esa-sceva/satcom-chunk-collection huggingface dataset:
# Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State
## 4 Experiments and Results
### Robot Stability on Various Surfaces with Various Slope Angles
Under the proposed method, the variation trend of adhesive strength of the robot is similar to that of the gecko. The success rate of climbing directly confirms the superiority of OIAC. For the glass surface, IBSS-8 started to fail at a slope angle of 85\({}^{\circ}\) under TTC and succeeded only once at 90\({}^{\circ}\). Under the proposed method, IBSS-8 failed only once at 90\({}^{\circ}\). For the Teflon surface, IBSS-8 failed completely at a slope angle of 80\({}^{\circ}\) under TTC but had a success rate of 50% at this angle under the proposed method. These results confirm that the proposed control method reduces the probability of robot instability during climbing. Besides, the climbing speed of the robot on the 90-degree glass surface with tripod gait (see Supplementary Materials' Video S1) is 0.35 cm/s, while that with trot gait is 0.6 cm/s. However, the movement of the robot is unstable under trot gait. The adhesion strength and peeling speed of the sole are the main reasons to limit the speed improvement. Another limitation for the proposed architecture is that it lacks an online trajectory adaptor.
## 3 Results and Discussions
### Temporal Variation Characteristics of Atmospheric Particulate Matter Concentration
This investigation also delved into the PM\({}_{2.5}\)/PM\({}_{10}\) ratio. This metric serves as an indicator of the balance between fine and coarse particulate matter within Chenzhou's atmospheric milieu. The average seasonal PM\({}_{2.5}\)/PM\({}_{10}\) ratios in Chenzhou are delineated as follows: winter (0.61) > autumn (0.58) > spring (0.54) > summer (0.48). Notably, the ratios for autumn and winter surpass 0.55, underscoring a prevalence of fine particulate matter pollution during these seasons in Chenzhou. Conversely, the ratios for spring and early summer predominantly hover around 0.50 or even dip below 0.45, signaling an ascendancy of coarse particulate matter pollution during these warmer months. This is particularly evident in the markedly low PM\({}_{2.5}\)/PM\({}_{10}\) ratio observed in summer. Viewed from a different angle, this underscores the pronounced influence of summer precipitation and southerly winds on particulate matter dispersion, with fine particles being more significantly impacted.
# Retrofitting Masonry Walls against Out-Of-Plane Loading with Timber Based Panels
## 2 Material and Method
### Experimental Program
#### 2.2.3 Stage 3: Large-Scale Test (Out-Of-Plane Bending Test on Masonry Wall)  
The knowledge gained from the small-scale tests (stage 2) was then used to perform out-of-plane flexural strength tests on six large-scale, single leaf, double wythe solid (\(1115\times 1115\times 215\) mm) masonry walls. The purpose of these tests [19] was to achieve the research aim, which was to evaluate how the timber panel aided the out-of-plane behaviour of the masonry wall. For the large-scale test, two similar specimens each were tested as plain, one-sided retrofitted and double-sided retrofitted walls. The plain walls were tested with both constant and variable precompression load to represent high in-plane compression usually present in URM walls. The retrofitted walls were constructed using OSB type-3 and adhesive anchor connection type (C1) that offer the best performance in the flexural bond strength of masonry prisms, as identified during the small-scale tests. The test program ensured that loading was applied on walls retrofitted with OSB timber on either the tension face alone or on both the tension and compression face of the masonry walls.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dimension** & **Typology of Test** & **Number of Tests** & **Relevant Code** \\ \hline \multicolumn{5}{c}{Stage 1: Material Characterisation} \\ \hline \multirow{3}{*}{Brick unit} & \multirow{3}{*}{\(215\times 102.5\times 65\) mm} & Dry density & 6 & [20] \\ \cline{3-5}  & & Water absorption & 6 & [21] \\ \cline{3-5}  & & Compression test & 6 & [22] \\ \hline \multirow{3}{*}{Mortar} & \multirow{3}{*}{-} & Dropping value & 6 & [23] \\ \cline{3-5}  & & Flow test & 6 & [24] \\ \cline{3-5}  & & 100 \(\times\) 100 \(\times\) 100 mm & Compression test & 6 & [25] \\ \hline \multirow{3}{*}{Masonry cube} & \multirow{3}{*}{\(215\times 215\times 215\) mm} & \multirow{3}{*}{Compression test} & 6 & unconventional with \\  & & & & & insight from [25] \\ \hline \multicolumn{5}{c}{Stage 2: Small-Scale Test (Flexural bond strength of masonry prism)} \\ \hline Plain MP & \(215\times 102.5\times 665\) mm & Four-point bending test & 3 \\ \hline Retrofitted MP & \(215\times 102.5\times 665\) mm & Four-point bending test & 6 (9 MPa with C1: adhesive anchor; and 3 MPa with C2: mechanical connection) \\ \hline \multicolumn{5}{c}{Stage 3: Large-Scale Test (Flexural strength of masonry wall)} \\ \hline Plain wall & \multirow{3}{*}{\(1115\times 1115\times 215\) mm} & \multirow{3}{*}{Four-point bending test} & 2 \\ \cline{3-5}  & & & & [16] \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Full experimental program.
