esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LoRaWAN Downlink Routing Control Strategy
### Objective Function of the Minimum Path Selectivity Routing Control Strategy  
By employing the triangle module operator [24] in fuzzy mathematics to realize the routing control of the minimum routing degree. The triangle module operator is shown in Equation (14):  
\[Y(x_{1,}x_{2})=\frac{x_{1}\cdot x_{2}}{1-x_{1}-x_{2}+2(x_{1}\cdot x_{2})} \tag{14}\]  
Figure 11: The path bandwidth occupancy rate at time \(t+T\).  
where \(x_{1},x_{2}\) represent the two parameters involved in the optimization integration, \(x_{1},x_{2}\in[0,1]\). In order to ensure the metrics consistency between different parameters, \(CB_{n},PS_{n}\) and \(T_{n}\) are normalized here according to Equations (15)-(17):  
\[CB_{n}^{\prime}=\frac{CB_{n}-CB_{\min}}{CB_{\max}-CB_{\min}} \tag{15}\]  
\[PS_{n}^{\prime}=\frac{PS_{n}-PS_{\min}}{PS_{\max}-PS_{\min}} \tag{16}\]  
\[T_{n}^{\prime}=\frac{T_{n}}{T_{\max}} \tag{17}\]  
where \(CB_{\min},~{}CB_{\max}\) are the minimum and maximum values of the bandwidth occupancy rate at time \(t\) in the alternative routing set, \(PS_{\min},~{}PS_{\max}\) represent the minimum and maximum values of the predicted bandwidth occupancy rate score at time \(t+T\) in the routing set, \(T_{\max}\) represents the maximum transmission delay that can be tolerated in the LoRaWAN downlink communication service. Based on the triangle module operator associative law, the path selectivity can be expressed with Equation (18).  
\[Q=\min\{CB_{n}^{*},Y(PS_{n}^{*},T_{n}^{*})\} \tag{18}\]  
The selectivity of multiple transmission paths between the source node and the destination node is calculated by Equation (18). When there is a downlink data packet transmission, the path with smallest \(Q\) is preferentially selected as the main transmission path. When the \(Q\) values of different paths are equal, the path with the smallest number of switches \(m\) in path \(R_{n}\) is preferentially selected. When \(Q\) and \(m\) are still the same, the path that is preferentially selected is the path less frequently used.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion  
For the characterization of the studies covered in this review, from the technical and application points of view, the following variables were considered:  
* Application area.
* Healthcare benefits.
* IoMT devices.
* Communication protocols.
* Networking technologies.
* Network technical requirements.
* Security mechanisms or protocols.  
The objective of the study is to identify the main characteristics from the technical and application points of view focused on healthcare, which are used in smart healthcare systems. In order to show the results graphically, pie diagrams were used that allow a simple and quick interpretation of the works' distribution, considering the variables proposed.  
When searching for papers, we identified great diversity in the publication types obtained. Since 5G network deployment is a relatively new topic, we found several papers that make proposals or describe prototypes under development. There are also implemented systems (although few) and theoretical studies with perspectives that deserve to be analyzed. Figure 4 shows the percentage of studies classified according to the type of work presented in each of the articles: 56.81% (\(n=25\)) of the studies are proposals--that is, unimplemented work, thereby containing almost no technical and safety features. However, they show theoretical support and propose detailed models, which contribute to the identification of analysis variables. On the other hand, 22.73% (\(n=10\)) of the articles report on real-world implementations. In general, these documents focus on the benefits, merits, and advantages of the systems, without neglecting technical characteristics and safety requirements.  
Figure 3: Global distribution and networks related to the articles in this review.  
Regarding the implementations, which comprise 15.91% (\(n=7\)) of the studies, the results are detailed for developed and functional systems which address issues related to security and privacy mechanisms, although not in their entirety.  
Finally, 4.55% (\(n=2\)) of papers describe prototypes' development, that is, models that serve as apparent and concrete representations of operational systems. These works focus on most of the variables analyzed, giving details of technical requirements, proposed applications, and safety considerations.  
The following sections review the results obtained in response to the research questions initially posed and that guided the research.  
RQ1: What Are the Healthcare Applications That Can Be Developed with the Implementation of 5G in Smart Health Systems?  
The medical system has dynamically evolved, and this has made smart health a reality. The development of these systems lies in the possibility of improving people's life quality. Figure 5 shows a classification generated based on the application developed in each work. We have identified three specific application areas: remote care, data transmission, and continuous real-time monitoring. In addition, in Table 2, articles that belong to each classification are listed.
## 4 Discussion
### Significance and Practical Application of Results  
This study has attempted to simulate the possible contamination of milk with the microorganism _E. coli_, which is widely used in food as an indicator microorganism and is among the most frequent contaminants of food. The treatments to which the HM was subjected for the present study are those normally used in HMBs, and allow us to evaluate the effect of possible contamination on the type of milk that is administered to the newborn.  
