esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### QoS Metric Modeling
#### 4.3.2 Network Throughput  
The network throughput represents the total number of successfully received information packets at the coordinator. It is important to maximize the number of successfully transmitted packets at the coordinator \(C\), because all information is critical in I2O WBANs. The optimization expression for maximizing the number of successfully received packets \(P_{s}\) can be formulated as:  
\[\textit{Objective:}\ Max\sum_{r}P_{s},\ \forall r\in T \tag{21}\]  
Subject to:  
\[P_{SR}>P_{RC},\ \forall S\in N,\ \forall R\in N \tag{22}\]  
\[E_{i}\geq E_{Tx\_min} \tag{23}\]  
\[P_{link}\geq P_{min} \tag{24}\]  
The objective function (21) aims to maximize the number of successfully received packets \(P_{s}\) during the network lifetime \(T\). Constraint (22) demonstrates that data packets may drop when data transmission occurs from \(R\) to \(C\). Constraint (23) points out that no data information transmission is possible when the remaining energy \(E_{i}\) is lower than the minimal required transmission energy\(E_{Tx\_min}\) as mentioned in (14). Constraint (24) states that the probability of a transmission link \(P_{link}\) should be no less than the minimal predetermined required value \(P_{min}\).
## 3 Results and Discussion
### Leaf Nutrient Composition  
The cation (Na, K, Ca, and Mg) concentrations were significantly affected by the second-degree interactions of salinity \(\times\) biostimulant and salinity \(\times\) harvest, except for K, which was also affected by the interaction of biostimulant \(\times\) harvest (Supplementary Material, Table S2). In regard to the anions, Cl content was significantly affected by all three of the second-degree interactions; S content only by the S \(\times\) H interaction and P content by the S \(\times\) B interaction (Supplementary Material, Table S2). As for the nitrate content, it was affected by all of the second-degree interactions. At lower salinity levels (EC0 and EC4) no significant differences were recorded between the biostimulant treatments, while at EC4 Auzym@application resulted in a significant decrease in Na content, which was not the case at the highest salinity level (EC6). At this salinity level, the highest overall Na content was recorded for this particular biostimulant without being significantly different from the other biostimulant treatment (Table 5). In regard to K concentration, it reached the highest value in plants that were not treated with biostimulants and which were irrigated with tap water, not being different from EC0 \(\times\) BA, all of the treatments of EC2, and BA \(\times\) EC4 (Table 5). For Mg concentration, only in EC2 was the treatment with Trainer@different from BC, while significant differences were also recorded from EC4 \(\times\) BA and EC6 (both BC and BT treatments) (Table 5). In the plants irrigated with tap water or water with low salinity (EC2), BT elicited a higher value of Ca concentration in the leaves, but in EC2 it was not different from BA; in higher salinity levels no differences were recorded between plants treated and untreated with biostimulants (Table 5). Biostimulant application reduced Cl concentration in leaves up to moderate salinity levels (EC4 treatment), while at the highest salinity tested (EC6) no significant differences were recorded between BA, BT, and BC (Table 5). Sulfur content was not affected by the factors tested (data not shown). In regard to P concentration, at low (EC2) and moderate salinity stress (EC4) the plants treated with Trainer@showed higher values, contrary to what occurred in EC0 and EC6, where the treatment with BA elicited higher values, although no significant differences were recorded between the two biostimulant products (Table 5). In accordance with our study, [PERSON] et al. [17] reported that increasing salinity in a nutrient solution resulted in increased Na content in lettuce
## 2 Experimental Program
### Test Setup  
The tests were conducted using a YE-1000F electro-hydraulic servo press (Bonwei Electromechanical Control Engineering Co., Ltd. in Hangzhou, China) with a maximum load capacity of 10,000 kN (Figure 9a). To simulate the realistic stress state of eccentrically compressed members, a hinged support structure composed of rollers and loading plates was installed at both ends of the specimen. Both rollers and loading plates were fabricated from Q235 steel, with their long sides matching the width of the specimen's cross-section (Figure 9b). Pre-marked load application points on the top and bottom surfaces of the specimen enabled the adjustment of the hinged support positions, thereby facilitating the application of various eccentric loads. Strain and displacement data at each loading stage were recorded using a DH3821 static strain measurement system (Donghua Test Technology Co., Ltd. Jingjiang, China).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & \begin{tabular}{c} **Strength** \\ **Grade** \\ \end{tabular} & \begin{tabular}{c} **Size (\(b\times h\times l\))** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} \(e_{0}\) \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} \(d\) \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} \(\rho_{s}\)** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Stirrup** \\ **(mm)** \\ \end{tabular} \\ \hline SCGC1 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 70 & 12 & 0.302 & 8@200 \\ SCGC2 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 280 & 12 & 0.302 & 8@200 \\ SCGC3 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 70 & 16 & 0.536 & 8@200 \\ SCGC4 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 140 & 16 & 0.536 & 8@200 \\ SCGC5 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 210 & 16 & 0.536 & 8@200 \\ SCGC6 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 280 & 16 & 0.536 & 8@200 \\ SCGC7 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 70 & 20 & 0.838 & 8@200 \\ SCGC8 & C30 & 250 \(\times\) 300 \(\times\) 1600 & 280 & 20 & 0.838 & 8@200 \\ \hline \end{tabular}
\end{table}
Table 7: Design parameters of SCGCs.  
