esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### VR Real-Time Remote System  
As previously stated, one the main drawbacks of a VR solution is the need for an expensive high-performance computer that renders the VE and relays the session's information to the therapist. To avoid this problem, the FarmDay application was designed to run on top of a real-time, remote VR system.  
The details of this system are out of the scope of this document, but its architecture can be explained briefly. In this real-time, remote VR system, the VR application runs on a remote high-powered cloud computer (the server), from which the rendered environment is encoded and streamed to a less powerful computer at the patient's home (the client), which in turn is connected to the VR HMD and controllers. This is possible through the use of an elastic down-sampling video compression codec [41]. This architecture relies on the continuous transmission of HMD and controller positions and interaction events from the client to the server. This way, the results of the interactions can be computed on the server and the video image relayed to the client. Figure 1 shows the architecture of this real-time, remote VR system. The FarmDay application is, therefore, run in the cloud server but used by the patient at their home. This way, the cost of running this setup for the user is greatly reduced.  
Every VR application designed to use this system must use the OpenVR libraries, since it must use four custom components developed for it:  
* A client inputs process that captures the HMD and controllers inputs and sends them to the server.
* A server virtual inputs driver that receives the input data from the client and feeds them to the actual VR application running in the server (in this case, FarmDay).
* A server video encoding driver that captures the rendered VE product of these interactions, encodes it, and sends it to the client.
* A client video decoding process that receives the encoded video, decodes it, and shows it in the client's HMD.
# Separation of Albumin from Bovine Serum Applying Ionic-Liquid-Based Aqueous Biphasic Systems
## 2 Materials and Methods  
### Chemicals and Reagents  
Several ILs were synthesized by us, namely tetrabutylphosphonium arginate ([P\({}_{4444}\)][Arg]), tetrabutylammonium arginate ([N\({}_{4444}\)][Arg]), tetrabutylphosphonium 2- (N-morpholino)ethanesulfonate ([P\({}_{4444}\)][MES]), tetrabutylammonium 2-(N-morpholino) ethanesulfonate ([N\({}_{4444}\)][MES]), 1-butyl-3-methylimidazolium 2-(N-morpholino)ethanesulfonate ([C\({}_{4}\)mim][MES]), tetrabutylphosphonium acetate ([P\({}_{4444}\)][Ac]), and tetrabutylammonium acetate ([N\({}_{4444}\)][Ac]), according to previously reported protocols [17; 19; 26; 27]. Details on the ILs synthesis are given in the Supporting Information (SI). The purity of all ILs were checked by \({}^{1}\)H and \({}^{13}\)C nuclear magnetic resonance (NMR), being \(>\)95%. The ILs synthesized are solid at room temperature and completely water-soluble. The IL 1-butyl-3-methylimidazolium acetate ([C\({}_{4}\)mim][Ac], 97% purity, was acquired from lolitec. The chemical structures of the studied ILs are presented in Figure 1.  
The salts used for the buffer solution preparation were potassium phosphate dibasic trihydrate (K\({}_{2}\)HPO\({}_{4}\) 3H\({}_{2}\)O, extra-pure) from Panreac, and potassium phosphate monobasic (KH\({}_{2}\)PO\({}_{4}\), extra-pure) acquired from Fisher Chemical. Phosphate Buffered Saline (PBS) pellets (pH = 7.4) were acquired from Sigma.  
BSA (purity \(\geq\) 96%), IgG (purity \(\geq\) 98%) and bovine serum were acquired from Fischer Scientific, Innovative Research and Merck, respectively. All aqueous solutions were prepared with distilled water.
