esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Assessment of Cytotoxicity According to ISO: 10993-5: 2009  
#### 3.1.1 Quantitative Evaluation  
The data presented represent the average of three repetitions of LBMs cytotoxicity assays using the SRB method, and the error bars indicate the standard deviation (Figure 2). The standard deviation remains with little dispersion in all samples and the cytotoxic effect is found in acceptable limits. If the viability is reduced to \(<\)70% of the control; it had a cytotoxic potential [22], with the exception of the SF30 membrane, with a cytotoxicity of 64.64%. All other samples showed values higher than 70% (Table 3).  
Figure 3: Stereotypical micrographs of the different fibroblast culture media evaluations of all LBMs: 0 control group, **(a)** FC30, **(b)** FC50, **(c)** FC70, **(d)** FL30, **(e)** FL50, and **(f)** FL70. Bar 100 \(\upmu\)m.  
Figure 2: Average cytotoxic evaluation of LBMs in human fibroblast culture medium during the three cytotoxicity evaluations. Associated with the micrographs of Figure 3, **(***)** values with different sets are significantly different at \(p\)-value \(<\) 0.05 in the bilateral equality test for column means. The tests assumed equal variances.  
The different membranes showed a concentration-response relationship since cytotoxicity increased gradually with increasing concentration of the fibrous material. The result of the one-way ANOVA test shows (Table 3) that there is no significant difference between the cell viability and the different treatment conformations at probability value (\(p>0.05\)).  
Cytotoxicity evaluations performed in LBMs, for both LF and SF, at different concentrations were shown to have mild cytotoxic activity [22]. Using an average of the three evaluations of all the LBMs, an acceptable behavior of obtained results according to ISO: 10993-5:2009 is observed. A cytotoxic viability behavior of 64.64 and 77.65%, respectively, is observed in the sieved dressings FC30 and FL50. However, the SF70 sample reaches 93.52% cell viability--the lower the agavaceous load, the lower cell viability. On the other hand, the LBMs without screening have a downward behavior with FL30, FL50, and FL70, showing a cell viability of 93.18, 85.66, and 71.09%, respectively. The lower the agavaceous load, the greater the cell viability, which could indicate that the screening process could reduce the concentration of some types of toxic contaminant (Figure 2).  
Larger particles have a smaller surface area and lower porosity among the particles, in contrast to small particles that have a larger specific surface area with less porosity within it [39]. Since LBMs have a greater specific area with screened fibers, because of the particle size, they are provided with a better distribution, which leads to better cell growth.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Simulation of the Channel Clustering Algorithm  
In this section, we combine the physical factors that affect the state of the wireless channel with the channel bandwidth to form a training sample. The physical factors in the wireless channel are represented by the signal-to-noise ratio (SNR), and the value range is [0, 4.5] dB. The transmission bandwidth is [1000, 8000] KHz. The training sample data set consists of a data set that conforms to a  
Figure 17: FER simulation result.  
Figure 18: Average iterations simulation result at R = 0.5.  
Gaussian distribution and contains 2000 data points. The k-means++ algorithm is used to cluster data, and \(k=3\).The clustering result is shown in Figure 10.  
The data points with different symbols in Figure 19 indicate different categories. It can be seen that a series of unordered channel data is divided into three categories by the clustering algorithm.
# CNN Based Image Classification of Malicious UAVs
## 3 Results and Discussion
### Training Time Analysis  
Table 7 illustrates the mean and median training times for the various models when using the hyperparameters specified in Table 2. The model training was conducted with PyTorch scripts running on Google Colab with Graphics Processing Unit (GPU) acceleration. As Google Colab is a shared cloud-based service, service levels can change over time and so the values represented in Table 7 are subject to such variations. However, it can be clearly seen that VGG-16 is the slowest model to train, while AlexNet and MobileNet v2 are the quickest to train.
