esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review  
Our work focuses on the PLS of ISTNs from a signal processing perspective. Our contributions can be summarized as follows:  
* We classify PLS methods into four main categories: resource allocation, beamforming, cooperative communication, and physical characteristics. For each category, we provide a comprehensive review of state-of-the-art research conducted in recent years.
* In the area of resource allocation, we discuss various strategies designed to optimize the use of network resources while ensuring secure communication. These strategies often involve complex optimization techniques to efficiently manage the allocation of bandwidth, power, and other resources, aiming to maximize secrecy capacity and enhance overall network security.
* Beamforming is another crucial aspect of our review. We examine advanced beamforming techniques that focus signals in specific directions to improve signal quality and reduce the risk of eavesdropping. By leveraging optimization algorithms, these techniques enhance the secrecy capacity and energy efficiency of the network.
* Cooperative communication involves multiple network nodes working together to achieve secure communication. We highlight recent advancements in this field, including methods where nodes cooperate to create a more robust and secure communication environment. These methods often use optimization techniques to balance cooperation benefits with energy efficiency and security requirements.
* Finally, we explore physical characteristics that inherently enhance the security of ISTNs. This includes the exploitation of unique propagation properties of wireless signals, such as path loss, fading, and channel properties, to improve security. Techniques in this category often involve optimizing these physical properties to achieve better secrecy capacity and normalized secrecy capacity.  
By providing a detailed review of the latest advancements in resource allocation, beamforming, cooperative communication, and physical characteristics, we aim to offer valuable insights into how to enhance the PLS of ISTNs. First, we take a look at various performance metrics that are commonly used in providing PLS for a network.
## 3 Results and Discussion
### Out-Sample Testing  
Recall that in out-sample testing, the learning algorithm is trained on one dataset, while its performance is evaluated on another dataset. Thus, the model was trained on the Chile dataset, while the performance was then evaluated on the Hindukush and California datasets: this step was important in gauging the generalizability of the proposed approach. Tables 5-7 show the performance scores of various algorithms using out-sample testing:  
When the models were trained on the Chilean dataset, and evaluated on the Hindukush and California datasets, we found that LR achieved the highest accuracy (91.7  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Training on California**} \\ \hline \multicolumn{10}{c}{**Testing on Chile**} \\ \hline
**Approach** & **DNN** & **RF** & **SVM** & **LR** & **DNN** & **RF** & **SVM** & **LR** \\ \hline Accuracy & 99.44 & 91.2 & 73.4 & 90 & 99.33 & 85 & 78.86 & 99.5 \\ Precision & 99.45 & 54.2 & 36.8 & 80.63 & 99.44 & 50.14 & 34.14 & 85.97 \\ Recall & 96.86 & 55.37 & 49.98 & 75.72 & 99.01 & 50.04 & 50 & 69.48 \\ F1-Score & 98.14 & 52.42 & 42.39 & 77.59 & 97.62 & 45.3 & 40.58 & 71.74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training on California, and testing on Hindukush and Chile.  
Figure 3: Overall accuracy score of all approaches on each dataset.  
and 98.1, respectively); however, accuracy alone did not represent the complete picture. For instance, the low precision score of logistic regression reflected a higher rate of false positives. Likewise, the low recall score was an indication of a higher rate of false negatives. Therefore, achieving higher accuracy was not enough to declare an algorithm superior, and other factors needed to be considered as well. When we considered all the performance metrics, we observed that our proposed DNN technique achieved better results in comparison to the benchmark algorithms. The same trend was observed when the models were trained on the Californian dataset (ref Table 6), as well as on the Hindukush dataset (ref Table 7).