## 2 Results and Discussion  
### Instrumental Conditions  
In a first approach, the HPLC-DAD conditions including the UV/vis spectral data for the detection of five phenol compounds (3-nitropenol, 4-nitropenol, bisphenol-A, 4-\(n\)-octylphenol and 4-\(n\)-nonylphenol), as well as retention time characteristics were evaluated. In agreement with the UV/vis data obtained, the wavelength (\(\lambda_{\mathrm{max}}\)) of 226 nm was selected since it maximizes the detector's response to the five analytes. By combining a conventional reversed phase column with a mobile phase constituted by MeOH and water, a good response was obtained for all analytes by HPLC-DAD, showing suitable resolution within convenient analytical time (\(<\)20 min). The instrumental sensitivity was checked through the limits of detection (LODs) and quantification (LOQs) for the analytes,obtained by the injection of diluted calibration standards and calculated with a signal-to-noise ratio (S/N) of 3/1 and 10/1, respectively. Values of 13.0 and 25.0 \(\upmu\)g/L for LODs and LOQs, respectively, were obtained for 3 NOP and for the remaining analytes under study (4 NOP, BPA, OP and NP) the LODs and LOQs were 43.0 and 83.0 \(\upmu\)g/L, respectively. Subsequently, the instrumental calibration was performed with six standards solutions having concentrations ranging from 0.1 to 4.0 mg/L. From the data obtained, excellent linear dynamic responses were achieved for all analytes with determination coefficients higher than 0.9944. To evaluate the instrumental precision, repeated injections for each calibration level were carried out, resulting in relative standard deviations (RSD) below 5.0%. No carry over was observed by a series of replicate injections since the background was always below the instrumental LODs achieved.
## 3 Results and Discussions  
Based on the self-designed Schottky diodes and GaAs monolithic integrated technology, 0.34 THz triplers and sub-harmonic mixers were fabricated. To verify the effectiveness of the diode design method and simulation optimization method proposed in this article, three triplers and three mixers were assembled and measured for the research on performance consistency.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Performance Evaluation  
The sensitivity, specificity, precision, accuracy, F1 score and balanced accuracy were used in estimating the performance of the DenseNet121-based EEG normal abnormal classification method.  
\[Sensitivity=\frac{TP}{TP+FN} \tag{23}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{24}\]  
\[Precision=\frac{TP}{TP+FP} \tag{25}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{26}\]  
\[F1=2\times\frac{Sensitivity\times Precision}{Sensitivity+Precision} \tag{27}\]  
\[BalancedAccuracy=\frac{Sensitivity+Specificity}{2} \tag{28}\]  
where:  
* True positives (_TP_): the number of abnormal EEGs predicted as abnormal EEGs;
* False positives (_FP_): the number of normal EEGs predicted as abnormal EEGs;
* True negatives (_TN_): the number of normal EEGs predicted as normal EEGs;
* False negatives (_FN_): the number of abnormal EEGs predicted as normal EEGs.
## 6 Experimental Study
### Performance of Dynamic Scheduling  
In dynamic scheduling, methods such as NeuRewriter, which utilize intelligent agents to guide local search, cannot be directly applied as they involve iterative improvements on the entire result and are not suitable for real-time environments. Therefore, no comparison is made with these methods.  
