esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Operational Protocol of the Present Method  
To measure the individual viscosities of suspended blood and plasma, Figure 1(B-i) represents temporal variations of the three parameters (i.e., \(U_{b}\), \(l_{b}\), and \(\alpha_{b}\)) obtained through the overall operational protocols suggested in the present study. \(U_{b}\) and \(\alpha_{b}\) were used to obtain the viscosity values of each fluid; \(l_{b}\) was used to detect the specific time when the suspended blood or plasma flowed in the blood channel or co-flowing channel. Suspended blood (Hct = 50%) was prepared by adding normal RBCs to plasma. First, to measure its viscosity, the suspended blood was supplied continuously into the microfluidic device with a micropump (i.e., \(t<\) 180 s). Here, the frequency and voltage were set to \(f=300\) Hz and \(v=140\) au, respectively. The micropump was then turned off, and the ultrasonic transducer was turned on to separate plasma from the blood (180 s \(<\)\(t<\) 1680 s). Finally, the transducer was turned off. The plasma was supplied to the microfluidic device by turning on the micropump. Figure 1(B-ii) shows snapshots of the ultrasonic chamber and  
Figure 1: Proposed method for separating plasma and sequentially measuring individual viscosities of blood and plasma. (**A**) Experimental setup, plasma separation, and quantification of blood. (i) Schematic of the experimental setup, including, a microfluidic device, an ultrasonic transducer, two pumps (i.e., micro pump and syringe pump), and an image acquisition system. (ii) Plasma separation from the suspended blood with an elapse of time (i.e., \(t_{1}=t_{0}\), \(t_{2}=t_{0}+10\) min, \(t_{3}=t_{0}+10\) min, and \(t_{4}=t_{0}+10\) min). (iii) Quantification of blood velocity (\(U_{b}\)) and interface (\(\alpha_{b}\)). (**B**) Quantification of three parameters (i.e., interface, image intensity, and blood velocity) for suspended blood (i.e., normal RBCs suspended in plasma, and Hct = 50%). (i) Temporal variations of \(\alpha_{b}\), \(l_{b}\), and \(U_{b}\). First, the suspended blood was supplied for 3 min. Next, plasma was separated from the blood for 25 min; plasma was then supplied for 6 min. (ii) Snapshots of the ultrasonic chamber and microscopic images with respect to time (\(t\)) (\(t=0\), 180, 1620, and 2040 s).  
microscopic images with respect to time (\(t\)) (\(t\) = 0, 180, 1620, and 2040 s). Before \(t\) = 180 s, suspended blood was supplied and loaded into the microfluidic device. The co-flowing channel was partially filled with blood and 1 \(\times\) PBS, and the blood volume decreased in the ultrasonic transducer. Plasma separation continued at a specific time, ranging from \(t\) = 180 s to \(t\) = 1680 s. After the micropump was turned off, all the channels were filled with the reference fluid. At \(t\) = 2040 s, the plasma was loaded into the co-flowing channel. Based on the suggested operation protocol of the micropump and ultrasonic transducer, the three parameters were obtained, and further used to sequentially acquire the two viscosity values of the blood and plasma.
## 3 Results and Discussion  
### Basic Physical Properties Test  
#### 3.1.1.  
The moisture content that marks the transition of soil from a semi-solid state to a plastic state is called the plastic limit (\(W_{\mathrm{L}}\)), while the moisture content that indicates the transition from a plastic state to a liquid state is referred to as the liquid limit (\(W_{\mathrm{P}}\)). The plasticity index (\(I_{\mathrm{P}}\)) is defined as the difference between the liquid limit and the plastic limit. They are important indicators for evaluating claye  
Figure 4: Carbonation box.  
