esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation
### Results and Discussion
#### 5.2.2 Structural Integrity Evaluation  
Post-experimental structural analysis assessed the durability of both contact geometries after exposure to ultra-high-pressure conditions of 87.5 MPa. Figure 17 presents microscopic images comparing the plunger tip geometries of rectangular and concave contact geometries after completion of the test sequence.  
The rectangular contact geometry exhibited significant visible deformation at the contact edge, with permanent plastic deformation extending from the edge toward the center of the contact surface. As shown in Figure 17a, material displacement and edge distortion are evident in the rectangular specimen, indicating localized stress concentration that exceeded the compressive strength of the PAI material.  
For the concave contact geometry specimens, precision measurements detected no quantifiable permanent deformation within the measurement system's resolution limits. As illustrated in Figure 17b, the optimized concave contact geometry maintained its original profile after high-pressure exposure, with no observable distortion or material displacement. Consequently, we compiled deformation data only for the rectangular contact geometry specimens.  
Figure 16: Measured sealing pressure for rectangular and concave contact geometry specimens.  
We conducted quantitative measurements of permanent deformation depth using a precision digital profile measurement system. Table 13 summarizes the deformation measurements for the rectangular contact geometry specimens, which exhibited an average permanent deformation depth of 0.0580 mm with a standard deviation of 0.007 mm at the contact edge.  
Figure 18 shows the digital profile measurement results for the Result #1 specimen of the rectangular contact geometry, where \(x\) = 0 represents the position of maximum deformation with a measured depth of 0.0568 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Performance Metrics** & **Result \#1** & **Result \#2** & **Result \#3** & **Result \#4** & **Result \#5** & **Mean \(\pm\) SD** \\ \hline Permanent Deformation Depth (mm) & 0.0568 & 0.0495 & 0.0640 & 0.0655 & 0.0530 & 0.0580 \(\pm\) 0.007 \\ \hline \end{tabular}
\end{table}
Table 13: Permanent deformation measurements for rectangular contact geometry specimens.  
Figure 17: Plunger tip geometry after high-pressure exposure: (**a**) rectangular contact geometry (Result #1); (**b**) concave contact geometry (Result #1).  
Figure 18: Digital profile measurement of permanent deformation for rectangular contact geometry specimen (Result #1).
## 1 Introduction
Therefore, our evaluation needs to generally identify the reasons for discrepancies and focus on those problems related to the land surface solar radiation partitioning scheme. Current remote sensing (RS) data, and the application of photosynthetically active radiation (PAR) sensors in flux tower networks, provide appropriate observations for evaluating the CLM4 land surface radiation partitioning scheme. Some satellite FPAR datasets have been produced based on various retrieval algorithms and radiative transfer assumptions [23, 29, 30]. Although they differ in absolute values, most remote sensing FPAR products are compatible with one another in seasonal cycle and spatial patterns [26, 28, 31]. The application of PAR sensors in a flux tower network enables evaluations of the FPAR diurnal cycle. PAR sensors provide highly accurate measurements of PAR, and have been put into service in many flux tower sites [22, 32, 33]. A systematic FPAR observation should consist of four parameters: incident PAR, reflected PAR, PAR transmitted through the canopy, and PAR reflected by the ground. Transmitted PAR should be measured by a group of PAR sensors because it is highly location-sensitive, while PAR reflected by the ground could be ignored if the soil reflectance is low. Such an integrated observing system exists at limited sites [32]. Hence, it is of great potential to use both satellite-based and site-level observations to investigate CLM4 performance regarding FPAR dynamics and spatial patterns. We have three objectives: (1) to evaluate CLM4 performance in representing FPAR dynamics and spatial patterns; (2) to identify the agreements and disagreements between CLM4 FPAR and observations; and (3) to understand the reasons for those differences, and focus on the problems related to the land surface solar radiation partitioning scheme. In Section 2 we present the data and methods. The comparisons regarding FPAR dynamics and spatial patterns are shown in Section 3. Specific findings and implementations are discussed in Section 4, with conclusions drawn in Section 5.