## 3 Fuzzy Fault Diagnosis Method
### Judgment Principle for Fault Diagnosis  
Based on the above fuzzy signal processing algorithm, two specific judgment principles on fault diagnosis are given as follows:  
1. Maximum method  
Combined with the principle that a certain type of fault has the greatest probability, that type of fault will occur. A fault diagnosis method is given, which is called the maximum fault identification method. Suppose \(A_{i}\in F(U)\;(i=1,2,\cdots,n)\), denoted as \(A_{L}=\underline{A}\), \(A_{U}=\overline{A}\), for \(u_{0}\in U\). If there is \(i_{0}\), Equation (14) is established.  
\[\left\{\begin{array}{l}\underline{A_{i_{0}}}(u_{0})=\max\{\underline{A_{1}} (u_{0}),\underline{A_{2}}(u_{0}),\cdots,\underline{A_{n}}(u_{0})\}\\ \overline{A_{i_{0}}}(u_{0})=\max\{\overline{A_{1}}(u_{0}),\overline{A_{2}}(u_ {0}),\cdots,\overline{A_{n}}(u_{0})\}\end{array}\right. \tag{14}\]  
where, \(A_{i_{c}}(u_{0})=\left\langle\underline{A_{i_{0}}}(u_{0}),\overline{A_{i_{0}}} (u_{0})\right\rangle\), it is considered that \(u_{0}\) is relatively close to fault \(A_{i_{0}}\). That is to say, \(u_{0}\) and \(A_{i_{0}}\) are the same type of fault.  
2. Minimum difference method  
Combining the principle that the difference between a certain type of fault and the actual fault characteristics is the smallest, the type of fault occurs. A fault diagnosis method is given, which is called the minimum difference fault identification method. Suppose fuzzy signal set \(X=\left\langle X_{L},X_{U}\right\rangle\), fault set \(A_{i},B\in F(U)\) (\(i=1,2,\cdots,n\)). If there is \(i_{0}\), Equation (15) is established.  
\[\left\{\begin{array}{l}N(\underline{A}_{i_{0}},B)=\max\{N(\underline{A}_{1},B),N(\underline{A}_{2},B),\cdots,N(\underline{A}_{n},B)\}\\ N(\overline{A}_{i_{0}},B)=\max\{N(\overline{A}_{1},B),N(\overline{A}_{2},B), \cdots,N(\overline{A}_{n},B)\}\end{array}\right. \tag{15}\]  
where \(N(A_{i_{0}},B)=\left\langle N(\underline{A}_{i_{0}},B),N(\overline{A}_{i_{0} },B)\right\rangle\), it is considered that the difference between \(B\) and \(A_{i_{0}}\) is the smallest. That is to say, \(B\) and \(A_{i_{0}}\) are the same type of fault.
## 4 Discussion  
According to the findings of the property tests, biodiesel have lower calorific values than diesel and lower kinematic viscosity, density, and cetane number. The energy-exergy study revealed that when the volume fraction of biodiesel fuel increases relative to diesel, the heat loss via cooling water increases. This is because biodiesel fuel has a higher oxygen  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & \
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} & **df** & **Mean Square** & **F-Value** & _p-Value_ \\ \hline Model & 25.17 & 9 & 2.8 & 41,217.33 & \(<\)0.0001 & significant \\ A-load & 1.6 & 1 & 1.6 & 23,536.72 & \(<\)0.0001 & \\ B-Blendend & 0.0056 & 1 & 0.0056 & 82.34 & \(<\)0.0001 \\ AB & 0.0049 & 1 & 0.0049 & 72.39 & \(<\)0.0001 & \\ A\({}^{2}\) & 0.037 & 1 & 0.037 & 545.32 & \(<\)0.0001 & \\ B\({}^{2}\) & 0.0013 & 1 & 0.0013 & 19.1 & 0.0006 & \\ A\({}^{2}\)B & 0.0033 & 1 & 0.0033 & 48.13 & \(<\)0.0001 & \\ AB\({}^{2}\) & 0.0007 & 1 & 0.0007 & 10.44 & 0.006 & \\ A\({}^{3}\) & 0.0011 & 1 & 0.0011 & 16.94 & 0.001 & \\ B\({}^{3}\) & 0.0003 & 1 & 0.0003 & 4.15 & 0.0611 & \\ Residual & 0.0009 & 14 & 0.0001 & & & \\ \hline Lack of Fit & 0.0003 & 13 & 0 & 0.0397 & 0.9998 &
\begin{tabular}{c} not \\ significant \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical analysis (ANOVA) for Brake power.  
