esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Isomerization
[17], it is possible to determine the regions dominated by kinetics and chemical equilibrium through a detailed analysis based on temperature and the sum of the component flows for each series (C\({}_{4}\), C\({}_{5}\), and C\({}_{6}\)), compared with equilibrium compositions. In the case of isomerization, based on plant data reported by [PERSON] et al. [15], [PERSON] et al. [17] establish that, for the C\({}_{6}\) series, the chemical equilibrium region is reached in the first reactor at 162.8 \({}^{\circ}\)C and in the third reactor at 146.4 \({}^{\circ}\)C, while in the second reactor, chemical equilibrium predominates. Figure 3: Temperature profile along the three reactors of the isomerization process. Figure 2: Molar flow profiles in the three reactors for Schemes 1-2 (a), 3-4 (b), 5-6 (c), 7-8 (d) –8 of the C\({}_{6}\) series. In Figure 4, which presents the analysis for the components n-C6, 3-MP, and 2,2-DMB from Scheme 4, the results of this study are shown to be consistent with previous findings. The kinetic region predominates in the first and third reactors up to approximately 162 \({}^{\circ}\)C (Figure 4a) and 146.5 \({}^{\circ}\)C (Figure 4c), respectively, while the second reactor remains in equilibrium (Figure 4b). Figure 4: Mole fraction of the C\({}_{6}\) series C\({}_{6}\) (**c**) \(C_{6,i}/\sum C_{6,k}\) as a function of temperature in the first (**a**), second (**b**), and third (**c**) reactors of scheme 4. It is important to emphasize that the model proposed by [PERSON] et al. [17] is based on three reversible reactions, demonstrating that any reaction scheme that appropriately incorporates equilibrium information allows for a precise and consistent analysis of the process. The reaction extent profiles for each reaction in the C\({}_{6}\) series vary depending on the specific scheme studied. However, as observed in the molar flow profiles, all schemes lead to the same result, despite differences in the extent profiles. In contrast, for the reactions in the C\({}_{4}\) and C\({}_{5}\) series, as well as for the benzene saturation and cracking reactions, the reaction extent profiles show no variations between the schemes.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### The Ranging and Altimeter  
The concept of laser altimetry is straightforward. It involves measuring the time of flight for a laser pulse to travel to a surface and reflect back to the sensor. Equation (2) determines the distance \(z\) from the laser to the surface [99].  
\[z=\frac{c\Delta T}{2} \tag{2}\]  
where \(z\)[m] is the distance from the laser to the surface. \(T\)[s] is the time of flight for a laser pulse to travel to a reflective surface and back, and \(c\)[m/s] is the speed of light in a vacuum = 299,792,458 m/s. The calculated distance, combined with the satellite attitude platform position, leads to the precise three-dimensional spatial coordinates of the laser footprint point.  
When combined with the satellite's geolocation and orientation data, this range information enables the derivation of highly accurate three-dimensional coordinates of the laser footprint on the Earth's surface. Conventional spaceborne laser altimeters typically operate using high-energy pulses--ranging from 20 to 50 millijoules--and a low pulse repetition frequency (PRF) between 1 and 10 Hz [99].
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 5 Discussion  
High regional inequalities are often brought about by concentrated economic activity and growth in one specific area. Urban regions are often where economic activity is concentrated. In the end, inequities develop in every subject, including the realm of health. The hospital is often constructed in metropolitan areas as a basic health treatment facility. It seems understandable that a hospital would be built in this metropolitan location. The main motivation is to guarantee that community access is made simpler due to the availability of improved mobility options, including infrastructure and public transit. Every healthcare system has adopted equal access to hospitals, patient happiness, and respect for patient wishes as fundamental principles. Health planners and policymakers primarily focus on disentangling or downgrading inequities in healthcare consumption. This has to be done as part of the endeavor to raise the performance indicator for the healthcare system [47; 51].  
Further, the COVID-19 pandemic gave us a peek at how the healthcare business will continue to grow using cutting-edge technologies. It also highlighted the significance of remotely monitoring and treating patients. It is anticipated that IoT, 5G, and cloud computing will play a crucial role in the future of healthcare by linking everything from wearables to emergency services, medical campuses, and hospitals [52]. By lowering latency, enhancing dependability, and boosting security, new healthcare use cases will be enabled and will profit from the availability of these innovative technologies.  