This study warns of the risk to neonatal health posed by certain practices carried out during feeding in NICUs. Limited studies have been published that discuss the influence of milk holding times under NICU conditions (temperature and holding time) on microbial growth. The present study provides information on the growth of _E. coli_ under certain conditions (22 \({}^{\circ}\)C during 4 h) in HM subjected to various treatments, which may help to re-evaluate milk holding times. It also demonstrates the importance of avoiding contamination of milk during handling, since microorganisms such as _E. coli_ easily develop in milk. These conclusions allow for immediate clinical application.
## 2 Preliminaries  
### Tailless Aircraft Experimental Setup  
The studied tailless aircraft is of the medium-aspect-ratio and blended-wing-body configuration as shown in Figure 1. There are eight individually driven control surfaces, which could realize the traditionally defined control deflections, i.e., the elevator, the aileron and the rudder. The elevators are classified in left and right having positive deflection when turning downward, the ailerons are also classified left and right having positive deflection when the left surface goes up and the right one goes down. The split drag rudders (SDRs), each one positioned on one side of the wing, are composed of two deferential surfaces: a trailing-wing flap and a mid-wing spoiler. The positive deflection of the rudder is achieved when the left SDR splits symmetrically and the right one keeps at zero.  
From a flight test point of view, the wind tunnel model should be geometrically and dynamically similar to the full-scale aircraft. Hence, a 10% scaled model was fabricated with carbon fiber and aluminum alloy. The physical parameters, including the characteristic size, mass, and inertia, are carefully tuned as listed in Table 1. A spherical joint is selected to connect a vertical support rig to the airframe at the center of gravity (CG), which frees the aircraft's rotational motions (roll/pitch: \(\pm 40^{\circ}\) yaw: \(\pm 180^{\circ}\)) and eliminates the translational motions, as shown in Figure 2. The physical parameters of the scaled model are listed in Table 1. The model is equipped with not only the surface actuators, but also the sensors including two vanes for angle of attack (\(\alpha\)) and angle of sideslip (\(\beta\)), an inertial measurement unit (IMU) for angular rates (\(p,q,r\)), and an attitude and heading reference system (AHRS) for Euler angles (\(\phi,\theta,\psi\)). A flight computer is employed for the implementation of control laws and data acquisition (100 Hz) using the technique of rapid control system prototyping. For simplicity, the flight computer is placed outside the wind tunnel test section and wired to the onboard avinics. A \"pilot\" is responsible for handling the aircraft model with a stick.  
Figure 1: Layout of the tailless aircraft.
## 4 Temperature Effects on the Carrier-Injection Plasmonic Micro-Ring Modulator  
The effect of temperatures on the resonant peak originates from the thermos optical property of silicon, which is 0.000186/K [22]. The resonant peak shifts 0.11 nm/K as temperature varies, and the temperature changes 1 K are enough to render the device inoperable at the aiming wavelength [23]. Therefore, heating resistance and temperature control are necessary for the MRMs by thermo-optic resonant tuning [24]. However, the CIPMRMs we proposed are insensitive to temperature changes due to their low Q factor. Therefore, it needs no complex control circuit for resonant tuning.  
Figure 6a represents the resonant peak of CIPMRMs with 5 nm SiO\({}_{2}\) shifts from 1547.5 nm to 1552.5 nm as temperature varies from 280 K to 360 K. It can be seen that the resonant peak shifting depends on temperature linearly and that the result is 0.075 nm/K (Figure 6b shows), which is explained by the stable thermos optical coefficient of silicon.  
Figure 5: The transmission curve of the plasmonic micro-ring resonator with different SiO\({}_{2}\) thicknesses.  
Figure 7a shows the transmission curve of CIPMRM (5 nm SiO\({}_{2}\)) on the resonant state (1.13 V) and off the resonant state (1.21 V) at 1550 nm. When the temperature increases, the transmission curve has redshifts. Although the insertion loss rises and the extinction ratio decreases as the transmission curve has redshifted, the plasmonic micro-ring resonator sustains a high extinction ratio during temperature variation. When the temperature decreases, all of the transmission curve have blueshifts. Both the insertion loss and the extinction ratio of CIPMRM decrease. The insertion loss changes slowly, which causes the extinction ratio to decrease with decreases in temperature. The marked wavelength range represents permissible resonant wavelength shifts with temperature variation. We conclude that an ER of 9 dB is satisfied as temperature varies within the 60 K range for CIPMRMs (5 nm SiO\({}_{2}\)). The low Q factor promotes stable temperature for the micro-ring modulator. As for the CIPMRM with 40 nm SiO\({}_{2}\), it sustains an ER of 9 dB within 27 K due to its higher Q factor (Figure 7b).
