esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Sensor Requirements for A3R
### Ensure Communications
to 30 MHz) [58], the modern-day Link-16 waveform in the L Band (960 to 1215 MHz) [57], and newer waveforms to include the Multifunction Advanced Data Link (MADL) used on modern day fifth-generation fighters [59]. At the time of publication, no additional information was available about MADL. In the event the UA achieves a distance where the radar horizon prohibits communication, a BLOS waveform generator and aperture would need to be installed on both the UA and GCS. BLOS communications could occur between multiple aircraft at high altitudes and long distances or between Satellite constellations through Satellite Communication (SATCOM), which operate in the K Band (18 to 27 GHz) [49]. In the event an aircraft cannot receive BLOS information, if the architecture exists, information could be relayed from a BLOS capable aircraft over LOS to an aircraft unable to receive BLOS.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 5 Simulation Results
### Algorithm Stability Analysis  
In the dynamic selection algorithm of cluster heads, the number of cluster heads selected in every round reflects whether the algorithm is stable. Network stability will be higher when the number of cluster heads is within a smaller variation range. A total of 200 sensor nodes were selected for the experiment, and 20 rounds were randomly selected. Figure 4 shows the variation in the number of cluster heads for the three algorithms.  
As shown in Figure 4, the variation ranges of the number of cluster heads of LEACH, UCUBG and the present algorithm are [6; 19], [9; 16] and [11; 15], respectively. Compared with the LEACH algorithm, UCUBG and the present algorithm have better stability. In  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Network area/m\({}^{3}\) & 100 \(\times\) 100 \(\times\) 100 \\ Maximum communication radius \(R_{max}\)/m & 50 \\ Perception radius \(R_{g}\)/m & 20 \\ Initial energy \(E_{0}\)/J & 2000 \\ Signal frequency \(f\)/kHZ & 10 \\ Sensor nodes & 200 \\ Launch energy factor \(\lambda_{t}\) & 0.001 \\ Receiving energy factor \(\lambda_{r}\) & 0.001 \\ Packet length \(l\)/bit & 2000 \\ Nearest distance \(d_{min}\)/m & 0 \\ Longest distance \(d_{max}\)/m & 125 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of simulation experiments.  
Figure 4: Variation in the number of cluster heads.  
particular, the stability of the present algorithm is the most obvious. The LEACH algorithm applies a probability function to select cluster heads randomly, which cannot guarantee that the number of cluster heads is stable. UCUBG and the present algorithm have the influence of considering a variety of factors, which makes the choice of cluster head nodes more certain. Compared with the UCUBG algorithm, this algorithm considers the distance between nodes in addition to the number of neighboring nodes when choosing cluster heads, constructs a node density weight function and increments the node coverage redundancy index. In this way, cluster heads in the network are distributed more reasonably and this algorithm has a better stability.
## 3 Results
### Power Input Correlation
#### 3.1.4 Effect of Volume Number on Newton Number  
The exponential \(\gamma\) of the volume number is decided by changing the liquid volume \(V_{L}\)[18]. Since merely the volume number is affected by the fill volume, exponential \(\gamma\) is decided by changing the volume number only. [PERSON] et al. [18] found that \(Ne=0.27\cdot(V_{L}/D^{3})^{0.44}\) was constant at shaking frequencies of 160 rpm, 20%-60% relative filling volume in a 20-L vessel using polypropylene liquid with different viscosities of 0.89-12 mPa\(\cdot\)s and shaking diameters of 5 cm. The functional correlation between Newton number and volume number is obtained by  
\[Ne=C_{4}(\frac{V_{L}}{D^{3}})^{\gamma} \tag{14}\]  
The exponent of liquid volume in this correlation of the Newton number in our experiment differs from \(-1.047\), \(-1.1\), \(-1.126\), \(-1.205\) values with a coefficient of determination greater than 0.9999 obtained for different liquids as shown on Figure 5. This result is consistent with the investigation of the liquid distribution between shaking frequencies. Due to the cylindrical orbitally shaken bioreactor design, the liquid and reactor walls have higher liquid viscosity during shaking, so the volume number is more affected. Therefore, the Newton number as stated in Equation (2) is not suitable for OSB cylindrical vessels.  