Figures 9 and 10 present the experimental results. Based on the experimental results, it is observed that in the dynamic scheduling scenario, the performance of all algorithms is generally inferior to that in static scheduling. This is attributed to the reduction in candidate sub-tasks, making it challenging to achieve optimal scheduling. However, reinforcement learning-based scheduling algorithms exhibit more significant performance variations compared to other methods. It is concluded that when the algorithm lacks the ability to observe the global context, the decisions made tend to be locally optimal within the considered subset of tasks, but may not be globally optimal.
## 2 Materials and Methods
### Evaluation of Antioxidant Activity
#### 2.4.2 Free Radical Scavenging by ABTS Assay  
The free radical scavenging capacity of _G. hederaca_ extracts was also studied using the ABTS radical cation decolorization assay [44], which is based on the reduction of ABTS\({}^{+}\) radicals by the antioxidants of the tested plant extracts. The ABTS\({}^{+}\) radical was prepared by mixing equal amounts of two stock solutions (7 mM ABTS solution and 2.45 mM potassium persulfate solution) and allowing them to react for 16 h at room temperature in the dark. The working solution was then prepared by mixing 3.9 mL of ABTS\({}^{+}\) with 140 mL of 5 mM phosphate buffered saline (pH 7.4) to obtain an absorbance of (0.70 \(\pm\) 0.02) at 734 nm using a spectrophotometer. Fresh ABTS working solution was prepared daily. The amount of 2850 \(\mu\)L of ABTS working solution was allowed to react with 150 \(\mu\)L of plant extracts in a test tube for 8 min in water bath incubation at 30 \({}^{\circ}\)C. The absorbance was measured at 734 nm using a Varian Cary 300 bio UV-VIS spectrophotometer (Agilent, Santa Clara, CA, USA). Trolox was used as the standard and PBS as the blank. The calibration curve was plotted in the range of 0.9-10 \(\mu\)g/mL. The results are expressed in \(\mu\)g Trolox equivalents/g of extract mass (\(\mu\)g(TE)/g) based on the calibration curve according to the following formula:  
\[\text{ABTS value ($\mu$g TE/g of extract) = (c \times v)/m} \tag{3}\]  
where c is the Trolox concentration (\(\mu\)g/mL) of the corresponding standard curve of the plant extract, v is the sample volume (mL) and m is the weight of the plant extract (g) [45].
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 2 Problem Definition
### Target System
#### 2.1.1 Dynamic System  
The output (\(Y(t)\)) of a dynamic system is affected by the inputs (\(X(\tau)\)) for \(\tau\leq t\); in other words, not only the input at the present time (\(u(t)\)) but also its past inputs (\(u(\tau)\)) will affect the system (for \(\tau<t\)) [20]. For a simulation of the dynamic system, simulation time is divided into many small steps (i.e., discrete time tick). The system model evolves its states at each time step, assuming that the states are unchanged during a time step. This is a typical characteristic of discrete time simulations; thus, the discrete time system models are usually the most intuitive to grasp all forms of the dynamic system models [18].  
The dynamic system using a discrete time simulation has numerous applications. For example, a compartment dynamic model was simulated to describe the dynamic response and distribution characteristics of the gasifier [21], and trajectories of unmanned aerial vehicles were modeled for collision avoidance [22]. In this paper, the maneuvering behaviors of a UUV are modeled based on the DTSS, which is a generalized form of a Mealy system [18].  
Figure 1 shows the structure of a DTSS model and the following specification is its formal representation [23]. The DTSS model consists of three variable sets (i.e., input, output, and state sets) and two functions--state transition and output functions. Input, output, and state variables have continuous values according to the simulation time. The state transition function,\(f(t)\), updates the state at the next time instant given the current state and input values. Note that a derivative equation is used to specify the rate of change of the state variables for the state transition function. The output function, \(g(t)\), also requires state and input variables. At any particular time instant on the time axis, given the current state and input values, the function computes the output values. In the DTSS model of Figure 1, the state variable is represented by a circle and the two functions are depicted with squares.  
\[DSM=\langle X,Y,S,f,g\rangle\]  
\(X\) is the set of inputs;  
\(Y\) is the set of outputs;  
\(S\) is the set of continuous states;  
\(f:\frac{d}{dt}S(t)=f(S(t),X(t),t)\) is the state transition function;  
\(g:Y(t)=g(S(t),X(t),t)\) is the output function.  