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 6 Nose-to-Brain Delivery of L-DOPA
### Clinical Trials  
In 2018, a randomized, double-blind clinical trial was launched in Australia, which studied the performance of a Precision Olfactory Delivery device (POD) with L-DOPA versus placebo. The investigation was focused on evaluating the safety, tolerance, and pharmacokinetics/pharmacodynamics of intranasal L-DOPA when administered alongside carbidopa--a L-DOPA decarboxylase inhibitor, during an \"OFF\" episode. Adult males and females, from 40 to 80 years of age, diagnosed with idiopathic PD (by UK Brain Bank Criteria) with modified Hoehn & Yahr (H&Y) stage I-III during an ON period at first visit and prone to OFF episodes (when their usual PD medication has worn off) were included. The study took place in five PD treatment centers. The trials started with the application of a spray-dried levodopa formulation at three escalating dose levels: 35 mg, 70 mg, and 140 mg, followed by an oral dose of a carbidopa. Subsequently, a combined formulation was developed with 7 mg of carbidopa and 70 mg of L-DOPA. The results indicated that as the dose of L-DOPA increased, the maximum concentration (Cmax) and the AUC proportionally increased over the first 2 h. The average time needed to reach the maximum plasma concentration (Tmax) was longer than expected or desired. Given this result, additional tests and improvements were required. However, the majority of patients reported a high level of comfort using the POD device and they declared a preference for it over their current drug delivery method. Overall, the study highlighted the functionality of the POD device, showcasing its adaptability in clinical applications. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nano-Carrier System** & **Size/Zeta Potential** & **Active Targeting** & **Scientific Result** & **Reference** \\ \hline Multi-lamellar liposomes & \(\begin{array}{c}302-2432\ nm/\\ -27.56 to \(-29.50\) mV\end{array}\) & n.a. & \(\begin{array}{c}\text{Chemical stabilization}\\ \text{of L-DOPA}\end{array}\) & [186] \\ \hline Mixed liposomes co-loaded & & & & \\ with N,N-dimethyl-1-tetradecylamine ammonium & 121 \(\pm\) 13 nm/+21 mV & n.a. & \(\begin{array}{c}\text{Chemical stabilization of}\\ \text{L-DOPA and elevated}\\ \text{ex vivo permeability}\end{array}\) & [187] \\ bromide (TDAB) surfactant & & & & \\ \hline Chitosan pharmacosomes & \(\begin{array}{c}125.5\pm 5.2\ nm/\\ +33.7\pm 0.5\ mV\end{array}\) & n.a. & \(\begin{array}{c}\text{Improved ex vivo permeability;}\\ \text{better recovery of locomotor}\\ \text{activity and neuronal tissue}\end{array}\) & [188] \\ \hline \hline \end{tabular} n.a.—data not available.  
\end{table}
Table 4: Lipid-based and hybrid nanocarriers for nose-to-brain delivery of L-DOPA.  
study concluded the need to address the challenges related to the drug kinetics following intranasal administration before proceeding to further clinical trials [189].
## 4 Efficiency and Fairness Signal Optimization Model
### Optimization Model Construction
#### 4.2.2 Optimization Model  
In this paper, based on the classical signal timing optimization model, the objective function is replaced with the one proposed in the previous section, and the constraints on the conversion rate and the degree of delay sacrifice are added. Thus, the signal optimization model considering fairness is obtained with the following Equation (20) as the objective function and Equation (21) as the constraints, where the first and second in Equation (21) are the phase green time length constraints; the third is the cycle length constraint; the fourth is the conversion rate constraint, which is required to be greater than or equal to 1; and the fifth is the delay sacrifice degree constraint.  
\[s.t.\left\{\begin{array}{l}5<g_{i}<(C-L)\\ g_{i}>(C-L)(q_{i}/s_{i})\\ 15m\leq C\leq 220\\ \frac{(H-H^{\prime})/H^{\prime}}{(D-D^{\prime})/D^{\prime}}\geq 1\\ 105\%\cdot D^{\prime}\geq D\geq D^{\prime}\\ i=1,2\cdots m\end{array}\right. \tag{21}\]  
In Equation (20), \(D=\frac{\sum_{i=1}^{m}q_{i}\cdot d_{i}}{\sum_{i=1}^{m}q_{i}}\), \(H=-\sum_{i=1}^{m}k_{i}lnk_{i}\), and \(k_{i}=\frac{d_{i}}{\sum_{i=1}^{m}d_{i}}\). In Equation (21), \(g_{i}\) represents the phase \(i\) green time; \(g_{i}=(C-L)\cdot\lambda_{i}\); \(s_{i}\) represents the phase \(i\) saturation flow; \(L\) represents lost time; and w represents delay sacrifice, and its value is greater than 1.