## 4 Experimental Detail
### Object Identification  
In the experimental work of the object identification task, 100 different 2D objects have been taken. Each object is having a unique spatial allocation of sensations, i.e., each object is a permutation and combination of different sensations. The object is shown in Figure 15, where each sensation is represented by a unique color. Similar sensations are present in different objects but at different locations.  
Figure 15: Object representation.  
The object identification is made through navigating of fingers over the object whose results are shown in the results section. Three sensors are placed on each finger, whereas one of the sensors is considered as a base sensor. At every movement, each sensor generates a grid code in a parallel way. Each sensor except the base sensor generates the grid code from the current grid code of base sensor and its relative position from the base sensor.  
This parallel exploration of any environment reduces the number of palm movements in learning and recognition as well.  
If the size of an individual sub-region in a 2D object is bigger, then multiple grid codes can be allocated to the same sensation. As a result of that, during the recognition by touch, the observed sensation from a sub-region can recall multiple grid codes. The multiple grid codes create ambiguity in making the comparison. The ambiguity problem can be solved using the following steps:  
1. Observe the sensation of the base sensor, and recall the grid code corresponds to the observed sensation.
2. Other sensors observe their sensations and recall their corresponding grid codes called sensed grid code.
3. Next, each sensor integrates their relative position of the base sensor with the grid codes of base sensors to generate new grid codes called path integrated grid code.
4. The next comparison will be made between the path integrated grid codes and the sensed grid codes. Those sensed grid codes found similar will be chosen to activate their associated place neurons.
5. The objects which are associated with the activated place neurons will be in the list of the active object, and others will be inhibited for future activation.
6. A similar process will be repeated again and again until a single object survives in the activation list.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 4 The Proposed Scheme
### Report Verification on the Base Station  
Upon receiving a signed report \(\{V,e,\sigma\}\), where \(V=\{CID,ID_{ch},L_{e},t_{e}\}\), the BS verifies the signed report and recovers the original event report \(M\) from the legitimate signed report as follows:  
1. First, it checks the freshness of the report with the help of \(t_{e}\).
2. Next, it checks that d \((L_{e},L_{ID_{ch}})\leq R_{s}\), where \(L_{ID_{ch}}\) is the location of \(v_{ID_{ch}}\) stored in the BS.
3. It then verifies the cluster signature in the report using the public key \(Q_{CID}\) of cluster \(CID\). It first computes \(d=H(e\mid V)\), \(R=\sigma P-dQ_{CID}\), and \(C=ENC^{-1}(X(R),e)\), then checks that the \(\{ID_{ch},L_{e}\}\) abstracted from \(C\) are the same as those in the report.  
When any check is incorrect, the report will be dropped immediately. Otherwise, the BS recovers the original event report \(M=\{CID,ID_{ch},E,L_{e},t_{e}\}\) from \(C\) and \(V\).
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 3 Hyperlipidemic Rabbit Models for the Development of Drugs
### HDL, Apo A-I, Apo A-I\({}_{Milano}\), and Apo A-I Mimetic Peptides
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rabbit**} & \multirow{2}{*}{**Drug**} & \multicolumn{4}{c}{**Plasma Lipids**} & \multirow{2}{*}{**Atherosclerosis**} & \multirow{2}{*}{**Reference**} \\ \cline{3-3} \cline{5-6}  & & **HDL-C** & & & & \\ \hline \multirow{4}{*}{JW (male)} & Dalcetrapib (100 mg/kg) & 1.3-fold \(\uparrow\) & & 6\% \(\downarrow\) & 12\% \(\uparrow\) & \\  & Dalcetrapib (300 mg/kg) & 1.9-fold \(\uparrow\)\({}^{*}\) & - & 21\% \(\downarrow\) & 3\% \(\uparrow\) & \\
