esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 4 Problem Statement  
### Terminology and Assumptions  
As in most related works, we discretize the flying space, as illustrated in Figure 3. The collection of edges of the grid form a set of \(\mathbb{R}^{3}\) coordinates denoted as \(\mathcal{P}\). These edges define potential locations for UAVs. Each UAV mounts one AP. As such, the words UAV and AP may be used interchangeably for the remainder of this work.  
Figure 3: UAVs placement scenario.  
Throughout this paper, we use the following definitions and terminology:  
* Drones are denoted by the set \(\mathcal{D}=\{1,2,...,D\}\). Their location is given by the set \(\mathcal{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},...,\mathbf{x}_{D}|\mathbf{x}_{i}\in\mathcal{P},i\in \mathcal{D},\mathbf{x}_{i}\
eq\mathbf{x}_{j}|_{\forall j\
eq i}\}\), where \(\mathbf{x}_{i}\in\mathcal{P}\) represents the three-dimensional coordinates of drone \(i\).
* Ground users are denoted by the set \(\mathcal{U}=\{1,2,...,U\}\). Their location is given by \(\{\mathbf{w}_{k}|k\in\mathcal{U}\}\), where \(\mathbf{w}_{k}\in\mathbb{R}^{3}\) represents the coordinates of user \(k\).
* The subset of users (i.e., WiFi clients) associated to the AP installed at drone \(i\in\mathcal{D}\) is denoted by \(\mathcal{C}(i)\subset\mathcal{U}\). Thus, the number of ground users covered is given by \(\mathcal{C}=\sum_{i=1}^{D}|\mathcal{C}(i)|\).
* \(R(i)\) represents the expected speech quality level (from 0 to 100) for users associated to the AP installed at drone \(i\in\mathcal{D}\).
* \(T_{\text{Flight}}(i)\) is the expected flight duration for drone \(i\) (see Equation (7), UAV's endurance). The average flight duration of the set of UAVs deployed, \(\bar{T}_{\text{Flight}}\), is: \[\bar{T}_{\text{Flight}}=\frac{1}{D}\cdot\sum_{i=1}^{D}T_{\text{Flight}}(i)\] (8) For the sake of simplicity and tractability, the following assumptions are considered:  
* The terrain has known dimensions, and it is an open area.
* The number of drones available for the initial deployment has an upper bound \(D_{\text{max}}\).
* Ground users have a smartphone with a compatible VoIP application installed (i.e., using a known codec).
* User positions are known. This assumption is commonly found in literature and could be implemented through user's smartphone's GPS or image processing.
* APs' channelization is arranged in such a manner that interferences between adjacent radio channels are negligible.
## 3 Simulation and Experimental Setups
### Experimental Phantom and In Vivo Carotid Studies  
In addition to simulation, we tested our methods on the publicity available CPWC data [48]. The experimental datasets were collected using a Verasonics Vantage 256 research scanner and a L11-4v linear array probe (Verasonics Inc., Redmond, WA, USA). The specific parameters for the scanner and the acquisition system are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of elements & 128 \\ Element pitch & 0.30 mm \\ Element width & 0.27 mm \\ Center frequency & 5.208 MHz \\ Sampling frequency & 20.832 MHz \\ Pulse bandwidth & 67\% \\ Excitation & 2.5 cycles \\ Number of transmit angles & 75 \\ Transmit angles & \([-16^{\circ},16^{\circ}]\) \\ Angle spacing & \(0.43^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of acquisition system for simulation and experiment.  
The proposed methods were compared with DAS, DMAS, SLSC, and CMSB. A tapered 25% Tukey window with an F-number of 1.75 was implemented in the receive aperture in DAS method. The \(M_{0}\) was set to 23 in SLSC to obtain a high contrast performance, and the kernel size was set to \(\lambda\). In simulation and phantom experimental studies, the \(\delta\) was set to 1.3 in LOCw-CMSB, and \(\delta_{max}\) was set to 1.3 in CMSB (\(\delta=\delta_{ac}\)) and LOCw-CMSB (\(\delta=\delta_{ac}\)). In the in vivo carotid study, \(\delta\) was set to 0.5 in LOCw-CMSB, and \(\delta_{max}\) was set to 0.5 in CMSB (\(\delta=\delta_{ac}\)) and LOCw-CMSB (\(\delta=\delta_{ac}\)). Additionally, the maximum subarray length \(L_{max}\) was set to \(N/2\) in all CMSB-based methods to achieve a balanced performance. The kernel size in LOC was set to \(5\lambda\) to keep a good speckle, where \(\lambda\) is the wave length. We added a Gaussian distributed noise with an SNR of 20 dB to the in phase/quadrature (IQ) channel data prior to beamforming. All reconstructed ultrasound images are shown in a 60 dB dynamic range, except for SLSC images, which are displayed in [0, 1].