Figure 4: Relationship between Newton number and the product (Re x Fr) of different filling volumes.  
**(a)** Water; **(b)** 5 vol% glycerin; **(c)** 10 vol% glycerin; **(d)** 20 vol% glycerin.  
Figure 5: _Cont._
## 5 Validation and Verification Aspects
### Adaptation of V&V Techniques for EEWS  
Verification and validation (V&V) techniques are crucial for ensuring the reliability and effectiveness of earthquake detection and warning systems in the context of IoT. These systems use a combination of sensors, data analysis algorithms, and communication technologies to detect and respond to earthquakes in real time. However, the adaptation of V&V techniques for these systems presents unique challenges and opportunities.  
One challenge is the need for a robust and reliable sensor network. EEWS relies on a sensor network to detect and measure seismic activity. These sensors must be calibrated and tested regularly to ensure that they are functioning correctly. V&V techniques can help to ensure that the sensor network is reliable and accurate by providing a framework for testing and calibration. More precisely, to ensure the reliability and accuracy of the entire sensor network, a statistical calibration and testing approach can be employed. This involves validating a representative sample of sensors rather than individually testing each sensor, given the large number of sensors typically used. Statistically, the majority of sensors will be sufficiently calibrated and functioning properly at the time of an earthquake.  
Another challenge is the desirability of real-time data analysis and action plans. In earthquake detection systems, timely and accurate decision-making is critical for minimizing damage and saving lives. V&V techniques can help to ensure that the data analysis algorithms used in these systems are accurate and reliable. This can involve testing the algorithms under a variety of conditions and scenarios, as well as ensuring that they are able to operate in real time.  
In addition to challenges, there are also opportunities for the adaptation of V&V techniques for IoT earthquake systems. One opportunity is the use of simulation and modeling. V&V techniques can be used to create simulations and models of earthquake scenarios to test and validate the performance of the detection and warning systems. This can help to identify potential weaknesses in the system and inform improvements.  
Another opportunity is the use of crowdsourcing and citizen science. V&V techniques can be used to validate and integrate data from citizen scientists and volunteers who contribute to earthquake detection and warning systems. This can help to improve the accuracy and reliability of the system while also engaging the public in the process.  
In conclusion, the adaptation of V&V techniques for IoT earthquake systems presents both challenges and opportunities. By leveraging V&V techniques, developers can ensure that these systems are reliable, accurate, and effective in detecting and responding to seismic activity. This can help to minimize damage and save lives in the event of an earthquake.
## 2 Optical Interconnection Architecture  
In this section we briefly introduce a general multi-plane switching architecture, which is the basis, technology-independent version of the RR-based switching backplane we have investigated in this study, followed by a description of the design procedure for routing assignment.  
The RR-based switching backplane exploits transmitter wavelength tunability as switching mechanism: wavelength \(h\) selection by the source LC transmitter is then translated into selection of a target destination LC by means of a passive wavelength-routing structure composed of waveguides, star couplers, and RRs.  