Figure 14: The variation of the sustainability index.  
concentration, which causes better combustion, a rise in cylinder temperature and an increase in heat loss via cooling water.  
The primary reason for the system's inefficiency is the exergy being destroyed by irreversible processes, mostly burning. Other factors which affect the exergy destruction include energy losses from heat transfer and exhaust gas energy losses. By devoting further research, we may be able to determine how the fuel energy can be utilized by the engine more efficiently based on these variables.
## 2 Materials and Methods
### Feature Selection through Evaluating the In-Class and Among-Class Variance  
Execution of the classifier highly depends on the discriminative strength of the extracted features [40]. The performance of the classifier could be significantly improved by assessing the in-class and among-class variance of the training dataset. We divide the entire dataset into two groups i.e., training and testing sets. Let the dataset have \(k\) classes and \(N_{tr}^{c}\) be the number of training samples for each class \(c\); in total, we create \(k\times N_{tr}^{c}\) training images. Extract the invariant features of PCET, ZM, PCT, and RM at some maximum order of moments \(M_{max}\). Thereafter, construct a training set matrix, \(F_{tr}^{c}\), \(n,\ell\in 0,1,\ldots M_{max}\) and \(c\in 0,1,\ldots k\) for all classes and all training images and arranged in a matrix form. The mean value of the moments of each class, \(\mu^{c}_{n\ell}\) is defined as  
\[\mu^{c}_{n\ell}=\frac{1}{N^{c}_{tr}}\sum_{t\,=\,1}^{N^{c}_{tr}}F^{c}_{n\ell}(t) \tag{8}\]  
For the sake of discriminative capability of the moment's in-class, we compute the variance of each moment's in-class as follows:  
\[V^{c}_{n\ell}=\frac{1}{k}\sum_{c\,=\,1}^{k}\sum_{t\,=\,1}^{N^{c}_{tr}}\left|F^ {c}_{n\ell}(t)-\mu^{c}_{n\ell}\,\right|^{2} \tag{9}\]  
In similar manners, the total variance of all training samples is calculated by  
\[V^{T}_{n\ell}=\sum_{c\,=\,1}^{k}\sum_{t\,=\,1}^{N^{c}_{tr}}\left|F^{c}_{n\ell} (t)-\mu^{T}_{n\ell}\,\right|^{2} \tag{10}\]  
where, \(\mu^{T}_{n\ell}\) is the mean value of the moments of the complete training samples and given as,  
\[\mu^{T}_{n\ell}=\frac{1}{k\times N^{c}_{tr}}\sum_{c\,=\,1}^{k}\sum_{t\,=\,1}^ {N^{c}_{tr}}F^{c}_{n\ell}(t) \tag{11}\]  
Finally, we define the discriminative power of PHTs of the order \(n\) and repetition \(\ell\):  
\[DP_{n\ell}=\frac{V^{T}_{n\ell}}{V^{c}_{n\ell}} \tag{12}\]  
It could be observed from Equation (11) that a high value of \(DP_{n\ell}\) results in small in-class variations concerning the total variations. This means that the higher value of \(DP_{n\ell}\) will lead to higher discrimination ability of the interrelated moments. Therefore, it could be anticipated that feature selection using \(DP_{n\ell}\) attain the higher accuracy of the proposed classifier compared to the traditional approaches. All the feature vectors are arranged in descending order, and we select only those moments PHTs and RM which have the highest \(DP_{n\ell}\).
