esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Pedicle screw fixation and fusion is the standard procedure commonly used to treat a variety of spinal disorders, including degeneration, fracture, deformity, malignancy, and infection [1, 2, 3, 4, 5, 6]. However, screw loosening is still one of the common complications that requires additional revision for long-term clinical satisfaction [7, 8, 9, 10, 11]. In cases of screw loosening, replacement with larger diameter screws is always performed [12, 13, 14, 15, 16]. The pullout force imposed by the pedicle reaches 60% in normal density vertebrae and 57% in osteoporomic vertebrae [13, 15]; unfortunately, this salvaging procedure potentially increasesthe risk of pedicle wall violation with a subsequent loss of fixation and potential neural injury [12; 17; 18; 19; 20].  
Dual-thread screws with coarse threads in the vertebral body and fine threads in the pedicle have been proven to be more useful fixation screws than traditional screws; notably, conical shaped dual-thread screws possess more robust pullout strength than traditional cylindrical screws [21; 22; 23; 24; 25]. A pedicle screw with more preserved bone stock could become the primary methodological choice as it decreases the risk of pedicle breach when exchanging a screw of the same core size in revision surgery. In a previous study, dual-thread screws were designed by equipping a fine thread adjacent to the standard coarse thread on the proximal shaft and proven to possess improved pullout force [24]. In the current study, a newly designed proximal-conical dual-thread screw was designed as a proximal-conical-shaped shaft with fine threads and a distal cylindrical shaft with coarse threads. It is geometrically reasonable to expect the proximal-conical shape to preserve more pedicle bone stock than other designed screws. The purpose of this biomechanical study was to determine whether a newly designed proximal-conical dual-thread screw provides fixation strength comparable to that of a traditional screw.  
The purpose of the study was to evaluate whether a newly designed proximal-conical dual-thread screw with less bone occupancy provides fixation strength comparable to that of a traditional screw and whether it would be suitable for use in clinical practice. The insertion torque and pullout force of six different geometric configurations of screws composed of three screw shapes (cylindrical, conical, and proximal-conical) and two thread profiles (single and dual) were compared.
## 4 Assessment of Performance
### Aggregate Relocation Distance via Movement  
The results were all within 10% of the sample mean, with a 90% confidence interval. The analysis is carried out using the results of all three proposed approaches, including PACR [15] and SNR [25]. We have got the results right here. The entire distance travelled for relocation vs. the number of nodes added is depicted in Figure 2. The proposed strategy surpasses existing baseline techniques, according to the results, because only recovery nodes are shifted during operation. The number of cascaded relocations is reduced as a result. As a result, the suggested methodology has a shorter average distance travelled than the other methods investigated. The proposed method consistently outperforms previous strategies as the network's node count grows. The main reason for this is that the proposed method restricts node motions, which causes more network fragmentation. In terms of performance, PACR outperforms other algorithms. The key reason for the increase in performance is PACR working approach. As the number of nodes in the network rises, the network's backbone becomes stronger, allowing the recovery mechanism to become more competent and trustworthy, resulting in improved performance. Aside from that, PACR assumptions are unrealistic, and in the event of a topology change, they would result in a significant increase in packet exchange costs. When compared to PACR, Figure 3 indicates that our technique still performs admirably.  
\begin{table}
\begin{tabular}{c c} \hline
**Simulation Parameters** & **Values** \\ \hline
**Area of Simulation** & 800 \(\times\) 800 m\({}^{2}\) \\ \hline
**No. of nodes** & 150 to 300 \\ \hline \(R_{c}\) & 30 to 170 m \\ \hline \end{tabular}
\end{table}
Table 3: Parameters for simulation.
## 3 Numerical Results  
This section demonstrates the performance of the LIS for a multi-user system through simulations using the Monte Carlo method and analyzing the validity of the proposed approximations for various analysis scenarios in terms of the number of antennas at the LIS panel, the number of users in the system, the number of antennas at the base station, and the [PERSON] parameter for the phase error distribution.  