This study makes use of IoT, 5G, cloud, and AI technologies, which have the potential to be very beneficial. After examining how these technologies are used and how they enhance healthcare, this article offers patients, particularly those who reside in rural locations with few healthcare options, a remote healthcare alternative. The study aims to provide distant patients with the best care possible while adhering to specific limitations. The suggested strategy assumes that the patient and doctor are in constant communication. IoT sensors affixed to the patient's body gather his vital signs. If there is any irregularity in the vitals, the smart application on the patient's smartphone quickly sends data through 5G to the cloud [53; 54]. The doctor accesses patients' information from the cloud and evaluates their vitals. If the vital signs are somewhat out of the ordinary, the doctor will prescribe medicine and deliver it to the patient through the cloud, 5G, and a smartphone app. If the doctor determines that the severity of the vitals is high, he connects to the healthcare facilities using telehealth technology and sends the vitals to the appropriate healthcare division. In this manner, patients get prompt medical attention, even in rural areas. A real-life case study and mathematical modeling were used to assess the proposed system. The findings imply that cutting-edge contemporary technologies help enhance healthcare facilities.
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
###### Abstract  
The effects of bulk flow pulsations on film cooling in gas turbine blades were investigated by conducting large eddy simulation (LES) and Reynolds-averaged Navier-Stokes simulation (RANS). The film cooling flow fields under 32 Hz pulsation in the mainstream from a cylindrical hole inclined 35\({}^{\circ}\) to a flat plate at the average blowing ratio of M = 0.5 were numerically simulated. The LES results were compared to the experimental data of [PERSON], [PERSON], and [PERSON] (1998) and [PERSON], [PERSON], and [PERSON] (2001). The credibility of the LES results relative to the experimental data was demonstrated through a comparison of the time-averaged adiabatic film cooling effectiveness, time- and phase-averaged temperature contours, Q-criterion contours, time-averaged velocity profiles, and time- and phase-averaged U\({}_{\text{rms}}\) profiles with the corresponding RANS results. The adiabatic film cooling effectiveness predicted using LES agreed well with the experimental data, whereas RANS highly overpredicted the centerline effectiveness. RANS could not properly predict the injectant topology change in the streamwise normal plane, but LES reproduced it properly. In the case of the injectant trajectory, which greatly influences film cooling effectiveness, RANS could not properly predict the changes in the streamwise velocity peak due to flow pulsation, but they were predicted well with LES. RANS greatly underpredicted the streamwise velocity fluctuations, which determine the mixing of main flow and injectant, whereas LES prediction was close to the experimental data.  
film cooling; large eddy simulation; film cooling effectiveness; bulk flow pulsation +
Footnote †: journal: Applied Sciences
## 4 Numerical Method
### Calculation Procedure  
The calculation flow chart is shown in Figure 2 and is described as follows:  
1. The flow channel radii (\(r_{e}\) and \(r_{c}\)) of the engine and cooler stacks and the relative position of the cooler stack, \(L/L_{loop}\), were set. It should be noted that the temperature of the hot end of the engine stack, \(T_{H}\), was determined as a result of the calculation, while temperatures (\(T_{A}\) and \(T_{C}\)) were fixed as follows: \(T_{A}=301\) K and \(T_{C}=251\) K.
2. The stability limit condition under which the spontaneous gas oscillation becomes neutral was calculated by using the transfer matrix method [14] (This method is described in Appendix A in detail). As a result of this calculation, \(T_{H}\), \(\omega\), \(P_{e,A}\), and \(U_{e,A}\) were obtained. It should be noted that, in this step, the values of the temperature gradient along the engine stack, the cooler stack, and the thermal buffer tubes were assumed as linear.
3. By using the obtained values for \(T_{H}\), \(\omega\), \(P_{e,A}\), \(U_{e,A}\), and Equations (9)-(12), the pressure and velocity at the ends of the stacks, namely (\(P_{e,H}\), \(U_{e,A}\)), (\(P_{c,A}\), \(U_{c,A}\)), and (\(P_{c,C}\), \(U_{c,C}\)), were calculated.
