esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Model-Based Ultrasound Envelope Statistics Parametric Imaging Techniques
### Ultrasound Nakagami Imaging  
The Nakagami statistical model was first introduced into the biomedical ultrasound field by [PERSON] [29]. The probability density function (PDF) of the Nakagami model is defined by [29]  
\[f_{\rm N}(r)=\frac{2m^{m}r^{2m-1}}{\Gamma(m)\Omega^{m}}\exp\Bigl{(}-\frac{m}{ \Omega}r^{2}\Bigr{)}U(r), \tag{2}\]  
where \(\Gamma(\cdot)\) and \(U(\cdot)\) denote the gamma function and the unit step function, respectively; \(\Omega\) is the scale parameter; \(m\) is the shape parameter. For \(m<1\), the envelope statistics is pre-Rayleigh distribution, meaning there are a small amount of scatterers (\(<\)10) randomly distributed in the ultrasound resolution cell. When \(m=1\), the envelope statistics becomes Rayleigh distribution, meaning there are many scatterers (\(\geq\)10) randomly distributed in the ultrasound resolution cell. When \(m>1\), the envelope statistics turns into post-Rayleigh distribution, meaning there are periodic scatterers or local high-concentration scatterer aggregation, in addition to many scatterers (\(\geq\)10) randomly distributed in the ultrasound resolution cell (Figure 2). Therefore, the Nakagami distribution is a generalized statistical model for ultrasound backscattering.  
The Nakagami distribution can be regarded as an approximation of the HK model [10]. Because of its low computational complexity, the Nakagami distribution has become the most widely used statistical model in the field of medical ultrasound; typical applications of Nakagami model have been summarized by [PERSON] et al. [30]. The estimation methods of Nakagami \(m\) parameter mainly include moment-based estimators [19; 31; 32] and maximum likelihood estimators [30; 32; 33]. It should be noted that window-modulated compounding (WMC) Nakagami imaging was proposed by [PERSON] et al. [34] for simultaneous improvement of image resolution and smoothness of Nakagami images.  
Animal models [19; 31] and clinical studies [35] have shown that the \(m\) parameter estimated by the moment-based estimators increases with the increasing severity of hepatic steatosis. Using a rat model (\(n=24\)), [PERSON] et al. [31] reported that ultrasound Nakagami imaging is well correlated with the amount of the total cholesterol (\(r=0.86\); \(p<0.0001\)) and triglyceride (\(r=0.79\); \(p<0.0001\)) in the liver tissue, respectively. [PERSON] et al. [19] investigated three-dimensional WMC Nakagami imaging for characterizing hepatic steatosis in a rat model (\(n=18\)); the Nakagami \(m\) parameter had a correlation \(r^{2}=0.94\) with the methionine-choline-deficient diet weeks. [PERSON] et al. [35] investigated ultrasound Nakagami imaging for assessing hepatic steatosis of 107 patients; the reference standard was an ultrasonographic scoring system [36]. A correlation \(r=0.84\) (\(p<0.0001\)) was reported.
## 2 Theoretical Background
### LEACH Protocol  
The LEACH proposed by [30] employs a hierarchical structure routing technique. In addition to providing data fusion, the LEACH protocol's primary purpose is to improve the efficiency with which sensor nodes utilize the power they consume and to maximize the amount of time a network may remain operational by combining many different types of approaches, including cluster-based routing and MAC-layer techniques [31]. The sensor nodes that make up the network are responsible for delegating tasks to one another. For instance, one of the nodes is designated as the cluster head, while the rest are classified as leaf nodes or non-cluster heads. The sensed data by sensor nodes (leaf) are transmitted to the cluster head within a particular cluster group. The cluster head is responsible for transmitting the collected data to the sink node or the base station. Nodes in a cluster consume significantly less power than the cluster head. When the cluster head dies, the leaf nodes lose their ability to communicate.  
