esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### AMP Type
#### 2.5.1 Single AMP Type
The efficacy of AMPs as antimicrobial drugs is reduced by their low proteolytic stability, suboptimal bioavailability, and relatively high manufacturing costs. In this respect, [PERSON] et al. described the synthesis of small molecules based on biphenyllyboxamide (15c), which possessed Gram-positive and -negative antibacterial activity and disrupted (35%) of _S. aureus_ biofilm [119]. The compounds appeared to be non-toxic. GH12 is an example of an AMP that has been de novo designed and synthesized with an emphasis on anti-dental activity. [PERSON] et al. investigated the AMP's effect in a complex biofilm model in vitro and a rat caries model in vivo. GH12 reduced the exopolysaccharide and lactic acid production in a S. mutans biofilm, as well as the abundance of caries-associated bacteria in a rat model and maintained microbial diversity in multispecies dental plaque-derived biofilms obtained from healthy volunteers [120]. In a similar publication, the same lead author investigated the pH responsiveness of GH12, showing that this AMP generated much lower minimal bactericidal concentrations and minimal inhibitory concentrations at pH 5.5 as compared to pH 7.2. The relevance being that dental caries are associated with acidification of biofilms on the affected teeth [121]. Consecutive exposure to sub-lethal levels of GH12 tended not to generate GH12 resistance, although the acquisition of resistance could not be ruled out. TAT-RasGAP317-326 is a cell-permeable chimeric peptide derived from the p120 Ras GTPase-activating protein that has anticancer properties via membrane lysis and cell death. [PERSON] et al. showed that this new AMP could limit biofilm expansion and prevent biofilm formation by _P. aeruginosa_ and _A. bumanmili_, while also inhibiting _S. aureus_ biofilms [122]. [PERSON] et al. used 'Collection of AMPs (CAMPR3)' software to cleave AMP ILTI obtained from plant seeds, which exhibits biological activity against pest insects. The result was the 198 amino-acid peptide KWI18 [123]. KWI18 reduced bacterial (_P. aeruginosa_) and fungal (_C. parapsilosis_) biofilms.
## 1 Introduction  
The primary function of pedicle screw systems is to maintain spinal stability while fusion occurs. However, in weakened or osteoporonic bone, the bone-screw interface is often poor and prone to failure, resulting in screw loosening or back-out after surgery. Transpecular instrumentation in patients with osteoporosis is difficult because of the challenge in achieving sufficient fixation strength. In addition, biomechanical studies have shown a reduction in the pull-out strength of pedicle screws in osteoporonic bone, which can ultimately lead to failure of internal fixation [1, 2, 3]. As such, fixation problemsare common in patients suffering from osteoporosis, and gaining sufficient pedicle screw fixation is a major challenge for spinal surgeons. Loosening of pedicle screws is a leading cause of non-union, pseudarthosis, and back pain after surgery.  
One method to improve the interface strength between pedicle screws and surrounding bone in osteoporotic patients is to use a bone-cement-augmented pedicle screw, which has been shown to increase the pull-out strength [4; 5; 6]. However, complications such as cement leakage outside the vertebral body and difficulty in removing the fixed screw have been reported. Symptomatic cement leakage with augmented screws has been reported at up to 17% [7; 8], while [PERSON] et al. indicated that perivetbral cement leakage occurs in 73.3% of cases, but most are clinically asymptomatic [9]. Besides cement augmentation, changing the screw design, including diameter, length, and thread design, may be used to improve fixation [10; 11; 12; 13]. Because the holding power of the bone-screw interface is poor in osteoporosis, increasing the diameter of the screw may improve fixation and stability [14]. However, the maximum diameter of the screw is limited by the anatomical shape of the pedicle, and so the viable size range for the screw is limited.  
A previous study by the authors demonstrated that having the proximal 1/3 of the pedicle screw left unthreaded significantly improves the pull-out strength and withdrawal force in comparison to a fully threaded screw [15]. The authors considered that this novel screw design could also improve the fatigue life of the pedicle screw in cases where only partial screw insertion is required [16]. Hence, this study aimed to evaluate the fatigue life and stress distribution of proximally unthreaded screws in accordance with American Society for Testing and Materials (ASTM) F1717 [17] and using finite element analysis. The results were compared with those obtained from fully threaded pedicle screws.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 6 Experimental Results
### End-to-End Latency  
We call end-to-end latency the amount of time between the moment a more generates a new UDP datagram, and the moment it reaches the root. It is computed at the root using a timestamp inside the datagram.  