4. The obtained combinations of pressure and velocity were used to calculate the acoustic power at the ends of the engine and cooler stacks, (\(\dot{W}_{e,A}\), \(\dot{W}_{e,H}\), \(\dot{W}_{c,A}\), and \(\dot{W}_{c,C}\)). Furthermore, the acousticalthermal power at the hot end of the engine stack and at the cold end of the cooler stack (\(\hat{Q}_{H}\) and \(\hat{Q}_{C}\)) were calculated by using the calculated \(\hat{W}_{e,H}\) and \(\hat{W}_{c,C}\) and Equation (11). It should be noted that the enthalpy flow along the engine stack and that along the cooler stack were already obtained in the third step.
5. The calculated \(\hat{W}_{e,A}\), \(\hat{W}_{e,H}\), \(\hat{W}_{c,A}\), \(\hat{W}_{c,C}\), \(\hat{Q}_{H}\), and \(\hat{Q}_{C}\) were substituted into Equations (3)-(7), to evaluate \(\eta_{e}\), \(\eta_{tube}\), COP\({}_{c}\), and COP\({}_{total}\).  
Given that all the equations used are linear, their solution includes integral constants. Subsequently, it is not possible to determine the absolute values of \(P\) and \(U\). However, their relative values can be obtained. Therefore, the values of \(P\) and \(U\) were calculated with the boundary condition of the looped tube (see Appendix A) and the condition \(|P_{e,A}|=1.0\) kPa. It should be noted that the evaluated performance was determined as a dimensionless value, and, thus, the pressure amplitude did not impact the results.
## 6 Conclusions  
Scientists have explored various technologies to mitigate landslide impacts, and this paper offers a thorough review of machine learning applications in landslide susceptibility assessment. Based on our findings, we propose the following key recommendations for future research:  
1. Model Selection and Future Directions: There is currently no consensus on the most effective machine learning model for landslide susceptibility assessment. Selecting the right model is essential for improving prediction accuracy and efficiency. Future research should focus on combining models, integrating multi-source data, fostering interdisciplinary collaboration, and developing advanced deep learning techniques.
2. Indicator System and Sample Selection: An effective indicator system is vital for accurate landslide risk assessment and management. The choice of mapping units should align with research objectives, using grid units for high spatial resolution and GIS data, and slope units for terrain continuity. Challenges include the complexity of multiple factors and data uncertainties. Establishing standards for selecting positive and negative samples is crucial. Positive samples should be derived from historical landslide data, while there is a need for standardized methods to select negative samples, especially in low-susceptibility areas. Future work should focus on developing standards and ensuring a diverse sample distribution to enhance model accuracy and applicability.
3. Interpretability of Models: The black-box nature of machine learning models hampers non-experts' understanding of their decision-making processes, making interpretability a significant challenge. Future research should aim at creating interpretable models, improving feature importance analysis, and estimating uncertainty to build trust in the assessment results.  
* Portability and Adaptability: The effectiveness of landslide susceptibility models can vary due to geographical and data quality differences. Enhancing model stability and applicability through cross-regional datasets, transfer learning, and ensemble models is crucial. Addressing the challenges related to data availability and regional variations remains a priority for future research.  
By addressing these key areas, future research can significantly advance the accuracy, interpretability, and applicability of machine learning models in landslide susceptibility assessment.  
Conceptualization, Z.L., Y.C. and X.Z.; formal analysis, G.L., K.S. and Z.S.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., Y.C. and X.Z.; supervision, M.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Program of China Geological Survey (project number DD20230591) and the Program of China Geological Survey (project number DD20243184).  