In this study, the SINR was simulated for each user using the parameters of each channel involved in the total link, being the Nakagami-\(m\) channels from the base station to the LIS, from the LIS to the user, and the complex normal direct channel from the base station to the user, which may be strong for near-field communications and weak for far-field communications. The SINR histograms were generated by the Monte Carlo method, simulating the resulting channel several times, and the contours of the histograms are shown close to the PDF of the Gamma distribution.
# A Novel Inverse Simulation Method of Helicopter Maneuvering Flight
## 3 Application to the Helicopter System
### Construction of a Transitional Vector  
In the inverse simulation of the helicopter system, the prescribed output vector \(\mathbf{y}_{\rm DES}\) usually contains the elements \(x_{\rm E}\), \(y_{\rm E}\), \(z_{\rm E}\) and \(\psi_{\rm BP}\)[15]. Thus, its first-order differential form is:  
\[\dot{\mathbf{y}}_{\rm DES}=\begin{bmatrix}\dot{x}_{\rm E}\\ \dot{y}_{\rm E}\\ \dot{z}_{\rm E}\\ \dot{\psi}_{\rm BP}\end{bmatrix}=\begin{pmatrix}\mathbf{T}_{z}(-\psi_{\rm PE})\mathbf{ T}_{y}(-\theta_{\rm PE})&\mathbf{0}_{3\times 1}\\ \mathbf{0}_{1\times 3}&1\end{pmatrix}\begin{bmatrix}\dot{\mathbf{S}}_{\rm P}\\ \dot{\psi}_{\rm BP}\end{bmatrix} \tag{12}\]  
where \(\mathbf{T}_{y}(\cdot)\) and \(\mathbf{T}_{z}(\cdot)\) are the Euler transformation matrices relative to the respective y- and z-axes. \(\dot{\mathbf{S}}_{\rm P}=\begin{bmatrix}\dot{S}_{\rm P},\ 0,\ 0\end{bmatrix}^{ \sf T}\in\mathcal{R}^{3}\), \(S_{\rm P}\) is a displacement variable, and \(\dot{S}_{\rm P}\) is the scalar of the flight velocity, i.e., \(V_{\rm P}\).  
Thus, the prescribed vector \(\mathbf{\eta}(t)\) can be constructed in the form:  
\[\mathbf{\eta}(t)=\begin{bmatrix}S_{\rm P},\ \theta_{\rm PE},\psi_{\rm PE},\ \psi_{\rm BP}\end{bmatrix}^{ \sf T}\text{ or }\mathbf{\eta}(t)=\begin{bmatrix}V_{\rm P},\ \theta_{\rm PE},\psi_{\rm PE},\ \dot{\psi}_{\rm BP}\end{bmatrix}^{ \sf T} \tag{13}\]  
Because \(V_{\rm P}\) and \(\theta_{\rm PE}\) are in the longitudinal channel, and \(\psi_{\rm PE}\) and \(\psi_{\rm BP}\) are in the directional channel, the constructed vector \(\mathbf{\eta}(t)\) can theoretically prescribe almost any three-dimensional maneuvering flight. Thus, our method has good versatility. Note that although the element \(\psi_{\rm BP}\) does not affect the elements of the flight path, i.e., \(\dot{x}_{\rm E}\), \(\dot{y}_{\rm E}\), and \(\dot{z}_{\rm E}\), different descriptions of \(\psi_{\rm BP}\) can indicate different maneuvers, as follows:  
1. \(\dot{\psi}_{\rm BP}=0\) and \(\psi_{\rm BP}=0\) indicate a maneuver with no sideslip; e.g., pull-up and hurdle-hop.
2. \(\dot{\psi}_{\rm BP}=0\) and \(\psi_{\rm BP}\
eq 0\) indicate a maneuver with a certain sideslip; e.g., sidestep and sideslip.
3. \(\dot{\psi}_{\rm BP}\
eq 0\) indicates a maneuver with a certain yawing rate; e.g., hovering turn and pirouette.
