esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Results of Asymmetrical Construction  
Based on the above analysis, an uneven deformation is observed in the supporting pile and tunnel frame structure when the asymmetric construction method is adopted. To strictly limit the deformation of the tunnel frame structure during the construction process, a symmetric construction method is put forward. A symmetrical soil excavation with a simultaneous construction of a tunnel frame structure is adopted in symmetric construction, and the inclination deformation can be limited effectively as expected during the whole construction. Figure 12 shows the deformation of the supporting pile subjected to symmetric construction. The deformation of the supporting pile is mainly observed in Section \(BC\), which is caused by the squeezing of soil, while Section \(AB\) is constrained by the tunnel frame structure, and no obvious deformation is observed. The maximum deformation of the southern supporting pile is 4.04 mm, and the deformation decreases after the construction of the connecting section with a residual deformation of 1.51 mm. The maximum deformation of the northern supporting pile is 4.14 mm after excavation, and the residual deformation is 1.62 mm after the construction of the connecting section.  
Figures 13 and 14 show the deformation of the structural slab and structural wall in the tunnel frame structure subjected to symmetric construction. There is a uniform floating  
Figure 11: Lateral deformation of structural wall subjected to asymmetric construction: (**a**) northern wall, (**b**) middle wall and (**c**) southern wall (unit: mm).  
Figure 12: Lateral deformation of supporting pile subjected to symmetric construction: (**a**) southern pile and (**b**) northern pile (unit: mm).  
deformation of about 13 mm in the structural slab during the soil excavation case, and it increases by 1 mm after the pile cut and then drops to an even floating value of about 6 mm during the period of structure construction. Generally, the slabs deform uniformly when subjected to symmetric construction. As for the structural wall, the deformation is well controlled within 0.5 mm during the whole symmetric construction procedure. Such a small deformation is negligible in engineering practice.  
To ensure a positive function on deformation control in symmetrical construction, uniform and synchronous construction measures on both sides of the Huayuan foundation pit are necessary, and the adverse effects caused by the time-space effect should be reduced. For example, the excavation of the soil behind supporting pile should be carried out in symmetrical multiple steps. The construction procedure can be performed by an inverse method where the connecting structure is constructed from top to bottom right after the soil excavation to reduce the unloading effect caused by soil excavation.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 2 Materials and Methods
### Functional Gait Assessment  
The included subjects performed four standardized gait tests prior (without the system) and after the testing period (with the system turned \"On\"). The performed gait tests included the Timed \"Up and Go\" Test (TUG) [33; 34], the Four Square Step Test (FSST) [33], the 10 Meter Walk Test (10 MWT) [34] and the 2 Minute Walk Test (2 MWT) [35]. The TUG was used to evaluate the parameter risk of falling [36; 37], the FSST to evaluate the parameter coordination [35], the 10 MWT to evaluate the parameter walking speed [36] and the 2 MWT to evaluate the parameter endurance [37]. Gait tests were performed in a 30 m corridor marked at 1 m intervals, with performance times recorded using a stopwatch. The TUG, FSST and 10 MWT were each performed three times, with mean values calculated afterward, while the 2 MWT was performed only once. For the 10 MWT, the walking  
Figure 4: Flow chart of the study protocol. The assessment procedure for home testing of the vibrotactile feedback system (VTFS) involved the recruitment of subjects reporting gait instability who met the inclusion criteria. Participants were equipped with a VTFS and instructed to engage in home-based testing for a minimum of 30 days. Preceding the testing period, subjects underwent four gait tests without the system. Following the completion of the home testing period, the subjects were required to repeat the gait tests, this time with the VTFS activated. After the testing period, subjects rated their experience with the VTFS on a 5-point Likert scale and indicated whether they would like to continue using the system. (Images VTFS: Copyright Saphenus Medical Technology GmbH; Icons & flow chart created in BioRender. [PERSON] (2024) ([[https://BioRender.com/a98g068](https://BioRender.com/a98g068)]([https://BioRender.com/a98g068](https://BioRender.com/a98g068)), accessed on 9 October 2024).  
speed was calculated by dividing 10 m by the time required to perform the test. For the 2 MWT, the distance covered within 2 min was recorded using the 1 m intervals on the floor. Subjects were permitted to use walking aids (e.g., crutches, canes), but assistance from others was not allowed during the gait tests.