## 4 Results and Discussion
### Reliability Test  
In statistics and psychometrics, reliability is commonly used as a measure to determine the overall consistency. Reliability is the consistency of the results when the experiment is replicated under the same conditions. A reliability test was conducted in this study to ensure that the reliability of the study was good enough. Study results must be reliable, otherwise the research questions will not meet the aims of the study, which in turn will fail to be generalizable, making the findings useless. Generally, the coefficients of reliability would be in the range of 0 to 1, where \"0\" means too many errors, and \"1\" indicates no error. This value indicates the amount of error in the study results [14]. Cronbach's Alpha was used to conduct the reliability analysis in this study. In assessing a unidirectional latent construct, Cronbach's Alpha plays a role as a measurement scale of reliability, quantifying the goodness of items or variables [15]. Alpha values higher than 0.8 indicate that the items in a study or test are high in internal consistency [16]. A Cronbach's Alpha coefficient of 0.7 is generally considered to be the minimum acceptable value.  
Table 3 shows that the values of Cronbach's Alpha obtained for the three latent variables--risky behavior, risk perception, and positive affect--were 0.833, 0.945, and 0.889, respectively. These values indicate that the three variables have a level of internal consistency, which shows that the study is reliable.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Forms of Telemedicine
#### 3.4.1 Asynchronous Telemedicine  
Asynchronous telemedicine, sometimes referred to as store-and-forward telemedicine, is a method in which physiological data captured from the patient are conveyed to medical experts based on a request for offline assessment [4, 63, 65, 75, 81, 82, 83, 84, 85]. It includes online storage and access of medical records by medical personnel. Because it uses low bandwidth, the cost of the process can be reduced. One major drawback of this method is the absence of any means of consultation between the patient and the doctor, which by implication may lead to wrong interpretation and diagnosis. Furthermore, it requires distribution of the patient's medical history and data, which may incur security and privacy concerns. Dermatologists, radiologists, and pathologists most typically employ asynchronous telemedicine [43].
## 2 Unmanned Ship Positioning System and Its Multi-Sensor Data Fusion Framework
### Framework of Multi-Sensor Data Fusion  
In order to improve the reliability, fault tolerance, and accuracy of multi-sensor data fusion for the unmanned ship for water quality detection, a new multi-sensor-data-fusion positioning framework based on particle filtering as, shown in Figure 3, was designed. From the figure, it can be seen that the longitude and latitude information of the unmanned ship was first unified in time and space, and projected into the local coordinate system constructed by the ZigBee multi-node module through coordinate transformation, so as to realize mutual complementarity among sensor data. Then, through the consistency inspection of the multi-sensor data, the fault data are weighted and corrected to improve the fault-tolerant performance of the data fusion algorithm. At the same time, the corresponding confidence factor was given according to the confidence distance of the positioning data, and the confidence factor was associated with hierarchical sampling to improve the fusion efficiency and accuracy of the data fusion algorithm. After that, the basic particle filter was used to de-noise the multi-sensor positioning data to improve the reliability of the  
Figure 2: Test platform for unmanned-ship positioning system.  
data samples. Finally, an improved particle-filter algorithm was designed to fuse and filter the multi-sensor positioning data to achieve accurate positioning of the unmanned ship.
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 2 Preliminary and Related Work
### Specifications of IEEE 802.15.6
#### 2.1.3 PHY Layer of WBAN  
As previously mentioned, in the specifications of the IEEE 802.15.6-2012 standard, there are three communications that could be utilized in the communication of WBAN: NB, UWB, and HBC, respectively. As a typical case, the physical layer processing of NB communication is analyzed as follows.  
The standard Physical-Layer Protocol Data Unit (PPDU) for NB is illustrated in Figure 5. Every PPDU contains three main components: the Physical-Layer Convergence Protocol (PLCP) preamble, the PLCP header, and the Physical-Layer Service Data Unit (PSDU).  