## 4 Experiments and Results
### Evaluation Metrics  
In this paper, the segmentation performance is evaluated using Area Under the Precision-Recall Curve (AUPR), Dice coefficient (Dice), and Intersection over Union (IoU) [35; 36]. Higher scores in these three metrics indicate the better segmentation capability of the proposed method.  
The AUPR reflects the ability of a method to enrich true positive samples, which is the area accuracy under the precision-recall curve, and mAUPR is the average of the AUPR values for all lesion types.  
Figure 4: Samples of original images and preprocessed images.  
The Dice coefficient is a similarity metric that measures the overlap between the segmented region and the actual lesion area. The mDice is the average of the Dice coefficients for all lesion types. The Dice coefficient is calculated using the following formula:  
\[Dice=\frac{2\times TP}{2\times TP+FN+FP} \tag{9}\]  
IoU represents the ratio of the intersection to the union between the segmented region and the actual lesion area. The mIoU is the average IoU value obtained for all types of lesions. Specifically, the IoU is calculated using the following formula:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{10}\]  
In Equations (9) and (10), the following definitions are used: _TP_ (true positives) represents the number of pixels correctly segmented as lesion pixels. _TN_ (true negatives) represents the number of pixels correctly segmented as background pixels. _FP_ represents the number of pixels incorrectly segmented as background pixels, while they are actually lesion pixels. _FN_ represents the number of pixels incorrectly segmented as lesion pixels, while they are actually background pixels.
## 5 Analysis of the Hybrid Propagation Channel Model
### Comparison and Analysis at Different Polarizations  
Two sets of data with both RMSE and MAE values less than 10 were selected to analyze the trend of path loss with the horizontal distance between the transmitting and receiving antennas for different polarizations, i.e., when the number of scattered rays is nine and the scattering angles are 4.8\({}^{\circ}\), 4.9\({}^{\circ}\), 5.1\({}^{\circ}\), and 5\({}^{\circ}\), as shown in Figures 10 and 11. 'VH' indicates that the transmitting antenna is vertically polarized and the receiving antenna is horizontally polarized. 'P_mea_VV' indicates that the VV polarization was selected in the measurement. Overall, the path loss increases and the received electric field decreases with increasing distance in different polarizations. The path loss in the 'HH' and 'HV' polarizations is less than in the 'VV' and 'VH' polarizations. Although the scattering angles are very close, the path loss and the electric field obtained at each receiving point are different when the scattering angles are 4.8\({}^{\circ}\), 4.9\({}^{\circ}\), 5.1\({}^{\circ}\), and 5\({}^{\circ}\) and the number of scattered rays is nine. Moreover, there is a large difference between these values. The above results  
Figure 9: The trend of path loss with distance around the scattering angle of 5\({}^{\circ}\) when the number of scattered rays is 9. (**a**) The scattering angle is from 4.1\({}^{\circ}\) to 5\({}^{\circ}\) and (**b**) the scattering angle is from 5\({}^{\circ}\) to 5.9\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scattering Angle (\({}^{\circ}\))** & **RMSE (SP)** & **MAE (SP)** & **Running Time (s)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: RMSE, MAE, and runtime at a scattering angle of 5\({}^{\circ}\) when the number of scattered rays is 9.  
indicate that the scattering angle exhibits a great influence on the received electric field at each receiving point.
## 4 Experimental Results
### Firewall Performance under Real Operative Conditions  
After the firewall performance was evaluated in controlled conditions, where Ethernet frames of selected protocol, size and data throughput were generated, the system was tested in real operative conditions. The experimental setup in this case is presented in Figure 9, where the proposed firewall is placed between a PC used to generate real network traffic (Port A) and the Internet (Port B). Another PC was used to acquire the firewall statistics at regular time intervals of 1 s using the USB-UART interface.  