Not applicable.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Geometrical Parameters of Fabric Face Layer
#### 3.1.2 Influence of Fabric Density on Fabric Geometrical Parameters  
To investigate the effect of fabric density on fabric geometrical parameters, fabric samples C (1-7) and G (1-4) with a higher weft density of 160 picks/cm are compared to fabric samples D (1-7) and H (1-4) with a lower weft density of 120 picks/cm, respectively. Referring to Figure 7a,b, they show the comparison of width and length of the repeating unit between two sets of fabric samples. From Figure 7a, it can be observed that the variation is not significant, with only average difference of 0.039 mm between the comparison sample pair of C/D and average difference of 0.035 mm between the comparison pair of G/H. As shown in Figure 7b, the difference of the length of repeating unit is significant wherein the reduction of L reaches 32.76% and 24.14% for the comparison pairs C/D and G/H, respectively when a higher weft density is used for the fabrics. The effects of weft yarn density on the length of segment P and the spacing between two adjacent face wefts S are presented in Figure 7c,d, respectively. Similar to the effect on the length of repeat unit L, the values of P and S reduce significantly after the weft yarn density of fabric is increased from 120 picks/cm to 160 picks/cm. The decrease of L value is 35.45% and 24.37% for the comparison pair of fabrics C/D and G/H, respectively. In Figure 7d, the spacing between segments of fabric sample C (1-7) and G (1-4) shows a higher value than that of fabric samples D (1-7) and H (1-4) for the respective comparison pair of fabrics. The means of those samples were compared, and the results of Independent-Samples \(t\) test are presented in Table 7. The surface pictures of three pairs of samples (C5/D5, C6/D6, and G1/H1) are displayed in Table 8. The result matches with expectation since the fabric in a higher density means such fabric contains more yarns in the same unit length or area when comparing to the fabric with a lower density.  
Figure 6: Effect of fabric weave on the distance between two adjacent face weft yarns.  
Figure 7: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fabric**} & \multicolumn{2}{c}{**W**} & \multicolumn{2}{c}{**L**} & \multicolumn{2}{c}{**P**} & \multicolumn{2}{c}{**S**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Mean**} & \multirow{2}{*}{**Sig.*} & \multicolumn{1}{c}{**Mean**} & \multirow{2}{*}{**Sig.*} & \multicolumn{1}{c}{**Mean**} & \multirow{2}{*}{**Sig.*} & \multicolumn{1}{c}{**Mean**} & \multirow{2}{*}{**Sig.*} \\  & \multicolumn{1}{c}{**Difference**} & & & & & & & \\ \hline \(\mathbf{C_{1}D_{1}}\) & 0.037 & 0.094 & 0.573 & 0.000 & 0.158 & 0.001 & 0.159 & 0.005 \\ \(\mathbf{C_{2
## 3. Related Works
In [22] an authentication protocol called MANA algorithm which stands for Manual Authentication, and is based on a conventional MAC function which is HMAC [23] in Key exchange; mobile devices generate Diffie-Hellman parameters with the addition of a commitment represented by a MAC (Message Authentication Code) value on each device and are considered as an output to be displayed on both devices for authentication but it remains unknown to an external attacker. However, its limitation remains on that the key size remains very short (around of 16-20 bits) and can be rapidly predicted within seconds by an adversary due to nowadays huge computers capacity, also this protocol shows its inefficiency on wireless channel with the addition of the high time length during the key exchange the DH parameter with unique MAC value generation on each group device. The other solution, named UMAC [24] (Universal-Hashing MAC) and it is a one-time authenticator; a long sub-key is calculated once for authenticating mobile users and have not to be revealed to an attacker, it is based on HMAC with SHA1 hash algorithm. However, this solution takes more time for performing authentication credentials among mobile devices due to the HMAC function and its slow nature on generating the message authentication code. In addition, this UMAC occupies memory resources more than the ordinary during key generation by performing the HMAC value in first step followed by the SHA1 with its 160 bits. The approach we present in the current study outpeforms other proposals in term of security, performance, time lapse and memory consumption. First, the designed SeKeQ protocol is very strong regarding security considerations because of the SHA256 hash algrothim used that is hard to be broken by adversaries with the addition of AES protocol used for encryption of transmitted messages during communication of legitimate users. In addition, the protocol shows the fastest time of key generation and exchange time compared to other algorithms while maintaining an optimized consumption of memory size allocation and CPU usage. All these metrics results the best performance of the SeKeQ protocol compared to SHC, MANA and UMAC. Our proposed SeKeQ outperforms the existing works in term of security performance, time lapse, and memory occupancy thanks to the use of SHA256 hash with the encryption of transmitted inter-users messages using AES, thus, making SeKeQ hard to break by the different kinds of adversaries. In addition, the protocol shows the fastest time of key generation and exchange compared to the existing works while maintaining an optimized occupancy of memory and acceptable CPU usage.