* PLCP Preamble  
As seen in Figure 5, the 90-bit PLCP preamble is the initial data package that needs to be sent for every PPDU to assist the receiver in data packet detection, timing synchronization, and carrier recovery. The data packet detection, timing synchronization, and the carrier recovery are presented next.  
* PLCP header  
Following the PLCP preamble, a 31-bit PLCP header that contains a 15-bit PHY header, a 4-bit Header Check Sequence (HCS), and 12-bit Bose Chaudhuri Hocquenghem (BCH) parity bits, can be found. The BCH code with 19 information bits and 12 parity check bits (31,19,2) is a shorter version of standard BCH code (63,51,2), which provides up to \(t=2\) error bit correction capability. The purpose of the PLCP header is to provide the system configuration parameters related to the receiver.  
The PHY header is constructed by a 3-bit rage, 8-bit length, 1-bit burst mode, and 1-bit scrambler seed, while two bits are reserved. The detailed encoding methods and corresponding meaning of them are specified in the IEEE 802.15.6 standard.  
* PSDU  
The PSDU is the component that contains the data from the Medium Access Control (MAC) layer. More precisely, it consists of a 7-byte MAC header at the beginning of the sequence, a 2-byte FCS at  
Figure 5: Standard Physical-Layer Protocol Data Unit (PPDU) structure. PLCP, Physical-Layer Convergence Protocol; PSDU, Physical-Layer Service Data Unit; HCS, Header Check Sequence.  
the end of the sequence, and a 0-255-byte MAC frame body in the middle containing the data. In this paper, the MAC frame body length is fixed to 255 bytes to simplify the control logic of the transmitter.  
The block diagram of the baseband transmitter is illustrated in Figure 6. According to the IEEE 802.15.6 standard, the modulation for the preamble, PLCP header, and PSDU is \(\frac{\pi}{2}\)-Differential Binary Phase Shift Keying (DBPSK), \(\frac{\pi}{2}\)-DBPSK, and \(\frac{\pi}{4}\)-Differential Quadrature Phase Shift Keying (DQPSK), respectively.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)
### Sub-Criteria of Real-Time Information Sharing
#### 4.2.2 Financial Performance  
For more than two decades, financial performance has been an effective strategy to improve the effectiveness and efficiency of logistics service companies [3; 109]. Reducing costs and improving logistics performance efficiency are success factors in enabling companies to sustain their economy in a highly competitive market [6; 110; 113]. The level of financial performance will recompense the complexity of logistics operations in their supply chain by PSS and PLM [75; 76]. A high financial performance reduces cost, high revenue, and the permeation of effectiveness and efficiency [3]. Therefore, financial performance improves logistics performance and reduces cost; investments into reducing cost can be analyzed to check the feasibility of the investment throughout the supply chain, such as rate of return (ROR), Activities-Based costing (ABC), Return of Investment (ROI), and Return of Asset (ROA).
## 4 Discussion  
### Shifting Trends in Target Species and Fishing Habits in the Danube Delta--Marine Zone  
The rapa whelk, an invasive gastropod that is native to the Far East (Sea of Japan, South China Sea), was originally introduced to the Black Sea between 1930 and 1940 [52; 53]. _R. vetosa_, a voracious predator with no natural enemies and competitors for food, soon spread throughout the basin and is blamed for destroying native flat oyster beds (_Ostrea edulis_, [PERSON], 1758) [54; 55]. It was first reported on the Romanian coast in 1963 [54]. During the 1960s, _R. vetosa_ recorded an exponential development, with the population reaching considerably high values [56]. Due to its high fecundity and wide tolerance to salinity, water pollution, and oxygen shortage, the rapa whelk population continued to thrive in the following decades, while other benthic species in the area suffered from deteriorating ecological conditions [30].  
Although it is a species considered to have a major negative impact on mussel stocks [8], _R. vetosa_ has a high commercial value, mainly due to its gastronomic and nutritional qualities [57]. Thus, it became an object of exploitation on an industrial scale for most of the Black Sea riparian countries, being a valuable bioresource. _R. vetosa_ caught the interest of economic operators in Romania as it can be exploited with smaller expenses than other target valuable species [50].  