Figure 5: Thermal constants analyzer.  
index of CL-MgO carbonized and solidified loess are presented in Figure 6. The results indicate that the plastic limit of the treated loess samples increased with higher additive content, showing a notable increase of approximately 13%. Conversely, both the liquid limit and the plasticity index exhibited decreasing trends, with the liquid limit decreasing by 11% and the plasticity index decreasing significantly by 33%. These findings align with the results of [PERSON] et al. [39], who reported similar behavior in silt treated with MgO. From a chemical reaction perspective, when reactive MgO undergoes hydration, it releases a significant amount of OH\({}^{-}\) ions. These ions interact with the charged surfaces of soil particles, triggering ion exchange and modifying the electrochemical properties of the soil. This reaction alters the structure of the double electric layer surrounding the soil particles, thereby affecting the interaction strength between the soil and water molecules [40]. From a microstructural standpoint, the hydration of MgO produces magnesium hydroxide (Mg(OH)\({}_{2}\)), which acts synergistically with CL [41; 42]. This magnesium hydroxide (Mg(OH)\({}_{2}\)) is distributed in a flocculent form within the soil pores and interacts with CL on the surface of soil particles. This synergy promotes particle rearrangement and aggregation, reducing the soil's specific surface area and decreasing its water adsorption capacity. The combined physical and chemical processes lead to significant macroscopic changes in soil properties. Specifically, the plasticity range of the solidified soil is reduced, reflecting a decrease in soil plasticity. These changes enhance the engineering performance of carbonized and solidified loess, making it more suitable for construction and environmental applications.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 4 Security Analysis and Performance Evaluation
### Security of Session Key
#### 4.2.2 Forward Secrecy  
In our scheme, \(CS\) computes the session key without the use of secret keys \((x,y,z)\), avoiding compromise its security in case that an attacker knows the secret keys. Let us suppose that, an attacker knows \((x,y,z)\), he/she cannot compute the correct session key because it does not contain the secret keys. Thus, the attacker cannot create a valid session key.
# Improving Named Entity Recognition for Social Media with Data Augmentation
## 4 Experimental and Analysis
### Ablation Study  
We designed ablation experiments to evaluate the data enhancement effectiveness of the DANER model. The ablation experiments were set up as follows.  
* DANER. DANER is the proposed method.
* DANER without attention. The attention mechanism is removed, and the entities are labeled by CRF after direct vector fusion.
* DANER without weight word. In data augmentation, pre-trained vectors are not weighted, and data augmentation and semantic transformation are performed directly on the embedded vectors.
* DANER without data augmentation. The model with data augmentation removed is degraded to BERT + Bi-LSTM + CRF neural network model.  
We used the WN16 dataset as the experimental dataset, and the other settings were calibrated in the same way as in the previous experiments. We performed each round of experimental training five times and report the average scores of F1 in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**WN16**} & \multicolumn{3}{c}{**WN17**} & \multicolumn{3}{c}{**ON5e**} \\ \cline{2-10}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Bi-LSTM-CRF & - & - & - & - & - & - & 86.04 & 86.53 & 86.28 \\ BERT & - & 49.02 & 54.36 & - & 46.73 & 49.52 & - & - & 89.6 \\ XLNET & 55.94 & 57.46 & 56.69 & 58.68 & 49.18 & 53.51 & 89.72 & 91.05 & 90.38 \\ AESINER & - & - & 55.14 & - & - & 50.68 & - & - & 90.32 \\ IftreNER & - & - & - & - & - & 50.52 & - & - & - \\ HGN & **59.74** & 59.26 & 59.50 & **62.49** & 53.10 & 57.41 & 90.29 & 91.56 & 90.92 \\
**Ours** & 58.37 & **65.23** & **61.61** & 58.11 & **68.89** & **63.04** & **90.42** & **91.65** & **91.03** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of methods on datasets (%).  
As can be seen through the experiments, the performance decreases after removing the attention mechanism, which indicates that the use of attention can effectively explore the information location and labeling of entities. The absence of weighting the vectors enhances the probability of non-entities acquiring labels, which leads to a decrease in the experimental results. When the data enhancement module is removed from the model, the performance receives a greater impact, which illustrates the importance of integrating data enhancement into the model.
