covalent immobilization carriers like sodium alginate/graphene oxide beads via amide bonds, which retained 52% of initial pectinase activity after 30 days in 0.25 M citrate buffer (pH 4.0) at 4 \({}^{\circ}\)C [20]. Meanwhile, pectinase immobilized onto chitosan-grafted magnetic nanoparticles has exhibited a better storage stability, retaining 74% of initial activity after 75 days in 5 mM sodium acetate buffer (pH 4.5) at 4 \({}^{\circ}\)C [42].  
To determine the reusability of immobilized pectinase onto nylon 6/6 threads and pellets, enzyme activity was measured at 40 \({}^{\circ}\)C for 30 min for 20 successive cycles (Figure 5). After each cycle, substrate solution was removed and nylon 6/6 carriers were washed with 0.2 M sodium acetate buffer (pH 4.8) for 10 min before adding new substrate solution. Additionally, in this case, the enzyme inactivation obeyed exponential decay and there was no statistically significant difference in inactivation of immobilized pectinase onto nylon 6/6 threads or pellets, but the activity dropped to 40% after five cycles. After 20 successive cycles, the remaining activity of the immobilized pectinase was 22.0 \(\pm\) 1.6%, indicating the good reusability of the immobilized enzyme. An earlier study by [PERSON] et al. [40] reported that polygalacturonase immobilized onto nylon-6 particles retained 50% of its initial activity for three cycles and only 17% after seven cycles. Recent studies on covalent immobilization of pectinase onto magnetic nanoparticle carriers reported better reusability potential where immobilized enzymes retained 50 to 60% of initial pectinase activity after 15 cycles [25; 42].  
Figure 4: The storage stability of immobilized pectinase in 0.2 M sodium acetate buffer (pH 4.8) at 4 \({}^{\circ}\)C.
# Analysis and Optimization of Output Low-Pass Filter
## 3 Frequency and Filter Component Normalization
### Harmonic Distortion and Attenuation
Each filter is characterized by a quality factor of 2 and a cutoff frequency set at 1 kHz, with the harmonic spectrum measured at a switching frequency of 4 kHz. Additionally, each filter configuration meets the grid's THD requirements for a \(Q\) of 5 and a normalized cutoff frequency below approximately 0.3 pu. As a result, in order to comply with the grid's THD standards, the cutoff frequency should be limited to around 0.3 pu. In summary, filter configurations FC 2 and FC 4 could meet grid THD requirements over a wider range of \(Q\) and \(f_{c}\) than filter configurations FC 1 and FC 3. This information increases the flexibility of designing second-order low-pass filters in terms of quality factor and cutoff frequency. However, when deciding on a filter topology, the power factor and damping resistor loss should be considered.
## 3 Results and Analysis
### Response Surface Experimental Design and Result Analysis
The response surfaces of normal stiffness (\(x_{2}\)) per unit area, tangential stiffness (\(x_{3}\)) per unit area, and bonding radius (\(x_{6}\)) in relation to the relative error (e) are shown in Figure 11d-f. From Figure 11a,d, it can be observed that when the bonding radius (\(x_{6}\)) remains constant, the maximum shear force increases with the normal stiffness (\(x_{2}\)) and tangential stiffness (\(x_{3}\)) per unit area, while the relative error initially decreases and then increases. When the tangential stiffness (\(x_{3}\)) per unit area is held constant, both the normal stiffness (\(x_{2}\)) per unit area and the bonding radius (\(x_{6}\)) increase gradually, leading to an increase in the maximum shear force, with the relative error initially decreasing and then increasing. Similarly, when the normal stiffness (\(x_{2}\)) per unit area remains constant, an increase in both tangential stiffness (\(x_{3}\)) per unit area and bonding radius (\(x_{6}\)) contributes to an upward trend in the maximum shear force, with the relative error first decreasing and then increasing. It is noteworthy that the maximum shear force and relative error show more significant changes with variations in normal stiffness (\(x_{2}\)) and tangential stiffness (\(x_{3}\)) per unit area. