esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OFDM & Orthogonal frequency division multiplexing \\ UFMC & Universal filtered multicarrier \\ BER & Bit error rate \\ MA & Multiple access \\ ITU & International telecommunication union \\ CP & Cyclic prefix \\ ICI & Intercarrier interference \\ ISI & Intersymbol interference \\ OOB & Out of band \\ QAM & Quadrature Amplitude Modulation \\ MC-CDMA & Multicarrier code division multiple access \\ CDMA & Code division multiple access \\ TDM & Time division multiplexing \\ BS & Base station \\ IFFT & Inverse fast Fourier transform \\ LS & Least squares \\ FFT & Fast Fourier transform \\ MMSE & Minimum mean squared error \\ ZF & Zero forcing \\ PDF & Probability density function \\ IDFT & Inverse discrete Fourier transform \\ SNR & Signal-to-noise ratio \\ AWGN & Additive white Gaussian noise \\ TDL-A & Tapped delay line A \\ PDP & Power delay profile \\ RE & Resource element \\ MUI & Multiuser interference \\ MAI & Multiaccess interference \\ \end{tabular}
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 3 Research Method
### Tourism Safety System Design Method
#### 3.2.3 Collection of Data from Tourists via Applications and Smartwatches  
The framework for obtaining data from tourists via mobile applications is shown in Figure 4.  
In reference to Figure 4, we have developed an application compatible with smartwatches featuring Wear OS, an Android-based platform for application development. Before initiating the system, it is necessary for the smartwatch to establish a Bluetooth connection with a smartphone. To enable access to all sensors on the smartwatch, the getOpenSensorService is activated within the developed application. Specifically, we select the cardiac sensor (selectSensor(\"HeartRate\")) and the step counter (selectSensor(\"Steps\")). These sensors are configured to read data continuously every 5 s (ListenerSensor 5 s). Consequently, every 5 s, the application retrieves readings from the heart sensor (getSensor(\"HeartRate\")) and step count (getSensor(\"Steps\")). Subsequently, the pedometer sensor value is utilized to calculate calories burned (calculateCaloriesBurnt). This data, comprising heart rate, step count, and calorie count, is then transmitted via Bluetooth to the Health application for Tourist on the connected Smartphone. Within the Health application for Tourist, a function named Read_sensor is employed to retrieve sensor data from Bluetooth every 5 s.
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 3 Methodology
### Notation  
Matrices are represented in uppercase bold letters, for instance, \(\mathbf{A}\), while vectors are represented in boldface lowercase letters, for instance, \(\mathbf{a}\), and \(\mathbf{a}_{i}\) is the \(i\)th element of \(\mathbf{a}\). \(\mathbf{A}_{i}\), and \(\mathbf{A}_{sj}\) denote the \(i\)th row and \(j\)th column of a matrix \(\mathbf{A}\); therefore, the element of the \(i\)th row and \(j\)th column of the matrix is represented by \(\mathbf{A}_{ij}\). The trace of \(\mathbf{A}\) is defined by \(\text{tr}(\mathbf{A})\) and the transpose of \(\mathbf{A}\) is defined by \(\mathbf{A}^{T}\). In addition, \(|\mathbf{A}_{ij}|\) is the absolute value of \(\mathbf{A}_{ij}\), \(\|\mathbf{A}\|_{F}\) is the Frobenius norm of \(\mathbf{A}\). If \(\mathbf{A}\) is positive definite, we have \(\mathbf{A}\succ 0\), while it is positive semi-definite (psd), we have \(\mathbf{A}\succeq 0\).  
