esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Experimental results show that the accuracy of orbit and clock corrections varies in different SBAS systems. The accuracy of orbit and clock corrections of EGNOS and SDCM systems are significantly better than that of the other three SBAS systems, followed by WAAS, while MSAS and GAGAN provide orbit and clock correction with a relatively worse accuracy. In terms of SBAS ionospheric corrections, the accuracy of ionospheric corrections provided by EGNOS, WAAS, and SDCM are about half a meter, while MSAS and GAGAN suffer from further accuracy degradation of about 3 decimeters. When comparing with GPS broadcast orbit, clock and ionospheric correction, the SISRE degrades with application of SBAS orbit and clock corrections while the accuracy of SBAS ionospheric corrections is still much better than that of GPS broadcast ionospheric corrections.  
It should be noted that the SBAS message availability of SDCM PRN 125 is 85.9%, which might affect the assessment slightly. In addition, the service areas of WAAS, EGNOS and SDCM are mainly in mid- and high-latitude regions, while GAGAN and MSAS provide service in low- or mid-latitude regions. To a certain extent, it may affect the accuracy comparision of ionospheric corrections from different SBAS systems. At last, each of the three systems (WAAS, EGNOS and SDCM) has one GEO satellite whose corrections are not included in the evaluation. However, considering that the accuracy of orbit and clock corrections from the same SBAS system only show minor differences, using only two satellites for the assessment of WAAS, EGNOS and SDCM should have only slight impacts on the results.  
Several associated aspects which are not discussed in this paper still require further study. Firstly, the assessment under different ionosphere condition needs to be further conducted given that the ionosphere condition during the evaluation period in this paper is relatively mild. Secondly, the consistency between the evaluation results and integrity information broadcasted by different SBAS systems need to be further investigated. Nonetheless, the results of this paper are beneficial for high-precision GNSS positioning using SABS corrections or combining SBAS corrections with other source of corrections. As we know, IGS RTS has been providing openly and freely accessible high-precision correction since 2013. The accuracy of real-time orbit is about 5 cm, and the real-time clock has a standard deviation of 0.1-0.15 ns ([[http://www.igs.org/rts/monitor](http://www.igs.org/rts/monitor)]([http://www.igs.org/rts/monitor](http://www.igs.org/rts/monitor))). Although there is only one IGS analysis center, CNES, broadcasting RTS ionospheric message, and the SBAS ionospheric  
Figure 16: The bias and STD values of GPS broadcast ionospheric correction errors for each selected IGS station.  
corrections are still competitive in terms of accuracy [47]. Therefore, it is a good choice to combine RTS multi-GNSS orbit/clock corrections and SBAS ionospheric corrections to conduct real-time multi-GNSS single-frequency PPP.
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
## 2 Materials and Methods
### Decision Variables and Constraints  
The current flowing from the photovoltaic array is denoted by \(I_{PVt}\), where \"\(t\)\" identifies the minutes within the prediction horizon of 2 h. This index also depends on the variable \"\(z\)\", which identifies the absolute time at which the algorithm is. At the beginning of the day, for example, \"\(z=1\)\" is adopted and is updated at each algorithm run, in order for it to be applied to a full day or any period of time that is required. The variable \(\beta_{t}\) represents the energy cost. \(I_{bi}\) is the instantaneous charging current of the battery bank whose charging and discharging limits obey (1). \(K_{2}\) is the maximum charging current value. \(-K_{2}\) is related to the maximum discharging, and current \"\(i\)\" is the battery charging current.  
The prediction control algorithm used is based on the receding horizon strategy [22], which consists in the horizon being rolled forward as the optimization algorithm is solved. As an example, the first two hours of the cost and photovoltaic curves are utilized to run the algorithm, utilizing \(t\) as the index. At the end of the algorithm's first run, \(t\) is reset and the calculated currents are used for the system operation in the first minute. Then, \(z\) is incremented and a new prediction window of 2 h is used by employing the second minute as the initial one.  