## 4 Experimental Results
### CNN Model Performance Results
Categories such as air conditioner, car horn, engine idling, and jackhammer were generally classified correctly. However, some confusion was observed between children playing and dog bark, children playing and drilling, and children playing and street music. Particularly, the children playing category was confused with several different sounds. Although categories like gunshot, siren, and drilling were mostly classified correctly, occasional misclassifications did occur. These results indicate that while the model performs with high accuracy overall, there is some confusion between similar sounds. The model's performance is \begin{table}
\begin{tabular}{l l l l l} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Air\_conditioner & 0.93 & 0.93 & 0.93 & 195 \\ Car\_horn & 0.95 & 0.95 & 0.95 & 91 \\ Children\_playing & 0.81 & 0.84 & 0.83 & 205 \\ Dog\_bark & 0.84 & 0.82 & 0.83 & 182 \\ Drilling & 0.86 & 0.92 & 0.89 & 202 \\ Engine\_idling & 0.93 & 0.94 & 0.94 & 216 \\ Gun\_shot & 0.97 & 0.85 & 0.91 & 87 \\ Jackhammer & 0.95 & 0.92 & 0.93 & 187 \\ Siren & 0.97 & 0.93 & 0.95 & 199 \\ Street\_music & 0.84 & 0.86 & 0.85 & 183 \\ Accuracy & 0.90 & 0.90 & 0.90 & 1747 \\ Macro avg & 0.90 & 0.90 & 0.90 & 1747 \\ Weighted avg & 0.90 & 0.90 & 0.90 & 1747 \\ \hline \end{tabular}
\end{table} 
Table 1: Classification performance of CNN model on UrbanSound8K dataset. generally positive, as indicated by the high correct classification rates and relatively low confusion rates. Overall, the model was observed to correctly classify most sound categories, although some confusion was noted, particularly in the \"children playing\" and \"drilling\" categories. This suggests that certain sounds may share similar features, making it challenging for the model to differentiate them. The model's overall performance is strong, as evidenced by high accuracy rates and minimal confusion.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 3 System Model  
In this section, we present the system model for both cooperative communication and Wi-Fi offloading. An OFDMA-based downlink within a sector of a two-tier macro cell (MC)-SC network is considered with \(N\) SCs and \(M\) UEs. The SCs consider in the MC network are placed adjacent to each other and \(K\) unique OFDMA resource blocks (smallest unit of an OFDMA system) are assigned to each of them. We denote the small-cell BSs, UEs and RBs set as \(\mathcal{I}=\{1,2,\ldots,M\}\), \(\mathcal{J}=\{1,2,\ldots,N\}\) and \(\mathcal{K}=\{1,2,\ldots,K\}\), respectively. A time interval \(T\) is considered for which the resources are allocated in both the strategies while assuming quasi-static channel conditions. As already mentioned, this paper compares cooperative communication with Wi-Fi offloading. These two scenarios are shown in Figure 1,b, respectively. Table 1 describes the notations used throughout the paper.
## 3 Results and Discussion
### Geobia
#### 3.2.3 Results of Classification Algorithms  
The SVM classification algorithm (Figure 11a) enabled distinguishing the class of olive canopies. The majority of the olive trees in the study area are well known in their surroundings. However, there are some shortcomings in the class of other vegetation, where areas that belong to the class of other vegetation are incorrectly classified as olives in some areas. Moreover, the volume of the olive canopies in certain parts is reduced due to the influence of shadows, especially on the axial side of the canopies. Classification algorithm MLC (Figure 11b) classifies and differentiates the shadows much better than the olive tree canopies class. On the other hand, the algorithm recognizes less the differences between the olive class and other vegetation, i.e., it more often classifies olives under the other vegetation class. The RTC algorithm separates the olive canopies class accurately and shows the fewest shortcomings compared to others (Figure 11c). RTC differentiates less well the spatial distribution of classes within the study area. Namely, shadows are very poorly recognized, while surfaces covered with karst are very often classified under the soil class. But, since the object of research are olive tree canopies, RTC ultimately shows very good results.  
Figure 10: MS model with 10-9-8 bands layout.
## 3 Results and Discussion
### Effect of Organic Grinding Aids on the Grinding Property of Converter Steel Slag
#### 3.4.1 Effect of GA on the Sieving Residue of Steel Slag Powder  
As shown in Figure 9, it can be seen that the sieving residue of steel slag powder with GA is lower than that of the blank group (i.e., without any GA) after the same grinding time, indicating that ground steel slag with GA is smaller in particle size. The reducing effect of GA on the sieving residue is gradually enhanced with the increasing of grinding time before reaching 50 min grinding time. At the initial grinding period (before 20 min), the role of GA is small, but it becomes obvious after 20 min and reaches an optimum grinding role at 50 min grinding time.
