esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 3 System Model
The signal to noise plus interference ratio (SNIR) of IoT device \(i\) on subcarrier \(k\) is modeled as  
\[\Gamma_{k,i}(t)=\frac{b_{k,i}(t)p_{k,i}(t)\big{|}g_{k,i}(t)\big{|}^{2}}{\sum_{ f=1,\ |g_{k,f}(t)|^{2}<|g_{k,i}(t)|^{2}}^{2}b_{k,f}(t)p_{k,f}(t)\big{|}g_{k,f}(t) \big{|}^{2}+\sigma^{2}}, \tag{8}\]  
Figure 1: Illustration of the elevation angle in case of static UAV [21]. \(g_{k,i}(t)\) is the channel gain between the _UAV_ and IoT device \(i\) on subcarrier \(k\). \(g_{k,i}(t)\) which is invariant in one time slot but varies over different time slots, can be modeled as:  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]0  
where \(h_{k,i}(t)\) is the small scale fading of complex Gaussian distribution given by \(h_{k,i}(t)\)\(\sim\)\(\mathrm{CN}(0,\sigma^{2})\). URLLC uses short packets to ensure low latency transmission, so Shannon capacity, which acts as the upper bound in terms of data rate, can no longer be applied. Consequently, the user data rate at finite blocklength transmission [23] is given by  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]1  
where, the decoding error probability \(\epsilon\)[24] is given by  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]2  
Such that the \(Q\) function and \(V\) are defined respectively as  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]3  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]4  
where \(D\) states the packet size, and \(m\) represents the blocklength of the channel. The sum rate can be calculated as  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]5  
Assume that packet arrival of the IoT device \(i\) follows a Poisson process with the average arrival rate as \(\lambda_{i}\) packets per second. The average service rate of the same IoT device is assumed to be \(\mu_{i}\) packets per second. Both \(\lambda_{i}\) and \(\mu_{i}\) are statistically identical and independent distributed. The queuing system model of the IoT device is shown in \[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]6 Then, the average delay \(d_{avg,i}\), which is based on the _M/M/1_ queuing model [25], can be given by the following formula:  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]7  
where \(S_{i}\) and \(R_{avg,i}\) are the packet size and the average rate of IoT device \(i\) respectively. \(R_{avg,i}\) can be given as the rate of device \(i\) averaged over time as follows:  
\[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]8  
where \(T_{slots}\) represents the total number of time slots. \[A=\eta_{LOS}\ -\eta_{NLOS}, \tag{2}\]9 Queuing system model of the IoT device [26].
## 3 Results and Discussion
### ANFIS Model

Table 1: The root mean squared error (RMSE) and the coefficient of determination (R2) for _Salmonella_ populations by ANFIS models generated with different types of input membership functions (MFs) at the training and prediction phases. network. It can generate if-then rules automatically, and no relevant background knowledge is required. Therefore, it could be easily applied to various situations, making it a powerful tool for the modelling and prediction of microbe populations and the shelf life of food products. The ANFIS system could play an important role in the early warning of foodborne pathogenic microorganisms.
## 7 Conclusions  
In this paper, we extended previous research on measuring Tor latency and usability: for the technical measurement in terms of diversification and duration and for the user acceptance in terms of user cancelation rates. We included further statistics from web usability studies and we included our own results based on an interactive survey. Both extensions helped us to improve the significance and clarity of the usability analysis of the Tor anonymization tool. In particular, we analyzed the performance of the Tor network by comparing direct Internet access against a Tor-anonymized Internet access. Those tests were performed on different nodes around the world to gather data based on various locations. Enormous amounts of data were accumulated during a period of 38 days totaling nearly 4.5 million requests for each connection type.  