Until beam trawl fishing was legislated in 2013, rapa whelk specimens were harvested manually, with divers, but with a poor yield and high operational expenses, the maximum catch being achieved in 2012 (588 t). The use, starting from August 2013, of the beam trawl, led to a progressive increase in the amounts of rapa whelk harvested, which were almost 15 times higher in 2017, when the catch represented 98.6% of the total catch made on the Romanian coast [50]. At the beginning of the large-scale exploitation of this species, estimates highlighted a stock value of approximately 17,500 tons, with an annual TAC value of approximately 10,000 t/year [30]. Starting from 2018, rapa whelk catches entered a downward trend, so that in 2020 they decreased by almost 40% compared to the catch in 2019. This was mainly caused by the reduction in the _R. vetosa_ stock, as well as by the restrictions of the COVID-19 pandemic, which affected both fisheries and the hotels-restaurants-catering (HORECA) industry [41].  
Although the intensive fishing of this species functions as a regulator of the stock size on the Romanian coast, there are major concerns regarding the impact of beam trawling on the seabed, even more so in a protected area sheltering sensitive and ecologically valuable habitats.
## 3 Results and Discussion
### Multi-Objective Optimization  
The optimization plots obtained using response optimizer (courtesy: Minitab) for the two scanners are shown in Figure 17a. The desirability value for each case should either be one or close to one in order to validate this analysis. Since, the composite desirability (0.9405) is close to 1, it indicates that the objective of highest accuracy in the minimum scanning time with the smallest number of triangles can be accomplished with a laser power of 50%, shutter time of 6 ms, and resolution of 3 mm Similarly, the composite desirability for the white light scanner is close to 1 (0.8446). Hence, a shutter time of 8 ms and resolution of 1.5 mm can be used to achieve highest accuracy in the minimum scanning time with the smallest number of triangles as shown in Figure 17b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Controlling Factors**} & \multicolumn{2}{c}{_p-Value_} \\ \cline{2-3}  & **Laser** & **White Light** \\ \hline Laser Power & 0.183 & - \\ Shutter Time & 0.444 & 0.289 \\ Resolution & 0.000 & 0.000 \\ Laser Power*Shutter Time & 0.264 & - \\ Laser Power*Resolution & 0.125 & - \\ Shutter Time*Resolution & 0.388 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(p\)-Values using ANOVA for the laser-based and white light handheld scanners (triangle numbers).  
Figure 14: Main effect plots for deviation: (**a**) laser scanner; (**b**) white light scanner.  
Figure 16: Main effect plots for triangle numbers: (**a**) laser scanner; (**b**) white light scanner.  
Figure 17: Optimization Plots—handheld: (**a**) laser scanner; (**b**) white light scanner.  
Figure 15: Main effect plots for scanning time: (**a**) laser scanner; (**b**) white light scanner.  
The accuracy comparison reveals that the laser scanner (deviation = 0.1285 mm) provided better accuracy than the white light scanner (deviation = 0.1995 mm). However, the white light scanner required less scanning time (2.6585 min) than the laser scanner (10.3607 min) as shown in Figure 17. Similarly, the number of triangles generated by the white light scanner (142,659) was less than the number of triangles generated by the laser scanner (148,800). The triangle number is important because it determines the computer processing time, as well as the accuracy. In particular, a larger number of triangles improves accuracy, but it also increases the processing time. There should be a balance between the desired accuracy and the processing time. The processing time is defined as the time taken by the software to convert the polygon model into a final CAD model. Therefore, the data acquired by the laser scanner requires a longer processing time as compared to the polygon model captured by the white light scanner.
# A Water Tank Level Control System with Time Lag Using CGSA and Nonlinear Switch Decoration
## 2 Tank Level Model  
Assumption. An idealized single tank exists, where both input and output regulating valves are in action to achieve the set height. The key mathematical notations used in this model are listed in Table 1.  
The difference between the inflow and outflow is  
\[\Delta Q_{i}-\Delta Q_{0}=\frac{dv}{dt}=A\frac{dh}{dt} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Descriptions** \\ \hline \(Q_{i}\) & Steady-state value of input water flow \\ \(\Delta Q_{i}\) & Increment of input water flow \\ \(Q_{0}\) & Steady-state value of output water flow \\ \(\Delta Q_{0}\) & Increment of output water flow \\ \(h\) & Liquid level height \\ \(h_{0}\) & Steady-state value of the liquid level \\ \(\Delta h\) & Increment of liquid level \\ \(u\) & Regulating the opening of valves \\ \(A\) & Cross-sectional area of the water tank \\ \(R\) & Resistance of load valves at the outflow end \\ \(\Delta u\) & Change in opening of control valve \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and descriptions.  
where \(V\) is the tank liquid storage volume.  
\[\Delta Q_{i}=K_{u}\Delta_{u} \tag{2}\]  
where \(K_{u}\) is the valve flow coefficient.  
\[Q_{0}=A_{0}\sqrt{2 gh} \tag{3}\]  
where \(K_{u}\) is the cross-sectional area of the output tube.  