Figure 1: An example of a dynamic system model: Discrete time system specification (DTSS) model.
## 6 Conclusions  
This paper proposes a SAIOR algorithm based on the narrow downlink bandwidth, high received SNR characteristics of LEO constellation signals, and the periodicity of PFM interference signals. The algorithm concentrates the PFM interference energy originally dispersed over a wide bandwidth into a few frequency bins in the resampled data through appropriate secondary sampling, thereby enhancing interference aggregation. This effectively reduces the overlap between LEO satellite signals and interference, consequently minimizing damage to desired signals during interference suppression. Considering the effects of PFM interference parameters such as bandwidth, carrier frequency, modulation period, and intensity, the algorithm was tested using Iridium signals from the LEO system through anti-jamming simulations and real data experiments. The results demonstrate that, compared to traditional algorithms, this method effectively eliminates single/multi-component PFM interference, improving interference suppression performance under conditions of narrow bandwidth and high power. It significantly enhances anti-PFM interference performance in LEO satellite anti-jamming scenarios.  
Conceptualization, L.Y.; methodology, L.Y.; validation, L.Y.; formal analysis, L.Y.; investigation, L.Y.; resources, H.Q., D.X. and H.S.; data curation, Y.Z., C.M. and X.F.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y., H.Q., H.X., D.X., D.H., B.G., H.S., Y.Z., H.Z., N.X., Z.L., C.M. and X.F.; project administration, H.Q., B.G., H.Z. and Z.L.; funding acquisition, D.H., H.S., J.S. and F.C.; visualization, J.S. and F.C.; supervision, B.G., H.Z. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are inconvenient to be directly disclosed. The data presented in this study are available upon request from the corresponding author.  
We would like to thank the editor and the anonymous reviewers for their useful feedback that improved this paper.  
The authors declare no conflicts of interest. The company had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.
## 3 System Model and Problem Formation  
In this section, we first describe models, assumptions, and calculations used throughout the paper; and then we formulate our problem. We divided notations into two tables; Tables 1 and 2.  
\begin{table}
\begin{tabular}{l l} \hline \(v_{i}\) & Wireless mesh node \(i\) \\ \(v_{n}\) & Total numbers of network nodes \\ \(R_{v_{i}}\) & Numbers of IEEE802.11 hybrid radio available to \(v_{i}\) \\ \(c_{k}\) & Total number of orthogonal channels \\ \(e_{i,j}\) & Link from node \(v_{i}\) to \(v_{j}\) \\ \(e_{m}\) & Total numbers of network edges \\ \(I_{R}\) & Interference range of communication node \(v_{i}\) \\ \(d_{v_{i}v_{k}}\) & Euclidean distance between nodes \(v_{i}\) and \(v_{k}\) \\ \(k_{max}\) & Maximum number of orthogonal channels \\ \(f_{y}\) & Total number of network flows \\ \(cc_{em}\) & Channel capacity assigned to link \(e_{m}\) \\ \(r_{em}\) & Residual capacity of link \(e_{m}\) \\ \(p_{x}\) & Total number of alternative paths for flow \(f_{y}\) \\ \(d_{f_{y}}\) & Affordable delay limit of flow \(f_{y}\) \\ \(pl_{f_{y}}\) & Affordable PLR limit of flow \(f_{y}\) \\ \(RC_{e_{i,j}}\) & The residual capacity of link \(e_{i,j}\) \\ \(CC_{e_{i,j}}\) & Total assigned capacity to link \(e_{i,j}\) \\ \(f_{o}\) & The amount of capacity used by traffic flow \(f_{o}\). \\ \(F_{e_{i,j}}^{{}^{\prime}}\) & The set of flows traversing from link \(e_{i,j}\) \\ \hline \end{tabular}
\end{table}
Table 1: Notations.  
Table 1 presents the notations used in System Model, Interference Model, Assumptions, Calculations, Problem Formation, and ILP-Based Solution while Table 2 presents the notations used in LNR-RCTD Algorithm.
