esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Adaptive Dynamic Surface Controller
Let \(\zeta=-\hat{\varsigma}_{\rm d}\) be the third virtual control input, the virtual control law is designed as follows:  
\[\overline{\varsigma}_{\rm d}=\frac{V_{\rm a}}{RT_{\rm s}d}\bigg{[}\bigg{(} \frac{A_{\rm a}x_{2}}{V_{\rm a}}-\frac{RT_{\rm s}a}{V_{\rm a}}\bigg{)}\hat{x}_ {3}+\frac{1}{\tau_{2}}(\overline{\varsigma}_{\rm d}-\varsigma_{\rm d})-k_{3}s_{ 2}\bigg{]}, \tag{22}\]  
where \(k_{3}>0\) is a positive control gain. Similarly, the signal \(\overline{\varsigma}_{\rm d}\) is fed to a low pass filter to obtain a new one \(\zeta_{\rm d}\) for the next design step:  
\[\tau_{3}\dot{\zeta}_{\rm d}+\zeta_{\rm d}=\overline{\zeta}_{\rm d},\quad \zeta_{\rm d}(0)=\overline{\zeta}_{\rm d}(0),\ \tau_{3}>0, \tag{23}\]  
where \(\tau_{3}\) is the filter parameter. _Step_ 4: Define the second surface error as \(s_{3}=\zeta-\zeta_{\rm d}\), its time derivative can be derived as:  
\[\dot{s}_{3}=\dot{\zeta}-\dot{\zeta}_{\rm d}=-\dot{\hat{x}}_{4}-\dot{\zeta}_{\rm d }=-\frac{RT_{\rm s}}{V_{I}}(\hat{\overline{m}}_{\rm v}-\hat{\overline{m}}_{\rm a })-\frac{1}{\tau_{3}}(\overline{\zeta}_{\rm d}-\zeta_{\rm d}). \tag{24}\]Similarly, the following control law \(q_{\rm d}\) for \(\hat{\vec{n}}_{\rm v}\) is proposed:  
\[q_{\rm d}=\hat{\vec{m}}_{\rm a}-\frac{RT_{\rm s}}{V_{\rm f}}\Bigg{[}\frac{1}{\tau_ {3}}(\overline{\zeta}_{\rm d}-\zeta_{\rm d})-k_{4}s_{3}\Bigg{]}, \tag{25}\]  
where \(k_{4}>0\) is a positive control gain. Once the \(q_{\rm d}\) is calculated, the desired effective valve orifice area \(A(u)\) can be calculated by:  
\[A(u)=\left\{ \begin{array}{ll}\frac{q_{\rm d}}{K_{\rm q}(p_{\rm v},p_{\rm v},T _{\rm s})}&q_{\rm d}>0\\ \frac{q_{\rm d}}{K_{\rm q}(p_{\rm v},p_{\rm v},T_{\rm s})}&q_{\rm d}\leq 0 \end{array} \right.. \tag{26}\]  
Thus, the input signal \(u\) for the proportional-directional control valve could be obtained according to the relation between the input signal and effective valve orifice area. The proposed pressure observer based adaptive dynamic surface control is illustrated in Figure 3.
## 8 A Look-Ahead, Missing Functionality and Research Challenges
### Missing Sub-Aspects  
#### 8.2.1 Monitoring Dynamic Cloud Federations  
Intercloud middleware [390] enables application managers to deploy their applications across multiple independent cloud platforms that are located in geographically different data centers. It also enables an application to more autonomously delegate parts of its application components to those cloud providers that offer suitable pricing models or SLA. Also, customers of applications may want to run parts of the application nearby or on their own trusted infrastructure in order to protect their data or because the data is too big to move.  
While existing CO frameworks offer basic support for customizing the placement of applications across multiple cloud providers, they lack support for observing and dynamically exploiting the different VM types that are offered by a cloud provider in terms of pricing models and SLAs. For example, customer-facing latency-sensitive applications require VMs that can be leased for a longer period at a lower-cost, while non-critical batch workloads can acquire VMs that are only run when idle datacenter resources are available. The design of an autonomic monitoring service that is able to detect and annotate these different pricing models and SLAs in the cloud federation is thus lacking in current CO frameworks. It is neither possible to monitor for events about changes in pricing models and SLAs of cloud providers.  