In Figure 1, \(N\) line cards, each one comprising a tunable-wavelength transmitter (TX) and a fixed-wavelength receiver (RX), are divided into \(S\) Input-Output planes (_h_ from 0 to _S_-1); at each Input plane, _N_/_S_ TXs are connected to the _plane selection circuit_ (PSC). For instance, TX and RX of each LC are depicted as separated, though they are actually co-located on board of each LC. Routing properties of such a circuit let each TX select the output port corresponding to the plane a certain RX belongs to. Each selection-circuit output port is connected to one of the \(S\) couplers (SCs) (_S_:1) that convey signals in order to set-up connections between TXs and RXs which lie into different planes. Signals coming from different transmission planes are coupled together and an Erbium Doped Fibre Amplifier (EDFA) is placed to restore signal optical power. A _receiver selection circuit_ (RSC) separates channels directed to different RXs of the same Output plane. The number of planes \(S\) is allowed to range from 1 to \(N\). If _N_/_S_#N, then [_N_/(_S_-1)] cards will be connected to the first _S_-1 planes, while the remaining ones will occupy the first available positions within the last plane.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
## 3 LTE-U Coexistence Mechanism
### Fairness in Unlicensed Spectrum  
LTE-U is deemed successful if its coexistence with Wi-Fi is fair. A fair coexistence approach must provide all coexisting networks with equal opportunities to access the medium. However, this kind of fairness is limited between a system of the same type and a system having similar system parameters. In an LTE-U network scenario, both technologies are diverse, having major design dissimilarities. The normalized throughput achieved by both systems will be a good indicator of the fairness. That is, when presenting LTE-U into a shared channel, the effect on the existing STAs should be similar to that of adding the same type of STAs. Hence, in the remainder of this paper, the Jain fairness index on the achieved throughput is implemented as key performance metric for the fairness evaluation. The normalized throughput for each participating network is achieved based on the ratio of its achieved throughput to the maximum throughput attained during the standalone operation.  
\[\textit{Jain~{}fairness~{}index}~{}(\mu_{k})~{}=~{}\frac{(Th^{\textit{wifi}}_{k} ~{}+~{}Th^{LTE-U}_{k})^{2}}{2[\left(\left(Th^{\textit{wifi}}_{k}\right)^{2}+~{} (Th^{LTE-U}_{k})^{2}\right]} \tag{11}\]  
where, \(Th^{\textit{wifi}}_{k}\) and \(Th^{LTE-U}_{k}\) are the normalized throughputs attained by LTE-U and a Wi-Fi network.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Delayed Self-Heterodyne Method
#### 3.3.4 Beat Frequency Power Spectrum Analysis  
In 2006, [PERSON] et al. achieved sub-kHz linewidth measurements using the gain compensated R-DSH method [102]. The signal light circulates multiple times in the delay loop using a fiber amplifier to compensate for the loss of a 25 km delay fiber, resulting in an equivalent delay length of more than a hundred km. The beat frequency power spectrum was fitted using the Voigt function to eliminate spectral broadening caused by \(1/f\) noise. The Lorentz linewidth is 0.68 kHz, and it is demonstrated that the measured Lorentz linewidth is independent of the fiber delay length.  
In 2015, [PERSON] et al. reported an ultra-narrow-linewidth measurement method based on Voigt contour fitting [49], which filtered out the spectral line broadening caused by \(1/f\) frequency noise and restored the Lorentz line shape of the laser from the measured self-heterodyne spectrum. The actual spectrum of a single-frequency BEFL with a 45 cm EDF as a mixed-gain medium was measured and compared with that of the direct measurement method, as shown in Figure 8. The fitting linewidth is consistent with that of the heterodyne beat frequency method. The approach improves the resolution and is suitable for any type of laser compared to a long-fiber self-heterodyne.
# Reinforcement Learning Based Resource Management for Network Slicing
## 4 Resource Trading System Using a Markov Decision Process
### Action Space  
In a given state \(s_{t}\), the tenant selects an action \(a_{t}\) from the set of possible actions defined as  
\[A=[+\sigma,+2\sigma,+4\sigma,+8\sigma,-\sigma,-2\sigma,-4\sigma,-8\sigma,0], \tag{9}\]  
where \(\sigma\) represents the resource trading unit. For instance, if a positive/negative action is taken, the agent increases/decreases the amount of resource being bought from the provider. \(a_{t}=0\) means that the agent keeps maintaining the amount of resource. We define a possible action set \(\bar{A}\) in the action space \(A\). In each trading interval \(h\), \(\bar{A}\) is constrained by the amount of the allocated resource.  