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Effect** & **Degrees of Freedom** & **Mean Square** & **F** & \(\boldsymbol{p}\) \\ \hline Model & 4339.0 & 9 & 482.11 & 30.32 & \textless{}0.0001 ** \\ A-\(x_{2}\) & 2163.18 & 1 & 2163.18 & 136.03 & \textless{}0.0001 ** \\ B-\(x_{3}\) & 1373.14 & 1 & 1373.14 & 86.35 & \textless{}0.0001 ** \\ C-\(x_{6}\) & 345.06 & 1 & 345.06 & 21.70 & 0.0023 ** \\ AB-\(x_{2}x_{3}\) & 342.07 & 1 & 342.07 & 21.51 & 0.0024 ** \\ AC-\(x_{2}x_{6}\) & 48.02 & 1 & 48.02 & 3.02 & 0.1258 \\ BC-\(x_{3}x_{6}\) & 2.89 & 1 & 2.89 & 0.1817 & 0.6827 \\ A\({}^{2}\)-\(x_{2}\)\({}^{2}\) & 19.53 & 1 & 19.53 & 1.23 & 0.3044 \\ B\({}^{2}\)-\(x_{32}\) & 33.75 & 1 & 33.75 & 2.12 & 0.1885 \\ C\({}^{2}\)-\(x_{62}\) & 14.43 & 1 & 14.43 & 0.9074 & 0.3725 \\ Residual & 111.32 & 7 & 15.90 & & \\ Lack of fit & 56.49 & 3 & 18.83 & 1.37 & 0.3714 \\ Pure error & 54.83 & 4 & 13.71 & & \\ \hline \hline \end{tabular} Note: ** indicates extremely significant (\(p<0.01\)), \(x_{2}\) is the normal stiffness per unit area, \(x_{3}\) is the tangential stiffness per unit area, and \(x_{6}\) is the bonding radius.  
\end{table} 
Table 8: Analysis of response surface variance.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 7 Performance Trade-Offs  
In this section, we review the performance of our proposed schemes A-DBR, C-DBR, CA-DBR and B-DBR with the existing base scheme WDFAD-DBR. A-DBR reduces void nodes by adaptively adjusting the transmission range and achieves reduced energy consumption in dense regions; while WDFAD-DBR makes a routing decision based on the weighting sum of depth difference up to two nodes. Therefore, the possibility of confronting a void hole still exists, which results in packet loss and high end-to-end delay. C-DBR has reduced end-to-end delay at the cost of high energy consumption as compared to A-DBR due to clustering. CA-DBR experienced less consumption of energy and low end-to-end delay at the cost of high APD. B-DBR is able to minimize the void node probability, which results in high PDR and reduced end-to-end delay at the cost of high APD. Other proposed schemes have increased APD as they avoid the shortest route to the sink in order to avoid collision and void holes. The summary of simulation results is presented in Table 3 and the trade-offs are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **RE-PBR** & **WDFAD-DBR** & **B-DBR** & **A-DBR** & **C-DBR** & **CA-DBR** \\ \hline Network lifetime & 88\% & 87\% & 89\% & 85\% & 88.4\% & 90\% \\ End-to-end delay & 15\% & 09\% & 10\% & 13.6\% & 14.6\% & 14.8\% \\ APD & 22\% & 30\% & 35.8\% & 21\% & 27\% & 26\% \\ PDR & 90\% & 91.5\% & 93\% & 94.2\% & 95\% & 96\% \\ Alive nodes & 60\% & 60\% & 62\% & 64\% & 63.3\% & 64\% \\ Packet drop ratio & 10\% & 8.5\% & 7\% & 5.8\% & 5\% & 4\% \\ Energy consumption & 38\% & 39\% & 24\% & 21\% & 26\% & 22\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized simulation results.  
Figure 13: Comparison of the packet drop ratio.
# Reference Models for Digital Manufacturing Platforms
## 3 Reference Models Alignment
### Industrial Internet Consortium Reference Model
#### 3.4.2 Usage Viewpoint  
The usage viewpoint describes how the system realizes the fundamental capabilities identified in the business viewpoint, through their decomposition into tasks (units of work) and activities (how the system is used) between different system components. This viewpoint addresses the concerns of expected system usage, typically represented as sequences of activities involving human or logical users. The main elements of the usage viewpoint are:  
* Task: Basic unit of work, such as the invocation of an operation, a transfer of data, or an action of a party, carried out by a party assuming a role.
* Functional Map: Map of the functions or functional components of the task.
* Implementation Map: Map of the implementation component the task relies on for its execution.