## 4 Discussion
### Validity  
Both the IMUs and PSW detected changes in FPA with each gait modification strategy implemented. The mean difference between the two measurement systems in baseline conditions was negligible (0.33\({}^{\circ}\)). IMUs seemed to amplify the \(\Delta\)FPA compared to the PSW for the gait modification strategy conditions by 15-30%. A significant difference of 2.7 degrees between the systems was found when performing the gait modification strategies. A similar study found a comparable difference in FPA of 2.6 degrees between a foot-worn inertial sensor and a motion capture system [20]. It is unclear whether the location of the IMU in detecting deformation of the foot (when loading in extreme positions) could have caused a varus or valgus effect on the FPA. \(\Delta\)FPA amplification could also have been caused by sensor movement, since the lumbar and foot sensors were placed above clothing and shoes. Sensors were secured as firmly as possible, but some unwanted movement may have been possible. When using the systems interchangeably, which is not recommended, one should apply a correction for FPA for toe-out and toe-in gait.  
The Bland-Altman plot for baseline gait indicates a good agreement between the two measurement systems. The other two Bland-Altman plots (toe-out gait and toe-in gait) show a proportional bias. The IMUs amplified the values (more negative with toe-in gait and more positive with toe-out gait) compared to the PSW values. This can be seen in the scatterplot with all gait types combined in Figure 2. The regression line shows a positive slope and predicts the difference between the systems significantly well (\(B=17,\ p<0.001\)). The R\({}^{2}\) value shows that 23% of the total variation in the difference between the systems can be explained by the mean between the systems. The limits of agreement of the Bland-Altman plots stayed proportionate, the variability consistent, and with negligible outliers, suggesting good validity during implementation of gait modification strategies.  
A similar study, comparing spatiotemporal gait parameters between the Opal sensors and the GAITRite(r), found a comparable trend, where the Opal sensors consistently overestimated the FPA measurements compared to the GAITRite(r) values, which increased as the gait variability increased [21]. Another study, comparing another wearable IMU system to 3D motion capture found an ICC of 0.94 between the two systems, which is higher than the ICC found in this study (ICC = 0.87) [7]. However, the other study used target FPAs with visual feedback, which would reduce the amount of individual gait variation.
## 5 Simulation Results
### Turn-Off Analysis  
Although the proposed technique is effective in damping the turn on oscillation, an increase in the source inductance may lead to the self turn-on of \(T_{\text{HS}}\) during its turn off. The circuit shown in Figure 7 was simulated to deep such an aspect, resulting in the \(v_{\text{CS}}\) voltages shown in Figure 10a with \(R_{\text{g,off}}=0\,\Omega\) and \(L_{\text{s}}=0,10,20,30,40,50\,\text{nH}\). It is worth noticing that the \(v_{\text{CS}}\) increases once \(T_{\text{HS}}\) is off as the \(di_{\text{sw}}/dt<0\) causes the \(V_{\text{L_{s}}}\) to be negative, thus, if the voltage across the gate inductance is neglected, it is  
Figure 9: Switching energy related to \(T_{\text{HS}}\) during its turn-on (dashed line) and the maximum ratio between the \(v_{\text{sw}}\) and the ideal trapezoidal waveform spectra.  
\[v_{\text{GS}}=R_{\text{g,off}i_{\text{g}}}-L_{\text{s}}\frac{di_{\text{sw}}}{dt}. \tag{8}\]  
If \(v_{\text{GS}}\) is higher than the transistor threshold, the self-turn on phenomenon occurs. As far as the considered circuit is concerned, \(V_{\text{th,min}}=2.5\,\text{V}\), thus the source inductance should be lower than 30 nH, resulting in the \(L_{\text{s}}\) optimal value not to be sufficiently high to cause such an issue. However, if the source value found from the optimization method would have been higher, (8) states that the voltage drop across the gate resistance counteracts the effect of the source inductance, meaning that the gate resistance at the turn-off can be increased to avoid the self turn-on. More precisely, if \(R_{\text{g,off}}\) is increased up to 10 \(\Omega\), the corresponding \(v_{\text{GS}}\) voltages are shown in Figure 9(b). In addition to the presence of the Miller's plateau, it can be noticed that no self turn-on event occurs even in the case of \(L_{\text{s}}=50\,\text{nH}\), however, the power dissipation during the turn-off of the high-side transistor increases.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 2 System Model
### Channel Model  
Consider a time-varying channel characterized by \(P\) propagation paths. For the \(p\)th path, \(h_{p}\), \(\tau_{p}\), and \(v_{p}\) represent the channel gain, delay, and Doppler shift, respectively. The DD domain-channel response can be written as\[h(\tau,\
u)=\sum_{p=1}^{p}h_{p}\delta\big{(}\tau-\tau_{p}\big{)}\delta\big{(}\
u- \
u_{p}\big{)}, \tag{6}\]  
where \(\delta(\cdot)\) is [PERSON]'s delta function. The delay and Doppler shift are given by  
\[\tau_{p}=\frac{l_{p}}{M\Delta f},\quad\
u_{p}=\frac{k_{p}+\kappa_{p}}{NT}, \tag{7}\]  
where the delay indices \(l_{p}\) and Doppler shift indices \(k_{p}\) are integers and \(\kappa_{p}\in(-1/2,1/2]\) represents the fractional part of the Doppler shift.