For example, if a slice does not have enough resource to satisfy the requirements, i.e., \(R_{n,t}<D_{n,t}\), it certainly has to purchase more bandwidth, whereas if \(R_{n,t}=D_{n,t}\), one more action, \(a_{t}=0\), is also available. Otherwise, all actions are available. As a result, \(\bar{A}\) can be determined as follows:  
\[\bar{A}=\begin{cases}[+\sigma,+2\sigma,+4\sigma,+8\sigma],&\text{if }R_{n,t}<D_{n,t},\\ [+\sigma,+2\sigma,+4\sigma,+8\sigma,0],&\text{if }R_{n,t}=D_{n,t},\\ A,&\text{otherwise}.\end{cases} \tag{10}\]
# New Space Engineering Design: Characterization of Key Drivers
## 3 Results and Discussion
### Discussion per Application Domains
#### 3.2.4 Further Aspects  
Alongside small satellite deployment and space servicing operations, more conventional missions continue to play a significant role in the sector [21; 23]. These include large satellite systems in higher orbits, primarily manufactured by major aerospace industries for communication purposes, which still represent a substantial portion of the market [8]. In parallel, scientific probes and observation platforms, such as the Psyche mission by NASA or the Jupiter Icy Moons Explorer (UUICE) by ESA, continue to play an important role in space exploration. These types of missions require larger platforms, more extensive development times, and significant investments. Therefore, they are less aligned with the cost-efficiency and agility principles of New Space and more representative of legacy approaches. Moreover, the field of Earth Observation is particularly active also in this context, especially in support of monitoring Earth's climate changes, leading to missions such as ESA's Biomass.
## 5 Applications and Discussions  
To demonstrate the accuracy of ABFT-SMM to approximate the nonlinear system, the method is applied to a benchmark problem named chaotic Mackey-Glass time series prediction, a case of identification of the electrical water heater system and the modeling of waste heat utilizing process, respectively.
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 3 Enhancing Cache Robustness via Per-Face Popularity
### Per-Face Popularity with Dynamic Aging (PFP-DA)  
Although the original PFP scheme showed promise, a couple lingering issues were identified. Namely, that the scheme did not consider recency at all, and that the content frequency lists maintained by the popularity manager could be arbitrarily large. The first issue is a problem since a content object that might have been popular a significant time ago would remain in the cache, even if it was not currently popular, simply due to the extreme number of requests received once upon a time. This, of course, reduces the reflection of true locality for a given cache and contributes to natural cache pollution, in which there is no attacker, yet content objects continue to reside in the cache even though they no longer improve the hit rate significantly. The second issue, the ever-increasing content frequency list size, is clearly a significant problem when considering performance and scalability issues. Therefore, a revised scheme was needed to address these issues.  
Borrowing insight from the Least-Frequently Used with Dynamic Aging (LFU-DA) [20] algorithm, we modified our original PFP algorithm to include recency considerations, resulting in the Per-Face Popularity with Dynamic Aging (PFP-DA) scheme. The dynamic aging aspect of LFU-DA directly affects the cache itself. However, in PFP-DA, the dynamic aging aspect affects each of the content frequency lists. Therefore, aging affects the actual cache, but indirectly. In the PFP-DA scheme, the content frequency lists are limited to a finite size based on the actual cache size. In our implementation, the lists are twice the size of the true cache (e.g., 200-size lists for a 100-size cache). For the majority of cache sizes, this will essentially solve our first problem, in that the list sizes are no longer theoretically unbounded.  