# Model Test Analysis of Subsurface Cavity and Ground Collapse Due to Broken Pipe Leakage
## 2 Testing Program
### Testing Procedure  
Considering the complex and diverse forms of pipeline breakages observed in practice, three common types of pipeline breakages were studied: wall perforation, circumferential damage, and vertical damage. Based on these types, three different model tests (Figure 5) were set up to study the influence mechanism of underground cavity development and the degree of ground collapse under different sizes and types of cracks in the pipeline.  
Figure 4: Model filling division.  
Figure 3: Arrangement of monitoring points.  
Group 1: Pipe wall perforations. Circular cracks were created in the middle of the pipe of 20, 30, and 40 mm in diameter to study the effect of crack size on ground collapse.  
Group 2: Circumferential damage. Circumferential damage tests were conducted by varying the size of the circumferential cracks to one quarter and half the value of the circumference, both 10 mm wide. Simulations were performed on two types of pipe water seepage conditions: surge leak (water pushing out from the defective point over an area no larger than one third of the pipe section) and spray leak (water spewing out from the defect point or when the area of the leaking water surface is greater than one third of the pipeline section).  
Group 3: Vertical damage. Vertical damage tests were conducted with spacing sizes of 10 mm and 20 mm, with truncated pipes inserted from the ends of the pre-drilled holes in the sidewalls; the crack size was adjusted by controlling the distance between the ends. The different types of pipe leakage conditions helped simulate water erosion damage in different erosion areas.  
The experimental pipe was placed based on the model filling procedure, and the pipe crack was set above. Subsequently, the experimental pipe was connected to the water pump and the filter water tank through a PVC hose. During the experiment, the water pump delivered water to the experimental pipeline at a constant pressure of 0.2 MPa. To simulate the peak and low-peak water consumptions observed in practice, the pump was operated for 1 min and stopped for 20 s.  
Figure 5: Types of experimental groups: (**a**) wall perforation group; (**b**) circumferential damage group; (**c**) vertical damage group (black fonts for scale reading).
### Crop Response Assessment
Though the volatility of AW raises some concerns over N losses, the very small quantity of product applied teamed with the fast incorporation of the product into soil, the soil's buffering characteristic, and the controlled moisture conditions of the experiment caused negligible NH\({}_{3}\) volatilization, thus ensuring ANR results comparable to the synthetic fertilizer treatment. Nevertheless, it must be considered while making conclusions that the results show high standard deviations. This was particularly evident in the case of AW + CaE\({}_{50\%}\). The replicates of this treatment exhibited a non-homogenous N uptake, thus resulting in variable NFRV results within the treatment. The analysis of soil parameters after harvest gave further insights into the effect of tested RDFs on plant growth. The high pH of AW, as discussed before, could result in its volatilization after application into the medium. Proper fertilizer management techniques can help in reducing N volatility to the environment [78; 79], and care must be taken while applying such concentrated volatile N products on field scales. The RDFs AN and AW inherently have high EC values, but due to their higher N content, smaller quantities of the RDFs (882 mg and 468 mg, respectively) were applied in each pot. Hence, no significant impacts on crop growth were observed, but, overall, a negative correlation was detected between the EC of soil and the fresh weight of plants (Pearson Correlation = \(-\)0.647; \(p\) < 0.001). Research shows that salinity in soil can curb plant growth by creating osmotic stress [80; 81; 82]. Since lettuce is a salt-sensitive plant [83], the poor yield observed in plants fertilized with CaE and AN + CaE could be a result of osmotic stress, also made apparent by previous correlations. Furthermore, as expected, a positive correlation was discovered between the EC and soil Na content (Pearson Correlation = 0.845; \(p\) < 0.001). CaE and the blends have inputs of Na from the product. As seen before, in the case of plant Na concentration and N uptake, the soil treated with these products also shows significant differences with the unfertilized control. CaE\({}_{100\%}\) and both blends at both doses exhibited a significantly higher total Na in the soil in comparison to the CAN at the respective doses. However, stronger conclusions regarding the behavior of these RDFs cannot be made before testing them under field conditions in long-term trials and with diverse crop types.