The experiment results quantitatively confirmed the common intuition that one has to accept performance losses while using the Tor network. User waiting times exhibit a large spread, ranging from taking twice as long as to nearly a hundred times longer while using the Tor network. The median ratio while using the Tor network is around 7.8 times slower. Concerning the loss of usability in exchange for improved anonymity while browsing the web via Tor, we can say that for core latency, the median of all Tor requests was 7.8 times higher than the median of the direct connection. Furthermore, the experiments revealed that Tor latency seems to fluctuate more, _i.e._, the actual duration of an HTTP request via Tor is harder to anticipate for the user. The overall latency that a user finally experiences is approximated by page latency, estimating the download of a complete web page. Our results indicate that at least 75% of all direct requests are faster than 75% of all Tor requests.  
Based on the results of our experiments, we provided a mapping that measures the expected increase in web user cancelation rate while using Tor. Comparing page latency between Tor-based and direct requests, there is a difference of 64% or 65% according to the lab experiment, respectively the user survey, in expected cancelation rate. This is a strong indicator for potentially high user frustration when using the Tor anonymization network.  
We suggest that a usability improvement in terms of a massive latency reduction would significantly increase the adoption of Tor by new users, and thereby increase the anonymity of current users as well. On the other hand, if anonymization technology should become part of a Future Internet [14], our research offers first steps towards an empirically grounded analysis of corresponding performance requirements.
# Influence of Cargo Luggage on the Vertical Drop Crashworthiness of Aircraft Mid-Fuselage Section
## 5 Influence of Cargo Luggage on Fuselage Section Crash Response
### Acceleration Response of Passenger Seat Rail  
To study the load transmitted from the fuselage cargo structure to the cabin during the crash, the acceleration response of the cabin seat rail was mainly studied. A total of six acceleration measuring points were selected on the inner floor guide rail of the cabin, as shown in Figure 19. The vertical lines in Figure 19 represent the fuselage frames, and the numbers are FS1-FS7 from left to right. The green and blue lines in the horizontal direction indicate the inner and outer floor guide rails of the cabin, respectively. The six acceleration measuring points are located at the intersection between fuselage frames FS2, FS4, and FS6 and the inner floor guide rail of the cabin, respectively.  
Figure 18: Proportion and value of energy absorption of each component.  
The acceleration response curves of the models of the passenger cabin seat rail without and with cargo are compared in Figure 20. The comparison shows that although the acceleration of the fuselage without cargo luggage was slightly greater than that of the fuselage with cargo luggage when the first overload peak occurred, the second overload peak of the model with cargo luggage was greater than that of the model without cargo luggage and subsequently reached the maximum value of acceleration there. Therefore, cargo luggage increases the overload suffered by passengers in a vertical crash.  
According to the technical index of the overload tolerance limit of the human body in all directions in CCAR-25, the airworthiness standard of transport aircraft in China, the tolerance value of the human body to vertical acceleration is \(-\)15-25 g. The acceleration measured at the six acceleration measuring points of the cabin guide rail selected in this study was within this range. Therefore, although the presence of cargo luggage increases the overload suffered by passengers in a vertical crash, it remains within the safe range, and the influence of seats and cushions in a vertical crash was not considered in this study; therefore, the result is still safe.  
Figure 19: Schematic of selection of the acceleration measuring points of passenger seat rail.  
Figure 20: Comparison of acceleration responses of guide rail with and without cargo luggage in the fuselage section. **(a)** FS2-left. **(b)** FS4-left. **(c)** FS6-left. **(d)** FS2-right. **(e)** FS4-right. **(f)** FS6-right.