In a learning task that contains multiple classes of data, we usually have dataset \(\{\{\mathbf{X}_{si},\mathbf{y}_{i}\}\in\Re^{D}\times\Re^{1},i=1,2,\ldots,N\}\), where each \(\mathbf{X}_{si}\) represents a sample and \(\mathbf{y}_{i}\in\{1,2,\ldots,C\}\) is the class of that sample, \(C\geqslant 2\) is the total number of classes and \(N\) is the total number of samples. For a linear dimensionality reduction task, we hope to find a projection matrix \(\mathbf{W}\) and obtain the \(d\)-dimensional representation \(\mathbf{Z}_{si}\) of \(\mathbf{X}_{si}\) by \(\mathbf{Z}_{*i}=\mathbf{W}*\mathbf{X}_{*i}\), where \(\mathbf{Z}_{*i}\in\Re^{d},i=1,2,\ldots,N\), \(d<D\) is the dimensionality of the output.
## 2 Three-Dimensional Dubins Path  
The Dubins path is the shortest path between two points with a specific direction of motion in a plane. It is either a CSC or a CCC path, where C represents circular arc and S represents straight line. The CCC path is formed by three consecutive circular arcs that are tangential to each other, and the CSC is composed of a straight line and two circular arcs, where the radius of the circular arc is chosen equal to the minimum radius of rotation of a vehicle which corresponds to a given maximum curvature constraint. We only consider a CSC path since the CCC path is complex and known as suboptimal in most cases, except that the distance between the two waypoints is too close [20].  
In this section, we present a new 3D Dubins path method that extends the 2D Dubins path by applying a simple constraint to the pose. Our method is straightforward for waypoint guidance problems.  
Suppose that \(\mathbf{p}_{i},i=1,...,N\) is a sequence of waypoints in a 3D space, which might be generated by a path planner or pruner considering obstacle avoidance or other constraints. The problem is to fit these waypoints with the shortest path satisfying the minimum radius of rotation \(R\).  
For each waypoint \(\mathbf{p}_{i}\), we define a pose or direction vector \(\mathbf{e}_{i}\) as an unit vector pointing to the next waypoint \(\mathbf{p}_{i+1}\).  
\[\mathbf{e}_{i}=\frac{\mathbf{p}_{i+1}-\mathbf{p}_{i}}{\|\mathbf{p}_{i+1}- \mathbf{p}_{i}\|_{2}} \tag{1}\]  
At the final waypoint \(\mathbf{p}_{N}\), a direction vector is designated as an approaching angle to the waypoint.  
This definition of a direction vector is very natural in the waypoint guidance problem and is the only constraint, which makes it possible to extend a 2D to a 3D Dubins path. In this way, all three sequential waypoint tuples \(\{\mathbf{p}_{i},\mathbf{p}_{i+1},\mathbf{p}_{i+2}\}\) and their associated two-direction vectors \(\{\mathbf{e}_{i},\mathbf{e}_{i+1}\}\) are located on the same plane \(plane_{i}\). The consecutive waypoint tuple \(\{\mathbf{p}_{i+1},\mathbf{p}_{i+2},\mathbf{p}_{i+3}\}\) and their associated direction vectors \(\{\mathbf{e}_{i+1},\mathbf{e}_{i+2}\}\) are also all on another plane \(plane_{i+1}\). Moreover, the waypoint segment line \(seg_{i+1}\), which connects the waypoints \(\mathbf{p}_{i+1}\) and \(\mathbf{p}_{i+2}\), becomes the intersection line of two adjacent planes, \(plane_{i}\) and \(plane_{i+1}\), as shown in Figure 1.
# Changes in the Volatile Profile of Wheat Sourdough Produced with the Addition of Cava [PERSON]
## 3 Discussion
### Analysis of Volatile Compounds in Different Sourdough Samples
#### 3.2.1 Alcohols  
Alcohols can be produced by both sugar fermentation (short-chain alcohols) and amino acid metabolism (long-chain alcohols) [30; 31], and are usually characterized by green and herbaceous odor notes [32]. In fact, microbial amino acid metabolism may be increased during the back-slopping steps of fermentation as a protection against acidic stress and to maintain the redox balance, transforming peptides and amino acids into higher alcohols [33].  