## 3 Experimenting Results
### Adaptability
Horizontal black line represents the anomaly detection threshold and dashed blue line splits data by each failure type. In addition, each failure type contains all its magnitude versions stacked one after another incrementally, causing a staircase-like behaviour given that higher failure magnitudes result in higher damage indexes. Figure 10: Damage indexes of correct test data before (**left**) and after transfer learning (**right**) using 2D-CNN-AE model in data of same die one week later. Horizontal black line represents the anomaly detection threshold. Then, the anomaly detection threshold was adjusted in the retrained algorithm, using the techniques presented in Section 3.1. Percentile 90 and percentile 95 thresholds on correct validation data were tested, and failure validation data were also used to search for the best threshold. None of these thresholds worked correctly given that damage indexes on training and validation data are smaller than the ones for test correct data. Therefore, all test samples, either correct and failure, are above selected thresholds, which is caused by model overfitting that is unable to differentiate anomalous and correct strokes. All in all, transfer learning works correctly to adapt the model for data of the same die in different periods of time. However, it does not work to adapt the model trained with data of one die to data of different die. The reason is that data of the same die are collected for the same forming process and under similar operating conditions, but different dies perform different forming processes and have different operating conditions. Therefore, one model for each die should be created, but then they could be reused over time.
## 4 Dynamic Analysis of the PIM System  
The internal algorithm of the ANCF investigated in this study was developed using MATLAB (2024a). To further validate the effectiveness of the model, a simulation of the deployment process of a large floating structure at a water depth of 1700 m was conducted. The model depicts a work vessel moored at the sea surface on the left end, with a towing cable extending from it, and a floating structure attached to the cable's end. The cable's characteristics, including flexibility and highly nonlinear behavior, were incorporated as established by the models developed in earlier sections, primarily simulating the free deployment process of the large floating structure. Additionally, simulations conducted with ADAMS software (v.31.1.0), based on catenary theory, were performed under the same conditions, and the results were then compared with those of the ANCF model.  
In practical applications of the multibody dynamics simulation, especially those involving flexible objects such as cables, ADAMS does not offer a direct modeling method and can only perform approximate simulations using existing modules and constraints. After a thorough comparison of the advantages and disadvantages of different modeling methods for flexible bodies in this software, this paper adopts the approach of adding bushing motion pairs between each small cylindrical segment to simulate cables based on the catenary theory, thereby enhancing the practicality and accuracy of the model simulation [46].  
Additionally, the Euler-Lagrange method used by ADAMS belongs to the second type of model, yielding a set of differential algebraic equations for the multibody dynamics model obtained. Its built-in HHT solver is employed to solve the equations, similar to the solution method used in this paper. Therefore, the results of the two models can be effectively compared and analyzed.
## 7 Cross-Layer Design Schemes  
### Introduction  
Cross-layer protocols design specific to WBANs is a relatively immature research area but has the potential to deliver greater efficiencies than single layer adaptation schemes. Cross-layer protocol research in the comparable WSN area is more established with many schemes able to demonstrate significant energy efficiency improvements via the optimization of two or more layers. Generally, cross-layer schemes can be divided into _loosely coupled_ and _tightly coupled designs_. Loosely coupled protocol designs focus on adapting the parameters available at a lower layer to optimize the performance at a higher layer. For example channel conditions at the physical layer could be utilized at the network layer as part of an intelligent routing algorithm. In the loosely coupled approach the individual layers within the protocol stack remain, but the parameters normally accessible only to the next highest layer could be made available to higher layers to provide performance gains or better/more informed decision making at the upper layers. In the tightly coupled approach to cross-layer design, the different layers are optimized together to form one complete solution to an optimization problem. Performance gains for tightly coupled designs should be greater than loosely coupled as they do not incur the same stack communication overhead, however this may be at the expense of protocol transparency and maintenance. This section highlights the benefits of cross-layer interaction across specific layers and details some of the relevant cross-layer schemes and the benefits they deliver to low power wireless communications.
## 5 Control Algorithms for UAV Networking
### Centralized Control Algorithm
#### 5.1.1 Leader-Follower
This helps the UAVs compensate for aerodynamic disturbances and avoid collisions between UAVs within the formation [88]. Several test systems have been built, including quadrotor UAVs, UWB positioning systems, ground control stations, and wireless communication systems. The control algorithms have been tested on the MATLAB platform and on real hardware such as Pixhawk with the ROS operating system. These tests demonstrate the feasibility of the leader-follower method in UAV swarm control [89; 90].