## 3 Results
However, there was a more substantial pressure difference when the backpressure was increased to 540 psi, signaling hydrate formation within the core. [PERSON] and [PERSON] [20] reported that the threshold pressure for CO\({}_{2}\) hydrate formation under static conditions at 4 \({}^{\circ}\)C is 315 psi. Figure 6 shows the observed inlet and outlet pressures during injection at 4 \({}^{\circ}\)C with a backpressure between 540 and 580 psi initially. The minimal pressure difference indicates no hydrate formation in the core during that time. But a noticeable pressure differential was revealed when the backpressure reached 590 psi, confirming that hydrates had formed inside the core. Figure 4: Observed pressures during CO\({}_{2}\) injection at 2 \({}^{\circ}\)C.  
Figure 5: Observed pressures during CO\({}_{2}\) injection at 3 \({}^{\circ}\)C.  
The last test was run at a core temperature of 5 \({}^{\circ}\)C, which was supposed to have a minimum required pressure of 350 psi in static conditions [20]. Figure 7 depicts a very low difference between the inlet and outlet pressures initially when the backpressure was varied between 580 and 640 psi, and it indicates no formation of hydrates in the core. But a significant rise in inlet pressure was seen continuously when the backpressure was changed to 650 psi. The increasing difference between the inlet and outlet pressures implies that hydrates formed inside the core. This behavior--minimal pressure difference at lower backpressures and significant pressure difference at higher backpressures--provides clear evidence that CO\({}_{2}\) hydrate formation is strongly dependent on both temperature and pressure. The results confirm that for each temperature, there is a critical pressure above which hydrates start to form, obstructing the CO\({}_{2}\) flow. This agrees with the findings of [PERSON] and [PERSON] [20], who predicted similar pressure thresholds under static conditions. Additionally, it demonstrates that even under dynamic flow conditions, the formation of hydrates follows a similar trend where the higher the temperature, the higher the pressure required for hydrate formation.
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 4 Discussion  
In this review, we provide a review of various AR-based studies and applications in the field of orthopedics. What emerged from the development of this work is that AR technology is a growing trend, and its adoption in orthopedics has gained increasing attention and paved the way for new possibilities in the training and execution of surgical procedures. Nowadays, many of the studies still concern preclinical and proof-of-concept results, but many are still dealing with the incorporation of these technologies into daily clinical practice and with the study of adequate workflows to facilitate this process and their feasibility.  
Overall, the results obtained from the studies describe AR technology as a new method capable of improving precision, reducing risks and radiation for both the patient and the physicians, and optimizing surgical times in various fields of orthopedic surgery. Furthermore, in addition to the intraoperative technology aspects, AR is increasingly emerging as a valuable tool for training physicians and as an effective rehabilitation tool. Nonetheless, the clinical differences of AR in terms of cost reductions and improvements in patient care will need to be carefully assessed in future research. Currently, the effect that AR and HMD have on cognition, perception, and concentration are not completely clear and this is the main cause why it is not yet commonly implemented in daily clinical routine. The reasons are manifold, and this review aims to highlight the issues that were found to be relevant in the scientific community, shed light on the aspects that still need research and investigation, and emphasize the future challenges for this environment. In fact, with the computing power currently available and its expected growth, AR is destinated to experience an increased usage in all the augmented humanity scenarios, including orthopedics.  
In response to RQ1, statistical analysis was performed in Table 1 and summarized in Figure 2. The answer to our first research question was discussed previously in Section 3.  
The following subsections discuss the limitations and challenges found by the authors in the analyzed articles and answers to research questions posed in Section 2. In particular, in Section 4.1, the answer to RQ2 is detailed, and limitations regarding AR visualization and related hardware are addressed, in Section 4.2, the answer to RQ3 was considered, along with a description of the types of registration and tracking used, and their corresponding advantages and disadvantages. Finally, in Section 4.3 implications for future research are discussed.