Low-energy adaptive clustering hierarchy LEACH [4; 30] is one of the most efficient and simple clustering and routing protocols in wireless sensor networks. The core objective of the protocol is to minimize the overall network energy consumption requirements for creating and maintaining clusters in order to improve the lifetime of the network. The general working principle of the LEACH protocol is hierarchical where nodes transmit to cluster heads, and the cluster heads aggregate the data and forward it to the base station (sink). Each node uses a stochastic algorithm at each round to determine whether it will become a cluster head in this round. Nodes that have been cluster heads cannot become cluster heads again for a period of \(\mathrm{P}\) rounds, where \(\mathrm{P}\) is the desired percentage of cluster heads. Thereafter, each node has a \(1/\mathrm{P}\) probability of becoming a cluster head again. At the end of each round, each node that is not a cluster head selects the closest cluster head and joins that cluster.  
The algorithm of the LEACH protocol operates in two phases: setup and steady state. In the first phase, the cluster and cluster head will be determined. The amount of energy that is still available is used by each node to determine whether or not it will act as a cluster head (CH) throughout the session. The cluster head broadcasts a message to all other nodes that become a CH, then the nodes request a CH to join the cluster. Based on the number of nodes joining the cluster, the CH creates a TDMA schedule which contains slots of time equal to the number of member nodes in the cluster. The second phase is responsible for identifying the process of data transmission [4; 32].
## 6 Conclusions  
To make full use of the attractive benefits of OFDM in the DVB-C2 system, it is very important to keep the frequency alignment between the transmitter and the receiver. In this study, a reduced-complexity and robust CFO and SFO estimation method was suggested in the DVB-C2 OFDM system, which is based on a block-by-block estimation. The block-by-block estimation was used to make the LSE be complexity effective and noise robust. The optimization of the LSE scheme played an important role in minimizing a bias from non-symmetrically located CPs. Since the amount of phase rotation of the proposed estimation scheme caused by frequency offsets increases in proportion to the number of observed symbols, the proposed method could provide robustness against noise. It has been demonstrated by the presented results that the proposed block-by-block estimation method is proven to be computationally efficient, offering an improved estimation performance, when compared with the conventional estimation methods. Therefore, the proposed frequency-offset estimation scheme can be used to maximize the benefits of OFDM in digital cable communication systems.  
[PERSON]-[PERSON] developed the estimation method and conducted computer simulations. [PERSON]-[PERSON] analyzed the performance of the presented detection method and verified the numerical results.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048819).  
The authors declare no conflict of interest.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 5 Experimental Results  
RQ1: Which Network Protocol of AODV, OLSR, or DSDV Shows the Highest PDR Performance in the Reconnaissance Scenario?  
First, we compared the FANET protocols, AODV, DSDV, and OLSR in terms of PDR to identify a suitable protocol for the reconnaissance scenario. In FANETs, node density is sparse, making communication between nodes difficult. Therefore, we evaluated the performance of AODV, DSDV, and OLSR by increasing the number of nodes by five in our simulation. By doing so, we also could understand the impact of node density on the performance of FANET protocols.  
Figure 1 shows the packet delivery ratio for each mobility model used for reconnaissance. In the figure, the x-axis represents the number of nodes, and the y-axis represents the packet delivery ratio. From Figure 1a-e, AODV shows the highest PDR. In our reconnaissance scenario, the topology of nodes changes quickly and frequently. Therefore, AODV, a reactive method that determines routes whenever needed, yields relatively high PDR performance. On the other hand, DSDV stores and manages all nodes in a table. The excessive overhead of updating the table could be one of the reasons that DSDV yields the lowest PDR performance.  
In addition, we checked our assumption that the PDR performance of a FANET protocol would increase as the node density increases. As expected, the PDR performance of AODV and OLSR increased to some extent and then began to decrease. For example, in Figure 1a, the PDR performance of AODV increases up to 15 nodes (81%) and then decreases. The PDR performance of OLSR increases up to 15 nodes (60%), and then degrades from  
Figure 1: Packet delivery ratio: (**a**) SRWP, (**b**) MP, (**c**) RDPZ, (**d**) EGM, and (**e**) DPR.  
20 nodes. However, in the case of DSDV, the PDR performance did not increase even if the node density increased. DSDV shows the best performance (46%) with five nodes.  