## 6 Numerical Results
### Performance Evaluation of the Proposed LRT-BER Fusion Rule
\(P_{fa}=0.1\), \(P_{d}=0.6\), and \(P_{\rm FA}=0.01\). Figure 5: ROC curves for the LRT-BER fusion rule based on CF closed-form performance evaluation with \(N\) = 20. The average received SNR is \(\bar{\gamma}_{b}=-5\) dB. Each local detection index is assumed to be equal to each other (\(P_{fa}\) = 0.1, \(P_{d}\) = 0.6). The system alarm probability is \(P_{\rm FA}\) = 0.01. LRT-BER fusion rule with the Nakagami-2 assumption outperforms that with the Ricean-2 and Rayleigh assumption. The fusion rule with Rayleigh fading assumption exhibits the worst detection performance. This behavior can be explained easily. The Rayleigh fading assumption is the worst case, since the line of sight component of the received signal is not included. In Figure 5, we also give the detection performance of a single sensor system, which is marked as the intersection of the dashed lines. We can find that the detection performance of the proposed fusion rule with BPSK and 2 FSK can be enhanced by fusing. However, when the DPSK modulation is employed, the fusion system does not provide enhanced performance. This is caused by the large transmission error of DPSK modulation, which is also derived from Equations (7), (9) and (10). From the above results, we can conclude that the non-ideal transmission channel can deteriorate the system detection performance and even exhibits worse performance than a single sensor.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 3 Fundamentals of Acoustic Emission Technique
### Parameters of Acoustic Emission Signals
For example, AE energy, duration, and frequency centroid exhibit greater sensitivity to shear fractures in rock joints compared to amplitude and rise time. [PERSON] et al. [103] concluded that energy and peak frequency are effective indicators of rock crack growth processes. Different loading modes also result in varying predominant parameters. Therefore, careful recognition of the significance of these parameters is essential for tailored quantitative analyses of specific source processes in geomaterials.
## 3 Basic Principles of Pulsed Operation in a Laser
### Active/Passive Mode-Locking  
As a preface to the Kerr lens mode-locking mechanism, we provide a general and intuitive description of the mode-locking techniques that preceded Kerr lens mode-locking. We refer the reader interested in a detailed study to the corresponding literature [19; 20; 21; 22], but a quick review is important to set the background for the Kerr lens mode-locking technique. In general, mode-locking establishes preference for pulsed operation, either in loss or in gain. Mode-locking techniques are either **active**, where an external modulation inside the cavity enforces pulsed operation, or **passive**, where the preference for pulsed operation is introduced by an additional intra-cavity nonlinear response.  
Active mode-locking can be achieved by intra-cavity amplitude modulation (AM) or frequency modulation (FM) at an exact multiple of the cavity repetition rate, \(f_{rep}\)[19; 20; 23; 24]. The first method uses the modulator as a fast shutter in the cavity, where pulsed operation is achieved by synchronizing the modulation rate with the round trip time of the pulse, so a pulse will always pass through the modulator when the \"shutter is open\", cutting away the pulse edges and pushing it towards shorter periods. The second method introduces a phase/frequency shift to light arriving outside of the temporal window of mode-locking, thus constantly pushing its spectrum, outside of the gain bandwidth. Pulses can therefore evolve only during the short time window of \"no phase\", similar to the window of \"no loss\" with AM modulation.  
Passive mode-locking is usually achieved by introducing an effective saturable absorber into the cavity, which reduces the loss for high intensity light due to the saturation of the absorbing medium [21; 25; 26]. Therefore, CW will suffer constant absorption losses, whereas a pulse with a high peak power will quickly saturate the absorber by the leading part of the pulse, considerably reducing the loss for the main part of the pulse. The saturable absorber then acts as a shutter that the pulse itself activates with a much faster modulation rate than electronically-based shutters. For short pulses, one prefers a fast response of the saturable absorber to intensity variations (short recovery time) [22].
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 2 Methods for Generating Cryptographic S-Boxes
### Construction from Smaller Components  
A Boolean function can be implemented in (AND, XOR) algebra (or any other Boolean algebra). The minimum number of AND-gates in the (AND, XOR) representation of a Boolean function is called the multiplicative complexity (MC). The MC is an important property for various problems connected to S-boxes, such as logic circuit minimization, algebraic cryptanalysis, and optimal masking against higher-order power analysis attacks. However, low multiplicative complexity can conflict with other S-box criteria, such as nonlinearity and differential uniformity.  
In [9], we provided an analysis of multiplicative complexity for all \(4\times 4\) bijective S-boxes and showed that MC of any \(4\times 4\) S-box is at most 5. It is, however, difficult to determine the MC of an existing (larger) S-box. Thus, in [46], we described the process of generic construction, which can be applied in the construction of strong \(n\times n\) S-boxes with low multiplicative complexity. Instead of analyzing an existing complex Boolean function, we constructed an S-box from smaller primitives with known multiplicative complexity.