The dominant alcohol quantified was 1-hexanol in the control sourdoughs and in the sourdoughs formulated with Cava lees (Table 2). 1-Hexanol is usually one of the dominant alcohols produced in sourdough [5; 7; 8], as well as in sparkling wines [31; 34; 35; 36], along with the other alcohols reported in this study, such as 1-pentanol, 1-octanol and 2-ethylhexanol [32; 35]. It contributes odors of cut grass, sweetness, resin, flowers and green, and it originates from fermentation and lipid oxidation (linoleic and linolenic acids) [2; 5]. Actually, 1-hexanol was also identified in the lees samples (Table 2), which can support the fact that Cava lees seem to retain volatile compounds on their surface during the biological ageing process [15; 36]. In addition, it has been reported that heteroformative bacteria produce a greater quantity of hexanol than homofermentative LAB [5; 31; 37; 38]. In fact, [PERSON] et al. (2020) [38] proposed that facultatively heteroformative LAB, such as _Lactiplantiobacillus plantarum_ (formerly _Lactobacillus plantarum_), can produce 1-hexanol via pathways other than the reduction of hexanal and that it can facilitate the production of hexanal, resulting in more substrate to transform into the corresponding alcohol.  
1-Butanol was the dominant alcohol in sourdoughs with 2% Cava lees (249.65 \(\pm\) 29.86 mg/kg). It can be observed that the mentioned compound increases its concentration by the addition of Cava lees. This higher alcohol has been reported in wine fermentation [39; 40] and is also commonly found in sparkling wines [32; 35; 36; 41].  
In summary, most of the alcohols identified in the sourdough samples increased their concentration with the addition of Cava lees (Table 1). In addition, the alcohols found in Cava lees increased their concentration in sourdoughs formulated with lees. The highest values were reached at 2% Cava lees (\(p\) < 0.05), which may be due to higher survival rates among the microorganisms fermenting the sourdough. In fact, it has recently been reported that Cava lees have a growth-promoting effect on different species of LAB in vitro and in sourdough [14; 20].
## 3 Sensor Positioning Principle
### Track Calculation Principle of Inertial Sensors  
In recent years, due to the emergence of various miniaturized and low-cost inertial devices, the application field of track estimation based on inertial sensors has gradually expanded and can be applied to personnel navigation and positioning. In pedestrian navigation and positioning, the acceleration curve of pedestrian motion is similar to a sine wave, and each complete sine wave in the acceleration curve exactly corresponds to a walking cycle. Therefore, the statistics of steps are actually to identify and count the walking cycle from the acceleration curve and calculate the step size based on this. Therefore, correctly identifying the walking cycle is the key to the track calculation of inertial sensors.  
\[\left\{\begin{array}{l}x_{k+1}=x_{k}+l_{k}\cos\alpha_{k}\sin\beta_{k}\\ y_{k+1}=y_{k}+l_{k}\cos\alpha_{k}\cos\beta_{k}\\ z_{k+1}=z_{k}+l_{k}\sin\alpha_{k}\end{array}\right. \tag{10}\]  
where \((x_{k},y_{k},z_{k})\) is the position coordinate at time \(k\), \(l_{k}\) is the step size of step \(k\), \(\alpha_{k}\) is the pitch angle at time \(k\) and \(\beta_{k}\) is the heading angle at time \(k\). Pedestrian track estimation does not require high hardware requirements, and the calculation overhead is small, so it has good application value. However, due to the poor performance of the internal sensor devices of commercial mobile phones, it is easy to form a large deviation after a certain time accumulation and cannot be corrected. Therefore, other means are needed to correct the positioning information of PDR.