# Dynamic Response of Slope Inertia-Based Timoshenko Beam under a Moving Load
###### Abstract  
In this paper, the dynamic response of a simply supported beam subjected to a moving load is reinvestigated. Based on a new beam theory, slope inertia-based Timoshenko (SIBT), the governing equations of motion of the beam are derived. An analytical solution is presented by using a coupled Fourier and Laplace-Carson integral transformation method. The finite element solution is also developed and compared with the analytical solution. Then, a comparative study of three beam models based on the SIBT, Euler-Bernoulli and Timoshenko, subjected to a moving load, is presented. The results show that for slender beams, the dynamic responses calculated by the three theories have marginal differences. However, as the ratio of the cross-sectional size to beam length increases, the dynamic magnification factors for the mid-span displacement obtained by the SIBT and Timoshenko beams become larger than those obtained by the Euler-Bernoulli beams. Furthermore, until the ratio is greater than 1/3, the difference between the calculated results of the SIBT and Timoshenko beams becomes apparent.  
slope inertia-based Timoshenko beam; moving load; integral transformation; shear inertia; dynamic response +
Footnote †: journal:  
is negligible [28] and the physical meaning of the term itself is not clear [4]. For example, [PERSON] et al. [29] and [PERSON] et al. [15] respectively considered the rotary inertia caused by shear deformation and proposed the truncated Timoshenko beam theory (TTBT). However, further studies have shown that the TTBT is asymptotically consistent, for it cannot be variationally derived [22].  
Recently, [PERSON] et al. [22; 30; 31; 32] proposed a slope inertia-based Timoshenko (SIBT) beam theory by means of the variational approach. Using the deflection slope instead of the bending rotation in the kinetic energy expression, the SIBT model eliminates the fourth-order time derivative term associated with the Timoshenko model and appears to be preferable to the TTBT [22]. [PERSON] et al. [4] carried out analytical and numerical studies on the SIBT model to clarify the physical meaning of its governing equation terms and reveal its distinct features in the prediction of modes and frequencies. Although the SIBT seems to be more comprehensive and preferable, a fundamental study of the SIBT beam under a moving load problem is not available yet.  
In this paper, based on the SIBT theory, the governing equations of motion of the beam under a moving load are established. An analytical solution to this problem is presented. Next, the finite element solution is proposed and validated. We present a comparative study of the SIBT, Euler-Bernoulli and Timoshenko beam models subjected to a moving load. Finally, concluding remarks are given.
## 3 Results
### Habitat Quality Change  
#### 3.2.1 Spatiotemporal Change in Habitat Degradation  
The average habitat degradation degree from 1980 to 2020 was evaluated through the InVEST method. As illustrated in Figure 4, the temporal pattern reveals three distinct phases of habitat degradation. The sharp increase in the 1980s coincided with rapid economic development, followed by a relatively stable period in the 1990s despite continued degradation. While the acceleration resumed in the 2000s, the recent plateauing trend suggests a potential transition in environmental pressure, though at persistently high levels.  
Subsequently, the degradation degree was classified as five levels through the natural breaks: very slight (0-0.015), slight (0.015-0.03), moderate (0.03-0.09), high (0.09-0.17), and severe (0.17-0.34) (Figure 5). Table 5 presents the area and proportion of each level. Over the last four decades, the analysis reveals a concerning shift in habitat quality distribution. The systematic decrease in better-preserved habitats alongside the expansion of degraded areas suggests a continuous environmental pressure pattern. The most notable transformation occurred in the conversion from very slightly degraded to more severe levels, indicating a gradual erosion of excellent-quality habitats.  
Over the last four decades, China's habitat degradation has generally exhibited a west-to-east gradient of increasing severity, closely aligned with the country's topographic variation. The spatial pattern of habitat degradation demonstrates a clear association with China's development patterns. A distinct degradation gradient emerges from the relatively preserved western plateaus to the intensively developed eastern regions, aligned with the nation's economic geography. The spatial concentration of severe degradation is particularly evident in urban agglomerations and their surrounding areas, where the urban-rural transition zones show the most significant habitat degradation.  
Figure 4: Change in the average habitat degradation degree (1980–2020).