Figure 7(a) plots the evolution of latency over time. As shown in Figure 7(b), 90% of the data reaches the root after 10, 25, and 35 s for FSK 868 MHz, OFDMM 868 MHz and O-QPSK 2.4 GHz, respectively. Figure 7(c) shows a long-tailed distribution of latency for shorter range PHYs.  
With 41 slots per slotframe and 40 ms timeslot, each mode on average gets one transmission opportunity every 1.68 s. Given the increased number of hops for lower range PHYs, the higher latency of O-QPSK 2.4 GHz, compared to that of FSK 868 MHz, was to be expected.  
Figure 8: _Cont._  
Figure 7: Cumulative Density Function (CDF) of buffer occupancy over the last 15 mins of the experiment. Having more than 15 entries occupied in the buffer (the red line) leads to data packet drops.
## 6 VLC-IoT: Existing and Proposed Approaches
### Multiple Access Technique in VLC-IoT  
Conventional multiple access techniques used for RF communication are usually deployed in most VLC-IoT networks. Moreover, most of these techniques are implemented without considering the unique characteristics of VLC. Nonetheless, a few techniques have also been proposed and developed for VLC.
# Wireless Remote Control for Underwater Vehicles
###### Abstract  
Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link.  
underwater multimodal networks; underwater acoustic networks; ROV wireless control; DESERT Underwater network simulations; underwater optical communications +
Footnote †: journal: Marine Science and Engineering
# A Cost-Optimized Model for Dynamic Port Hinterland
## 3 Methodology
### Methodological Proposal
#### 3.2.3 Warehouse Costs (_Whc_)  
Warehouse Costs (_Whc_) represent a major logistics expenditure encompassing rent, depreciation, overheads, staffing, insurance, and taxes [55], gaining prominence with e-commerce growth [72]. Modern practices require strategic placement of intermediate storage facilities for cargo consolidation, with companies often utilizing multiple locations to optimize operational efficiency [53].  
The _WHC_ calculation is based on warehouse size and geographical location, computed as a location-specific unit value multiplied by square footage [57]. The required space is determined by two key factors: goods type (volume) and service rotation time, being a direct contribution of time in costs. This relationship can be formulated following methodologies from [PERSON] et al. [54] and [PERSON] [52], incorporating modern warehouse design principles [51]:  
\[WHC=\lambda\cdot S, \tag{10}\]  
where \(\lambda\) represents the unit cost of the warehouse [52] and \(S\) denotes the surface area, which can be determined by:  
\[S=\delta\cdot CM\cdot RT, \tag{11}\]  
where \(\delta\) is the warehouse occupation density for any product, defined as square meters per kg [54], \(CM\) is the daily consumption in kg of the product, and \(RT\) is the rotation time of transport service (in days).  
By combining Equations (10) and (11), a comprehensive expression for _WHC_ is derived:  
\[WHC=\lambda\cdot\delta\cdot CM\cdot RT. \tag{12}\]  
This formulation provides a practical means of calculating _WHC_ for various scenarios in logistics planning and optimization.  
Recent technological advances, particularly robotics and artificial intelligence, are reshaping _WHC_ calculations through improved space utilization and operational efficiency [82]. Sustainability considerations are also transforming _WHC_ models, incorporating energy efficiency, waste management, and carbon footprint metrics [58]. As warehouse operations evolve, future research must develop adaptive _WHC_ models that integrate these technological and environmental factors while addressing modern supply chain complexities.