## 5 Discussion and Conclusions
This study underscores the significance of selecting a robust post-processing method for EPFs, whether for meteorological or hydrological applications. Overall, this study underscores the potential benefits of EPFs for enhancing both precipitation forecasting and streamflow predictions when they are adeply post-processed. Such methodologies could be extrapolated for application in other mountainous terrains. Nonetheless, the research is not devoid of constraints; likewise, the paucity of observational data from mountainous terrains and the uncertainties germinating from the deployment of a solitary hydrological model pose constraints. Additionally, the post-processing methods adopted in this study are applied to a single weather variable at a single location, neglecting the intersite and intervariable dependence structures of forecast variables [46]. It is well worth exploring further the potential advantages of integrating multisite and multivariable correlations during post-processing, which may amplify the precision of streamflow forecasts. Conceptualization, Y.X. ; Data curation, Y.X. and Z.Y. ; Funding acquisition, Y.X. and Y.L. ; Investigation, Y.X. and X.Z. ; Methodology, T.P. and Z.Y. ; Project administration, X.Z. ; Resources, Y.L. ; Validation, T.P. ; Writing--original draft, Y.X. ; Writing--review & editing, Y.L., X.Z. and Y.R. All authors have read and agreed to the published version of the manuscript. This work is partially supported by the Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science (ZH2102000105 and 242202000907), the Key Research Project of Hubei Meteorological Bureau (2022Y26 and 2022Y06), the Basic Research Fund of WHIHR (202304), and the Hubei Provincial Natural Science Foundation and the Meteorological Innovation and Development Project of China (2023 AFD094, 2023 AFD096 and 2022 CFD129). Not applicable. The meteorological observations are publicly archived and can be accessed through the China Meteorological Administration. Streamflow observations can be retrieved from the Shuibuya Hydropower Station. Public datasets from ECMWF, CMA, JMA, and NCEP utilized in this study are available at: [[https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/](https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/)]([https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/](https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/)). The authors confirm that there are no known conflicts of interest associated with this publication.
# The Influence of Tomato and Pepper Processing Waste on Bread Quality
## 1 Introduction
For instance, the incorporation of high-fiber ingredients into the dough can disrupt the formation of the gluten network by creating new hydrogen bonds or altering the conformation of disulfide bridges. Such changes can influence the rheological properties of the dough by improper folding of polypeptide chains or aggregation of protein complexes [19]. The addition of fiber-rich ingredients, due to the presence of hydroxyl groups in their structure that can form hydrogen bonds with water, can increase the water absorption capacity [20]. While the high water absorption capacity of fiber can have a positive effect on the baking properties of the mixture [21], it can lead to dehydration of the gluten network, resulting in reduced elasticity and decreased resistance to mixing [22]. The weakening of the gluten network structure by vegetable by-products such as TP and PB has also been confirmed in previous studies examining changes in the secondary structure of the gluten network [23]. These findings further support the notion that the addition of fiber-rich ingredients can have a significant impact on the rheological properties of dough and the overall quality of bread. Therefore, the objective of this study was to examine the effects on the rheological properties of dough and the quality of bread after incorporating vegetable processing waste from tomatoes (TP) and bell peppers (PB) into a recipe for wheat bread. Additionally, the physicochemical features and nutrient composition of the bread were analyzed to assess the potential for producing bread with increased nutritional value, particularly in terms of dietary fiber, while maintaining the desired quality characteristics preferred by consumers. This research aimed to contribute to both diversifying the range of bread products and reducing food waste by utilizing vegetable processing waste.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 1 Introduction
