esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Algorithm Complexity Analysis  
The waveform superposition algorithm calculates the jamming power based on (1), (16), and (17). The computational complexity of the normalized field pattern is \(O(n)\), and the computational complexity of calculating \(\theta^{\prime},\phi^{\prime}\) in (16) is \(O(n)\). Equation (17) contains two summations with a computational complexity of \(O(n^{2})\), so the waveform superposition algorithm has a complexity of \(O(n^{2})\). Similar to the waveform superposition algorithm, the array-based algorithm relies on (16), (23), and (25), which has a complexity of \(O(n^{2})\). However, since the angle of each point on the sphere concerning the node needs to be iterated in the waveform superposition algorithm, the running time is increased. The array-based algorithm in this paper only needs to focus on the transmitted power and distribution of the nodes, reducing the computational complexity.  
Under the same conditions, the running times of the two algorithms are compared. The results are shown in Figure 10. With the increase in the number of nodes, the calcula  
Figure 8: Jamming power comparison between the array-based algorithm and the waveform superposition algorithm.  
Figure 9: The relative errors between the array-based algorithm and the waveform superposition algorithm.  
tion complexity of \((\theta^{\prime},\phi^{\prime})\) increases, so the running time of the waveform superposition algorithm increases significantly. In contrast, the running time of the array-based algorithm is maintained within 1 s, basically unaffected by the number of nodes, which can shorten the response time of the jammer and improve the response speed of electronic warfare in practical applications.
## 3 Results  
### Study Population  
Seventy TMD-pain patients provided informed consent for study participation, seven patients were excluded due to missing headache classification from the patient file and three were excluded because they failed to return the 12-week questionnaire. From the included 60 patients, 83.3% were female and the age ranged from 18 to 71 years. Myalgia was the most common TMD-pain subtype (61.7%), followed by a combination of myalgia and arthralgia (30.0%) and arthralgia alone (8.3%). Migraine was present in 36.5% of the sample, TTH in 35% and headache attributed to a TMD in 28.3% (Table 1). Based on the post hoc power calculations with the standard deviation found in the present study (i.e., SDpooled = 2.6), power of 80% and \(\alpha\) = 0.05, the sample size needed to show a clinically significant change was 16. All headache groups consisted of more than 16 participants each.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Outcome Measure** & **Type of Pain Compaint** & \begin{tabular}{c} **Baseline** \\ **Mean** \(\pm\) **sd** \\ \end{tabular} & \begin{tabular}{c} **Week** \(\pm\) **sd** \\ \end{tabular} & \begin{tabular}{c} **Week \(\pm\) **sd** \\ \end{tabular} & \begin{tabular}{c} **Week \(\pm\) **sd** \\ \end{tabular} & \begin{tabular}{c} **Week \(\pm\) **sd** \\ \end{tabular} & \begin{tabular}{c} **Difference between** \\ **Cambrianians** \\ **F (\(\varphi\)-Value)** \\ \end{tabular} & \
\begin{tabular}{c} **Change over Time** \\ **F (\(\varphi\)-Value)** \\ \end{tabular} &
\begin{tabular}{c} **Complaints** \\ **Time** \\ **F (\(\varphi\)-Value)** \\ \end{tabular} \\ \hline \multicolumn{7}{c}{**Migazine (N = 22)**} \\ \hline