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 2 Problem Description  
In this section, the problem description of MSOOP for LEO satellite constellations is detailed. The additional discussion on the design of satellite constellations will not be conducted in this paper. The homogeneous Walker-\(\delta\) constellation consisting of the same optical satellites is used as the studied case. The constellation configuration is described in Section 2.1; the mathematical formulation of MSOOP is described in Section 2.2; the constellation communication network topology is described in Section 2.3.
### Calculation Method of Tunnel Wall Displacement Based on the H-B Criterion
Under this assumption, the dilatancy coefficient \(\eta\) and the wall displacement \(u_{\rm w}\) in Equation (16) are as follows:  
\[u_{\rm w}=R_{0}-\sqrt{{R_{0}}^{2}+(1-\eta)(R_{\rm P}^{2}-R_{0}^{2})+(u_{\rm P} -2R_{\rm P})u_{\rm P}}. \tag{16}\]  
The meaning of the parameters in the equation is consistent with previous definitions, where \(E\) is the deformation modulus of rock mass, determined by Equation (9); \(\eta\) is the expansion coefficient of the rock mass; \(\mu\) is Poisson's ratio; \(R_{\rm P}\) is the radius of the plastic zone, determined by Equation (11); \(\sigma_{\rm R}\) is the radial stress on the elastic-plastic interface; \(u_{\rm P}\) is the displacement on the elastic-plastic interface; and \(u_{\rm w}\) is the displacement of the tunnel wall. Compared to other strength criteria, such as the Mohr-Coulomb criterion, the Hoek-Brown (H-B) criterion provides a more intricate representation of rock mass behavior by considering three key geological factors: lithology, rock mass quality, and in situ stress. This criterion captures the rock mass behavior under diverse stress conditions, employing parameters that reflect the hardness and fragmentation degree of the rock mass, offering a more nuanced understanding than the Mohr-Coulomb criterion, which relies solely on cohesion and the angle of internal friction [27; 28; 29].
## 3 Influence of PM and stator Arcs on Torque Production  
The \(\theta_{sp}\), \(\theta_{yp}\), \(\theta_{st}\), and \(\theta_{t}\) are important factors to impact the achievement of a better magnetic flux path and influence torque production. So, the influence of PM and stator arcs is investigated to objectively achieve a high average torque with low ripple torque in optimal multi-tooth HE-DSDPM. The optimization procedure can be explained by the flowchart, as indicated in Figure 2. Firstly, the initial design specifications and electrical constraints are parameterized to be constant values for structural optimization based on Table 1. Secondly, the \(\theta_{sp}\), \(\theta_{yp}\) and \(\theta_{t}\) are defined as possible variables and the \(\theta_{st}\) is calculated by the determined \(\theta_{sp}\) and \(N_{s}\) because they are interrelated. This optimization is based on a genetic algorithm (GA) to reach the objective of this investigation. Then, the boundary condition of these variable parameters is limited to reduce the structural distortion of the machine design. If the variable parameters are not appeared in the limited condition, they return the loop until the possible variable parameters are done. Finally, the 2-D FEA is utilised to calculate the torque production and then, investigate for optimal machine structure. The parametric model of multi-tooth HE-DSDPM is supportive of this consideration as indicated in Figure 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Initial Parameters** & **Units** & **Values** \\ \hline Outer stator radius, \(R_{so}\) & mm & 50 \\ Inner stator radius, \(R_{si}\) & mm & 30 \\ Stator teeth radius, \(R_{st}\) & mm & 34 \\ Outer rotor radius, \(R_{ro}\) & mm & 29.5 \\ Inner rotor radius, \(R_{ri}\) & mm & 10 \\ Rotor pole arcs, \(\theta_{r}\) & degrees & 12 \\ Stack length & mm & 50 \\ Steel type & - & Silicon steel 1010 \\ PM type & - & NdFeB—N35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial design specifications and electrical constraints of the multi-tooth HE-DSDPM.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Initial Parameters** & **Units** & **Values** \\ \hline Magnetic flux remanent & T & 1.23 \\ Armature current, \(I_{ac}\) & A & 8 \\ Current density & A/mm\({}^{2}\) & 2 \\ Slot opening PM arc, \(\theta_{sp}\) & degrees & 3–32 \\ Stator yoke PM arc, \(\theta_{sp}\) & degrees & 1–12 \\ Stator tip arc, \(\theta_{st}\) & degrees & 28–57 \\ Stator teeth arc, \(\theta_{t}\) & degrees & 13–30 \\ \hline \hline \end{tabular}
\begin{tabular}{l c} \hline \hline
**Set electrical constraints** \\ \hline Possible \(\theta_{\pi}\), \(\theta_{\pi}\), and \(\theta_{t}\) \\ \hline \hline \end{tabular}
\begin{tabular}{l c} \hline \hline
**Calculate** & \(\theta_{st}=\dfrac{360}{N_{s}}\cdot\theta_{sp}\) \\ \hline \hline \end{tabular}
\begin{tabular}{l c} \hline \hline
**Go** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: The flowchart of the optimization procedure.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 4 Results  
In this section, we compare the cluster holding time, cluster retention rate, message propagation rate, and overhead of the proposed clustering protocol compared to the existing CPB.  