It is important to highlight that the battery charging limits do not necessarily have the same values, as they can be defined based on the battery type and user restrictions. Current \(I_{git}\), as observed in (2), corresponds to the portion that flows into the grid or is absorbed from it based on the charging current that was initially generated. The limits set for \(I_{git}\), identified in (3) as \(K_{5}\), are related to the power of the bidirectional converter that interfaces the system and the grid. According to Figure 1, it is relevant to state that positive values for \(I_{g}\) correspond to the current flow from the grid to the electrical charging station.  
\[-K_{2}\leq I_{bi}\leq K_{2} \tag{1}\]  
\[I_{git}=I_{bi}-I_{PVt} \tag{2}\]  
\[-K_{5}\leq I_{git}\leq K_{5} \tag{3}\]
## 3 Results and Discussion
### Three-Dimensional Fluorescence Spectroscopy  
Fluorescence spectroscopy has shown great potential for the characterization of food products because they contain many fluorescent molecules (fluorophores) whose emissions can be considered characteristic fingerprints of specific food samples [34; 35]. In this study, fluorescence spectroscopy was applied to this category of herbal food supplements (botanicals), and the observed changes in intrinsic fluorescence were studied via plotting the fluorescence values as images (with intensity represented via color coding) of two-dimensional fluorescence excitation-emission landscapes, in which the characteristic circular patterns were used to identify regions that could be attributed to specific fluorophores. Figure 3 shows the contour plots of the six different species for each spectral range (Figure 3a,b) that were evaluated in the spectroscopic analysis. To provide a meaningful qualitative evaluation of the fluorescence data, both the ranges used for recording the fluorescence spectra are considered in the discussion.  
According to the literature, it is important to consider that the emission maxima of many different phenolic compounds can be observed in approximately the same spectral region. However, thanks to the combination of the information obtained via both the spectral ranges considered, it is possible to observe two different spectral regions as a result of the fluorescent properties of BDs. Excitation in the more energetic wavelength of 250 nm results in an emission between 400 and 440 nm, while excitation at wavelengths longer than 300 nm results in an emission between 420 and 450 nm [36]. The first spectral region reported in Figure 3a is ascribable to phenolic acids and cinnamic-like and benzoic-like acids, which present maximum emissions between 400-440 nm. This peak, which was detectable in all the samples analyzed, has higher intensity values in CS and QP fluorescence emission spectra that are also rich in ellagic and gallic acids according to their chromatographic characterizations. As reported by [PERSON] et al. [37] for green tea samples, at 270/420 nm, it is also possible to study the contents of catechins, which contribute to the generation of this broad peak. However, the second spectral range (Figure 3b) allows one to better characterize the emissions relating to other flavonoid compounds, especially quercitrin and quercetin, which present excitation/emission maxima at 310/430 nm, as reported by [PERSON] et al. [38]. This characteristic peak has been observed in all six species of BDs, with higher intensities in the QP, CA, FE and FC samples, confirming the outcomes of the quantitative analysis (data are shown in the Supplementary Materials, Figure S1).
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 4 Improved Detections
### The Decision Feedback-BNFT (DF-BNFT) Detection  
The decision-feedback BNFT (DF-BNFT) detection--sketched in Figure 1 in blue--uses the NFT causality property to detect symbols in the time domain, without any FNFT, differently from the I-FNFT and DF-FNFT [13]. For \(k=1,\ldots,N_{b}\), the \(k\)-th symbol is selected as the one that minimizes the euclidean distance with the received signal before \(t_{k}\), provided that the first \(k-1\) symbols have been already decided. This detection avoids the detrimental impact of the FNFT on the noise, differently from the other strategies. Furthermore, while the I-FNFT and the DF-FNFT also rely on the trick to avoid small values on \(|a(\lambda)|\), the DF-BNFT does not, since the nonlinear spectrum is not evaluated at the RX. The pseudocode forDF-BNFT detection is shown in Algorithm 3.  