**Disability Score** & **TMD complaints** & \(2.9\pm 3.1\) & \(2.9\pm 2.4\) & \(2.4\pm 2.8\) & \(1.0\pm 1.1\) & \\
**0-10** & **Headache complaints** & \(4.6\pm 3.0\) & \(4.4\pm 2.7\) & \(2.7\pm 3.4\) & \(1.8\pm 2.0\) & 17.162 (0.000) & 8.707 (0.001) & 1.928 (0.159) \\
**CF1** & **TMD complaints** & \(48\pm 2.6\) & \(5.0\pm 2.6\) & \(4.1\pm 2.3\) & \(3.2\pm 2.0\) & 2.740 (0.113) & 6.229 (0.004) & 0.578 (0.677) \\
**(0-10)** & **Headache complaints** & \(5.7\pm 1.6\) & \(5.7\pm 1.7\) & \(4.9\pm 1.8\) & \(3.8\pm 1.7\) & & \\ \hline
**Days with pain (0-30)** & **TMD complaints** & \(16.2\pm 11.1\) & \(11.9\pm 8.4\) & \(10.6\pm 8.1\) & \(10.0\pm 9.4\) & 3.470 (0.077) & 4.378 (0.017) & 2.236 (0.117) \\
**Headache complaints** & \(13.3\pm 8.8\) & \(9.3\pm 5.7\) & \(9.8\pm 5.3\) & \(5.8\pm 8.6\) & & \\ \hline
**Days with Disability (0-30)** & **TMD complaints** & \(3.8\pm 6.0\) & \(16.2\pm 2.0\) & \(2.8\pm 3.6\) & \(1.4\pm 2.9\) & 4.448 (0.047) & 1.724 (0.196) & 0.920 (0.450) \\
**Headache complaints** & \(4.6\pm 5.8\) & \(37.5\pm 5.0\) & \(35.4\pm 4.2\) & \(1.6\pm 2.9\) & & \\ \
# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 2 Study Area  
The study area is the Middle Volga region of Russia. The territory is located in the central part of the East European Plain [59]. The Middle Volga region's agro-industrial complex (AIC) is of all-Russian importance. The region holds one of the top places in the country to produce grain, including the valuable grain crop--wheat. Agriculture is characterized by high efficiency due to very favorable natural conditions. At the same time, the agricultural sector of the territory has a substantial impact on natural-territorial complexes, primarily on the soil cover. Given the intensity of plowing, cattle grazing, and natural conditions conducive to the development of exogenous processes, this area is highly affected by soil erosion. This situation is not new; it is not accidental that the territory of the Middle Volga region is historically called an erosion belt. At the same time, anti-erosion measures in recent decades are of a point and haphazard nature, mainly due to the poor study of the rate and development direction of the process, primarily rill erosion.  
The study area is 345,477 sq. km. The macroregion includes Mari El, Tatarstan, Chuvashia, Saratov, Samara, Ulyanovsk, and Penza regions. The Middle Volga region is located in the forest landscape zone in the north, the forest-steppe landscape zone in the center, and the steppe landscape zone in the south (Figure 1).  
The relief of the Middle Volga region has a spotted asymmetry of slopes: the average height--146 m, maximum elevation--316.8 m, and minimum--13.2 m [60]. The region's climate is moderately continental and continental in the south, varying wildly from region to region. Average annual precipitation ranges from 471 to 549 mm in the north to 264-424 mm in the south.  
The steepness of the slopes of the territory, in general, is favorable for agriculture; a quarter of the region is steep to 1 degree, and a significant part of the territory is characterized by steepness in the range of 1-2.5 degrees, rising to 5 degrees in the area of Bugulminsko-Belebeyevskaya upland [60]. Erosion risks are not uniform and vary from region to region [61]. Soil-forming rocks are not homogeneous and are mainly clayey and heavy loam. There are sandy soils and medium-loam in the north of the Republic of Mari El region and light loam in the forest-steppe zone (Penza Oblast). Soils vary; thermozems of various subtypes predominate, mostly leached (Haplic Chernozems), as well as ordinary (Calcic Chernozems), typical (also Haplic Chernozems), and southern (Haplic Chernozems too)(steppe soils). The remaining types have a clearly expressed zonal pattern, where the gray forests (Haplic Greyzems) are typical for the forest territories and north of the forest-steppe zone [60].  
Figure 1: The study area. The 0—Saratov region, 1—Penza region, 2—Republic of Chuvashia, 3—Samara region, 4—Republic of Tatarstan, 5—Republic of Mari El, and 6—Ulyanovsk region.