## 4 Fuzzy Logic Model
### Inference Engine
Thus, the triangular fuzzy value provides an easy solution for the described problem with two parameters, _i.e._, center point and the distance between the extreme points and the center point. To show the membership function for energy \(\beta\) and time \(\alpha\), the fuzzy controller generates an output value from the set of (0,1). Due to the diverse assigned tasks, the energy consumption of active nodes in each round may vary. Thus, the active time of a node in a specific round does not indicate the same level of energy consumption in each round. To guarantee a network lifetime \(\alpha\), WSNs are largely dependent on the active time of sensor nodes. Therefore, in order to select the active nodes among all nodes, the fuzzy controller considers both remaining energy and active time. The imprecisions or uncertainties in fuzzy logic are controlled by a probability value, which is defined by the membership function. Figure 3: Fuzzy membership function for energy function. In addition to a guaranteed network lifetime, real-time delivery can be achieved by reducing the number of hops between source and destination. Consequently, the number of hops can be reduced by expanding transmission power. Thus, the proposed scheme uses transmission power control in the sensor nodes as a grip to reduce end-to-end delay. The fuzzy controller is based on the capability of changing the transmission power of a node to achieve real-time service under a guaranteed lifetime \(\alpha\). Thus, the fuzzy controller assigns value to the output from the set of (0, 1), as shown in Figure 4b, to present the membership function for velocity \(V\) in a pre-configured lifetime \(\alpha\). The proposed fuzzy controller selects the next relay node hop _ETR_ to forward real-time packets with increased transmission power in order to achieve the packets' deadline. On the other hand, the NTR nodes use greedy forwarding and energy-aware routing to transmit data packets towards the sink node for non-real-time packets.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors
#### Radar Sensors  
The usage of 2D images generated by a radar sensor, along with artificial intelligence, can be translated into the substitution of video cameras, allowing the establishment of a parking slot status through a convolutional neural network training that has the radar information as input data and throws slots status as results [57]. These sensors give the possibility to use one powerful device to cover a great number of parking slots. However, it requires a system that can process all those signals.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
###### Abstract  
Lakes in arid regions play an important role in regional water cycles and are a vital economic resource, but can fluctuate widely in area and volume. This study demonstrates the use of a multisensor satellite remote sensing method for the comprehensive monitoring of lake surface areas, water levels, and volume for the Toshka Lakes in southern Egypt, from lake formation in 1998 to mid-2017. Two spectral water indices were used to construct a daily time-series of surface area from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), validated by higher-resolution Landsat images. Water levels were obtained from analysis of digital elevation models from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), validated with ICESat Geoscience Laser Altimeter System (GLAS) laser altimetry. Total lake volume peaked at 26.54 \(\times\) 10\({}^{9}\) m\({}^{3}\) in December 2001, and declined to 0.76 \(\times\) 10\({}^{9}\) m\({}^{3}\) by August 2017. Evaporation accounted for approximately 86% of the loss, and groundwater recharge accounted for 14%. Without additional inflows, the last remaining lake will likely disappear between 2020 and 2022. The Enhanced Lake Index, a water index equivalent to the Enhanced Vegetation Index, was found to have lower noise levels than the Normalized Difference Lake Index. The results show that multi-platform satellite remote sensing provides an efficient method for monitoring the hydrology of lakes.  
l 2019 11 158 10.3390/rs11020158
## 4 Techniques Used in Academia to Improve Performance Metrics
### Spatial Diversity and Cooperative Diversity  
Antenna diversity can be achieved by equipping the transmitter and/or receiver with multiple antennas. If the antennas are sufficiently apart from each other, and the local scattering environment is rich enough, then each antenna will see independent fading. References [15] and [16] use spatial diversity to improve the reliability of the link. In [17] the authors study the antenna switching in a WSN setting in a laboratory. They analyze their results for two types of data links _i.e._, with and without feedback channel. In the former scenario, the transmitter switches the antenna if it does not receive any acknowledgment, and in the latter scenario, the transmitter sequentially sends multiple copies of the same packet on a different antenna. References [18] and [19] explore the use of directional antennas in WSNs and show their potential to provide increased throughput and reduced latency.  