# Fail-Safe Topology Optimization Using Damage Scenario Filtering
## 2 Theory of Fail-Safe Structural Topology Optimization
### Sensitivity Analysis
Subsequently, we can establish a density iteration scheme for the topology optimization of fail-safe structures as follows:  
\[B_{e}^{k}=\frac{\sum\limits_{i=1}^{m}\frac{h^{i}}{H}p\left(\rho_{e}^{k}\right) ^{p-1}q_{e}}{\lambda v_{e}} \tag{13}\]  
Compared to traditional topology optimization methods, the density iteration scheme introduces the variable \(B_{e}^{k}\). The presence of \(B_{e}^{k}\) in the fail-safe topology optimization method prioritizes local damage scenarios that cause a significant increase in the structural strain energy. It achieves this by adjusting the optimization direction to mitigate the adverse effects of these damage scenarios.
# The Influencing Factors of CO2 Utilization and Storage Efficiency in Gas Reservoir
## 4 Results
### Dip Angle  
The variation curves of CO\({}_{2}\) content and CH\({}_{4}\) recovery at the long core outlet under different dip angles were shown in Figure 13. CO\({}_{2}\) breakthrough occurred at 0.7 PV CO\({}_{2}\) injection with a 64.31% recovery at the same time when \"high injection and low production\" with a 45\({}^{\circ}\) dip angle was adopted. CO\({}_{2}\) breakthrough occurred at 0.88 PV CO\({}_{2}\) injection with a 75.91% recovery at the same time when \"low injection and high production\" with a 45\({}^{\circ}\) dip angle was adopted. The experiment shows that the gravity segregation occurs between the dense supercritical CO\({}_{2}\) and the light CH\({}_{4}\). The gravity belongs to the driving force during the \"high injection and low production\" process, so CO\({}_{2}\) is easier to break through, and there is a wider transition zone and a lower recovery. The gravity belongs to the resistance force during the \"low injection and high production\" process, which could inhibit CO\({}_{2}\) breakthrough and achieve higher recovery.  
The relation curves between CO\({}_{2}\) storage efficiency and injection volume under different dip angles are shown in Figure 14. The storage efficiency curves increased in fixed slope before CO\({}_{2}\) breakthrough, and the increase gradually slowed down after breakthrough. The ultimate recovery in \"high injection and low production\" process was lower than other simulations because of earlier CO\({}_{2}\) breakthrough. The analysis indicates that due to the gravity effects, there is a longer transition zone and an earlier CO\({}_{2}\) breakthrough time when CO\({}_{2}\) is injected at the high spot. More CO\({}_{2}\) is produced after an earlier breakthrough, so the storage efficiency is lower. Furthermore, as the injection position becomes lower, the gravity gradually transforms from a driving force to a resistance force. The CO\({}_{2}\) breakthrough is inhibited, and the storage efficiency becomes higher.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks  
[PERSON]  
1 Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3, Canada;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.Z.A.); [EMAIL_ADDRESS] (V.B.M.)  
[PERSON]  
1 Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3, Canada;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.Z.A.); [EMAIL_ADDRESS] (V.B.M.)  
[PERSON]  
1 Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3, Canada;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.Z.A.); [EMAIL_ADDRESS] (V.B.M.)  
[PERSON]  
1 Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3, Canada;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.Z.A.); [EMAIL_ADDRESS] (V.B.M.)  
Footnote 1: email: {[EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS]  
drones. Please note that it is also possible that each node in the cluster can communicate with the relay node and that a single or a few nodes keep administrative functions.
4. Relay drones which need to leave the cluster and return to the ground station in order to re-charge the batteries. Relay drones collect the data from the cluster head and hand them over to the central substation.  
Usually, drones have small batteries that could last for 20 to 30 min only, so that it is crucial to have communications over short distances, small contention in access protocol and a lightweight security scheme. The UAV network should be self-forming and reorganizing, in addition, fault and delay tolerant [1; 2]. In this work, we propose a secure disaster surveillance UAV system built over drones that communicate using IEEE 802.11 ah standard. This technology features multiple Restricted Access Windows (RAW) which can be used to isolate intra-cluster from cluster to relay communications [8; 9]. In combination with multiple physical channels and higher communication range, this technology is a serious candidate for reliable multi-cluster drone networks.  