```
1:Data: Samples of the received signal \(\bar{\mathbf{r}}=(\bar{\mathbf{r}}_{1},\ldots,\bar{\mathbf{r}}_{N_{b}})\)
2:Result: The detected symbols \(\hat{\mathbf{x}}_{1},\ldots,\hat{\mathbf{x}}_{N_{b}}\)
3:for\(k=1,\ldots,N_{b}\)do
4:for\(X_{m}\in\{\)16-QAM constellation\(\}\)do
5: Perform all the TX operations except for precompensation, to obtain to obtain \(\mathbf{r}_{k}^{(m)}\), given \(\hat{x}_{1},\ldots,\hat{x}_{k-1},X_{m}\)
6: Evaluate \(d_{m}=||\mathbf{r}_{k}^{(m)}-\bar{\mathbf{r}}_{k}||_{2}\)
7:endfor
8: Obtain \(\hat{x}_{k}=\operatorname*{argmin}_{X_{m}}d_{m}\)
9:endfor
```  
**Algorithm 3** DF-BNFT detection  
As far as it concerns the computational cost, the DF-BNFT requires \(M\) BNFT. Indeed, using the Nystrom conjugate gradient method [29; 30], as for the DF-FNFT, at each step, the digital waveforms corresponding to 16 different symbols should be performed only in the considered time slot, since the samples on prior time instants, are taken from previous steps.
## 3 System Model
### Adaptive Thermal-Energy-Aware Routing Protocol in WBAN  
The perseverance of energy and evasion of temperature-rise are the primary concerns for the WBAN network life. To achieve efficiency and stability of the network, these two challenges need to be dealt with collectively. The potential problems are related to send data packet (from source to destination) with less energy consumption and less temperature for every communication. The existing routing approaches for WBAN addressed these issues individually. However, frequent communication using an optimal route with high energy and low temperature will ultimately result in energy exhaustion and overheating of that route or death of a node. In this research, this problem is resolved by presenting a protocol \"ATEAR--Adaptive Thermal-/Energy-Aware Routing in WBAN\". This protocol provides the mechanism of selection of optimal multihop paths among multiple available routes to avoid frequent partitioning of WBAN. This optimization is achieved by discovering multiple relay nodes from the source to destination, and each node is given a cost value based on the temperature and energy metric. Every time before sending the data, the forwarding node is selected depending upon the cost function. Thus, communication will be carried out using multiple options of the nodes  
Figure 2: Blockchain-enabled WBAN architecture.  
avoiding a single route or node used continuously. ATEAR is a reactive source protocol initiated by the source node to forward the data using alternative options. Our proposed ATEAR scheme constitutes two phases, i.e., the initialization phase and data transmission phase.
## 4 Performance Evaluation
### Analysis of End-to-End Delay
Then, the \(E2 ED\) of RSBP can be expressed as follows:  
\[E2 ED(RSBP)=nBNodes\times len(BS)=nBNodes\ \times(0.192+(6+p)\times 0.032)(ms) \tag{18}\]  
where \(len(BS)=t_{mr}+t_{turnon}+t_{rm}+t_{ppd}+t_{tx}\) and \(t_{mr}\), \(t_{turnon}\) and \(t_{rm}\) are assumed to be zero in simulation. In SSMAb, rewriting Equation (10), we get \(len(BS)\) as follows:  
\[len(BS)=((CW+1)\times 0.32+(6+p)\times 0.032)\ (ms) \tag{19}\]  
where \(\max(rdelay)=CW\ x\ delay\_slot\), \(delay\_slot=20\) symbols delay, t\({}_{CCA}\) = 8 symbols delay, \(t_{ppd}\) = 12 symbols delay, and one symbol delay = 0.016 ms. Rewriting Equation (14) with Equations (11) and (19), we get:  
\[E2 ED(SSMAb)\geq((H-2)\times N+1)\times len(BS) \tag{20}\]  
From Equations (16)-(18) and (20), the \(E2 ED\)s for the four broadcast protocols are compared in Table 3. The \(CW\) value for SSMAb is set to three since it is good enough for both 30 nodes and 75 nodes according to simulation results in Figure 9 later. The \(E2 ED\)s of SSMAb for 30 nodes and 75 nodes are given 60.7 ms and 98.1 ms, respectively. Furthermore, it shows good \(E2 ED\) comparable to RSBP for 30 nodes due to low tree depth. Glossy achieves the smallest E2 ED of all since it does not use random delay before rebroadcasting. We believe that about 100 ms is sufficiently small to satisfy the time bound of command delivery in industrial monitoring and control applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} Dimension = 100 \(\times\) 100 (m\({}^{2}\)), \\ \#Nodes = 30, R = 10 m, p = 100 bytes \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons on the lower bound of E2 ED. Packet delivery ratio (PDR) is the ratio of the number of nodes that received a message to the total number of nodes. Packet processing load (PPL) is the number of messages that each node received or overhears and also transmits on average per each broadcast message. Average energy consumption (AEC) is the average amount of energy consumption per node during the total simulation time.
# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 3 Mobile Crane Repositioning Route Optimization Method  
RPOS searches for a reposition route plan that minimizes the mobile crane operating time. All computational processes were implemented using MATLAB R2014b-based software. RPOS comprises of four modules: (1) site data modeling, (2) identifying feasible locations for the mobile crane, (3) GA-based near-global optimal reposition route planning for the mobile crane, and (4) outputting the near-global optimal mobile crane reposition plan and operating time report. A detailed description of each module is provided below.
## 7 Simulations and Discussion
### Packet Delivery Ratio  
The packet delivery ratio is calculated by dividing the total number of data packets that have reached their destinations by the total number of data packets sent from sources.  
The tables below present statistics for a 100 nodes network that uses OLSR without any malicious nodes and OLSR with malicious nodes.  
The statistics presented in Table 2 are related to the use of the standard OLSR protocol. Indeed, we can notice that the average PDR is 95%, while it also presents the performance of the OLSR protocol in the case of the existence of a malicious node. The purpose of the simulations is to assess how the introduction of varying numbers of malicious nodes, which intentionally discard received packets, affects the overall percentage of packet drops.  
Figure 16: Scenario 2: Node_(1) Routing table.  
Figure 15: Scenario 1: Node_(1) MPR table.  
Indeed, the presence of malicious nodes in the network is combined with a remarkable degradation of OLSR performance. This is evidenced by the degradation of the value of the average PDR, which is only 48.16%.  
On the other hand, when our proposed modified OLSR is under attack, the PDR is better than the standard OLSR under attack for 100 nodes. The results, as shown in Table 3, show a remarkable improvement in PDR when our malicious node detection algorithm is integrated within the OLSR protocol. This is due to the fact that our proposed solution allows the effective and efficient detection of malicious nodes. This detection allows each node under attack to choose between two possibilities: (1) to disconnect from the rest of the network. Indeed, it is the obvious solution when a node has only one relay to connect with other nodes and this relay node is unfortunately malicious, or (2) to trigger the process of electing another nearby node as MPR. This process will be preceded by blacklisting the malicious node.  
Table 3 clearly shows the efficiency of our solution compared to the unmodified version of OLSR. Indeed, our solution allows the OLSR protocol to better resist attacks. Indeed, our solution allows the OLSR protocol to better resist attacks by ensuring better packet transport reliability for different attack circumstances. This is illustrated by the guarantee of a PDR far exceeding that of the unmodified version of the OLSR protocol. Obviously, in extreme attack scenarios corresponding to a high percentage of attackers, the performance of our solution approaches that of the unmodified OSLR protocol.
## 10 Novel Imaging Using CXI
### Coherence-Contrast Imaging  
As shown in Equation (2), CXI can obtain three types of images with absorption contrast, phase contrast, and degree of coherence contrast. The third type has a limitation, though: quantitative analysis is not possible at present because it is related not only to the X-ray scattering by the small structure of the sample but also to the decrease in visibilty due to the interference X-ray optical path deviation. However, it can compensate for the narrow dynamic range of CXI.  