0.25\% Chol diet & for 12 weeks & (12 weeks) & & (12 weeks) & (Aorta, & \\  & & & & & gross lesion) & \\ \hline \multirow{2}{*}{NZW (male)} & Toretragib & 3.6-fold \(\uparrow\) & & 28\% \(\uparrow\) & 59\% \(\downarrow\)\({}^{*}\) & \\  & (60–90 mg/kg) & (16 weeks) & - & (16 weeks) & (Aorta, & \\  & & & & & gross lesion) & \\ \hline \multirow{2}{*}{NZW (male)} & K-312 (10 mg/kg) & \(\approx\)4.7-fold \(\uparrow\)\({}^{*}\) & \(\approx\)33\% \(\uparrow\) & & \(\approx\)54\% \(\downarrow\)\({}^{*}\) & \\  & K-312 (30 mg/kg) & \(\approx\)5.1-fold \(\uparrow\)\({}^{*}\) & \(\approx\)18\% \(\uparrow\) & - & \(\approx\)55\% \(\downarrow\)\({}^{*}\) & \\ \multirow{2}{*}{0.25\% Chol diet} & for 18 weeks & (18 weeks) & (18 weeks) & & (Aorta, & \\  & & & & & gross lesion) & \\ \hline \multirow{2}{*}{JW, Japanese white; NZW, New Zealand white; Chol, cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol. Plasma lipids and atherosclerosis are expressed as \% or fold-change compared with those of control rabbits (\({}^{*}\)_p_ \(<\) 0.05).} & \multirow{2}{*}{Miyasawa} \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 4: _Cont._The apo A-I mimetic peptide, 18-amino acid peptide (18A), was originally designed as a model peptide of the class A amphipathic helix by [PERSON] in the 1980s [114]. 18A with PC formed a discoidal complex, which was structurally similar to apo A-I. The 18A-PC complex showed similar functions to apo A-I such as cellular cholesterol efflux, activation of lecithin-cholesterol acyltransferase activity, and anti-inflammatory effects. The structure of 18A was refined and derivative peptides were synthesized, including Ac-18A-NH\({}_{2}\), 4F, 5F, and Ac-hE18A-NH\({}_{2}\)[115]. To evaluate the effects of lipid metabolism and atherosclerosis, mimetic peptides were tested in rabbits [116; 117; 118]. [PERSON] et al. and [PERSON] et al. showed anti-atherosclerotic effects of the apo A-I mimetic peptide in cholesterol-fed [117] and WHHL rabbits [118].
## 4 Results and Discussion
### Parametric Studies
#### 4.2.4 Effect of Tensile Reinforcement Ratio  
Figure 16 shows the comparison of the load-displacement curves according to the change in the amount of tensile reinforcement in the RC beam strengthened with Fe-SMA strip. The solid and dotted lines in Figure 16 are the load-displacement curves of the specimen with and without the Fe-SMA strips, respectively.  
As the parametric variable, the amount of tensile reinforcement was determined by considering the minimum reinforcement ratio (\(\rho_{min}\)) in ACI 318 [27] and the maximum reinforcement ratio (\(\rho_{max}\)) which satisfies the minimum allowable strain for the flexural  
Figure 16: Comparison of load-displacement curves for the different amount of steel reinforcement.  
Figure 15: Effect of the number of Fe-SMA strips: (**a**) Comparison of load- displacement curves; (**b**) Comparison of load stages for the number of Fe-SMA strips.  
behavior of the RC beams. The minimum reinforcement ratio and maximum reinforcement ratio are calculated in Equations (5) to (7). The maximum reinforcement ratio is expressed with net tensile strain in extreme tensile steel when the concrete strain reaches 0.003 of the strain in the extreme compression fiber of the concrete in accordance with ACI 318 [27]. The amount of tensile reinforcement considered were 142.6 mm\({}^{2}\), 285.2 mm\({}^{2}\), 427.8 mm\({}^{2}\), and 570.4 mm\({}^{2}\). The initial crack load, yield load, and ultimate load of the two cases (un-strengthened and strengthened with the Fe-SMA strips) are summarized in Table 2.  
\[\rho_{min}=\max\left(\frac{1.4}{f_{y}},\ \frac{0.25\sqrt{f_{c}^{\prime}}}{f_{y}}\ \right) \tag{5}\]  
\[\rho_{max}=0.85\beta_{1}\frac{f_{c}^{\prime}}{f_{y}}\left(\frac{0.003}{0.003+2 \varepsilon_{y}}\right) \tag{6}\]  
\[\beta_{1}=0.65\leq 0.85-0.007(\beta_{1}-28)\ \leq\ 0.85 \tag{7}\]  
where, \(f_{y}\), \(\varepsilon_{y}\) represent the yield stress and yield strain of the steel reinforcement, respectively. \(\beta_{1}\) is the ratio of depth of equivalent rectangular stress block to depth of neutral axis.  