* Role: Set of capacities assumed by an entity to initiate, participate in the execution of, or consume the outcome of a task.
* Party: Agent (human or automated) that has interest and responsibility in the execution of a task. An agent executes a task assuming a role with the right capacities for the execution of the task.
* Activity: Specified coordination of tasks (and possibly other activities) required to use or operate the system, consisted of the following elements:  
Figure 6: Industrial Internet Reference Architecture (IIRA) Reference Architecture Viewpoints.  
* Trigger: Conditions that initiate an activity, optionally associated with a role responsible for initiating or enabling the execution.
* Workflow: Organization of tasks (sequential, parallel, conditional, iterative).
* Effect: Difference in the state of the system after the successful completion of an activity.
* Constraints: Characteristics that must be preserved during the execution of the activity.  
This way, the usage viewpoint captures the description of the technical details and the step by step analysis of the different use cases. In the definition of the workflow of the activities, the different components of the system and the exchange of information between them are identified.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Security  
One of the most fundamental issues for any digital system is security. If a drone BS is interrupted by an attacker, for instance, the UEs served by that drone BS are more likely to lose connection than the UEs served by ground BSs. If a drone is operated by attackers, the UEs supplied by terrestrial BSs may face significant interference due to LoS links. When drones are utilized for cellular communications, it is critical to ensure the security of drone systems. The security and safety issues will become more critical with small drones, and the massive growth in drones having fast movements and the capability of long-distance travel. Drones' security vulnerabilities and threats are still a challenge that need further study. Therefore, drone security and privacy concerns with various mobility scenarios must be highlighted, discussed, and addressed, particularly drone vulnerabilities, threats, and attacks. Therefore, further research and enhancements must be accomplished in this area.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 4 Resource Request Queue Reconstruction and Collaborative Scheduling Method
### Link Resource Competition Optimization
Ideally, we can consider that the resource utilization reaches the maximum, then the time consumed by resource scheduling is the minimum when all resource requests are satisfied. Therefore, we designed the following algorithm to improve the utilization of link resources. According to Algorithm 1, we first create virtual source nodes and sink nodes according to the resource amount that needs to transmit on the full link at a particular time \(t\) in the spatial information network (lines 1-5), converting the multi-commodity network flow problem into a single source and single sink problem. Then, we use the Ford-Fulkerson algorithm to find the maximum flow in the network and the flow value that can circulate on each edge (line 6). Based on the time consumed by the unit data on the link, we calculate the maximum time consumed by the entire network at the current moment \(t\). Finally, we obtain the minimum data transmission time (lines 8-9).
## 3 Results
### Considering the Permeability Law of Gradation and Effective Stress  
To describe the relationship between effective stress and permeability under different Talbot power exponents, the power function is introduced based on Figure 9, and the relationship between Talbot power exponents and parameters a and b is given, as shown in Figure 10.  
As can be seen from Figure 9, there is an obvious exponential relationship that exists between the permeability and effective stress for different Talbot power exponents \(n\):  
\[k=bv_{c}^{a} \tag{7}\]  
where \(k\) represents permeability, m\({}^{2}\); \(\sigma_{\mathrm{c}}\) represents the effective stress, MPa; \(a\) and \(b\) are parameters.  
The power exponent \(a\) determines the shape of the permeability curve. When \(a\) is negative, it reflects the degree of the decrease in coal permeability as the effective stress increases, embodying the inhibitory effect of the Talbot power exponent on permeability. The coefficient \(b\) generally determines the overall longitudinal position and magnitude of the function. When \(b\) is positive, it represents the amplitude effect of the effective stress on permeability.  
Figure 10 shows that as the Talbot power exponent increases, the parameters change to varying degrees of change. Similar to the permeability-Talbot power exponent curve, three distinct stages can be identified: the first stage (I): \(n<0.4\); the second stage (II):  
Figure 10: The relationship between fitting coefficient and Talbot power exponent.  