Instead of using the frequency of interests for a given content when determining if it belongs in the cache itself, PFP-DA uses the key, \(K_{i}\) for a given content object \(i\). This key is calculated as follows:  
\[K_{i}=F_{i}+L \tag{3}\]  
The value \(F_{i}\) is the frequency for the given content object, \(i\), and \(L\) is the _age factor_. \(L\) is initialized to 0, and then whenever a content item is to be replaced, \(L\) is updated to be equal to the key, \(K_{i}\), of item \(f\), that is, the item being removed. Succinctly, this means \(L=K_{f}\). With respect to PFP-DA, aging is utilized with the rankings for each of the faces rather than overall content caching.
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 2 Spectral Distortions in the MPEG-H 3 DA BR
Consequently, some unwanted spectral distortion may occur during the step if multiple objects are in the same processing band. Therefore, a rigorous approach is required to solve the issue with minimal artifacts and low computational complexity as a standard technology.
### Navigating Vehicular Ad Hoc Networks  
When crafting routing protocols for VANETs, it is imperative to consider the trifecta of security, mobility, and scalability. These routing protocols delineate the mechanisms by which two nodes or routers communicate within a computer network [15]. Additionally, these protocols are instrumental in discerning the optimal candidates to steer data packets along their intended path. It is worth noting that many factors, including road conditions, traffic dynamics, physical obstacles such as buildings, and the broader environmental context, significantly influence the routing protocols in VANETs. Additionally, the dynamics of vehicular movement and potential disruptions within the network further contribute to the complexity of these protocols. The ad hoc routing protocols, crucial in VANETs, can be neatly categorized into three distinct types: hybrid, proactive, and reactive, each offering unique approaches to tackling the intricate challenges of vehicular communication [16].
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 1 Introduction
### Motivation  
In [1], the authors used SVM, Cloud, Spatially-Organized Distributed Echo State Network (SODESN), Bayes, and Hidden Markov Models (HMM) for classification of faulty data. Whereas, in [5], fault detection algorithms were used to detect fault in nodes and replace them in time. This ensures that a failed node does not affect and manipulate the function of the entire sensor network and system decisions. The faulty data detected can be used as a reference for the system update, which enhances  
Figure 1: Types of classifiers.  
the security and reliability of the network. Six classifiers are applied in this paper for the fault detection process, and results are compared on the basis of six faults; two of them are induced artificially in the datasets.
# FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
## 3 Measurement of Grain Orientation Distribution and Anisotropic Elastic Constants  
### Distribution of Grain Orientation  
To simulate the ultrasonic wave propagation behavior, it is necessary to model the distribution of the grain orientation, which is the macroscopic pattern of the grain orientation in austenitic welds. For the simulation, grain orientation obtained from micrographs can generally be used [3; 5; 9]. In a previous study, the authors proposed a grain orientation prediction methodology based on computational mechanics and an optimization technique [5]. In this study, a micrograph was used to determine the distribution of grain orientation in austenitic welds. Figure 2(a) shows the schematic of the DMW specimen and the coordinate system, and Figure 2(b) shows the weld section of the DMW specimen fabricated for this study. The thickness, top width, and weld root of the welded zone were 30.0 mm, 39.7 mm, and 5.1 mm, respectively. The base materials were carbon steel (SA508 Gr.3) and stainless steel (STS304). This weld included a buttering part between the austenitic weld and the carbon steel parts, and the material of the welding and buttering part was alloy 152M.  
Figure 3: DMW specimen.  
To model the grain orientation of the DMWs, the specimen was thoroughly etched, and the grain orientations were characterized using scanning electron microscopy (SEM). The macrographs of the weldment section were meshed with a 2 mm \(\times\) 2 mm size mesh [12], after which the grain orientations were carefully marked in the macrographs (yellow lines in Figure 3(a)). The measured grain orientations from each meshed area were used to interpolate the grain orientations in any position. The interpolated grain orientations, as shown in Figure 3(b) via red lines, were used as input information for the FEA.
###### Abstract  
Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HeNetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.  
fifth generation (5G); handover (HO); heterogeneous networks (HetNets); mobility management (MM); quality of services (QoS); key performance indicators (KPIs) IEEEexample:BSTcontrol  
2023