Equation (3) can be converted to linear at the equilibrium point \((h_{0},Q_{0})\), then the liquid resistance R can be expressed as  
\[R=\frac{\Delta h}{\Delta Q_{0}} \tag{4}\]  
Substituting Equations (2) and (4) into Equation (1) and converting them into transfer function form, we receive  
\[G(s)=\frac{h(s)}{Q_{i}(s)}=\frac{K_{0}}{s(T_{0}s+1)} \tag{5}\]  
where, \(K_{0}=K_{u}R\), \(T_{0}=RA\). The linear model (5) is used for the design of the controller, while the non-linear model (1) (3) is used for system simulation to verify the robustness of the designed controller. Assuming that the height of the tank used for this paper is 2 m, the cross-sectional area of the tank is 1 m\({}^{2}\), the cross-sectional area of the output pipe is 0.05 m\({}^{2}\), the initial level is 0.5 m, and the maximum inlet volume of the tank is 0.5 m\({}^{3}\)/s, then \(R=2\), \(K_{u}=0.4\), \(K_{0}=0.8\), \(T_{0}=2\)[22].  
However, the simplified model responds nearly 40% faster than the actual situation [27]. To improve the simulation accuracy of the linear tank control system, a first-order inertial system \(1/5s+1\) was added to Equation (5), and it was verified that the regulation time was similar to the actual situation in [27]. Thus, this paper uses this model to improve the confidence of the system simulation, and the new model transfer function is  
\[G(s)=\frac{h(s)}{Q_{i}(s)}=\frac{0.8}{s(2s+1)(5s+1)} \tag{6}\]
# New Insights into the Biology of the Emerging Tembusu Virus
## 1 Introduction  
Tembusu virus (TMUV) is an emerging arbovirus (arthropod-borne virus), which was initially identified in Malaysia in 1955 from _Culex tritateniorhynchus_ mosquitoes and then occasionally reported in different surveys in Southeast Asia (SEA) during the 1970s [1, 2]. As with the Japanese encephalitis (JEV) and dengue (DENV) viruses, two of the most important endemic arboviruses in SEA, TMUV belongs to the genus Flavivirus in the Flaviviridae family. Until the first major outbreak of TMUV in ducks in 2010, the virus was largely neglected, and studies were scarce. Since 2000, new variants coined Sitiawan virus, duck Tembusu virus (DTMUV) or Baiyangdian virus (BYD) have been identified and reported to cause avian outbreaks [3, 4, 5]. However, the latter viruses are not isolated species since they are closely phylogenetically related to the original TMUV strain of 1955. Sitiawan virus, DYMUV and BYD are now classified in the TMUV clade/monophyletic group and distributed in different sub-clusters. In this review, we will refer to all these viruses as TMUV, except when specific descriptions have been associated with one sub-cluster.  
TMUV causes encephalitis and neurological disorders in avian populations, resulting in morbidity rates of up to 90%, associated with damage to the female reproductive system leading to severe loss of egg production in farms [4]. Since 2010, TMUV has caused several outbreaks and has been detected repeatedly in China and SEA, underscoring the emergence and circulation of this virus outside of its original geographic region. Given the recent regional expansion and associated large economic losses in the poultry industry, TMUV should now be considered an emerging infectious disease.  
To date, knowledge on TMUV ecology and biology remains incomplete, and the driving cause of its sporadic emergence remains unknown. This strongly contrasts with other emerging arboviruses causing human diseases such as yellow fever (YFV), DENV, West Nile (WNV), JEV and Chikungunya (CHIKV) viruses. In the last decade, importantprogress in the understanding of TMUV biology has been made through both experimental animal models and in vitro studies. Here, we review the epidemiology, virus-host interaction and cell biology of avian TMUV infection. We also discuss approaches to increase the knowledge of this virus in order to properly evaluate the associated risk of spillover in humans.