CPB is one of the most effective solutions to date on V2X's biggest challenge, which is mobility, and regards mobility as the biggest issue in this document. Because this is a problem that we pay attention to, this paper focuses on the comparison with CPB.  
If the algorithms and protocols presented in this paper are superior to CPBs, it can be demonstrated that the solution to the autonomous V2X clustering solution using vehicle paths is more effective than the algorithms outlined so far.
## 1 Introduction
### Outline  
This paper studies the optimization of jitter mechanisms for route discovery of reactive routing protocols and is organized as follows: Section 2 presents the basics of reactive routing protocols, describes the jitter mechanism for flooding optimization described in RFC 5148 [9] and discusses the main drawbacks of the application of jitter in this form to reactive routing protocols. The paper then introduces and studies the \"delay inversion\" effect, a specific issue related to the use of standard jittering for route request flooding in reactive protocols. Section 3 describes this effect, proposes an alternative for the jitter distribution (the \"adaptive jitter\") and discusses some additional variations of this alternative. Section 4 compares analytically the behavior of the standard (uniform) jitter and the proposed window jitter with respect to the \"delay inversion\" effect and also examines analytically other side effects of jittering in flooding performance. This is complemented with an experimental performance comparison of the proposed techniques and standard jitter. The characteristics and algorithmic performance of the compared techniques and variants are studied in Section 5 by way of graph simulations. The performance impact of the different jittering techniques, when applied to reactive routing protocols, is studied in Section 6 by way of network simulations. Section 7 discusses the results presented throughout the paper and examines the performance trade-off illustrated in the comparison between the considered jitter techniques and variations. Finally, this paper is concluded in Section 8.
## 2 Materials and Methods  
### Reagents  
Gold nanoparticles (30-nm gold colloid) were purchased from BBI solutions (Cardiff, UK). Monoclonal mouse antihuman GAPDH, PARP, cleaved caspase-3, apoptosis-inducing factor (AIF), cytochrome C (cyt C), and \(\beta\)-actin antibodies were purchased from Santa Cruz Biotechnology (Dallas, TX, USA). The polyclonal rabbit antihuman PD-L1 antibody, 5% bovine serum albumin (BSA), and Nunc(tm) Glass Bottom Dish (diameter 40 mm) were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Dulbecco's modified Eagle's medium and F-12 (DMEM/F-12), penicillin/streptomycin (100 \(\upmu\)g/mL), and fetal bovine serum (FBS) were purchased from Gibco (Brooklyn, NY, USA). 11-mercapquondecanoic acid (11-MUA), ethyl-dimethylaminopropyl carbodiimide (EDC), N-hydroxy succinimide (NHS), sulforhodamine B (SRB) sodium salt, RIPA (radio-Immunoprecipitation assay) buffer, and Tris-buffered saline with 0.1% Tween(r)-20 detergent were purchased from Sigma-Aldrich (Seoul, Korea). The LIVE/DEAD(tm) Viability/Cytotoxicity Kit, Alexa flour 488 goat antirabbit IgG, and SYTO 13 were purchased from Invitrogen (Carlsbad, CA, USA). The Bio-Rad Protein Assay was purchased from Bio-Rad Laboratories (Hercules, CA, USA). Advanced enhanced chemiluminescence (ECL) Western blotting detection reagents were purchased from Merck Millipore (Darmstadt, Germany).