## 4 Results and Discussion
### Transmission Range  
In our simulation, nodes transmit messages by using Bluetooth. In an environment with obstacles, the maximum transmission distance of Bluetooth is about 10 m. If we denote the transmission distance as \(r\) meters, the transmission range is a circle with a radius of \(r\) meters. Thus, we ran the simulation program to evaluate how our routing protocol performs under different transmission ranges. In our evaluation, we change the transmission radius varying from 4 m to 10 m, and we ran 20 times with each transmission radius for each protocol. Meanwhile, there are 126 nodes in the whole network, and we assign a buffer space of 5 M to pedestrians and automobiles, 50 M to trans.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Scene} & Simulated City & Helsinki city \\  & Simulation Time & 24 h \\  & Simulation Area & 4500 m \(\times\) 3400 m \\ \hline \multirow{3}{*}{Messages} & Messages TTL & 5 h \\  & Messages Size & 500 KB–1 MB \\  & Generating Interval & 25–35 s \\ \hline \multirow{3}{*}{Nodes} & Grouping & Pedestrians & Automobiles \\  & & ShortestPath & ShortestPath \\  & Movement Model & MapBased & MapBased \\  & Movement & Movement & Movement \\  & Transmission Interface & & Bluetooth \\  & Moving Speed & 0.5–1.5 m/s & 2.7–13.9 m/s \\ \hline Bluetooth & Transmit Speed & 250 KB/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common parameters for the simulation.  
Figure 3 shows the performance of three protocols under different transmission ranges. Figure 3a-c depicts that, even in a relatively short transmission range, Scheduling-PROPHT still gains better performance with a higher delivery rate, lower overhead and lower packet drop rate than others. This implies that our protocol can keep better transmission efficiency than others, with less cost.
## 5 Security Requirements
### Information Security Requirements  
**Integrity:** This property allows an IoT device to verify the integrity of received messages. The integrity of in-transit and stored data can be compromised by modification and fabrication. Smart devices should be able to identify such altered requests and should not process them.  
**Information Protection:** This property protects the privacy and confidentiality of the stored and exchanged information. Smart devices should avoid sending messages in plain text. These devices should adopt encryption techniques to preserve the confidentiality of the sensor information. Smart devices should implement policies that limit access to information and disclose to trusted parties to protect information privacy. As such, malicious entities will be prevented from learning security-critical information. For instance, there may be policies defined for an IoT network that do not allow a smart node to share sensor information with the neighboring nodes (devices that share the same radio signal).  
Figure 14: Complexity parameters for security solutions.  
**Anonymity:** This security property protects the privacy and confidentiality of the identity and locations of a smart device. Anonymity enables IoT devices to hide their identifiers and locations when they provide sensor information to users, clouds, and other devices. In a smart city, roadside sensors send traffic updates to cars, pedestrians, traffic lights, and clouds periodically. Anonymity ensures that the identity of the sensors and their deployment locations are not revealed when the sensors provide traffic information to smart cars and drivers.  
**Non-Repudiation:** This property prevents parties that are involved in a communication, such as devices, gateways, clouds, and users, from denying their participation in exchanging messages. A medical sensor cannot deny sending a patient's health conditions that it has previously sent.  