## 3 Results and Discussion
### Metal Release
Figure 6: Projected densities of states of surface Ni d-states as a function of Li\({}_{\text{x}}\) with down-spin density in green and up-spin in blue. E\({}_{\text{F}}\) is set to zero, indicated by the dashed purple line. Electron field splits are provided below to show the changes in Ni oxidation state. values of BV would be 2, 3, and 4 for Ni, Co, and Mn, respectively. We denoted the value of BV for the metal atom to be removed as BV_vac. The second descriptor based on the starting slab was the calculated number of the valence electrons on the metal, (Charge_vac), which was readily obtained from the DFT output file for the optimized geometry. As mentioned in the Introduction, reviewing the metal release studies in other CMOs, another feature we linked to \(\Delta G_{1}\) relates to the spin pairing and the vacancy-induced disruptions to it throughout the lattice. This is included in two descriptors, one being the magnetic moment of the metal to be released, (\(\rho_{\text{s}}\)) and the other is the sum of the magnetic moments of the neighboring metal atoms, (\(\Sigma\rho_{\text{s}}\)). The RFT model can assign the importance of each descriptor when predicting \(\Delta G_{1}\), shown in Figure 7a. We observed the magnetic moment of the vacancy is the most important in the case of delithiated NMC vacancies and obtained accurate predictions, shown by a testing R\({}^{2}\) value of 0.95 (Figure 7b). It is worth noting that remaining in the phase space of Ni\({}_{1/3}\)Mn\({}_{1/3}\)Co\({}_{1/3}\) limits the range of \(\Delta G_{1}\) values, shown by the tight groupings on the Ni, Mn, and Co release values, with Ni being the most favorable to release and Mn the least. This is a good starting point for descriptor selection and should be expanded to more complicated datasets with varied compositions and formulations of CMOs to obtain a large span of \(\Delta G_{1}\) values filling in the gaps seen in Figure 7b and include additional metals other than Ni, Mn, and Co to have a predictability beyond NMC.
## 5 Damage Detection and Localization
### Surface-Mounted Artificial Damage
From the analysis of Figures 10-15, several conclusions can be drawn regarding the damage detection and localization results using embedded PZT transducers compared to surface-mounted PZT transducers:  
**The Damage Index (DI) values:**  
The DI values for the S\({}_{0}\) mode are generally higher than those for the A\({}_{0}\) mode, indicating that the S\({}_{0}\) mode is more suitable for surface-related damage detection. The DI values decrease with increased thickness at both 50 kHz and 250 kHz, indicating that the DI values of the S\({}_{0}\) and A\({}_{0}\) modes of embedded UGWs are thickness-dependent. Compared to surface-mounted results, the DI values for the A\({}_{0}\) mode of embedded UGWs are lower, while those for the S\({}_{0}\) mode of embedded UGWs are higher. This is because the A\({}_{0}\) mode is out-of-plane dominant, while the S\({}_{0}\) mode is in-plane dominant. **Damage localization:**  
The surface-mounted artificial damage can be accurately located using embedded PZT transducers, even for thick composite panels (e.g., 9 mm). The embedded PZT transducers demonstrate better sensitivity in locating surface-mounted artificial damage in thick composite laminates compared to surface-mounted PZT transducers, as shown in Figure 15. These findings highlight the advantages of using embedded PZT transducers for damage detection and localization in thick composite laminates. The embedded transducers exhibit higher sensitivity and accuracy in locating surface-mounted artificial damage compared to surface-mounted transducers. Additionally, the choice of the appropriate UGW mode (S\({}_{0}\) or A\({}_{0}\)) depends on the nature of the damage and the specific requirements of the application. Figure 13: Localizations of surface-mounted artificial damage for the 2 mm thick panel at **(a)** 50 kHz and **(b)** 250 kHz using embedded PZT transducers and at **(c)** 50 kHz and **(d)** 250 kHz using surface-mounted PZT transducers (where the \"\(\cap\)\" is the position for real damage and the “\(\times\)” is the position for predicted damage).
## 1 Introduction  
Advances in wireless, sensor design and energy storage technologies have contributed significantly to the expanded use of Wireless Sensor Networks (WSN) in a variety of applications. Integrated micro-sensors with onboard processing and wireless data transfer capability, the most important components of WSNs, have already existed for some time [1,2]. However, at present, more efficient designs have successfully integrated a wide range of sensors. These sensors can monitor a large variety of environmental factors that can affect health including temperature, humidity, barometric pressure, light intensity, tilt, vibration and magnetic field intensity among others, using short-distance wireless communications.  