Figure 8: SCGC’s basic size and reinforcement.  
Figure 10 shows the placement of lateral displacement meters, strain gauges on longitudinal reinforcement, and strain gauges on concrete. Displacement meters were evenly arranged along the height of the column to capture lateral displacements at different stages. Additionally, a single displacement meter was placed on the side to monitor whether the specimen was subjected to unidirectional eccentric compression during loading. A crack width detector was used to observe and measure crack widths, while a ruler was used to measure crack spacing. Concrete strain gauges were evenly attached at the mid-height of the column to measure longitudinal strain at various stages. Strain gauges were also attached at the mid-section of the longitudinal reinforcement and 200 mm above and below this point to measure the strain behavior of the SSR at each stage.  
Figure 10: Arrangement of strain gauges and displacement gauges.  
Figure 9: Loading of specimen. (**a**) Column loading. (**b**) Load scheme.
## 2 Methodology
### Performance Metrics  
There are various statistical performance measurement criteria in the literature. The created models were appraised to test the prediction accuracy of models with 6 different statistical methods: correlation coefficient (R), RMSE (root mean-square error), Nash-Sutcliffe efficiency coefficient (NSE), and Kling-Gupta efficiency (KGE).  
The correlation coefficient (R) is given in Equation (16) [81]:  
\[\text{R}=\frac{\sum_{i=1}^{N}\left(W_{pi}-\overline{W_{p}}\right)\left(W_{oi} -\overline{W_{o}}\right)}{\sqrt{\sum_{i=1}^{N}\left(W_{pi}-\overline{W_{p}} \right)^{2}}\;*\;\sqrt{\sum_{i=1}^{N}\left(W_{oi}-\overline{W_{o}}\right)^{2}}} \tag{16}\]  
where \(W_{pi}\) is predicted \(W\) for \(i\), \(\overline{W_{p}}\) indicates the mean of the predicted \(W\) value, \(W_{oi}\) shows observed \(W\) values for \(i\), and \(\overline{W_{o}}\) indicates the mean of the observed \(W\) value.  
RMSE is given in Equation (17) [81]:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{t=1}^{N}\left(W_{oi}-W_{pi}\right)^{2}} \tag{17}\]  
NSE is sensitive to additive and proportional differences between forecasts and observations [82].  
NSE is calculated in Equation (18):  
\[\text{NSE}=1-\left[\frac{\sum_{t=1}^{N}\left(W_{oi}-W_{pi}\right)^{2}}{\sum_{ t=1}^{N}\left(W_{oi}-\overline{W_{o}}\right)^{2}}\right] \tag{18}\]  
[PERSON] et al. [83] proposed the Kling-Gupta efficiency (KGE), which is based on the decomposition of the Nash-Sutcliffe efficiency [82] into its fundamental components: correlation, variability bias, and systematic bias, for the calibration and evaluation process of hydrological models.  