Under the new operative conditions, the proposed firewall was tested in the following cases:  
1. Download/upload of a file (the results are reported in Section 4.2.1).
2. Access to websites with different restrictions on the firewall rules (the results are reported in Section 4.2.2).  
Figure 8: Measured value of the parameter \(\lambda\) in the case of UDP packets of different sizes and no inter-frame delay.  
In all the reported cases, the network traffic was also monitored with the software Wireshark (version 4.0.3) running on the PC connected to Port A as a reference to verify the correctness of the data provided by the firewall statistics.
## 3 Proposed Method
### Message Embedding
#### 3.1.3 An Example of Message Embedding  
In this subsection, we describe Example-2, an example of message embedding. In Phase-1, let \(\mathbf{F}\mathbf{P}\) = (95.9, 138.0, 155.1) denote a pixel represented by three floating-point values during the mid-phase of optimal color transfer. Let \(\mathbf{d_{sb}}\) = (1\({}_{3}\), 0\({}_{3}\), 2\({}_{3}\)) denote the 3-ary secret message vector to be concealed. First, we determine the minimum distortion vector, \(\mathbf{V_{sb}}\) = (1.1, 0.0, \(-\)0.1), where \(\mathbf{P^{\prime}}_{\mathbf{oucet}}\) = \(\mathbf{F}\mathbf{P}+\mathbf{V_{sb}}\) = (97, 138, 155) satisfies Equation (6), implying that \(\mathbf{P^{\prime}}_{\mathbf{oucet}}\) has embedded a secret message vector, \(\mathbf{d_{sb}}\). Next, let \(d_{\mathbf{a}m}\) = 2\({}_{7}\) represent the 7-ary secret message to be concealed, and \(\mathbf{W_{a}}\) = (1, 2, 3) be the weight vector. We determine the coefficient \(\mathbf{C_{vm}}\) = (0, 0, 1), which has the minimal length \(L_{min}\) = 1 and \(\mathbf{P^{\prime}}_{\mathbf{oucet}}\) + 3 \(\times\mathbf{C_{vm}}\) satisfies both Equations (7) and (8). In this phase, we produce \(\mathbf{P^{\prime}}_{\mathbf{oucet}}\) = (97, 138, 158), which has conveyed \(\mathbf{d_{sb}}\) and \(d_{\mathbf{a}m}\).  
In Phase-2, let \(\mathbf{d_{dn}}\) = (1\({}_{5}\), 3\({}_{5}\), 0\({}_{5}\)) be the 5-ary secret message vector to be conveyed, and \(\mathbf{W_{b}}\) = (1, 4) be the weight vector. From Equation (9) and Table 3, we derive r distortion vectors \(\mathbf{V_{1}}\) = (1, 0), \(\mathbf{V_{3}}\) = (\(-\), 1), and \(\mathbf{V_{0}}\) = (0, 0) for the secret messages, 1\({}_{5}\), 3\({}_{5}\), and 0\({}_{5}\), respectively. Finally, we apply Equation (10) and produce three stego component pairs: \(\mathbf{P^{\prime\prime}_{n}}\) = [97, 97] + [1, 0] = [98, 97], \(\mathbf{P^{\prime\prime}_{g2}}\) = [138, 138] + [-1, 1] = [137, 139], and \(\mathbf{P^{\prime\prime}_{b3}}\) = [158, 158] + [0, 0] = [158, 158]. Consequently, the stego pixels in dual stego images, \(I^{\prime\prime}_{s1}\) and \(I^{\prime\prime}_{s2}\), are \(\mathbf{P^{\prime\prime}_{s1}}\) = (98, 138, 158) and \(\mathbf{P^{\prime\prime}_{s2}}\) = (97, 139, 158), respectively.