As shown in Table 2, the initial crack load slightly increased as the amount of reinforcement increased regardless of the installation of the Fe-SMA strip. In addition, the yield load and ultimate load increased by more than 300% in the un-strengthened cases and more than 200% in the strengthened cases when the amount of reinforcement increased from 142.6 mm\({}^{2}\) to 570.4 mm\({}^{2}\). It was observed that the flexural strength at the yield load and the ultimate load is governed by the steel reinforcement. However, as the amount of reinforcement increased, the ductility index decreased to a maximum of 69.65%, due to a decrease in vertical displacement at the ultimate load. At the ultimate load, the strengthening efficiency dropped from 85.92% to 20.14% when the amount of steel reinforcement increased from 142.66 mm\({}^{2}\) to 570.4 mm\({}^{2}\). This result shows that the NSM strengthening by the Fe-SMA strip is more effective for RC beams with a low reinforcement ratio.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
###### Abstract  
Vehicular Ad Hoc Networks (VANETs) employ multichannel to provide a variety of safety and non-safety applications, based on the IEEE 802.11p and IEEE 1609.4 protocols. The safety applications require timely and reliable transmissions, while the non-safety applications require efficient and high throughput. In the IEEE 1609.4 protocol, operating interval is divided into alternating Control Channel (CCH) interval and Service Channel (SCH) interval with an identical length. During the CCH interval, nodes transmit safety-related messages and control messages, and Enhanced Distributed Channel Access (EDCA) mechanism is employed to allow four Access Categories (ACs) within a station with different priorities according to their criticality for the vehicle's safety. During the SCH interval, the non-safety massages are transmitted. An analytical model is proposed in this paper to evaluate performance, reliability and efficiency of the IEEE 802.11p and IEEE 1609.4 protocols. The proposed model improves the existing work by taking serval aspects and the character of multichannel switching into design consideration. Extensive performance evaluations based on analysis and simulation help to validate the accuracy of the proposed model and analyze the capabilities and limitations of the IEEE 802.11p and IEEE 1609.4 protocols, and enhancement suggestions are given.  
Vehicular Ad Hoc Networks (VANETs); Multichannel Medium Access Control (MAC) protocol; IEEE 1609.4 standard; Markov chain; M/G/1 queuing model; reliability; efficiency
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
## Materials and Methods
### Transfersomes Obtention  
Transfersomes containing VDG (T + VDG) were prepared as in previous works [12]. The liposomes were formulated using a mixture of SPC and NaCl, to which VDG dissolved in methanol was added in a ratio of 40:7:1.4 \(w/w\) (SPC:NaChol:VDG). Briefly, the aforementioned mixture was combined with a chloroform: methanol solution in a 1:1 \(v/v\) ratio. The solvents were then evaporated using a rotary evaporator at 120 rpm in a flask until complete removal, to obtain a lipid film at the bottom of the flask. The lipid film was subsequently hydrated with 1 mL of Tris-HCl buffer containing 0.9% \(w/v\) NaCl at pH 7.4 (40 mg of SPC per ml of rehydration solution). Finally, the liposomal suspension underwent size and lamellarity reduction using an automatic extruder (Transferra, Burnaby, BC, Canada) connected to a nitrogen gas tank (N\({}_{2}\)). The liposomal suspension was passed through a 100 nm pore-size polycarbonate membrane five times to achieve size reduction and uniformity, rendering unilamellar transfersomes with a diameter of about 100-120 nm. Transfersomes without VDG (T) were prepared in the same way, but without the addition of the drug.