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 3 System Model
Then, the average response delay, except for the transmission and propagation delays, is given by \(1/(C-\lambda)\) according to [PERSON]'s law [32], where \(C\) is the processing capacity of the controller and \(\lambda\) is the packet-in message arrival rates. In addition, the transmission delay can be calculated using the bandwidth of the control channel and the sizes of packet-in and packet-out (or flow-mod) messages. Moreover, the propagation delay can be affected by the medium type. These parameters can be checked using the network statistics reports from the forwarding nodes defined by the OpenFlow protocol [1]. Therefore, the controller can estimate the current response delay using the queuing, transmission, and propagation delays [33] and monitor the history of the response delays. However, we need to know the future response delay to determine whether to proactively cache rules or not. Therefore, based on the history of the response delays, which can be calculated as described above, estimation of the future delay can be conducted as follows. We assume that there are discrete delay levels which are matched one to one with the calculated delays for the scalable operation. This paper utilizes a long short-term memory (LSTM) neural network to forecast the delay, which is widely utilized for load and state prediction [20; 34; 35]. The core component of LSTM is to use memory cells which are composed of three controlling gates (Figure 3): the input gate (\(i_{t}\)), forget gate (\(f_{t}\)), and output gate (\(o_{t}\)) at time \(t\). The input gate decides to add the input data to the memory cell. In addition, the forget gate aims to forget or reset the last state of the memory cell. Moreover, the output gate controls the output data that can be propagated to the rest of the network. The equations for forward propagation are given by  
\[\left( \begin{array}{c}f_{t}\\ i_{t}\\ o_{t}\\ g_{t}\end{array} \right)=\left( \begin{array}{c}\sigma\\ \sigma\\ \sigma\\ \tan h\end{array} \right)W\left( \begin{array}{c}h_{t-1}\\ x_{t}\end{array} \right) \tag{3}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g_{t} \tag{4}\]  
\[h_{t}=o_{t}\odot\tan h(c_{t}) \tag{5}\]  
where \(x_{t}\), \(c_{t}\), and \(h_{t}\) are the input sequence, memory cell state, and hidden state at \(t\), respectively. In addition, \(\sigma\), \(W\), and \(\odot\) denote the sigmoid function which maps the input data to the range between 0 and 1, weight matrix, and the Hadamard product, respectively [34]. The input data sequence for the LSTM network are the response delays from the controller, which are determined by the packet-in messages at each time unit. Then, the output is the predicted response delay. An Adam optimizer is used to stochastically optimize the parameters in the model.
# Computational Wear Prediction of TKR with Flatback Deformity during Gait
## 1 Introduction
To obtain the wear characteristics in flatback patients due to differences from normal gait mechanisms, we used the finite element (FE) method in this study. Performing a wear experiment requires a lot of time and cost, so it is more efficient in terms of time and cost to predict and analyze knee artificial joint wear through computational simulation using finite element analysis. After the creation of the finite element model, finite element analysis was performed by applying constraint and loading conditions for Modified ISO 14243-3 in the case of people without flatback syndrome and for experimental results of previous research [15] in the case of flatback patients. Through this analysis, contact pressure and sliding distance for one gait cycle were obtained, and the wear depth was calculated by using Archard's Law [22; 23; 24]. The wear in the flatback patient's gait mechanism and the wear in the normal gait mechanism  
Figure 2: Knee implants replacements (reprinted with permission from ref. [9]. Copyright 2009 Elsevier). were compared by the wear area and contact area results after 5,000,000 cycles. The study about wear can contribute to guiding TKR design and positioning to reduce wear for flatback patients. This paper consists of five sections. Following this section, a finite element model is performed to derive the contact pressure distribution of the knee artificial joint. Then, Archard's Law, which derives the wear depth using the obtained contact pressure, is introduced, and the process for deriving the wear depth of the artificial joint bearing after 5,000,000 cycles is presented. In the third section, numerical results obtained by employing the finite element analysis model are exhibited and the accuracy of the results is verified by comparing the actual experimental results. Furthermore, we compare and analyze the contact pressure, maximum sliding distance, wear depth, and wear area of the normal gait mechanism and the flat gait mechanism. In the fourth section, the discussions related to the results are given. In particular, the effect of the difference in contact pressure very during the gait cycle due to the difference in the gait mechanism on wear was analyzed. Finally, conclusions are made in the final section.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 2 Preliminaries
### Gauss-Jordan Elimination  
Gauss-Jordan elimination is a technique in linear algebra used for solving linear equation systems, determining the rank of a matrix, and calculating the inverse of an invertible matrix. It involves transforming a given matrix into its reduced row echelon form using a sequence of row operations. Algorithm 1 describes the process of Gauss-Jordan elimination. Note that the operations performed between rows are also applied to the vector \(c\).  