Specific characteristics for UAV networks should be taken into consideration when designing a secure UAV system. First, the drone's limited energy (because of its bounded battery size) restricts the flying time to a specified period. Second, the drone's computation capability does not permit the performance of complex cryptographic operations. Our security scheme uses the lightweight Ring-Learning with Errors (Ring-LWE) crypto-system to protect the exchanged messages' confidentiality with low computation overhead. It also utilizes data redundancy and fingerprint features techniques.  
The remainder of the paper is organized as follows: Section 2 discusses related work related to drone networks from the networking, security and application aspects. Section 3 introduces our system model, security parameters, and design goals. Section 4 reviews the Ring-LWE Encryption Scheme. In Section 5, we present our proposed security scheme. Section 6 gives the security analysis, while Section 7 evaluates the performance of our scheme. Finally, Section 8 concludes the paper.
## 2 Materials and Methods
### Evaluation of Volatile Compound Profiles  
The VCs of samples were analysed using gas chromatography-mass spectrometry. A solid-phase microextraction (SPME) device with Stableflex(tm) fibre, coated with a 50 \(\upmu\)m-\(\upphi\) PDMS-DVB-Carboxen(tm) layer (Supelco, USA), was used for analyses. For the headspace extraction of the samples, 1 g of sample and 12 mL of 1 M phosphate buffer (pH = 3) were transferred to the 20 mL extraction vial, mixed, sealed with a polytetrafluoroethylene septum, and thermostatted at 60 \({}^{\circ}\)C for 30 min before exposing the fibre in the headspace. The fibre was exposed to the headspace of the vial for 10 min and desorbed in an injector liner for 2 min (splitless injection mode). The prepared samples were analysed with a GCMS-QP2010 (Shimadzu, Japan) gas chromatograph and mass spectrometer. The following conditions were used for analysis: injector temperature 250 \({}^{\circ}\)C; ion source temperature 220 \({}^{\circ}\)C; and interface temperature 260 \({}^{\circ}\)C. Helium was used as a carrier gas at 0.65 mL/min flow-rate. For the separation of VCs, a low polarity Rxi(r)-5 MS column (Restek, Centre County, PA, USA) (length 30 m, coating thickness 0.25 \(\upmu\)m-\(\upphi\)), inner diameter of 0.25 mm-\(\upphi\)) was used. The temperature gradient was programmed from starting at 40 \({}^{\circ}\)C (3 min hold) to 220 \({}^{\circ}\)C (5 \({}^{\circ}\)C/min) up to 310 \({}^{\circ}\)C (15\({}^{\circ}\)/min) (6 min hold). The VCs were identified according to mass spectrum libraries (National Institute of Technology spectral library (NIST11) and Flavors and Fragrances of Natural and Synthetic Compounds 2 spectral library (FFNSC2)). For identification purposes, alkane mixes (C8-C20) were analysed to obtain the retention indices of unknown compounds.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 3 Methodology
### Classification Algorithms
#### 3.2.3 Bidirectional Recurrent Neural Networks
The output of the GRU layer contains an encoded representation of the contextual embeddings in both the forward and backward directions, and it is then fed into a fully connected layer for classification to identify actionable warnings based on the encoded contextual information.