The enormous cost of providing health care to patients with chronic conditions requires new strategies to more efficiently provide monitoring and support in a remote, distributed, and noninvasive manner. Diverse European projects such as the \"HealthService24 Project\" are trying to improve the quality of medical attention by providing remote medical monitoring. These types of projects are currently developing mobile monitoring systems and integrating remote monitoring into their healthcare protocols to provide expanded healthcare services for persons who require monitoring and follow-up, but do not require immediate medical intervention or hospitalization.  
The importance of monitoring patient health is significant in terms of prevention, particularly if the human and economic costs of early detection can improve patient independence, improve quality of life, and reduce suffering and medical costs. The early diagnosis and treatment of a variety of diseases can radically alter healthcare alternatives or medical treatments. Prevention and effective control of chronic diseases has proven repeatedly to be more cost effective than conventional treatments at medical facilities. This is particularly true with chronic and incapacitating illnesses such as cardiovascular disease or diabetes. In the case of cardiovascular disease, 4% of the population over 60 and more than 9% of persons over 80 years of age have arrhythmias, or abnormal heart rates, which require occasional diminutive electrical shocks applied to the heart. Sensors can identify at-risk patients by monitoring and transmitting their real-time cardiac rhythms to medical professionals who can subsequently determine whether or not they require a pacemaker to assist establish and maintain normal sinus rhythm [3].  
Body sensor networks used to manage diabetes will one day involve implanted sensors, not only to monitor patient glucose levels, but also to administer insulin in a timely fashion. In sum, the abovementioned chronic diseases exemplify the need for biochemical and physiological continuous monitoring.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 5 Experimental Results
### GloU Loss vs. IoU Loss  
In this subsection, we explore the advantages of _GloU_ loss over _IoU_ loss based on an incoherent imaging training set. The results of the test set have been reported in Table 5. Moreover, Figure 7 shows the mAP values of the model trained with _IoU_ and _GloU_ losses against different _IoU_ thresholds, i.e., _IoU_ = {0.5, 0.55,..., 0.95}.  
The result in Table 5 shows that we can improve the performance of the model significantly by using _GloU_ loss as the bounding box regression loss. As shown in Figure 7, consistent improvement can be obtained across different values of _IoU_ thresholds. However, this improvement is not stable with the change of _IoU_ threshold. Nevertheless, by incorporating _GloU_ loss into our algorithm, we can slightly improve UAV detection performance in the laser active imaging domain.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric\textbackslash{}Training Set** & **Gray Images** & **Coherent Imaging** & **Incoherent Imaging** &
\begin{tabular}{c} **50\% Coherent and 50\%** \\ **Incoherent** \\ \end{tabular} \\ \hline PSNR & 20.03 & 21.70 & 22.54 & 22.22 \\ \hline SSIM & 0.39 & 0.61 & 0.83 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The metrics between different training sets and the real dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss\textbackslash{}Evaluation** & **AP** & **AP50** & **AP75** \\ \hline _IoU_ & 0.489 & 0.886 & 0.448 \\ \hline _GloU_ & 0.534 & 0.987 & 0.535 \\ Relative improv.\% & 9.20\% & 11.4\% & 19.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model performance between IoU and GloU losses.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Feasibility Test  
Since the network quality is still uncertain, the test was conducted at ground level. The Ground Control Station (GCS) was located at a specific place with good network quality, and the drone was brought inside a car traveling around the UKM campus. The waypoints are as shown in Figure 9. Since there is no suitable method to measure detail parameters to test the performance, this test aimed only to measure the throughput and  
Figure 8: Illustration of the drive test method.  