## 4 Results and Discussion
### Geostructural Interpretation
Similarly, in the last minor group, which is located in the northern exposure of the Kamila amphibolite, the fracture data at stations 22, 24, and 28 shows north-northeast-south-southwest and northwest-southeast directed thrusting dominance, while the data at stations 25, 26, 29, and 30 show strike-slip dominance, and the data at stations 23 and 27 shows extensional dominance (Figures 1 and 7). Figure 7: Stereographic projection of conjugate fracture data of 30 stations from Besham to Dasu along the KKH. Strike-slip stresses are dominant from stations **2–7**. Stations **8–30** are further divided into minor groups, depicting a dominant compression followed by strike-slip and extensional stresses. Our stress analysis based on stereographic projections shows that compressive stresses (thrusting) due to convergence are dominant, followed by strike-slip and extensional stresses. The strike-slip impact is due to the eastward syntaxial bend, termed as Nanga Parbat syntaxis, whereas the Indus syntaxis influenced stations 1 to 7, which also show dominant strike-slip stresses. Similarly, the extensional stress dominance is connected with the crustal stacking and collapse in the region, although they are determined to be low in proportion.
## 4 Results and Discussion
### Comparison of the Main Effects
#### 4.1.2 Main Effects of Factors on Packet Loss Ratio  
The figure below (Figure 4) contrasts the main effects of factors on the AODV packet loss ratio (PLR) obtained from the three DOE methods. It is clear that the normalized effects of the FFM cope perfectly with those from the TM, while the values of normalized effects given by the PBM present quite small differences.  
Figure 3: Comparison of main effects on throughput (TH) from FFM, PBM and TM.  
The trends of the main effects for factors ND, BKH and WMH from the FFM and TM are closer to those obtained from the PBM. They all have a positive effect on the packet loss ratio. The difference lies in the effect of the factor NC. We note that it has a slight positive effect from the PBM and TM. This effect, however, is not highly important in the FFM. These outcomes point out that in scaled-up networks as well as when the network flows go up, the AODV packet loss ratio increases correspondingly. Moreover, the black hole and the wormhole attacks impact considerably the AODV performance and disrupt the correct execution of the routing operations.  
Briefly, both the FFM and the TM produce closer results. These results show small differences with the PBM. We conclude that they all allow efficient estimates of the main effects within the defined ranges.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 1-BiSGRU-GA
#### 4.1.3 Evaluation Index  
The evaluation indexes used in the experiment include accuracy, precision, recall, F-measure, false positives (FPs), and false negatives (FNs). F-measure is a comprehensive evaluation index based on accuracy and recall. FP represents the number of false positives in the exception log, that is, it is actually abnormal and predicted to be normal. \(FN\) represents the number of false positives in the normal log, that is, it is actually normal and predicted to be abnormal. TP represents the correct predicted number of normal logs, i.e., true positive. \(TN\) represents the number of abnormal logs correctly predicted, i.e., true negative. Accuracy indicates the percentage of the number of normal logs and abnormal logs accurately classified in the total number of logs; the accuracy calculation formula is as shown in Equation (23):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{23}\]  
Figure 10: Application process of knowledge graph.  
Precision indicates the proportion of the number of detected true exception logs in the number of all detected exception logs, the calculation formula of precision is as shown in Equation (24):  
\[Precision=\frac{TP}{TP+FP} \tag{24}\]  
Recall indicates the percentage of the number of detected exception logs in the total number of exception logs in the data set, the calculation formula of the recall rate is as shown in Equation (25):  
\[Recall=\frac{TP}{TP+FN} \tag{25}\]  
The calculation formula of F-measure is as shown in Equation (26):  
\[\text{F-measure}=\frac{2*Precision*Recall}{Precision+Recall} \tag{26}\]
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification  
In this section, we present a detailed classification of reinforcement learning-based routing algorithms in flying ad hoc networks. This classification consists of three groups:  
* Based on the reinforcement learning algorithm;
* Based on the routing algorithm;
* Based on the data dissemination process.  
Figure 6 displays the proposed classification.
# Implementation Aspects in Regularized Structural Equation Models
## 3 Research Questions
### RQ1: Fixed or Estimated Regularization Parameter \(\lambda\)?  
In the first research question, RQ1, we consider the choice of the regularization parameter \(\lambda\) regarding statistically efficient parameter estimation if structural parameters, such as factor means or factor correlations, are the primary analytical focus. We study whether an optimal regularization parameter is obtained by information criteria or a pre-chosen regularization parameter. Using only a fixed value of the regularization parameter instead of estimating the regularized SEM on a sequence of regularization parameters would decrease the computational burden of the estimation.