\[\text{KGE}=1-\sqrt{\left(\alpha_{P}-1\right)^{2}+\left(\beta_{P}-1\right)^{2} +\left(\gamma_{RP}-1\right)^{2}} \tag{19}\]  
\[\beta_{P}=\frac{\mu_{G}}{\mu_{O}} \tag{20}\]  
\[\gamma_{RP}=\frac{CV_{G}}{CV_{O}}=\frac{\left(\frac{\sigma_{G}}{\mu_{G}} \right)}{\left(\frac{\sigma_{O}}{\mu_{O}}\right)} \tag{21}\]  
where \(\alpha_{P}\) represents the Pearson correlation between the data observed and GCM-analyzed data, \(\beta_{P}\) represents the bias rate, and \(\gamma_{RP}\) represents the variability rate. In Equation (5), \(\mu_{G}\) and \(\mu_{O}\) represent the average of GCM-analyzed and observed data, respectively. In Equation (6), \(CV_{G}\) and \(CV_{O}\) represent the coefficient of variation of GCM-analyzed and observed data, respectively. KGE values range from minus infinity to 1; values closer to 1 indicate higher model performance.  
Another method that used performance metrics for comparison is the RMSE-standard deviation ratio (RSR). This method's calculation is as follows:  
\[\text{RSR}=\left[\frac{\sqrt{\sum_{t=1}^{N}\left(W_{oi}-W_{pi}\right)^{2}}}{ \sqrt{\sum_{t=1}^{N}\left(W_{oi}-\overline{W_{o}}\right)^{2}}}\right] \tag{22}\]
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 2 GNSS Receiver with a 2 \(\times\) 2 Antenna Array  
In our dual-band 2 \(\times\) 2 URA receiver, each patch antenna element covers the L1 band with a center frequency of f\({}_{1}\) = 1575.42 MHz and E5a band with f\({}_{5}\) = 1176.45 MHz. The dual-polarized patches enable the reception of right-hand circular polarized (RHCP) as well as left-hand circular polarized (LHCP) signals [14]. A simplified block diagram of the receiver is shown in Figure 1. The antenna array provides a total number of 16 RF channels, namely 4\(\times\) RHCP and 4\(\times\) LHCP at f\({}_{1}\) and 4\(\times\) RHCP and 4\(\times\) LHCP at f\({}_{5}\). Each channel is amplified, filtered and down-converted. The down conversion in the heterodyne receivers must preserve the magnitude and phase relationships between all received antenna signals to preserve phase coherence and thus synchronicity between the channels. A calibration signal (CAL) is provided at each channel, to determine differences in the transmission characteristics between the receiver chains. The CAL signal consists of an artificial GNSS signal which is generated in the digital baseband and up-converted to the RF domain by the LO-signal. It passes through the entire signal processing chain of each frontend channel,is processed in the digital baseband together with the actual received GNSS signals, and is eventually compared to the initial calibration signal. This allows for a correction of gain and phase differences caused by imbalances between the channels.  
A prerequisite for the calibration algorithm is that the CAL signal be applied with identical amplitudes and phases to each receiver channel. In a compact antenna array, this condition can be met straightforwardly because all antenna elements are arranged on a common substrate; consequently, the calibration signal can be multiplexed using passive components like power splitters and distributed to the frontend channels by length-matched transmission lines. A similar conclusion holds for the distribution of the LO signal to the mixers of all receiver channels.  
For the case of a distributed antenna array receiver, the above-mentioned conditions are much more challenging to meet because of the required cabling in the calibration and LO distribution paths between the two sub-arrays. As a consequence, the signals depend on the cable characteristics in terms of mechanical length differences, thermal variations and so on, leading to unforeseeable phase variations.