## 3 Results
### Soil Surface Status after Penetration  
Figure 6a,b showed the soil status at the end of tests for the specimens with maximum and minimum force in both experimental and simulation results. During the penetration, rise occurred at the surface of soil 1 and soil 2 around the specimens. However, there was no obvious rise in soil 3, and the sinking was found in the soundings of the specimen. When talking about the rise height, the specimen C under soil 1 and specimen B under soil 2 was higher than that of specimen A under the same soil. Evident sinking (a radian on the soil surface close to the specimen) with soil 3 was found in specimen A, but not in specimen D, indicating the soil rise got by specimen D neutralized the sinking. It means that the better flowability of the soil was obtained when penetrating using bionic specimen. Conversely, it also means that the soil suffered more pressure and got higher soil strength when penetrating using specimen A. It can thus be assumed that the reason for the lower force obtained by bionic specimen was because the soil had more flowability and less soil strength during the test, which made the soil more easily penetrated.  
Figure 6c shows the force analysis during specimen penetration. The different surface rise level was obtained using different specimens, indicating the directions of force was different as well as soil moving status. According to [PERSON]'s Second Law, the crosswise force \(\mathrm{F_{x}}\) and the portrait force \(\mathrm{F_{y}}\) imposed on the soils were:  
\[\mathrm{F_{x}} =\mathrm{F21+F22}=\mathrm{F}\times\cos\varphi \tag{19}\] \[\mathrm{F_{y}} =\mathrm{F11-F12}=\mathrm{F}\times\sin\varphi \tag{20}\]  
A larger proportion of \(\mathrm{F_{y}}\) results in the higher rise for soil, and a larger proportion of \(\mathrm{F_{x}}\) subjected the soils to more pressure and more soil strength, which was unfavorable for the penetration. According to Equations (19) and (20), the \(\mathrm{F_{x}}\) and \(\mathrm{F_{y}}\) are related to the included angle \(\varphi\). between the forces and the horizontal direction; larger \(\varphi\) leads to smaller proportion of \(\mathrm{F_{x}}\) and larger proportion of \(\mathrm{F_{y}}\); and vice versa.  
To analysis the force in soil, the force direction imagines was obtained by simulation as shown in Figure 6d. The obtained \(\varphi\) was listed in Table 4. It believes that, no matter what kind of soils, the bionic specimen had a larger \(\varphi\) and then got a larger proportion for \(\mathrm{F_{y}}\) and a smaller proportion for \(\mathrm{F_{x}}\), indicating the bionic specimen did change the directions of forces imposed on the soils during penetration. The larger proportion of \(\mathrm{F_{y}}\) and smaller proportion of \(\mathrm{F_{x}}\) when using the bionic specimen leaded to the better flowability and lower soil strength, which is the mechanism for the higher rise for soils and lower penetration force.  
Figure 5: (**a**) The internal friction angle cohesion and (**b**) the cohesion of the soils before and after tests.
# Synthesis of Four-Link Initial Kinematic Chains with Spherical Pairs for Spatial Mechanisms
## 4 Results
The true function is modeled as a Gaussian function \(f(x)=e^{-x^{2}}\), while the approximation is derived from its Taylor series expansion, as shown in Figure 3.3 Key elements in the graph: the solid blue curve represents the true function \(e^{-x^{2}}\). The dashed red curve represents the approximation function using a Taylor series. The gray shaded area represents the error magnitude, which measures the deviation between the true and approximate functions. The approximation function closely matches the true function near \(x=0\). As \(x\) moves further from \(0\), the approximation deviates more, showing increasing the error [4]. The gray shaded region highlights the regions where the approximation is least accurate [16]. Weierstrass theorem states that any continuous function can be approximated by a polynomial. Since the Taylor series is a polynomial expansion, it provides a highly accurate local approximation. The error is small near \(x=0\) but grows significantly as \(|x|\) increases. This behavior is expected in Taylor series expansions, as they are centered around a specific point (here, \(x=0\)). The approximation function [17] is highly accurate near \(x=0\), but accuracy decreases for a larger \(x\). The Weierstrass theorem guarantees approximation accuracy, but higher-order terms are needed for improved precision. Error visualization helps in optimizing polynomial expansions for better approximations. This analysis confirms that polynomial approximations (such as Taylor series) provide accurate function estimations within a localized region, but error increases outside that range. Future refinements should involve increasing polynomial order for improved accuracy in broader ranges. Figure 3.4 Analysis of the accuracy of a polynomial approximation.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 4 Experimental Setup