## 3 Validation and Experimentation
### Empirical Validation  
This section focuses on the empirical validation of the proposal by engaging real users in a comprehensive study of the experience. This analysis was conducted from two complementary perspectives: quantitative and qualitative. Through direct interaction with the participants, the aim was to assess the usability and acceptance of the proposal. This endeavor provided an in-depth understanding of how the proposed implementation impacts and enhances the user experience in a practical, real-world setting.
## 2 Service Restoration Procedure  
Distribution networks are designed radially, and the power flow is controlled by the states of the circuit breakers and the interconnecting and sectionalizing switches. Figure 1 illustrates a simplified model of distribution networks. In Figure 1, the distribution feeder is divided into three load sections by three sectionalizing switches, and each load section is connected to another feeder through an interconnecting switch. This is the three-sectionalized and three-connected distribution network, which is one of the most reliable structures of the multi-sectionalized and multi-connected distribution networks [1]. Some switches cannot be operated remotely, and they are excluded from our discussion.  
In normal operating conditions, the states of the switches are decided to maintain the power supply reliability and power quality. Once trouble occurs in the distribution  
Figure 1: Distribution network model (normal condition).  
network, the resulting outage leads to significant impacts on activities in society. This is the strongest reason why the distribution network operators are required to immediately resume the service to as many customers as possible. Figure 2 illustrates an example of distribution network reconfiguration during the service restorative phases.  
As illustrated in Figure 2a, after detecting a fault, the distribution feeder with the faulty load section is isolated automatically by turning off a circuit breaker and sectionalizing switches. In Figure 2b, the circuit breaker and the sectionalizing switches turn on, sequentially starting from the circuit breaker toward the sectionalizing switches adjacent to the faulty load section. In this step, the faulty load section is identified. Next, as shown in Figure 2c, the interconnecting switches are operated to reactivate the power supply from other distribution feeders to as many outage sections as possible. Then, the distribution network returns to its original configuration after clearing the fault as shown in Figure 2d, which is the same as Figure 1 when no PVs are installed.  
On the other hand, the power flow conditions in Figures 1 and 2d are different if the PVs feed the power to the distribution feeders. In the emergency condition, the PVs connected to the isolated feeders are simultaneously disconnected to prevent their islanding operations. As shown in Figure 2b,c, the disconnected PVs automatically return to the distribution network but with time delays. Even after the distribution network has returned to its original configuration, as shown in Figure 2d, the distribution substation must supply extra power to compensate for the shortage of power caused by the delayed reconnection of PVs. However, the distribution network operators cannot know the reconnection status because it is not monitorable or controllable. In summary, the uncertainty caused by the unknown time delays leads to difficulty in estimating the amount and duration of extra power supply, and therefore, the service restoration problems become more complicated.
# Experimental Study on Microstructure of Unsaturated Expansive Soil Improved by MICP Method
## 2 SWCC Test Materials and Methods
### Sample Preparation of MICP Improved Expansive Soil  
2.2.1. Bacteria and Culture Media  
The microorganism used to improve the expansive soil was _Bacillus pasteurii_, purchased from the Chinese General Microbial Strain Collection (CGMCC) under the number ATCC1859. This strain is a nonpathogenic, Gram-positive bacteria, which has no adverse effects on humans or the biological environment, a good ability to secrete urease. and is present in natural soil [27]. The _Bacillus pasteurii_ used for the test is shown in Figure 1. The culture medium consisted of 20 g/L urea, 20 g/L agar, 15 g/L casein peptone, 5 g/L soybean peptone and 5 g/L sodium chloride. _Bacillus pasteurii_ was inoculated in a slant medium and incubated at 30 \({}^{\circ}\)C for 24 h and stored at 4 \({}^{\circ}\)C. The culture was inserted into an agar-free medium and incubated for 36-48 h in a constant temperature shaking incubator at a temperature of 30 \({}^{\circ}\)C and a speed of 150 r/min. The absorbance \(OD_{600}\) value of the bacterial liquid at a wavelength of 600 nm was measured using a spectrophotometer. When the \(OD_{600}\)\(\approx\)1.6, the bacterial solution is used to improve the expansive soil.