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 2 Configuration of the Antenna Element
### Slotted Waveguide with Metallic Cylinder Inside
Figure 8: The simulation model of the slotted waveguide element. Figure 7: The field distribution of the waveguide with a cylinder. (**a**) The electric field in the upper wall of the waveguide. (**b**) The electric field in the cross-section of the waveguide. Figure 9: (**a**) The simulated S21 (dB) curves of the single resonant slot when changing the cylinder height and (**b**) the enlarged view for h = 2.50 mm to 4.00 mm. Following the above discussion, we can manipulate the radiation efficiency by adjusting the height of the cylinder. The relation between the radiation efficiency of the slot and the cylinder height is shown in Figure 10; the cylinder height refers to \(N\hbar\) in Figure 6c. the radiation efficiency is defined as follows:  
\[Eff_{rad}=1-(S11)^{2}-(S21)^{2} \tag{3}\]  
where \(Eff_{Rad}\) refers to the radiation efficiency of the antenna element, S11 is the energy reflected back to the excited port, and S21 is the energy transmitted to the load or next element. A method for controlling the element's radiation efficiency is introduced in this section; it lays the foundation for the follow-up array design work. This section describes the working principle and design method of a compact circularly polarized antenna element, resulting in a miniaturized circularly polarized antenna element with an aperture size of \(0.48\lambda\times 0.48\lambda\). Additionally, this section introduces the method used to control the radiation efficiency of the element, which serves as the foundation for subsequent design considerations.
## 5 Applications of DRA
### Biomedical Applications
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref.** & **Geometry** & \(f_{r}\) **(GHz)** & **BW (\%)** & \(\varepsilon_{r}\) & **Gain (dBi)** & **Polarization** & **Applications** \\ \hline
[95] & Rectangular DR, array & 3.79–6.29 & 49.6 & 10.2 & 18–22 & LP & Unmanned aerial system \\
[96] & Rectangular DR, array & 23.25–24.93 & 6.97 & 16 & 9.5 & LP & Short-range vehicular radar \\
[97] & Cylindrical DR & 22.5–26 & 14.43 & 9.9 & 10.8–11.8 & CP & Automotive radar \\  & & 8.2–10.3 & & & & Weather monitoring \\
[98] & Cylindrical DR & (Element) & 22.7 & 20 & 7.7 & CP & and vehicle speed \\  & & 8.2–11.3 & 31.79 & 20 & 9.5 & & detection \\  & & (Array) & & & & & \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison between various DRAs for radar applications.
## 4 Analysis of Approaches to Modelling Agricultural Systems
### Participatory Approach  
The participatory approach is based on the active participation of experts or stakeholders. This approach is particularly useful and powerful when there is a lack of measured data (Figure 2). A conceptual framework of questions and systems thinking allows for the development of theoretical models or design concepts based on expert knowledge or stakeholders' engagement [46]. This approach is also useful to resolve conflicts of interest [47,48].  
Coupling different types of expertise during the process helps strengthen the development of a conceptual model [46] or to initiate other types of more complex modelling and scenario simulations [47,49]. Participatory approaches for system modelling or scenario development are useful to bring together expert and stakeholder knowledge from academic, political, and civil sectors. Engaging stakeholders along with scientists is increasingly used in environmental research for analysing global change impacts [50] or sustainable futures [51,52]. Some tools have been developed to structure the participatory approach and to help follow scientific protocols. For example, the RIO approach (Dutch acronym for Reflexive Interactive Design) aims to structurally address complex trade-offs and to contribute by process and design to change perspectives towards sustainable development avoiding conflict [46,53,54].  
While the participatory approach works very well at a fine scale to help farmers on economically viable and environmentally strong decisions related to farm management [55], it is often used as a discussion tool for structuring environmental problems and design solutions (i.e., climate change adaptation roadmap, water management, sustainability of agricultural sector) at a larger scale (regional to national) [46,56,57,58].  
**Strengths**:  
* At a regional to national scale, the participatory approach is well designed for policy- and decision-making if it is sustained by upstream numerical modelling.
* Expert consultation can be a quicker process than numerical modelling and it does not necessarily need data or modelling skills.  
Figure 2: Modelling approaches and associated complexity.  
When coupled with another approach, participatory process has a real benefit to integrate stakeholders' participation (no data can fill the \"real-world\" knowledge gap).
* When decision-making is the main aim, the participatory process is fundamental to gaining stakeholder buy-in.  
**Weaknesses**:
* The participatory approach can suffer several biases, including group think, depending on the point of view of experts/stakeholders.
* It is a qualitative approach and therefore hard to quantify for coupling with other type of approaches.