# A Survey on Change Detection and Time Series Analysis with Applications
## 3 Decomposition Methods into Time-Frequency Domain
### Hilbert-Huang Transform (HHT)  
The HHT is an alternative method of analyzing non-stationary time series with or without trends [48; 49; 50; 51]. The HHT is the result of the Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis (HSA). The EMD decomposes the time series into Intrinsic Mode Functions (IMFs) with a trend via a procedure called sifting, and then HSA applies IMFs to obtain instantaneous frequency data.  
An IMF is a function whose number of extrema and zero-crossings are either equal or differ by at most one. Moreover, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero at any point [49]. The sifting process identifies all the local extrema in time series \(\mathbf{f}\), then it connects all the local maxima by a cubic spline as the upper envelope and does the same for all the local minima to produce the lower envelope. Therefore, the upper and lower envelopes with mean \(\mu(t)\) should cover all the data points between them (this generally may not occur). Then the difference between the time series values and \(\mu(t)\) is ideally an IMF. Otherwise, the same process on the difference may be repeated, which is one of the weaknesses of HHT [49]. Denote this IMF by \(\mathbf{c}_{1}\). The sifting process will now continue on \(\mathbf{r}_{1}=\mathbf{f}-\mathbf{c}_{1}\) (residual) to obtain \(\mathbf{c}_{2}\) (IMF), and so \(\mathbf{r}_{2}=\mathbf{r}_{1}-\mathbf{c}_{2}\). The sifting process stops when residual \(\mathbf{r}_{n}\) becomes a monotonic function from which no more IMF can be extracted [48]. Therefore, the time series is decomposed into IMFs, i.e., \(\mathbf{f}=\sum_{j=1}^{n}\mathbf{c}_{j}+\mathbf{r}_{n}\). After obtaining the IMFs, \(\mathbf{c}_{j}\), the instantaneous frequencies, \(\omega_{j}(t)\), and instantaneous amplitudes, \(a_{j}(t)\), of IMFs can be obtained using the Hilbert transform, the convolution integral of \(\mathbf{c}_{j}\) with \(1/t\)[48; 49; 50; 51]. The  
Figure 4: Morlet wavelet of frequency 10 Hz and its analyses (1000 samples per second). (**a**) Sinusoids of frequency 10 Hz, (**b**) DFT spectrum of panel a, (**c**) a Gaussian function, (**d**) DFT spectrum of panel c, (**e**) a Morlet wavelet, and (**f**) DFT spectrum of panel e.  
distribution of amplitude \(a_{j}(t)\) in the time-frequency domain, \(H(t,\omega)\), can be regarded as a skeleton form of that in CWT [49].  
Although HHT is an empirical method based on the physical meaning of non-stationary time series, it also comes with several weaknesses, such as the limitation of EMD in distinguishing different components in narrow-band time series, end effects, and the mode mixing problem occurring in the EMD process [52; 53]. The performance and limitations of HHT with particular applications to irregular water waves are comprehensively investigated in [54]. As a result, an appropriate interpolation technique is needed to determine better envelopes for this method.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 3 System  
### Developed UAV  
Figure 1(a) shows the platform we used in the experiment. For fast motion, we selected a QAV250 fiber frame as the flying platform. For high-speed target recognition, we chose a XIMEA MQ003 CG-CM high-speed camera with a Theia SY110M ultra-wide lens. Considering the need for fast image processing and pose estimation, as the onboard companion computer, we chose a Jetson TX2, which is a low-power embedded platform launched by NVIDIA, including a 256-core NVIDIA Pascal GPU, a hex-core ARMv8 64-bit CPU complex, and an 8-GB LPDDR4 memory with a 128-bit interface. As the flight controller, we chose a Pixhawk2.1 Cube as the basic hardware for developing our own flight controller.  
Figure 1(b) shows the wiring diagram of our UAV. The high-speed camera transmitted a raw image to the companion computer via a USB3.0 cable. After the relative pose was calculated, the current position and setpoint were transmitted to the flight controller using the mavlink protocol via an FTDI USB to TTL cable. Finally, PPM signals were sent to ESCs for converting them to 3-phase signals to drive the brushless motors. The specifications of our platform are shown in Table 1.  