### Network Design and Configuration
In the event of node failure, gossip-based routing offers a high probability of successfully relaying information. The gossip-based approach, depending on the transmission probability, can be tailored to minimise energy consumption compared to continuous broadcasting. It also provides adaptability by allowing adjustments to various aspects, such as the speed of information dissemination (based on \(T_{prob}\)) and the reduction of transmission interference. However, as the network scales up in terms of both length and width, it can experience performance degradation due to interference. While the allocation of dedicated time slots can effectively address this issue, it introduces a trade-off by increasing the transmission delay. Consequently, gossip-based approaches prove highly effective, especially in the context of long, thin networks. We used the various combinations of the transmission probabilistic distribution (\(T_{prob}\)) in the presence of two and three active relay nodes, respectively. To evaluate the performance, we sent at least 1000 pings from the source to the destination node via all active nodes and noted the performance by calculating the Packet Reception Rate (PRR) at the Destination node (D) using the one-hop model. We used Equation (1) to calculate the ideal packet reception rate using various probabilistic values in the presence of different numbers of active relay nodes. \[1-((1-T_{prob})^{n}), \tag{1}\]  
where:  
* \(T_{prob}\) stands for Transmission probability;
* \(n\) stands for the number of relay nodes.
## 5 Simulation Results and Analysis
### Beampattern Analysis  
Figure 5 shows the transmit beampattern formed by the waveform matrix \(\mathbf{S}\) designed through the proposed ADMM-based approach for a DFRC system with 32 antenna elements.  
Figure 6 shows the transmit beampatterns as synthesized by the waveform matrix \(\mathbf{S}\) designed through the proposed ADMM-based approach and that of iterative optimization with amplitude weighting (IO-AW) as reported in [46]. In both cases, the systems have 16 antenna elements, and the radar target is located at \(\theta_{R}=0^{\circ}\) and the communication user at \(\theta_{C}=45^{\circ}\). Moreover, in both cases, the power of the desired radar waveform is designed to be 10 dB more than that of the communication waveform. As can be seen in the figure, the IO-AW method leaks power at \(-45^{\circ}\) or, in other words, makes a mirror lobe toward a direction where there is no communication user. Otherwise, the sidelobe levels of the two beampatterns are almost the same. Thus, the beampattern formed through the proposed method outperforms the beampattern formed through IO-AW.
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 10 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **References** & **Application** \\ \hline Deep autoencoders & [119]\({}^{\text{T}}\), [136]\({}^{\text{TF}}\), [137]\({}^{\text{TF}}\) & Gearbox, Tidal turbine, Train \\ \hline SAE & [101]\({}^{\text{T}}\), [114]\({}^{\text{TF}}\), [117]\({}^{\text{C}}\), [122]\({}^{\text{TF}}\), [139]\({}^{\text{TF}}\) & CNC machine, Bearing, Bridge \\ \hline DAE & [99]\({}^{\text{T}}\), [131]\({}^{\text{TF}}\) & Bearing, Train bogie \\ \hline SAE + DAE & [108]\({}^{\text{T}}\) & Bearing \\ \hline DBN & [97]\({}^{\text{F}}\), [100]\({}^{\text{TF}}\), [104]\({}^{\text{W}}\), [105]\({}^{\text{F}}\), [111]\({}^{\text{T}}\), [112]\({}^{\text{T}}\), [115]\({}^{\text{TF}}\), [116]\({}^{\text{W}}\), [123]\({}^{\text{T}}\), [124]\({}^{\text{W}}\), [129]\({}^{\text{T}}\), [134]\({}^{\text{T}}\), [135]\({}^{\text{T}}\) & Hydraulic system, Gas engine, Acoustic signals, Train, Transmission Chain, Gearbox, Milling machine, Top coal cavity Aircraft wing \\ \hline SAE + DBN & [118]\({}^{\text{TF}}\) & Bearing \\ \hline CNN & [98]\({}^{\text{T}}\), [103]\({}^{\text{F}}\), [91]\({}^{\text{W}}\), [128]\({}^{\text{T}}\), [132]\({}^{\text{T}}\), [133]\({}^{\text{T}}\), [134]\({}^{\text{T}}\), [135]\({}^{\text{T}}\), [144]\({}^{\text{F}}\) & Wind generator, Steel Frame, Structural \\  & & damage detection, Aluminum plate \\ \hline GRU + LSTM & [110]\({}^{\text{TF}}\) & Spur gear \\ \hline CNN + LSTM & [130]\({}^{\text{T}}\) & CNC milling machine \\ \hline ReLu & [106]\({}^{\text{T}}\) & Helicopter \\ \hline Transfer + SAE & [113]\({}^{\text{F}}\) & Gearbox \\ \hline Transfer + CNN & [121]\({}^{\text{T}}\) & Gearbox \\ \hline DNN classifier \({}^{\text{1}}\) & [142]\({}^{\text{TF}}\) & Cantilever beams \\ \hline Multiscale inner product & [120]\({}^{\text{T}}\) & Bearing \\ \hline \hline \end{tabular}  
* The DNN classifier can indicate deep NN, convolutional NN and recurrent NN, but [PERSON] [142] just noted a DNN classifier. Superscripts of preprocessing type: time (\({}^{\text{t}}\)), Fourier (\({}^{\text{F}}\)), wavelet (\({}^{\text{W}}\)), compressed sensing (\({}^{\text{C}}\)), time + Fourier (\({}^{\text{TF}}\)).  
\end{table} 
Table 2: Summary of deep-learning-based structural health monitoring by architecture. Data labeling is not required for unsupervised learning. Data labeling requires a lot of time and effort in specific applications. Deep learning is expected to be a tool for solving complicated vibration problems and contributing to efficient structural health monitoring. This summary is intended to be used in future studies as a possible guideline when newly emerged machine learning algorithms are used for vibration-based structural integrity monitoring. This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) [grant number 2019M3E5D1A01069363]. The authors declare no conflict of interest.
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method
### Analysis of Influencing Factors on the Distribution of the Magnetic Induction Intensity Formation
#### 6.4.4 Analysis of Influencing Factors on the Relative Angle Between Two Wells on the Same Plane  
The angle \(\theta\) of the two wells axes is adjusted, and the upper and lower electrodes are rotated at \(0^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) within the same plane of the two wells with Q as the center of the origin of the (\(X\),\(Y\),\(Z\)) coordinate system.  
Figure 8a illustrates the impact of different axis angles on the casing current. As the axis angles increase, the vertical distance \(d_{u}\) between the upper electrode and casing gradually increases, while the vertical distance \(d_{d}\) between the lower electrode and casing gradually decreases. The casing current gradually changes its flow direction at point M in a downward direction towards point M. When the included angle is \(90^{\circ}\)and thus making both well axes vertical, there is a reverse symmetrical distribution in the casing current amplitude with respect to the X-axis as its center, consequently resulting in zero casing current at ranging point M. The results indicate that an increase in the axis angle leads to the attenuation of the casing current amplitude along with the formation of reverse current flow on the casing. Furthermore, when both well axes are perpendicular to each other, a reverse symmetrical distribution is achieved.  
Figure 8b illustrates the influence of different axis angles on the induced magnetic field intensity of the drilling well axis. With the change of axis angle, the changes of \(d_{u}\) and  
Figure 8: The amplitude of the casing current and the intensities of the induced magnetic field on the axis of the drilling well at different angles between two axis in the same plane.  
lead to the vertical distance change between the position of the magnetic field measurement unit and the casing. The attenuation characteristics of \(B_{c}\) are similar to those of \(I_{c}\).
## 2 Mathematical Preliminaries
### Trajectory Optimization
The other three unknowns \(\{\lambda_{r_{0}},\lambda_{u_{0}},\emph{and}\lambda_{v_{0}}\}\) in Equation (17) and the minimum flight time \(t_{f}\) are obtained by solving a two-point boundary value problem in which the three scalar (final) conditions (18) and the last of Equation (20) are numerically enforced. For an assigned set of parameters \(\{a_{c},r_{0},r_{f}\}\) that define the diffractive sail propulsive characteristics and the mission scenario, the two-point boundary value problem has been solved through a hybrid numerical technique that combines direct methods with gradient-based routines.