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 3 Results and Discussion
### The Descending Rates of Es Layers and IVN
They represent southern mid-latitude (**i**–**l**), low-latitude (**e**–**h**), and northern mid-latitude (**a**–**d**), respectively, from bottom to top, and represent March–May (**a**,**e**,**i**), June–August (**b**,**f**,**j**), September–November (**c**,**g**,**k**), and December–February (**d**,**h**,**l**), respectively, from left to right. Figure 4: Local time–height distributions of EsOR of the northern hemisphere in different seasons and latitudes of 2014, representing low latitude to high latitude from bottom to top for spring (March–May), summer (June–August), autumn (September–November), and winter (December–February), respectively, from left to right. Figure 5: Local time–height distributions of the occurrence rate of IVN of the northern hemisphere in different seasons and latitudes of 2014, representing low latitude to high latitude from bottom to top for spring (March–May), summer (June–August), autumn (September–November), and winter (December–February), respectively, from left to right. Besides the atmospheric tides of the horizontal winds, some factors also play roles in the formation and development of the Es layers (i.e., the electric fields, the diffusion, the atmospheric gravity waves, the collision frequency of ions relative to neutral species, and so on) [23; 24; 39; 40; 41; 42; 43]. According to the results of [PERSON] et al. (2016) and [PERSON] et al. (2019), the electric field suppresses the occurrence of the Es layers in the daytime in equatorial regions [39; 40]. The diffusion suppresses occurrence and accelerates dissipation for the Es layer [23; 40]. At the same time, the atmospheric gravity waves contribute to the small-scale structures of the Es layers based on the simulations of [PERSON] et al. (2017) and [PERSON] et al. (2023) [41; 42], and may induce the formation of the multilayered sporadic E based on the result of [PERSON] et al. (2020) [43]. The enhancement of collision frequency induces the Es layers to separate with IVN in the vertical direction below the altitude of 110 km [24]. Additionally, a 3D simulation of [PERSON] et al. (2023) displayed the horizontal movement of Es driven by the horizontal transport of ions [44]. In the future, those processes need to be taken into account in our research with further study on the Es layers.
## 3 Artificial Intelligence Methods for GWL Modelling
### Hybrid Algorithms
By integrating optimisation algorithms with ANN, ANFIS, and SVM methods, this research demonstrates the model's superior performance in handling monthly observational data. The findings underscore the importance of advanced modelling approaches in addressing complex hydrological phenomena, particularly in regions vulnerable to groundwater fluctuations. Ref. [82] study evaluates the effectiveness of the ELM for GWL prediction in Andhra Pradesh, India. Through a comparative analysis with other single and hybrid models like ANN, SVM, and GP, the research highlights the ELM model's superior predictive capabilities, especially in Vizianagaram, India. This study underscores the significance of selecting appropriate modelling methods tailored to specific hydrogeological conditions. Ref. [83] investigate the effectiveness of the MLP-WA (While Algorithm) for predicting GWL in Yazd Province, Iran. This research compares the performance of MLP-WA with other hybrid models, such as RBF-WA and GP, focusing on temporal precipitation data as inputs. The findings highlight MLP-WA as the most effective hybrid model, outperforming other alternatives. This research emphasises the importance of incorporating wavelet analysis into machine learning methods to enhance predictive accuracy in GWL forecasting, particularly in regions with varying climatic conditions. Ref. [84] introduce the Hybrid Emotional Artificial Neural Network coupled with the Genetic Algorithm EANN-GA for estimating GWL in the Konan Basin, Japan. By combining FFNN with the Hybrid GTWANN approach, this study demonstrates robustness and effectiveness in providing accurate predictions, particularly in alluvial aquifers. Ref. [85] investigated the performance of hybrid Artificial Bee Colony and Particle Swarm Optimisation (ABC-PSO) ANN models for GWL forecasting in Manipal, Karnataka, India. This research highlights the significance of integrating optimisation algorithms with ANNs for enhanced predictive capabilities. Ref. [75] evaluated the Hybrid EANN-GA method for simulating spatial-temporal GWL in the coastal aquifer of the Konan Basin, Japan. This study demonstrates superior predictive skills by comparing the performance of EANN-GA with other models like GRNN and FFNN. Ref. [77] introduce the ANFIS-PSO method for GWL prediction in the Pahang watershed, Malaysia. This research demonstrates the model's effectiveness in providing reliable predictions by optimising six different lag attributes using PSO, GA, DE, and ANFI methods. The findings suggest that ANFIS-PSO outperforms other models in terms of prediction interval width and accuracy. Table S6 (Supplementary Materials) summarises the evaluated articles that used hybrid methods and draws a general conclusion.