However, deploying several antennas at each wireless sensor node is difficult due to constraints in terms of analog device power consumption, space limitation and simplicity in implementation. The collaborative (or cooperative) diversity proposed by [20, 21], creates a virtual antenna array by enabling the single-antenna network nodes to share their antenna through relaying. In this approach, nodes help each other to relay information in order to realize spatial diversity advantages. Significantperformance gains in terms of link reliability and diversity gain can be achieved by utilizing collaborative diversity. In [22, 23, 24, 25] authors study the cooperative communications in resource-constrained wireless networks and wireless sensor networks.  
WSNs require a distributed cooperative protocol, in which users independently decide with whom to cooperate at any given time. In distributed cooperative diversity, the challenge is how to establish ways in which the scheme can treat the users fairly, does not have too much overhead, and will be compatible with the system's multiple access protocols [22]. [PERSON] _et al_. explore a distributed partner assignment algorithm by applying the cooperative protocol when the channel has a high signal-to-noise ratio (SNR). Otherwise the network reverts to the non-cooperative mode [20].  
Receiver diversity in the form of route choices at each hop can also be considered as a type of spatial diversity in case of multipath routing scheme, which is discussed in Section 4.1.3.
## 5 Numerical Results and Discussion
### Asynchronous Networks
#### 5.3.2 Dual-Slot Networks  
For dual-slot asynchronous ISAL networks, as shown in Figure 12, we give the sensing SPEB of the step-by-step scheme and the integrated scheme in the four networks with different topologies in Figure 8.  
It can be found from Figure 12 that the energy optimization allocation strategy between the two slots in the asynchronous networks is consistent with that in the synchronous networks.
## 1 Introduction
A loop heat pipe presents promising alternative heat switch solutions in this sorption compressor application. This functionality is achieved by enabling the control of the temperature differential between the evaporator and the compensation chamber, as described in [20; 21]. To implement it, a 1D thermo-hydraulic lumped parameter model mathematical model, as in [22; 23; 24; 25; 26; 27; 28; 29; 30], capable of predicting design and thermal performance, is necessary. This model should account for transient responses, complex interactions involving two-phase fluid flow, heat transfer, flow through porous structures, dry-out and priming phenomena, as well as condensation and evaporation within the loop heat pipe. This article's objective is to introduce an analytical 1D thermo-hydraulic mathematical model, offering a comprehensive understanding of the non-linear behaviour and thermal performance of loop heat pipes when functioning as heat switches. Currently, there is a lack of mathematical modelling references to address how the loop heat-pipe responds thermally in transient simulations, considering various state modes, such as a de-primed wick. By considering the intricate dynamics of two-phase fluid flow, heat transfer, and various phenomena, our objective is to provide valuable insights for the design and optimization of loop heat pipes as heat switches, particularly for sorption compressors in systems used for space applications. The innovation of this article lies in its demonstration of how a lumped parameter model of a loop heat pipe can be effectively configured to illustrate its non-linear behaviour, resembling that of a heat switch. This non-linearity is a distinguishing feature due to the loop heat pipe having five (5) distinct operational states. These valuable insights will significantly contribute to improving the efficiency and performance of various engineering applications that require a transient simulation and cannot be analysed by a steady-state approach. This paper, which aims to develop a 1D thermo-hydraulic lumped parameter model [22; 23; 24; 25; 26; 27; 28; 29; 30] for a loop heat pipe working as a heat switch, is organized as follows: Section 2 presents the mathematical model of the loop heat pipe working as a heat switch, Section 3 focuses on the dimensioning parameters, such as geometry and fluid properties, which are used in the simulations, and the corresponding results are presented in Section 4. Section 5 involves a discussion on the challenges and achievements from the proposed model.