However, existing state-of-the-art [39; 40] has already performed extensive research in this area. It is thus expected that existing multi-cloud support of CO frameworks can be extended with pricing-aware and SLA-aware selection of VM types.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 6 Simulation Results
For longer-distance flights, a flight plan might use an efficient dynamically geofenced route to/from a high-altitude transit tube, potentially requiring a hybrid combination of dynamic flight planning and geofencing at UTM-managed altitudes and fixed corridor transit at altitudes managed by legacy ATM.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 3 MATE Algorithm
### TOA Tracking
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Discriminator Algorithm** & **Characterization** \\ \hline carrier wave & & The output is independent of the signal amplitude and is computationally intensive but accurate [23]. \\ \hline carrier wave & \(\varphi_{e}(n)-\varphi_{e}(n-1)/T\), where & The output result is independent of the signal amplitude, and the frequency discrimination is accurate but the calculation is large [24]. \\ \hline modulated & \((1-d)*(E-L)/(E+L)\) where & The normalized incoherent presubtracted back-envelope method with a linear region of operation is [\(-\)d, d] [22]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Discriminator algorithms and characteristics. \[a_{2}=2\xi \tag{12}\]  
where \(T_{s}\) is the data rate of the input filter instead of the sampling rate of the signal, \(K\) is the loop gain, \(\omega_{n}\) is the eigenfrequency, and \(\xi\) is the damping factor. The performance of the filter is affected by the loop bandwidth \(B_{L}\) and the eigenfrequency \(\omega_{n}\). The higher the \(\omega_{n}\), the shorter the time it takes for the loop to reach the steady state. The narrower the \(B_{L}\), the fewer frequency components are allowed to enter the loop, the better the filtering, and the more accurately the loop tracks the signal. However, \(B_{L}\) should not be too small; too small a \(B_{L}\) may filter out high-frequency components that vary normally in frequency and phase. Typical second-order loop filter parameters are as follows:  
\[a_{2}=1.414 \tag{13}\]  
\[B_{L}=\frac{1+a_{2}^{2}}{4a_{2}}\omega_{n}=0.53\omega_{n} \tag{14}\]
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 3 Prototype of 5G Integrated with TSN
### Setup of the 5G-TSN-Bridge  
#### 3.2.1 URLLC Testbed  
The standardization body for cellular communications, 3 GPP, has put a high emphasis on enabling ultra-reliable low latency communication (URLLC) from the very beginning of 5G standardization. The 5G wireless technology standard is the only one that has been designed to target the requirements of a broad category of use cases requiring high reliability with real-time communication. The 5G technology aims at enabling URLLC with scalable deployments in industrial automation. The 3 GPP body has specified several technical features to enable a high degree of reliability and low latency communication in Release specifications 15 and 16 on Industrial IoT [12]. The commercial availability of 5G products for Industrial IoT with TSN is still limited in the market. We use a pre-commercial 5G URLLC prototype with a standalone core network (cf. Figure 4) developed by Ericsson; it implements 5G time-critical communication features such as scheduling and feedback enhancements, robust control and data channels design, quality of service differentiation, prioritization handling, and efficient handling of the Ethernet traffic. In the URLLC system, the underlying algorithms and protocols are configured in a manner to satisfy real-time communication requirements.  
The 5G URLLC system operates at 28 GHz (5G N261 band) and uses 120 kHz sub-carrier spacing. The 5G URLLC is a pre-commercial prototype and standard compliant standalone system with an on-premises core network [29]. Our empirical results in Figure 5 show that for over-the-air transmissions in a production environment of an industrial shopfloor with strong multipath propagation delays, the URLLC system is able to achieve reliable low-latency communication. The latency distribution on the URLLC testbed is shown in Figure 6 for a large sample set of 100,000 packets transmitted with traffic profile as listed in Table 1. It can be observed that although the latency is bounded to 1 ms for 99.9% value, there are very few outliers that go up to ca. 2 ms. One may observe  
Figure 4: Setup of the URLLC testbed and the mobile robot in a shopfloor environment.  
that there is a spread in the latency distribution ranging from ca. 0.5 to 1.2 ms. The variations in the end-to-end latency in the URLLC testbed with over-the-air transmissions are caused due to several reasons. One of the reasons is the alignment delay between the packet reception in the URLLC testbed and the actual transmit opportunity in a TDD (time division duplex) system, where the same transmission spectrum bandwidth is alternatingly used for uplink and downlink transmissions. Moreover, the error control mechanisms impart an extra latency where an unsuccessful packet decoding at the destination is rectified through inherent HARQ (Hybrid automatic repeat request) and RLC (radio link control) retransmissions in the protocol stack. Finally, there are variations in the software/hardware packet processing delays both at the UE side and the network side.