The three offset stacks were converted into angle stacks, which were the inputs of the partial-stack inversion, and angle gathers, which were the inputs of the simultaneous isotropic inversion. The ranges of the near, mid, and far stacks were from 5\({}^{\circ}\) to 15\({}^{\circ}\), 15\({}^{\circ}\) to 25\({}^{\circ}\), and 25\({}^{\circ}\) to 40\({}^{\circ}\). The partial-stack inversion resulted in the \(Z_{P}\) and \(Z_{S}\) volumes at the central angles, 10\({}^{\circ}\), 20\({}^{\circ}\), and 32.5\({}^{\circ}\), from which the zero-offset properties were forecasted. The target zone consisted of two gas-bearing shaly sand reservoirs in the Malay basin: A and B. The training data of the Bayesian and MLFN models were gathered from the wells (\(\times 1\)) and (\(\times 2\)); however, the facies log was only obtained at the well (\(\times 1\)) due to the availability of the petrophysical cut-offs and oil-mud resistivity imaging data. Another challenge was that the \(Z_{S}\) log was only available at the well (\(\times 1\)), so it was estimated from other log properties by the MLFN neural network at the well (\(\times 2\)). The resulting \(Z_{P}\) and \(Z_{S}\) were compared to those obtained from isotropic inversion by using the impedance logs. The MLFN resulted in the most accurate zero-offset impedances. Therefore, the \(Z_{P}\) and \(Z_{S}\) volumes were applied to [PERSON]'s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. The \(Z_{P}\) and \(Z_{S}\) of MLFN and isotropic inversion were transformed into the near and far elastic impedances, which were the lithology predictors, and then into the Mu-Rho (MR) ratio, Lambda-Rho/Mu-Rho (LR/MR) ratio, and Poisson's ratio (PR), which are the fluid predictors. The litho-fluid model was applied to the inverted elastic volumes to forecast the litho-fluid facies distribution. The facies log was used to validate the predicted facies obtained from the isotropic and anisotropic approaches.
## 2 Backgrounds
### Data Rebalancing Methods  
In the practical fault-type classification problem, the proportion of samples of the minority class is often severely lower than that of the majority class, which restricts the learning performance. To resolve this problem, the data rebalancing methods are commonly used. They can be classified into the over-sampling method, which adds samples of the minority class, and the under-sampling method, which reduces samples of the majority class. In general, the resampling process is repeated until the balancing ratio, which is defined by the ratio of the number of samples in the minority class over that in the majority class, is equal to or greater than a threshold parameter value \(r\)\((0\ <\ r\leq\ 1)\). In the following, we introduce some representative rebalancing methods that were included in our framework.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
###### Abstract
Thus, the proposed algorithm allows sustaining the best connection for WBAN users that satisfies application requirements and guarantees seamless internet working. * We integrate a Content-Centric Networking processing module into the edge network devices of all the networks, such as the WiFi access point, UMTS base station, LTE eNodeB and the edge routers. This approach can support the WBAN architecture in terms of caching efficiency, reducing packet loss, and improving QoS and network quality. Thus, the proposed integration allows the patient to maintain the current connection that guarantees QoS requirements, avoids unnecessary handover and has energy efficiency. The remainder of this paper is structured as follows. Section 2 highlights some of the related studies that pertain to this paper. Section 3 describes our WBAN network architecture model. Section 4 shows our MADM handover algorithm, and we evaluate and discuss the results in Section 5. Finally, the paper is concluded in Section 6.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