\(0.4\leq n<0.8\) and the third stage (III): \(0.8\leq n<1.0\). The power exponent \(a\) generally shows a decreasing trend. As the absolute value of \(a\) increases, the function becomes steeper, and permeability shows greater sensitivity to effective stress, indicating that the inhibitory effect of the Talbot power exponent on permeability is enhanced. The coefficient \(b\) increases with the Talbot power exponent, and the curve height of the curve gradually increases, indicating a continuous increase in the amplitude effect of effective stress on permeability. It is observed that the changes in the parameters a and b occur in three stages, which is consistent with the permeability change in Figure 9. In the first stage (\(n<0.4\)), \(a\) decreases gradually, while \(b\) increases slowly. In the second stage (\(0.4\leq n<0.8\)), \(a\) decreases linearly, and \(b\) increases linearly. In the third stage (\(0.8\leq n<1.0\)), \(a\) decreases exponentially, and \(b\) increases exponentially.
## 3 Advanced Techniques in Fault Injection and Error Rate Estimation
### Fault Injection Method
#### 3.1.2 Fault Injection Control  
Accessing the configuration memory using SEM IP is possible by sending the corresponding command through serial communication. To apply a fault to a desired memory address, a comprehension of linear frame addresses is necessary. This is explained in detail in Reference [13]. In this paper, to inject faults into the Zynq-7000 series board (Digilent Inc., Pullman, WA, USA), the linear frame address for the board was analyzed, and a fault injection engine based on this analysis was integrated into the ACMFI tool.  
To execute the desired fault command, the SLICE location information of the CLB where the DUT is synthesized is required. The logic location file (.ll) provides the CLB location of the synthesized logic. However, because logic location is sensitive information that can potentially lead to bitstream hacking, not all of the information is disclosed. If the information about the circuit to be analyzed is not available in the logic location, users can verify the CLB location through the floor plan created during the implementation stage.  
There are two methods (linear frame address and physical frame address) that can be used to apply a fault to a desired address location using SEM IP [30]. In this paper, we only discuss how to generate fault injection commands using linear frame addresses (hereinafter referred to as LFA). The fault injection command using LFA requires 10 words of information along with \"N\". Each word is expressed as a hexadecimal ASCII word, and, as a result, 40 bits of information are required. These 40 bits of information consist of a fixed '11000000', 2 bits of hardware SLR code, 17 bits of linear frame address, 7 bits of word address, and 5 bits of bit address. To obtain this information, the offset information and frame offset information about the fault injection location are necessary, and they can be found in the logic location file.  
This explains how to create a fault injection command to inject a fault into the $BS4169 cell using the logic location file. We can verify in the logic location file that the offset of the cell in question is 4904411, and the frame offset is 1467. In this example, because non-SSI is set, the SLR code is '00'. Figure 5 illustrates the process of generating a fault injection command using the provided information.
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 1 Introduction
### A Two-Dimensional Attention-Based Method for Scene Text Recognition
The context modeling capacity is improved by taking into account both forward (left-to-right) and backward (right-to-left) information of text sequences, resulting in increased text recognition accuracy and resilience.
## 2 Materials and Methods
### Indirect Tensile Strength Test  
The indirect tensile strength (ITS) test, as indicated in AASHTO T283, is prevalently used to determine moisture damage resistance in asphalt mixture. The static indirect tensile strength of a specimen is determined with a loading rate of 51 mm/min. The peak load is recorded and divided by geometrical factors to obtain the split tensile strength as presented in Equation (1).  
\[S_{t}=\frac{2000P}{\pi tD} \tag{1}\]  
where \(S_{t}\) = Indirect tensile strength (kPa), \(P\) = maximum load (N), \(t\) = specimen height immediately before test (mm), and \(D\) = specimen diameter (mm).  
Two test conditions (i.e., dry and wet) were conducted. In the dry condition, samples were kept for 24 h at room temperature before being subjected to a test. In the wet condition, samples were kept in a temperature-controlled water bath at 60 \(\pm\) 1 \({}^{\circ}\)C for 24 h. The testing method was implemented in the Superpave system measured by the ITS testing machine to determine the susceptibility to moisture of the asphalt mixture. The samples subjected to two conditions were tested to obtain the results of  
Figure 2: Aggregate gradation.  
each tensile strength. Then, the tensile strength ratio (TSR) to determine the suitable mixtures for resistance to moisture damage was calculated as shown in Equation (2).  
\[\text{TSR}=\frac{\text{\emph{Indirect tensile strength} }(\text{\emph{wet}})}{\text{\emph{Indirect tensile strength} }(\text{\emph{dry}})}\times 100 \tag{2}\]