# Analysis of Hydrocarbon Enrichment in Tight Sandstone Reservoirs in the Eastern Baiyun Depression
## 2 Geological Settings
### Source Rock Formation and Characteristics in Baiyun Depression  
The Baiyun depression is characterized by lacustrine facies of the Eocene Wenchang Formation, transitional facies of the Lower Oligocene Enping Formation and Marine source rocks of the Upper Oligocene Zhuhai Formation. The development of lacustrine source rocks in the Wenchang Formation is controlled by fault basins, which are mainly distributed in the margin of depression-controlled faults. The source rocks of the Enping Formation include delta coal measures and shallow Marine mudstones, which are mainly distributed in depressions and troughs. The distribution of natural gas in the Baiyun sag is controlled by source rocks. The oil and gas found on the north slope of the Baiyun Sag is basically the same origin as coal-derived oil and gas, and the Ro value of natural gas maturity is in the range of 1.5%-1.8%, mainly from the coal measure source rocks of the Enping Formation. A number of gas fields, such as Panyu 30-1, have been found, and the gas reservoir is highly filled. The gas characteristics of Liwan 3-1 gas reservoirs in the south of the Baiyun Depression are different. The gas is wet, and its maturity is slightly lower than that of the natural gas on the north slope, and the maturity Ro value is about 1.5%. The natural gas mainly originates from the Marine mudstone source rock of the Enping Formation, and the condensate may also come from the Marine source rock of the Enping Formation [26].  
The average total organic carbon content of deep lacustrine mudstones in the Wenchang Formation is 2.94%, the average chloroform bitumen \"A\" content is 0.23%, and the H/C atomic ratio of kerogen is mostly 1.5-1.0 [27; 28]. The organic matter types are mainly II\({}_{1}\) type, and some are I type, indicating that the Wenchang Formation has a good source rock. At the same time, the hydrocarbon generation potential of the source rocksof the Wenchang Formation has been confirmed. For example, crude oil rich in C\({}_{30}\)-4 methylstane from the Wenchang Formation was found in well LGH-1, which reflects that the source material of the oil is middle-deep lacustrine algae and plankton in a reducing environment [28].  
A large set of Marine mudstones is developed in the Zhuhai Formation. The TOC values of the mudstones in the Baiyun Depression are mostly between 1.0% and 1.5%, and the S\({}_{1}\) + S\({}_{2}\) values are mostly between 2 and 4 mg/g. The hydrogen index values and the maximum pyrolysis peak temperature chart indicate that the mudstones in the Zhuhai Formation are type II\({}_{2}\) and the reflectance R\({}_{0}\) values of the whole petrovite range from 0.43 to 0.53%. These data show that the Marine source rocks of the Zhuhai Formation in this area are a set of effective source rocks.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Lasers
#### 3.4.1 Types of Lasers  
The first lasers used in dentistry were neodymium-doped yttrium aluminium garnet (Nd:YAG) lasers, typically emitting a wavelength of 1.064 \(\upmu\)m. In the first applications, the Nd:YAG laser was limited to a few soft tissue applications and was large and costly [122]. Carbon dioxide lasers, with a wavelength of 10.6 \(\upmu\)m, were then introduced, which provided quicker healing times and allowed for haemostatic effects, as the diameter now exceed the average diameter of red blood cells (around 7 \(\upmu\)m). CO\({}_{2}\) laser is absorbed easily by water and was therefore found to be very efficient on soft tissues [120].  
The use of the erbium-doped yttrium aluminium garnet (Er:YAG) laser, with wavelength 2.94 \(\upmu\)m, was an exciting development in dentistry, as it was one of the first lasers to work for both soft and hard tissue. Erbium can be absorbed by water in soft tissues and hard tissues but possesses less haemostatic ability than other options [85]. In addition, when water molecules in the tissue exposed to the laser absorb the energy and vaporise, they create bursts of gas to ablate the tissue, and this technique can be damaging to remaining healthy surfaces of teeth. Hence, in 2005, carbon dioxide lasers with a wavelength of 9.3 \(\upmu\)m were introduced, which led to minimal thermal or mechanical damage while ablating hard tissues--enamel and dentin at high speeds [81, 123]. Diode lasers may also be used in dentistry, and due to their excellent haemostatic ability, they are ideal for cutting through highly vascular soft tissues [85].  