## 2 Materials and Methods
### Methodology
#### 2.1.3 Phase III: Classification Model  
The consistency between the information collected by the statistical, displacement and damage analyses in the second phase is processed and crosschecked to classify the level of risk (in the presence of a mapped landslide) or attention required (in the absence of a mapped landslide) associated with each \(TZU_{radd}\) (Tables 1 and 2).  
The information gathered from the landslide inventory and the three indicators calculated in the second phase (susceptibility index, damage severity level, velocity) have been treated as dichotomous variables, so that only a positive or a negative outcome is possible for each one of them: \"yes\" or \"no\" for landslide; \">0\" or \"<0\" for susceptibility index; \"damaged\" or \"undamaged\" for damage severity level; \"moving\" or \"not moving\"for velocity. Therefore, when a \(TZU_{rad}\) is not covered by the damage survey or DInSAR data are absent, it is conservatively considered as \"damaged\" or \"moving\", respectively.  
The sixteen possible combinations are associated with four levels of risk (in the presence of a mapped landslide) or attention required (in the absence of a mapped landslide), by adopting the following criteria:  
* \"high\", when all the three indicators are positive;
* \"medium\", when two indicators are positive;
* \"low\", when one indicator is positive;
* \"very low\", when all the three indicators are negative.  
Based on this classification, the road network is partitioned in stretches exposed to different levels of risk (or attention) by aggregating the \(TZU_{rad}\). For road stretches where the information is not univocal (i.e., road sections exhibiting different levels of risk or attention), the highest level of risk or attention is considered.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 4 Numerical Simulations and Analyses
### Transient Development of Annular Pressure  
As shown in Figure 11, the transient development of annular pressure is calculated. The simulation result shows that annular pressure is mainly caused by thermal expansion. Therefore, the annular pressure shows a rapid increase and then a decrease, which is similar to that of the temperature. The simulated well displays a maximum annular pressure of 7.12 MPa, which poses a great threat to the wellbore's integrity.
## 4 Payload Development
### Mechanical Simulations  
Payload survivability had to be demonstrated under the various loads it would be subjected to during the landing and transportation to meet the CSA's safety requirements. With the gondola weighing 500 kg, the payload was expected to experience a maximum vertical acceleration of 70.56 m/s\({}^{2}\) and an estimated shock acceleration of 147 m/s\({}^{2}\). Therefore, the potential detachment of any components during landing was a significant concern. A series of Finite Element Modeling (FEM) analyses were conducted to confirm that the stresses detected were lower than the materials' yield strength. The simulations indicated a significant safety margin for both shear and axial stresses. Although the highest stress was observed at the bolt corner of the interface plate, it posed no significant risk.
## 5 Conclusions  
This study proposes an innovative method for meniscus segmentation using approaches based on YOLO series models and ensemble methods. Pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search are used for ensemble methods, and their performances are compared. Combining the strengths of these methods with the strengths of YOLO models contributes to the performance improvement in meniscus ROI area detection. According to the DCS, PPV, and Sensitivity metrics, the values of the proposed method in the test set were calculated as 0.8976 (\(\pm\)0.0071), 0.8561 (\(\pm\)0.0121), and 0.9467 (\(\pm\)0.0077), respectively. The same metrics were calculated as 0.9004 (\(\pm\)0.0064), 0.8876 (\(\pm\)0.0134), and 0.9200 (\(\pm\)0.0119) in the external set. In particular, the dynamic weighted multiple voting methods optimized with grid search showed superior performance in both datasets.  
This study's findings show that the proposed method for meniscal segmentation offers high reliability and applies to clinical averages. Addressing the limitations mentioned in the discussion section and increasing the diversity of the dataset will make it possible to test the method in a wider range of clinical applications in the future.  
This study makes an important contribution to the literature as it is the first known study to use the YOLO series for meniscal segmentation and optimizes its performance with ensemble methods.  