delay experienced by the drone and the GCS. The car speed was maintained at around 45 km/h during the test. The route is around 4.2 km, and the farthest point between the drone and the GCS is about 1.2 km.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 3 Butterfly Network
### Secure PLNC  
#### 3.3.1 Use of Secure CAF  
If no shared secret number is assumed between \(V_{1}\) and \(V_{2}\), it is difficult to realize the type of secrecy for the butterfly network presented in Section 3.2 under the problem setting of secure NC. Then, we consider the following assumption:  
**(A2)**: The pairs \((e_{1},e_{2})\), \((e_{4},e_{5})\), and \((e_{6},e_{7})\) are given as Gaussian MACs such as (2).  
In the network model given in Figure 2, only the channel \(e_{3}\) is a Gaussian channel with a single input. To achieve secrecy under the assumption (A2), we employ secure CAF in the Gaussian MACs appearing in this network model in the following way: In the Gaussian MAC \((e_{1},e_{2})\) at \(V_{3}\), the node \(V_{3}\) receives only the information \(M_{1}\oplus M_{2}\). Then, the node \(V_{3}\) forwards the received information to the node \(V_{4}\), and the node \(V_{4}\) receives the information \(M_{4}:=M_{1}\oplus M_{2}\). In the Gaussian MAC \((e_{4},e_{5})\) at \(V_{5}\), the node \(V_{5}\) receives only the information \(M_{4}\oplus(-M_{1})=M_{2}\). In the same way, the node \(V_{6}\) receives only the information \(M_{4}\oplus(-M_{2})=M_{1}\). That is, we employ secure CAF in the three Gaussian MACs at \(V_{3}\), \(V_{5}\), and \(V_{6}\). In this way, these uses of secure CAF realize the security (S2) under this method.
## 2 Background and Motivation
### Impact of Segment Resizing  
A common use of a hash table is to store key-value pairs, enabling associations between keys and values for dictionary implementation or determining if a key is part of a set of keys for set operations. When the initial capacity of the hash table is insufficient for the number of items to be inserted, hash table resizing becomes necessary. This resizing process incurs an overhead and affects the insertion throughput. The efficiency of hash tables relies on performing resizing operations at the lowest possible cost.  
If the bucket size is small, the hash table fills up quickly, triggering the hash function to initiate the costly rehashing operation, which impacts performance. Allocating a relatively larger hash table proves advantageous as it delays the need for resizing. In our scheme, intermediate resizing occurs during segment splitting to expand the space before a full-table rehashing is required. Segment splitting involves significantly fewer accesses to persistent memory compared to rehashing the entire table.  
In the ESH scheme, a bucket size of 256 bytes is used, allowing for the storage of more records once the hash table is created by the hash function. When this bucket becomes full, segment rehashing redistributes the key-value pairs to the buckets after the segment splits. This feature, combined with a larger bucket size, helps reduce the frequency of resizing operations, ultimately contributing to improved performance.
## 5 Conclusions  
In this paper, we proposed a method for reducing the computational complexity of third-order Volterra filters based on singular value decomposition, and evaluated its performance in reducing computational complexity and compensating third-order harmonic distortion. In the proposed method, the target third-order Volterra kernel is sliced into multiple two-dimensional filters by considering its symmetry, singular value decomposition is applied to the sliced two-dimensional filters, and the paths with small singular values are removed to achieve the reduction of computational cost while maintaining distortion compensation performance. The proposed method also achieves a reduction in computational complexity while maintaining the compensation performance of the nonlinear distortion. In addition, unlike conventional computational complexity reduction methods, the proposed method does not use adaptive signal processing, so the accuracy of the approximate model with reduced computational complexity is not degraded due  
Figure 6: Comparison of compensation of third-order harmonic distortion between proposed method and parallel-cascade truncated Volterra filter.  
to its adaptive error. Simulation results demonstrate that the proposed method has better harmonic distortion compensation performance than the parallel cascade truncated Volterra filter for the same amount of computation. Future works will include the application of the proposed method to nonlinear acoustic echo cancelers and nonlinear active noise control. In addition, it is necessary to compare the amount of computation and the performance of nonlinear distortion compensation with other nonlinear models.  
Conceptualization, Y.N.; Supervision, K.I. and Y.K.; Validation, K.I.; Writing--original draft, K.I.; Writing--review & editing, Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.