**Findings.** The FANET routing protocol suitable for our reconnaissance scenario is the reactive routing protocol AODV. In addition, the PDR performance of a FANET protocol does not continue to improve as the density of FANET nodes increases.
## 3 Results  
### Comparison of CST and Experimental Results  
Our experimental measurements first acquired \"target-free\" data of the tank filled with 90% glycerine-water. We then introduced a 16 mm target tube filled firstly with water and then with a salt-water mixture (10 mg/mL). Figure 9 depicts the received signal strength with and without the target as a function of the receivers for the first antenna transmitting. Comparing Figures 6a and 9a, we observe a very good agreement between CST simulations and experimental measurements for the target-free case. Differences can be attributed to factors that were not taken into account in the CST simulations, such as coupling between the cables, instrument noise, and interference from environmental factors. We note that the noise floor for our used VNA settings is \(-\)100 dB, and, therefore, signals below this value are susceptible to noise.  
We also assessed the impact of the target on experimentally measured signal propagation, in a similar manner to the CST simulation studies. Results are shown in Figures 9b and 10b, which plot the same data as in Figure 9a for the water target and salt-water mixture (10 mg/mL), respectively. Results from CST simulations for the salt-water target are shown in Figure 10a. Trends observed in both cases are very similar, except at 1.0 GHz where the experimental data is significantly different from the simulation results. The antenna operates less efficiently at 1.0 GHz, and we observed that the coupling between the cables causes a significant discrepancy between experimental and simulation data at lower frequencies. For frequencies where the antenna is more efficient, such as 1.5 and 2.0 GHz, there is good agreement between CST simulations and measured data, as shown in Figure 11.
# Maximally Deficiency: Treatments and Applications for Adolescents  
[PERSON]  
1 Oral and Maximilofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China; [EMAIL_ADDRESS]  
2  
[PERSON]  
2  
[PERSON]  
1 Oral and Maximilofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China; [EMAIL_ADDRESS]  
2  
Footnote 1: email: {[EMAIL_ADDRESS]}  
Footnote 2: email: {[EMAIL_ADDRESS]}  
Footnote 3: email: {[EMAIL_ADDRESS]}  
Footnote 4: email: {[EMAIL_ADDRESS]}  
Footnote 5: email: {[EMAIL_ADDRESS]}  
Footnote 6: email: {[EMAIL_ADDRESS]}  
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based on the age ranges found in the included literature: early adolescence (10-14 years), mid adolescence (14-16 years), and late adolescence (16-19 years). Additionally, for some studies, dental age is assessed through cone-beam computed tomography (CBCT) scans, which evaluate skeletal and mid-palatal suture maturation. This approach has further provided a refined dimension for categorizing patients, facilitating more precise treatment planning for adolescents [3, 4, 5, 6, 7].  
Adolescent maxillary expansion represents a dynamic and transformative process due to the active bone growth occurring during this developmental stage. The growth potential of the maxilla during adolescence provides a crucial window of opportunity for treatment, allowing for a range of orthodontic and surgical interventions tailored to individual needs. Orthodontic techniques have been extensively employed for correcting transverse deficiencies, with each approach demonstrating distinct advantages depending on the patient's age, growth stage, and the degree of suture interdigitation [8]. As alternative to orthodontic techniques, surgical approaches have shown promising outcomes by enhancing skeletal modifications and optimizing maxillary protraction in patients with complex conditions such as cleft palate or craniosynostosis [9]. Moreover, some of the surgical approaches have emerged as a powerful option for late adolescents, offering superior skeletal effects and minimizing adverse periodontal outcomes.  
For more severe or multi-dimensional deficiencies, surgical interventions such as orthognathic surgery are indispensable in addressing discrepancies simultaneously. This dynamic spectrum of treatment highlights the need for a comprehensive approach to selecting appropriate interventions.  
To address maxillary expansion from a broader clinical perspective, the present literature review aims to comparatively summarize the existing treatment approaches. This enables clinicians to better navigate the complexities of adolescent maxillary deficiency and understand the available treatment options in various scenarios.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 5 Experiment
### Sample Adaptability Analysis  
As the proposed method in this paper presents different results on different datasets, the sample adaptability of the method is analyzed. We investigate the effect of training sample size on the retrieval performance of the proposed algorithm on the MIRFLICKR-25K and NUS-WIDE datasets. The mAP values of Tri-CMH were recorded when the hash code length was set to 32, and the sample sizes were 2000, 5000, 7000, and 10,000. Figure 5 shows the variation of mAP with sample size for both datasets.  