A summary of various lasers currently in use in dentistry and some of their popular applications is presented in Table 1[82].
# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 2 Materials and Methods
### Literature Search  
#### 2.1.1 Search Strategy  
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [12]. A literature search was carried out across the PubMed, Scopus, and IEEE Xplore databases on 18 th February 2024. Only studies published from 2014 onwards were taken into account to ensure the focus remained on advanced reconstruction methods, particularly those leveraging more sophisticated machine learning (ML) and DL algorithms instead of rule-based techniques [2; 13]. The search string encompassed the main concepts of \"freehand\", \"ultrasound\", \"three-dimensional\", and \"artificial intelligence\", supplemented with synonyms and related keywords. The detailed search string employed for all databases can be found in Appendix A. An additional snowballing method was utilized, examining the references of the selected papers. All duplicates were removed during the search process.
# Cystic Fibrosis: Overview of the Current Development Trends and Innovative Therapeutic Strategies
## 3 Non-Gene Therapy for CF
### Small Molecules
To overcome this problem, a combination of an aminoglycososide antibiotic like gentamicin or tobramycin, and PTC124 (Ataluren) is used. This serves as an effective treatment regimen since it brings about the insertion of the correct amino acid on the CFTR protein [114]. Alternative therapeutic strategies currently being explored for CF therapy include the use of a combination of CFTR modulators and ENaC inhibitors or calcium-activated chloride channel (CaCCs) agents. Therefore, duramycin and denufosol have been reported as potential CF medicines owing to their ability to enhance the release of intracellular calcium (Ca\({}^{2+}\)) ions [111]. Amiloride, the first ENaC inhibitor to be used in CF therapy, blocks Na\({}^{+}\) ion channels. However, its use is currently limited, owing to its short half-life and rapid clearance from the body [116]. Although second generation derivatives of amiloride, like benzamil and phenamil, were developed, these also suffer from rapid clearance [117]. Therefore, third generation amiloride derivatives, such as GS-9411, have been developed; however, these demonstrate a potent potassium-sparing ENaC inhibitor effect, which contributes to hyperkalemia [118]. Owing to the low stability and common side effects of amiloride and its derivatives, researchers have been prompted to develop alternative therapeutic strategies that indirectly inhibit ENaC activity, for instance, inhibition of Channel-Activating Protease (CAP) [119]. Camostat, a potent CAP inhibitor, plays an important role in stopping the cleavage of the extracellular loops of ENaC, thus, reducing the transportation of Na\({}^{+}\) ions across the lung epithelial cells. Despite the significant effect of CAP inhibitors, some concerns like unexpected side effects and lack of efficacy, still exist [50]. SPX-101 (developed by Spyryx Biosciences, Inc. (Durham, NC, USA)), a novel ENaC inhibitor, marks the first nebulized formulation for use in CF therapy to reach clinical trials [120]. It is an inhaled small peptide which mimics the functions of the ENaC modulator, short palate, lung, and nasal epithelial clone 1 (SPLUNC1). It reduces the amount of ENaC on the plasma membrane, thereby reducing Na\({}^{+}\) ion uptake and ASL absorption. It has been reported that the SPLUNC1 modulator is inhibited in the acidic ASL environment and degraded by the action of proteases found in the CF patients [120]. However, SPX-101 serves as a promising mutation-agonistic ENaC modulator owing to its protease resistance and high stability at low pH [120]. Table 1 summarizes the small molecule therapies in CF and their targeted mutations.
## 7 Results and Discussions
### Integration of RES and BSU  
In order to minimize the consumed energy for further reduction of the total cost, RES and BSUs were integrated in homes. Figure 9 shows that the day-time load will be supported by RES, while extra energy will be stored in BSUs for running the load in peak hours. It drastically reduced the cost. The figure depicts that our proposed TG-MFO algorithm has intelligently not only shifted the load to day-time off-peak hours for cost minimization, but also reduced the PAR.