The process begins with forward elimination, transforming the matrix into an upper triangular form by ensuring that all elements below the main diagonal are zero. This transformation is achieved through row operations, including swapping rows, multiplying a row by a non-zero scalar, and adding or subtracting multiples of one row to another. The key step is to identify pivot elements, which are the leading non-zero entries in each row, and use these to eliminate the entries below them.  
```
0: A matrix \(H\) of size \(n\times n\), a vector \(c\) of size \(n\)
0: Transformed matrix \(H\) and updated vector \(c\)
1:for\(i=0\) to \(n-1\)do
2: Find the pivot in column \(i\) (value \(1\) in \(H[i:n,i]\))
3: Swap rows to move pivot to \(H[i,i]\)
4:for\(j=0\) to \(n-1\)do
5:if\(j\
eq i\)then
6: Eliminate row \(j\) by using row \(i\)
7:return\(H\), \(c\)
```  
**Algorithm 1**Gauss-Jordan Elimination
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
###### Abstract
In addition, plane-based map correction techniques can increase position accuracy by reducing noise in the maps. In this study, the performances of LOAM, A-LOAM, and HDL Graph SLAM algorithms in mapping studies applied in indoor and outdoor environments were tested, and the effects of loop closure and plane detection methods on the accuracies of maps were examined. Similar studies have been carried out on this subject; for instance, [PERSON] et al. [16] studied the accuracy of the Google Cartographer SLAM algorithm using the Velodyne VLP-16 sensor. [PERSON] and [PERSON] [17] studied deep learning-based methods for loop closure detection for visual and LIDAR SLAM. These authors consequently stated that the learning-based methods are computationally expensive, and their performance is dependent on the dataset used for training the network.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 6 Discussion  
This section presents a comprehensive comparison of various algorithms and metrics employed in different analyzed studies to evaluate their effectiveness and efficiency for medical data analysis [41]. Performance analysis is a crucial aspect of assessing the suitability of different methods for specific tasks, as it helps identify the strengths and weaknesses of each approach.  
* Study [28] demonstrated the superior performance of Ridge Regression (MAE = 0.82131) compared to other regression techniques, including logistic regression, Lasso, and ElasticNet;
- In [30], random forest achieved the highest R\({}^{2}\) score of 0.7780, followed by gradient boosting and XGBoost;
- Multiple linear regression models in [37] showed a higher R\({}^{2}\) (0.63) before the implementation of the diagnostic-therapeutic-assistance pathway compared to after (0.50).
* Neural network models in [29] achieved high accuracy rates (94.74%) after optimization through grid search and random search;
- Study [40] utilized the GPT-3 language model for hospital LoS predictions, yielding an MAE of 2.37 days and a median absolute error of 2 days.
* The decision tree model in [33] obtained an accuracy value above 80% when predicting patient admissions;
- Gradient boosting in [35] outperformed other algorithms, including an accuracy value of 82.41%, highlighting its effectiveness when handling complex medical data.
* The survival analysis methods employed in [32] achieved a concordance index of approximately 0.7, indicating moderate predictive power using patient information.
* The XGBoost algorithm in [38] demonstrated high predictive accuracy for total hip arthroplasty, with an RMSE of 2.03 and an R\({}^{2}\) of 0.89;
- Study [39] found the support vector machine to be the most accurate model for predicting patient admissions in emergency departments, although specific accuracy metrics were not provided.  
Based on the summarized results in Table 4, we can analyze and rank the algorithms to determine the best-performing methods for predicting the LoS in hospitals.  