###### Abstract
**Novel relay selection algorithm**: We introduce a novel relay selection algorithm, seamlessly integrated into the ContikiRPL framework, which significantly enhances reliability and energy efficiency in wireless sensor networks (WSNs) through cooperative diversity. 2. **Cooperative-aided routing protocol (CERP)**: We develop the cooperative-aided routing protocol (CERP), a new routing protocol for WSNs. CERP is founded on our innovative relay selection method and the conventional RPL, demonstrating substantial improvements in routing performance. 3. **Practical implementation and evaluation**: We not only propose these advancements but also demonstrate their practical applicability. We map the CERP protocol onto an Arduino-based node equipped with an Xbee module and design a functional prototype using the Contiki operating system (OS) and Arduino Mega board. Moreover, we rigorously evaluate the CERP protocol's performance using off-the-shelf Arduino-based nodes, establishing its real-world effectiveness. The remainder of this paper is structured as follows. Section 2 gives an overview of the RPL routing mechanism. Section 3 discusses related works dealing with the enhancement of the RPL protocol. Section 4 provides a detailed formulation of the relay selection algorithm and presents the description of the proposed cooperative-aided routing protocol based on RPL. Section 5 presents the prototyping and the performance evaluation of the proposed protocol. Finally, Section 6 draws out concluding remarks.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 4 Received Signal Strength Distribution Approximation  
### Conventional Received Signal Strength Distribution Approximation  
RSSI is the received signal strength measurement at the receiver, which is suitable for empirical channel modeling. The large number of measurements of RSSI is used in a statistical approach to analyze the channel propagation. Since Wi-SUN's smart meters are installed on utility poles along the side of the street, a dominant transmitted signal component exists, i.e., LOS path, which is significant enough for channel modeling in the AMI network. Therefore, the Rician distribution approach is used as a curve fitting of the cumulative density function (CDF) of the RSSI measurement data. The Rician probability density function (PDF) [9] is presented as,  
\[\mathrm{f}_{\mathrm{Rician}}(\mathrm{r})=\frac{2\mathrm{rK}}{\mathrm{r}_{\mathrm{ S}}^{2}}\exp\left(-\frac{\mathrm{K}}{\mathrm{r}_{\mathrm{S}}^{2}}\left( \mathrm{r}^{2}+\mathrm{r}_{\mathrm{S}}^{2}\right)\right)\times\mathrm{I}_{0} \left(\frac{2\mathrm{rK}}{\mathrm{r}_{\mathrm{S}}}\right), \tag{1}\]  
where K denotes the Rician K-factor, \(\mathrm{r}_{\mathrm{S}}\) denotes the non-centrality parameter and \(\mathrm{I}_{0}\) denotes the Bessel functions, in which  
\[\mathrm{K}=10^{\mathrm{K}_{\mathrm{dB}}/10}=\frac{\mathrm{r}_{\mathrm{S}}^{2} }{2\sigma^{2}} \tag{2}\]  
\(\mathrm{K}_{\mathrm{dB}}\) denotes the ratio of dominant component power and multipath component power in dB unit, in which  
\[\mathrm{K}_{\mathrm{dB}}=10\log\left(\frac{\mathrm{r}_{\mathrm{S}}^{2}}{2 \sigma^{2}}\right), \tag{3}\]where \(\sigma\) denotes the scale of a Rician distribution, with I\({}_{0}\) being the zero-order modified Bessel function, and r\({}_{\rm s}\) denotes LOS.  
The maximum likelihood estimation (ML) method is used for the Rician distribution estimation that fairly fits the measured RSSI probability density function. The non-centrality parameter (r\({}_{\rm s}\)), the scale parameter (\(\sigma\)) and the Rician K-factor of Rician distribution estimation fitting are determined in Equation (6). The likelihood function of the measured RSSI and the joint PDF of a sample of N independent observation \(\{\rm r\}\) are presented as,  
\[\rm L=\prod_{i=1}^{N}f_{Rician}(r) \tag{4}\]  
\[\rm logL=\rm log\prod_{i=1}^{N}\frac{2 rK}{r_{s}^{2}}\exp\left(-\frac{K}{r_{s}^ {2}}\left(r^{2}+r_{s}^{2}\right)\right)\times I_{0}\left(\frac{2 rK}{r_{s}}\right) \tag{5}\]  
The ML estimator determines the maximum value of L, or equivalently, logL, in Equation (5) to arrive at,  
\[\rm\hat{\mu}_{ML}=\arg\{max(\rm logL)\} \tag{6}\]  
The root mean-squared error (RMSE) is presented for the sample standard deviation and evaluates an error between the ML estimator and the PDF of the received signal strength, expressed as,  
\[\rm RMSE=\sqrt{\frac{\sum_{r}^{N}(\hat{\mu}_{ML}-f_{Rician}(r))^{2}}{N}} \tag{7}\]