**Message Freshness:** This security property ensures that IoT service consumers receive the most recent sensor data. Smart devices are expected to provide real-time information on IoT environments. This property enables a service consumer to validate that received messages are most recent and not re-played. For instance, medical IoT services located in the cloud should be able to verify whether a medical sensor sends real-time updates on the patient's health conditions.
# A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate
## 4 Formulation of Crisp Model  
Here, we considered an inventory system of fishery culture. The fishery culture in a lake was mainly assumed for our proposed model. Here, for simplification of our study, all stages of fishery culture were not considered. We considered only from the stage where the fingerlings are brought from the market and fed to reach the ideal size for customer demand. Additional assumptions of the proposed model are given below.  
Figure 6: Multiplication of two parabolic dense fuzzy numbers.  
1. We considered an additional cost for feeding the growing items.
2. Feeding cost depended on the weight of the items.
3. The growth function was approximated by a linear function.
4. We considered an idle cost for the growth period and cleaning period.  
A schematic overview of the proposed model is given in Figure 7.  
In Figure 8, \(y\) is the number of purchased items and \(g\) is the growth rate per fish per unit time. The initial approximate weight of each fingerling is \(v_{0}\) and final approximate weight (at the time of selling) of each fish is \(v_{1}\). At the beginning, \(t_{1}\) is the time required to clean the pond, and then the fingerlings are grown up during the time \(t_{2}\). After that, the fish are sold on the market during time \(t_{3}\). According to this consideration, the following equations are obtained:  
The total initial weight of fingerlings is \(q_{0}=yv_{0}\). (13)  
The total final weight of the fishes is\(q_{1}=yv_{1}\). (14)  
\[t_{2}=\frac{q_{1}-q_{0}}{yg}=\frac{v_{1}-v_{0}}{g}. \tag{15}\]  
\[t_{3}=\frac{q_{1}}{d}=\frac{yv_{1}}{d}. \tag{16}\]  
Cycle time \(T=t_{1}+t_{2}+t_{3}\). (17)  
Figure 7: Schematic overview of the proposed model.  
The main objective of this proposed model is to minimize the total cost (Z) per cycle, which includes the purchasing cost, holding cost, food procurement cost (FPC), operational cost (OC), and natural idle cost (NIC).  
\[Z=\text{purchasing}\,\cos\text{t}+\,\text{holding}\,\cos\text{t}+\,\text{ food procurement}\,\cos\text{t}+\,\text{operational}\,\cos\text{t}+\,\text{natural}\,\cos\text{t}, \tag{18}\]  
where purchasing \(\text{cost}=syv_{0}=\frac{sv_{0}dt_{1}}{v_{1}}\), holding cost \(=c_{2}T\), food procurement cost \(=\frac{ft_{2}(q_{1}-q_{0})}{2}=\frac{fdq_{3}(v_{1}-v_{0})^{2}}{2 gv_{1}}\), natural idle \(\cos\text{t}=n_{c}(t_{1}+t_{2})\), and operational \(\cos\text{t}=B\).  
Putting these cost values into Equation (18) yields the total inventory cost per cycle as follows:  
\[Z=\frac{sv_{0}dt_{2}}{v_{1}}+c_{2}T+\frac{ft_{3}(v_{1}-v_{0})^{2}}{2 gv_{1}}+n _{c}(t_{1}+t_{2})+B,\] \[=d\left\{\frac{sv_{0}t_{3}}{v_{1}}+\frac{ft_{3}(v_{1}-v_{0})^{2}} {2 gv_{1}}\right\}+\{c_{2}T+n_{c}(t_{1}+t_{2})+B\}, \tag{19}\]  
where,  
\[\left\{\begin{array}{c}\varphi=\frac{sv_{0}t_{3}}{v_{1}}+\frac{ft_{3}(v_{1} -v_{0})^{2}}{2 gv_{1}}\\ \psi=c_{2}T+n_{c}(t_{1}+t_{2})+B\end{array}\right. \tag{20}\]  
Thus, the crisp problem is given by  
\[Minimize\,\,Z=d\varphi+\psi, \tag{21}\]  
subject to the conditions in Equations (13)-(17) and (20).