The XGBoost algorithm stands out as the best-performing method for hospital LoS prediction, providing the highest R\({}^{2}\) Score of 0.89. The neural network also demonstrates high accuracy and robustness across multiple studies, making it highly effective for hospital LoS predictions when optimized. Tree-based models, like the gradient boosting algorithm and random forest, also offer strong predictive capabilities, emphasizing their importance in healthcare analytics. These findings suggest that advanced ML algorithms, particularly XGBoost and neural networks, are highly suitable for predicting hospital LoS, potentially leading to better resource allocation and improved patient care.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 4 Analysis and Simulation
### Influence of Altitude  
The altitude affects the efficacy of the test window and how quickly it needs to be changed. As the drone's altitude increases, its perspective widens, and the changes between two consecutive frames are more significant.  
Experimental Setup: In this experiment, we chose three locations in a neighborhood of Manhattan (Figures 4-6). In the simulations, the drone flew at three different altitudes (50, 100, and 200 m) at a consistent speed of 5 m/s (meters per second). For each altitude, the simulation ended immediately after the drone was out of the scope of the first frame (this  
Figure 3: Example of the simulator screen. The drone speed and altitude values are inserted on the left and submitted by the user (by clicking on the “Submit” button); the user clicks on the image and uses the arrows to move the black frame on the map; to finish, the user presses “Enter”.  
was done in each of the experiments described below), because we found that a correlation of zero is obtained when the drone is out of the first image's scope due to the fact that there is no longer any similarity to the first image.  
Results and Conclusions: The results are presented in Tables 1-3. Based on the results obtained, we calculated the RMSE, \(R^{2}\), and MAE. Several interesting insights can be derived from the results: (1) RMSE: as the altitude increases, the RMSE value decreases; (2) \(R^{2}\); while this tends to be a value of one, it can be seen that the \(R^{2}\) value decreases at \(10^{-3}\); and (3) MEA: when the value is close to zero we get a better match to reality.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Altitude** & **Linear Function** & **RMSE** & \(R^{2}\) & **MEA** \\ \hline
200 m & \(-0.224\)X + 97.984 & 1.2588 & 0.9984 & 0.9323 \\
100 m & \(-0.464\)X + 93.165 & 3.8040 & 0.9952 & 1.4468 \\
50 m & \(-1.072\)X + 100.797 & 5.7254 & 0.9934 & 2.0112 \\ \hline \end{tabular}
\end{table}
Table 1: The effect of a drone’s altitude on the performance; the results are presented for location #1 and various altitudes, with a drone speed of 5 m/s (18 km/h).  
Figure 4: Location #1: 200 m (in the **left** image), 100 m (in the **center** image), 50 m (in the **right** image).  
Figure 5: Location #2: 200 m (in the **left** image), 100 m (in the **center** image), 50 m (in the **right** image).  
Figure 6: Location #3: 200 m (in the **left** image), 100 m (in the **center** image), 50 m (in the **right** image).
## 3 MAC Protocols for CRBANs
### CR-Based MAC Protocol for Cognitive Wireless Sensor Body Area Networking
In the CR-MAC protocol, there are three successful packet transmission scenarios, as shown in Figure 2:  
* Single critical packet arrival and the arrival of one or more non-critical packets in the scenario represented by state S1
* Single non-critical packet arrival, in the scenario represented by state S2
* Single critical packet arrival, represented by state S3  
CR-MAC achieves not only relatively high throughput, but also QoS promising for traffic of different priority classes. In CR-MAC, the traffic of sensor nodes is classified into critical traffic and non-critical traffic. After critical traffic throughput is first analyzed, the output is then applied to the analysis of non-critical traffic throughput, which depends on the transmission of critical traffic. Critical and non-critical traffic are prioritized according to transmission power. The analytical and simulation-based results disclosed different practical design approaches as follows: In critical nodes,  
Figure 1: Collided packet transmission in cognitive radio (CR)-MAC. Figure 2: Successful packet transmission in CR-MAC. when the number of packet retransmissions increases, the throughput increases and the number of packet rejections decreases. In non-critical nodes, the throughput decreases and the packet rejection rate increases. When the number of packet retransmissions in critical nodes reaches a certain value, the throughput of critical and non-critical nodes reaches a saturation point.