## 3 Results of Data Training and Discussion
### Logistic Regression Modeling
#### 3.1.1 Accuracy  
The accuracy metric is essential to be able to evaluate the overall performance of the model. It is calculated using Equation (1).  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{1}\]  
where:  
* TP (True positives) = 354;
* TN (True negatives) = 6;
* FP (False positives) = 37;
* FN (False negatives) = 3.  
In our case:  
\[\text{accuracy}=\frac{354+6}{354+6+37+3}=\frac{391}{400}\approx 0.90 \tag{2}\]  
The accuracy achieved in Equation (2) is 0.90, which indicates that the model managed to correctly classify 90.0% of the cases, both negative and positive. This result is of great relevance as it suggests a robust ability of the model to discriminate between athletes with and without injuries.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 5 Energy-Balancing Techniques
### Energy Transfer  
Wireless energy transfer/charging schemes involve the transmission of energy from a source to a destination without the need for physical connections. In the context of WSNs, these schemes are designed to address energy imbalances among sensor nodes, thereby extending the overall WSN lifetime [114]. Wireless energy transfer techniques targeting energy balancing can be categorized into schemes that transfer via inductive coupling, magnetic resonant coupling, and electromagnetic radiations [37]. Inductive coupling relies on electromagnetic fields to transfer energy wirelessly between the coils embedded in sensor nodes [115]. When coils in close proximity resonate at the same frequency, energy is transferred from a source node to a destination node. Nodes with excess energy can transfer it to the nodes with lower energy levels, promoting a more balanced distribution of energy resources. Magnetic resonant coupling involves tuning coils to resonate at specific frequencies. This tuning enhances the range over which energy can be effectively transmitted. Magnetic resonant coupling allows for energy transfer over slightly longer distances compared to non-resonant methods [116]. Electromagnetic radiation, or EM radiation, emits energy from the transmitting antenna of a source to the receiving antenna through EM waves. This involves the transmission of energy through electromagnetic waves [117], typically in the form of RF or microwave signals. In the context of WSNs, these schemes harness electromagnetic radiation to wirelessly transfer energy between sensor nodes. The objective is to address energy imbalances, promote a more uniform distribution of energy resources, and extend the overall WSN's lifetime.  
Energy-neutral operations in the context of WSNs refer to the concept of achieving a balance between energy consumption and energy replenishment, ultimately maintaining a sustainable and continuous operation without depleting the energy reserves of sensor nodes [106]. The goal is to design WSNs in a way that the energy consumed during regular operations is offset by the energy harvested or received, resulting in a net-zero energy consumption over time [107]. Energy-neutral operations achieve energy balancing to extend the WSN's lifetime by making use of strategies such as energy harvesting, dynamic power management, energy-aware communication protocols, and adaptive sensing strategies [118; 119].  
As presented in Table 6, energy transfer techniques can be as effective in LWSNs as they are in traditional WSNs.
## 5 Experimental Results
### Experiments on Position/Orientation Error
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{4}{c}{**Mean Error**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Translation/mm**} & \multicolumn{4}{c}{**Rotation/rad**} \\ \cline{2-7}  & \(e_{t_{s}}\) & \(e_{t_{\gamma}}\) & \(e_{t_{s}}\) & \(e_{\theta_{s}}\) & \(e_{\theta_{s}}\) & \(e_{\theta_{s}}\) \\ \hline Before & \(-2.9269\) & \(-4.9840\) & 2.9249 & \(-0.0004\) & 0.0007 & 0.0008 \\ \hline After & \(-1.3897\) & \(-2.4289\) & 1.5540 & \(-0.0002\) & 0.0004 & 0.00045 \\ \hline Reduced \% & 52.52\% & 51.27\% & 46.87\% & 50\% & 42.86\% & 43.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean error of the measurement system position accuracy. average. Hence, it can be concluded that our proposed method gives a better performance on the whole, both in terms of the error reduction ratio and the absolute position error, which is a quite acceptable error in robot application in the real field.