As shown in Figure 5a, on the MIRFLICKR-25K data set, a high mAP can be achieved with a small number of samples, both for text retrieval based on images and image retrieval based on text. Furthermore, the mAP tends to increase as the training sample data increase. However, starting with a sample size greater than 7000, the mAP trend slows down but also rises. The method proposed in this paper is highly adaptable to this data set.  
As shown in Figure 5b, on the NUS-WIDE data set, the mAP tends to increase when the number of samples is less than 7000, whether the text content is retrieved based on image information or the image content is retrieved based on text information. However, when the number of training samples exceeds 7000, the mAP tends to decrease. The method in this paper does not adapt well to a larger sample size on this data set.  
Therefore, for the NUS-WIDE data set, the method in this paper is more adapted to small samples and can work best for experiments by extracting a portion of the data with convincing results.  
The results of the experimental analysis show that in the current stage of cross-modal retrieval tasks, the mAP results of Tri-CMH are all higher than those of other mainstream methods. The area below the PR curve is generally more significant than other methods, which is sufficient to demonstrate the effectiveness and practicality of the method in this paper. However, some things could be improved with the method in this paper. For example, the accuracy is better on the datasets IAPR-TC12 and MIRFLICKR-25K. The accuracy of the more extensive data set NUS-WIDE will be slightly lower than the individual better methods as the recall rate rises. The analysis of the sample adaptation ability by different sample sizes on different datasets shows that the method in this paper works best when the sample size is less than 7000. The trend of increasing mAP values slowed down for all values larger than 7000. Therefore, in future research, the method will continue to be optimized for larger data sets.
## 2 Basic Notions and Preliminary Results
### Graph and Matrix  
We first introduce some graph terminologies which can be found in [32]. A digraph \(g=(\
u,\epsilon)\) consists of finite set of nodes \(\
u=\{1,2,3,...,N\}\) and an edge set \(\epsilon\subseteq\
u\times\
u\). An edge of \(\epsilon\) from node \(i\) to node \(j\) is denoted by \((i,j)\), where the nodes \(i\) and \(j\) are called the parent node and child node of each other, and the node \(i\) is also called a neighbor of the node \(j\). Let \(N_{i}\) denote the subset of \(\
u\) which consists of all the neighbors of node \(i\). If the digraph \(g\) contains a sequence of edges of the form \((i_{1},i_{2}),(i_{2},i_{3}),...,(i_{k},i_{k+1})\), then the set \(\{(i_{1},i_{2}),(i_{2},i_{3}),...,(i_{k},i_{k+1})\}\) is called a path of \(g\) from node \(i_{1}\) to node \(i_{k+1}\) and node \(i_{k+1}\) is said to be reachable from node \(i_{1}\). if \(i_{1}=i_{k+1}\), the path is called a cycle. The adjacency matrix of \(g\) is defined as \(A_{g}=\left[a_{ij}\right]\in R^{N\times N}\) with nonnegative adjacency elements \(a_{ij}>0\Leftrightarrow(j,i)\in\epsilon\). The degree matrix \(\Delta_{g}=\left[\Delta_{ij}\right]\) is a diagonal matrix with \(\left[\Delta_{ii}\right]=\sum_{j=1}^{N}a_{ij}\), \(i=1,2,...,N\), element and the graph Laplacian of the weighted digraph \(g\) is defined by \(L=\Delta_{g}-A_{g}\) whose eigenvalues will be ordered and denoted as \(0<\lambda_{1}\leq\lambda_{2}\leq...\leq\lambda_{N}\). \(L\) corresponding eigenvector \(1=[1,1,...,1]^{T}\), i.e., \(L1=0\).  