## 4 Design and Implementation
### OOK Modulation
#### 4.1.2 Decoding in OOK Modulation  
Quick adaptive thresholding is used for decoding received signals. It calculates the moving average of grayscale values of the columnar sum of pixels from the cropped RoI. The algorithm consumes least memory for hardware implementation than other thresholding techniques such as Polynomial Fitting and Iterative Thresholding [24]. It is expressed mathematically as follows, where \(y_{i}\) be the grayscale value of a pixel at point \(i\) and assume \(f\!s(i)\) be the sum of the values of the last \(s\) pixels at point \(i\); hence it can be represented in Equation (1),  
\[f_{s}(i)=\sum_{n=0}^{s-1}y_{i-n} \tag{1}\]  
A much faster way to calculate the weighted moving average is to subtract \(1/s\) part of it and add the value of only the latest pixel instead of all \(s\) pixels; This is like emphasizing the grayscale values closer to the target value. Then the threshold in the quick adaptive thresholding is represented by Equation (2), where r is the adjustment ratio.  
\[T=\frac{r\sum_{n=0}^{S-1}(1-\frac{1}{S})^{n}y_{i-n}}{1}\sum_{n=0}^{S-1}(1-\frac {1}{S})^{n} \tag{2}\]  
The process of demodulation of the received OOK signal at the receiver is as shown in Algorithm 3. This process is divided into three parts, namely image preprocessing, conversion of image to pulses, and finally the main orchestrator function. In _Main_ function the video stream of an active Li-Fi transmitter is recorded which is further processed frames by frame. Each frame is passed to _Preprocessing_ function, which returns the RoI identified by using image processing techniques. The returned RoI is converted to a vector under the \(ImageToPulse\) function. Obtained vector value is appended to a predefined buffer of arbitrary size. The process is repeated till the size of the buffer is full. Finally, data normalization is performed on the buffer, and each value in the buffer is comparedwith the threshold obtained by the sliding window average. Based on the outcome of the comparison, the bit value is decoded.  
```
1:Initialize a Buffer(size)
2:functionImageToPulse(CroppedImage)
3:Return colourwise sum
4:endfunction
5:functionMain(video)
6:for each frame in the video do
7:Preprocessing(image) \(\triangleright\) As shown in Algorithm 1
8:ImageToPulse(CroppedImage)
9:Append the obtained vector to a buffer
10:if buffer size is full then
11:Normalize Values in buffer
12:for each value in the buffer do
13:if value > Sliding Window Average then
14:decode bit as 1
15:elseif value < Sliding Window Average then
16:decode bit as 0
17:endif
18:endfor
19:endif
20:endfor
21:endfunction
```  
**Algorithm 3** OOK Demodulation for OCC-LiFi Receiver.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
### Related Work
In addition, this paper creatively presented a re-selection mechanism to solve the problem that PSO is prone to fall into the local optimum so that the users with more significant channel gain can match subchannels with greater probability. In [28], aiming at the device-to-device (D2D) underlying cellular network, the author proposed a PSO algorithm to reasonably allocate user power to ensure the minimum user rate. Compared with the random and fixed power allocation algorithms, the proposed algorithm can maximize the throughput of cellular networks. However, the application of this algorithm in multi-cell scenarios remains to be studied. In [29], to ensure the system's spectral efficiency and maximize the system's energy efficiency, the author combined the cycle strategy rotation with the particle swarm optimization algorithm to allocate the downlink power of the NOMA system. In [30], the authors studied how to distribute power to maximize the throughput of a single input single output (SISO) NOMA system. In this paper, the authors proposed a power allocation scheme that distributed power equally to the subchannels and used a particle swarm optimization algorithm to allocate power to the subchannels. The two methods significantly improve system throughput and fairness between users compared to the water-filling-based method. Because the Doppler frequency shift easily exists in the downlink MIMO-OFDM high-speed railway system, it is necessary to consider the inter-carrier interference to maximize the system throughput under this background. For this mixed integer nonlinear programming problem, the author in [31] proposed to use the quantum-behaved particle swarm optimization (QPSO) algorithm to find the sub-optimal solution. In [32], that particle swarm optimization algorithm was also applied to the power allocation of uplink NOMA millimeter-wave (mmWave) communications. The NOMA mmWave system of the proposed solution performs significantly better than OMA, but the practical value of the approach is not high due to the two-user setup. In [33], the author improved the particle swarm optimization algorithm by adding a genetic algorithm to the particle swarm optimization algorithm and used it to solve the adaptive resource allocation problem of the cognitive radio system. The simulation results show that this novel power allocation algorithm has a better resource allocation effect and can adapt to the adaptive task of cognitive radio in different communication scenarios.