## 1 Introduction
An EKF-based pose estimation approach is provided for a robot autonomous localization system in a semi-structured indoor environment. An additional recent research effort was done by [PERSON] et al. [20], in which they presented a four-wheel robot with spring suspension. The robot was able to navigate autonomously in complex and uneven terrains. In order to overcome the expected problems of low mapping accuracy, the authors used the Kardo SLAM algorithm. The A* algorithm and DWA algorithm were used for global and local path planning, respectively. Another recent research on indoor robot localization was presented by [PERSON] et al. [21]. A new method named Text-MCL is presented, and it depends on text information and data from laser scanning. It is used mainly for repeated indoor geometric structures like long and narrow corridors. Experimental results have shown a convergence rate of 92%. Further investigation into accurate mapping for indoor mobile robots is presented by [PERSON] et al. [22]. The aim of this paper was to present a brief review of semantic mapping techniques, which provide geometric information in addition to general information and features about the objects of the indoor map. This technique raises the cognition of the mobile robot system and facilitates interaction with humans in collaborative environments. More examples of AWMRs with indoor navigation capabilities are further presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PPA** & **P or S** & **Sensors** & **Environment** & **Motion** & **Obstacle** \\ \hline
**[PERSON]**[23] & – & S &
\begin{tabular}{c} Pose, Velocity, \\ Current \\ \end{tabular} & Indoor & \(\mathrm{H}\) & Static \\ \hline
**[PERSON]**[24] &
\begin{tabular}{c} A*, PF, RRT*, \\ FMM, uFMS \\ \end{tabular} & S & – & Outdoor & – & Dynamic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of mobile robots and their path planning techniques, PPA: path planning algorithm, P: practical testing, S: simulation testing, H: holonomic, NH: non-holonomic. In this paper, the implementation of different path planning techniques is presented, compared and discussed on an Omni Wheeled Mobile Robot (4 OWMR). The new robot is provided with indoor navigation capability in the form of global and local navigation [29]. This robot is intended to be used in indoor offices and libraries with the presence of fixed obstacles or landmarks. The robot's main parts, specifications and kinematics are shown in Section 2. The robot control architecture is shown in Section 3. Several path-planning algorithms are intended for testing on the developed robot. Three common algorithms are chosen and explained in Section 4. Experimental implementation of the proposed techniques and optimization of the generated paths are presented in Section 5. A discussion of the obtained results is presented in Section 6. Finally, the conclusion and future work are presented in Section 7.
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Application of Knowledge Representations  
In three scenarios, knowledge representation results can be obtained through the framework of knowledge representation. Now, we will discuss how to apply knowledge representation to signal separation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} A-VTTCES-KF & Adaptive vector-tracking time-correlated error suppressed Kalman filter \\ \(C/N_{0}\) & Carrier-to-noise density ratio \\ DCM & Direction cosine matrix \\ DGPS & Differential global positioning system \\ DLL & Delay lock loop \\ DOP & Dilution of precision \\ ECEF & Earth-centered, earth-fixed \\ GM & Gauss-Markov \\ GMWM & Generalized method of wavelets moments \\ GNSS & Global navigation satellite system \\ GNSS-R & GNSS-reflectometry \\ GPS & Global positioning system \\ ICT & Information and communication technology \\ IF & Intermediate frequency \\ INS & Inertial navigation system \\ IoT & Internet of things \\ KF & Kalman filter \\ LiDAR & Light detection and ranging \\ LOS & Line-of-sight \\ LR & Loop rate \\ NCO & Numerically controlled oscillator \\ O-KF-VT & ordinary adaptive KF algorithm regardless of compensating the time-correlated error \\ PDOP & Position dilution of precision \\ PLAN & Position, Location And Navigation \\ PLL & Phase lock loop \\ PSD & Power spectrum density \\ PVT & Positioning, velocity, and timing \\ RAIM & Receiver autonomous integrity monitoring \\ RMSE & root-mean-square error \\ RTK & Real-time kinematic \\ SAR & Synthetic aperture radar \\ SDR & Software-defined receiver \\ ST & Scalar tracking \\ STD & Standard deviation \\ SV & Satellite vehicle \\
3D & Three-dimensional \\
3\(\sigma\) & Three-sigma \\ UAV & Unmanned aerial vehicle \\ VDLL & Vector delay lock loop \\ VT & Vector tracking \\ WNLS & Weighted non-linear least square \\ \end{tabular}