## 4 Proposed Multi-Feedback Interference Cancellation
### Problem Formulation and Solution
Hence, the likelihood functions of \(\mathbf{\tilde{y}}_{k}^{(b)}\) and \(\mathbf{\tilde{y}}_{k}\) given \(s_{k}\) can be separately evaluated by:  
\[\text{Pr}(\mathbf{\tilde{y}}_{k}^{(b)}|s_{k})\propto e^{-(\mathbf{\tilde{y}}_ {k}^{(b)}-\mathbf{h}_{k}s_{k})^{H}\frac{1}{\sigma_{v}^{2}}(\mathbf{\tilde{y}} _{k}^{(b)}-\mathbf{h}_{k}s_{k})} \tag{15}\]  
For simplicity, we assume QPSK is used as in the previous subsection, so \(s_{k}(i)=\pm 1,i=1,2\). Using Equation (15), the LLR can be computed as:  
\[\text{L}(s_{k}^{(b)}(1))=\ln\frac{\text{Pr}(s_{k}(1)=+1|\mathbf{\tilde{y}}_{k} ^{(b)})}{\text{Pr}(s_{k}(1)=-1|\mathbf{\tilde{y}}_{k}^{(b)})}=\frac{4\Re( \mathbf{h}_{k}^{H}\mathbf{\tilde{y}}_{k}^{(b)})}{\sigma_{\tilde{v}}^{2}}, \tag{16}\]  
and the conditional probability of \(s_{k}^{(b)}(1)\) given \(\mathbf{\tilde{y}}_{k}^{(b)}\) can be calculated by:  
\[\text{Pr}(s_{k}^{(b)}(1)=+1|\mathbf{\tilde{y}}_{k}^{(b)})=\frac{1}{1+e^{-\text {L}(s_{k}^{(b)}(1))}} \tag{17}\]  
Accordingly, \(\text{L}(s_{k}^{(b)}(2))=\frac{4\Im(\mathbf{h}_{k}^{H}\mathbf{\tilde{y}}_{k}^ {(b)})}{\sigma_{\tilde{v}}^{2}}\). From Equation (17), multiple conditional probabilities \(\text{Pr}(s_{k}^{(b)}(1)=+1|\mathbf{\tilde{y}}_{k}^{(b)})\) and \(\text{Pr}(s_{k}(1)=+1|\mathbf{\tilde{y}}_{k})\) can be obtained. Hence, the average conditional probability \(\text{Pr}(s_{k}|\mathbf{y}_{k}^{*})\) can be obtained by:  
\[\text{Pr}(s_{k}(1)=+1|\mathbf{y}_{k}^{*})= \sum_{b=1}^{B}\text{Pr}(s_{k}^{(b)}(1)=+1|\mathbf{\tilde{y}}_{k}^ {(b)})\text{Pr}(\mathbf{\tilde{y}}_{k}^{(b)}) \tag{18}\] \[+\text{Pr}(s_{k}(1)=+1|\mathbf{\tilde{y}}_{k})\text{Pr}(\mathbf{ \tilde{y}}_{k}),\]As mentioned above, \(\text{Pr}(\mathbf{\tilde{y}}_{k}^{(b)})=\text{Pr}(\mathbf{s}_{k}^{(b)})\), which can be obtained by the LLR from the channel decoder. Equation (18) can be rewritten as:  
\[ \begin{split}\text{Pr}(s_{k}(1)=+1|\mathbf{y}_{k}^{*})=& \sum_{b=1}^{B}\text{Pr}(s_{k}^{(b)}(1)=+1|\mathbf{\tilde{y}}_{k}^{(b)}) \text{Pr}(\mathbf{s}_{k}^{(b)})\\ &+\text{Pr}(s_{k}(1)=+1|\mathbf{\tilde{y}}_{k})\text{Pr}(\mathbf{ \tilde{s}}_{k}),\end{split}  \tag{19}\]  
The second term in Equation (19) can also be evaluated according to Equation (15). The LLR of \(s_{k}(1)\) for the channel decoder, with the use of Equation (19), can be easily evaluated by:  
\[\text{L}(s_{k}(1))=\ln\frac{\text{Pr}(s_{k}(1)=+1|\mathbf{y}_{k}^{*})}{\text{ Pr}(s_{k}(1)=-1|\mathbf{y}_{k}^{*})}. \tag{20}\]  
Hence, we have obtained the new LLR output by the multi-feedback cancellation algorithm in this section and can further use it in the channel decoder.
# Effect of Washing Temperature on Adsorption of Cationic Dyes by Raw Lignocellulosic Biomass
## 1 Introduction
The physico-chemical properties of the biosorbents were characterized using a variety of techniques, including BET, SEM/EDS, FTIR, ATG, pH at zero charge, and surface function analysis using the Boehm method [37; 38]. The impact of experimental parameters such as pH, contact time, initial dye concentration, adsorbent mass, ionic strength, and temperature was studied using a batch adsorption technique. Adsorption mechanisms were analyzed using three kinetic models (pseudo-first-order (PFO), pseudo-second-order (PSO), and intraparticle diffusion models), and four theoretical isotherm models (Langmuir, Freundlich, Sips, and Redlich-Peterson) were tested to determine the most appropriate model for dye adsorption.