Figure 2: Developed UAV. (**a**) Platform (**b**) Wiring configuration.  
Moreover, the GPU-based Harris corner algorithm was used for detecting features. This algorithm was built on VisionWorks [39], which is a CUDA software development package for computer vision and image processing.
## 3 Mixed Integer Linear Programming (MILP)/Mixed Integer Quadratic Programming (MIQP) Models
### Objective  
The overall objective of the optimization model is to minimize the total annual costs \(C_{total}\), which is a trade-off between investment costs for boilers, heat pumps and thermal storages on the one hand and energy costs on the other hand.  
\[\min\ C_{total}=\underbrace{(C_{invest}^{HP}+C_{invest}^{B}+\sum\limits_{i\ \in\ STO}C_{invest,i}^{C})}_{ investment\ costs}f_{a}+\underbrace{C_{invest}^{HP}+C_{invest}^{B}}_{annual\ energy\ costs} \tag{25}\]  
To consider energy and investment costs on the same basis, the annualization factor \(f_{a}\) is used to calculate annuities for the investments. In this case \(f_{a}\) corresponds to the reciprocal of the equipment's life expectancy.
## 1 Introduction
In LTE communication schemes, primary synchronization signals (PSS) are composed by Zadoff-Chu (ZC) sequences [14], complex-valued mathematical sequences with good properties to use in synchronization techniques [15]. In terms of channel estimation, the most usual approach in OFDM-based systems is either inserting known pilot symbols or training sequences in the transmitted frame [16; 17; 18]. There are several possibilities to adopt:  
* Block-type pilot allocation, used in the IEEE 802.11a/g/n standard, consists of the allocation of pilots into all subcarriers in the frequency-domain. Typically, they are designed for slow-fading frequency-selective channels when the OFDM symbols' duration is much smaller than the channel coherence time [19]. * Comb-type pilot allocation, which is used, for instance, in the IEEE 802.11a WLAN standard [20], corresponds to insert pilots at predefined subcarrier locations across the entire transmission time to resist fast channel time variations between OFDM symbols. However, it is crucial to guarantee that the spacing between each pilot subcarriers is much smaller than the channel coherence bandwidth is for effective and accurate estimation. Topologies where pilots are scattered over the time and frequency-domains can also be taken into account, enabling a good tracking relationship between the frequency selectivity and time variation of the wireless channel. Since the pilot's grid insertion is not made in all subcarriers or in fixed subcarriers across all time, better overall system performance can be achieved by reducing pilot density, thereby improving spectral efficiency [16]. In our work, we propose using a ZC preamble sequence to jointly perform channel estimation and frame synchronization, which can offer a close performance comparatively to the achieved with perfect CSI. Hence, this paper offers a perspective of the techniques that could perform well in the TIBWB-OFDM system, including the preamble block-type allocation strategy, the frame correlator algorithm, and channel estimators for a linear equalizer receiver structure or by adapting the iterative block decision feedback equalization (IB-DFE) [21; 22] structure. The main contributions of our work are to:  
* evaluate TIBWB-OFDM technique performance under imperfect CSI, showing the robustness of the technique; and
* propose a jointly frame synchronization and channel estimation technique for TIBWB-OFDM transmissions that is able to maintain good TIBWB-OFDM spectral and power-efficiency characteristics, and to acquire reliable CSI information. The remainder of the paper is organized as follows. Section 2 presents an overview of all required relevant scientific knowledge to provide a clearer perspective of the presented work. Section 3 introduces the proposed TIBWB-OFDM's joint channel estimation and frame synchronization strategy along with a set of adapted iterative equalizers. Section 4 presents the discussion and analysis of the results of channel estimation algorithms and frame detection for the TIBWB-OFDM system. Lastly, conclusions and future work proposals are presented in Section 5.