Conceptualization, M.A.S. and A.S.; Data curation, H.S. and Y.M.D.; Formal analysis, M.A.S.; Funding acquisition, M.A.S.; Investigation, M.A.S.; Methodology, A.S.; Project administration, A.S.; Resources, M.A.S.; Software, M.A.S.; Supervision, A.S., H.S., and Y.M.D.; Validation, A.S., H.S., and Y.M.D.; Visualization, M.A.S.; Writing--original draft, M.A.S.; Writing--review and editing, A.S., H.S. and Y.M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The current study is a retrospective randomized study and was approved by the local ethics committee (Research Protocol Number: 2024.02.01.02, Date: 04 April 2024).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This manuscript was prepared using part of [PERSON]'s PhD thesis, conducted at Istanbul University-Cerrahpasa.  
The authors declare no conflicts of interest.
## 4 Experimental Validation and Analysis
### Discussion and Analysis  
#### 4.2.1 Sensitivity Analysis of Weights Change  
The TOPSIS and AHP sensitivity analyses to weight variations are discussed in this section.  
1. Only the TOPSIS method was employed for weight calculation.  
As described in Section 2.2, the TOPSIS method requires raw evaluation indicator data for weight determination. However, when calculating weights for the criterion layer (\(U_{1}\), \(U_{2}\), \(U_{3}\), and \(U_{4}\)), the TOPSIS method cannot be directly applied because these criteria inherently lack raw data. This limitation demonstrates that the TOPSIS method is unsuitable for the multi-level index system structure shown in Figure 8.  
(2) Only the AHP method was employed for weight calculation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comparative Experiment** & **V1-PA-1** & **V1-PA-2** & **V1-PC** & **V2-PC** & **V2-PB** \\ \hline Evaluation score & 0.258 & 0.528 & 0.60 & 0.193 & 0.049 \\ Class & bad & good & good & bad & bad \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation scores.  
As outlined in Section 2.1, the AHP method typically requires the construction of an initial judgment matrix based on expert experience for weight calculation. For determining weights of the criterion layer, only one initial judgment matrix needs to be constructed. However, determining sub-criterion layer weights requires four initial judgment matrices, increasing subjective judgments. This multi-matrix construction process introduces additional subjective judgments, consequently increasing uncertainty in the evaluation results.
## 2 Materials and Methods
### Fluid Force Measurement  
Fluid force measurements were performed on eleven types of different soccer balls with a wind tunnel. The wind tunnel we used is an open type with a sirocco fan. The outlet's size is 0.38 m \(\times\) 0.38 m, and the length of the measurement section is 0.78 m. The velocity range was 3-29 m/s with \(\pm\)1.5% fluctuation, and the turbulence intensity was \(\pm\)1.0%. The fluid force was measured in the range of wind speed from 3 to 29 m/s every 1 m/s. This speed range covers most of the speed of a soccer ball that is kicked in a real game [16]. For shooting or a free kick, the ball is kicked out at about 30 m/s, and aerodynamic drag decelerates it to about 15 m/s [9]. The ball may also be kicked out at a slower speed when passing. The drag coefficients were measured by placing the ball so that the wind hit the upper panel face of the pictures shown in Table 1. The force acting on the soccer balls was measured by a three component load cell (\(Fx\), \(Fy\), \(Fz\)), LMC-3520A by Nissho Electric Works. The measured values were sampled at 1000 Hz for 9.0 s and averaged. The experimental conditions are shown in Figures 3 and 4. The ball was mounted with cobra-type rear support. The drag coefficient, \(C_{D}\), was defined by the following equation:  
\[C_{D}=\frac{D}{\frac{1}{2}\rho U^{2}\cdot A} \tag{2}\]  
where \(D\), \(r\), \(U\), and \(A\) denote the drag force of the ball, the density of the air, the uniform wind speed, and the projected cross-sectional area of the ball, respectively. Reynolds number (\(Re\)) is a dimensionless number defined as the ratio of inertial force to viscous force. Reynolds number, \(Re\), is defined as the following equation:  
\[Re=\frac{U\cdot D}{v} \tag{3}\]  
where \(\
u\) denotes the kinematic viscosity.  
Figure 1: Groove shape measurement.  
Figure 2: An example of groove shape measurement. The green line shows the shape of the ball surface, the red line indicates the depth of the groove, and the yellow area denotes the cross section of the groove.