**Lemma 1** ([33]).: _For a quasi-strongly connected graph \(g\), the graph Laplacian \(L\) have the \(N\) nonzero eigenvalues, which are all in the open right-half plane._
## 5 Experimental Results and Discussion
### Impact of Transmission Distance  
The above comparisons focus on 20 km SSMF transmission. For longer distances, the basic limitation can be still understood from channel response in the frequency domain. Figure 13a-d depicts the overall transfer function after 20/40/60/80 km SSMF transmission. Conventional IM with differential driven mode (black curve) is shown as benchmark. We found that, with the help of bias deviation (blue curve) and amplitude mismatch (green curve), the 3 dB bandwidth could be extended by \(\sim\)35%. However, since the frequency nulls were not cancelled, the available transmission bandwidth inevitably reduced inversely proportional to the square of fiber length. Fortunately, if time skew is adopted, the 3 dB bandwidth is kept around 20 GHz, showing much stronger robustness against power fading. A smoother response can be obtained by selecting the appropriate value of skew.
## 4 Conclusions  
In conclusion, we analyzed the E\({}_{2}\) (high) and A\({}_{1}\) (LO) phonons of the GaN epitaxial layer grown on the single crystal sapphire substrate by MOCVD with four different types of buffer layers. We found that the peak shift of the E\({}_{2}\) (high) phonon peak was lightest, and the results of XRD indicated that the dislocations and defects in the GaN films grown on the graphene/sputtered AlN buffer layer had been reduced. The phonon lifetimes of the A\({}_{1}\) (LO) phonon peak were shortest when GaN films were directly deposited on the graphene/sputtered AlN buffer layer, which was most conducive to carrier migration in this situation. Finally, the Raman intensity of 2D mode indicated that the graphene transferred on the sputtered AlN buffer was monolayer, which was more helpful to remote epitaxy and guided the nucleation of the GaN epilayer. Through Raman analysis of GaN, it is concluded that the GaN obtained on the graphene/sputtered AlN buffer layer was subject to stress-free conditions, and the single-layer graphene obtained by the transfer was also more conducive to nucleation in the growing process. This work will play an important role in the subsequent manufacture of GaN-based optoelectronic devices dominated by optical-phonon and carrier interaction.  
Conceptualization, Y.Z. and J.N.; methodology, C.Y., J.Z., J.N. and D.W.; investigation, C.Y., B.W. and Y.J.; resources, J.Z., J.N. and D.W.; data curation, YJ and Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.N.; project administration, J.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Science & Technology Special Project (Program No. 2017 ZX01001301), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019 ZDLGY16-08, 2019 ZDLGY16-03, 2019 ZDLGY16-02); Youth Science and Technology Nova Program of Shaanxi Province (Program No. 2020 KJXX-068); the Wuhu and Xidian University special fund for industry-university-research cooperation (Program No. HX01201909039) and Fundamental Research Funds for the Central Universities (Program No. JBF201101).  
The authors declare no conflict of interest.
# Technical Evaluation and Problem-Solving in the Reopening of a Thermal Bath Facility
## 5 Development Proposals
### Using Smart Sensors to Optimize Water Quality and Energy Use  
Modern technology allows the operation of the spa to be continuously monitored and optimized.  
* Water quality monitoring sensors These sensors continuously measure the water parameters so that the operation can react to changes in real time.
* pH and chlorine level sensors--automatically control the dosing of chemicals.
* Turbidity meters--immediately indicate if water quality deteriorates.
* Temperature sensors--optimize the heating system.
* Water level and flow sensors
* Automatic water refill--reduces unnecessary water waste.
* Leakage sensors--provide early warning of burst pipes or water loss.
* Smart pumps and water circulation systems
* Energy efficient pumps--reduce electricity consumption for pool water circulation.
* Timed and automated water recirculation--regulates water circulation based on traffic data.
* Smart Building Management Systems (BMS)
* A central system can control water temperature, disinfection and energy consumption.
* AI-based predictive models--analyze visitor numbers and weather data to optimize water consumption and heating.  
The introduction of water treatment systems and smart sensors can not only reduce the operating costs of the spa, but also improve the visitor experience and the sustainability of the spa. Automated water quality monitoring, energy-efficient water circulation and digital optimization are investments that pay off in the long term.