Figure 20 shows an example observation of a mouse foot. From left to right are the projection images obtained using degrees of coherence contrast, phase contrast, and absorption contrast [73]. In Figure 20a, the outline of the soft tissue is visualized in addition to the bone, while in Figure 20b, the soft tissue is visualized but the bone (femur) region is not visualized due to an unwrapping failure. In Figure 20c, soft tissue with little absorption is not visualized at all. Thus, for morphological observation, coherence-contrast imaging, which images \(\gamma\) (the degree of coherence), is useful for expanding the dynamic range.
# Numerical Analysis of the Settlement Behavior of Soft Soil Improved with Stone Columns
## 3 FE Analysis Results
### Effect of Stone Column Strength  
The influence of the stone column aggregate strength upon the settlement performance for Footing 4 is shown in Figure 18 for friction angles of 40\({}^{\circ}\), 42.5\({}^{\circ}\), and 45\({}^{\circ}\). A negligible reduction in settlement is observed with increasing column stone aggregate strength at the working applied pressure up to 50 kPa. However, a significant reduction in settlement is observed at the ultimate pressure (greater than 150 kPa) with increasing stone column strength. This may be explained as when the stone column load increases, the column is in a plastic state where the angle of internal friction is mobilized. Thus, the effect of the stone column strength is more pronounced on columns with the largest degree of plasticity.
## 2 The Scenario, Architecture and Optimization Strategy of the UEMS for ADR
### The Architecture of the UEMS  
The architecture of the UEMS is depicted in Figure 1.  
The gateway, which is one of the critical components of the UEMS, is regarded as a middleware between the cloud layer and the sensor layer. It reports the data acquired from sensors to cloud while delivering commands generated by cloud layer to actuators. Besides gateway, smart sockets and temperature sensors are also essential for the UEMS. Smart sockets, which are physically connected to electric heaters, support appliance-based metering and direct load control. Temperature sensors are responsible for acquiring indoor temperature, which is beyond the capability of the smart sockets but necessary for the ADR control strategy of the UEMS. The smart sockets and the temperature sensors are collectively referred to intelligent terminals in this paper.  
Additionally, intelligent terminals communicate with the local gateways according to MQTT-SN protocols, which relies on non-IP wireless networks. Incidentally, ZigBee technology is adopted in this paper to support the local non-IP wireless networks. The communication between the cloud layer and the gateway layer is based on MQTT protocols, which relies on IP-based protocols such as TCP/IP. The MQTT/MQTT-SN would be introduced in detail in Section 3.  
Figure 1: The architecture of the UEMS.
## Experimental Results
### The Impact of Packet Size  
The maximum size of an IEEE 802.15.4 frame is 127 bytes. Many experiments were conducted with different packet sizes. Our goal was to evaluate the impact of this parameter on the radio link when nodes ware deployed at ground level. We considered five sensors linearly placed on the ground with a distance of 3 m between two consecutive sensors. The nodes were configured to use channel 26 for communications and 0 dBm as transmission power. We varied the application payload from 2 bytes to 102 bytes.  
Figure 8 shows the PRR and RSSI CDF for radio links of length 3 m (Figure 7(a),d), 6 m (Figure 7(b),e), and for all links in the network (Figure 7(c),f). For presentation purpose, only three application payloads are shown in this figure.  
The results do not show a significant difference concerning the RSSI distribution, regardless the application payload. Nevertheless, Figure 7(a)-c presents a clear difference between the PRR distribution for a very small application payload (2 bytes, i.e. only the sequence number as payload in the packet) and larger ones (42 and 82 bytes). Figure 9 shows that, with an application payload of 2 bytes, there are  
Figure 8: CDF of PRR and RSSI for distances 3 m and 6 m and for all links in the network for different packet sizes.  
more intermediate radio links in the network, while for other message sizes more good radio links are present in the network. Similar remarks can be made concerning the radio link asymmetry. Indeed, the data presented in Table 2 show that, when the application payload was 2 bytes, the average difference of the measured RSSI or PRR on forward and backward radio links was the highest. The percentage of unidirectional wireless links was also larger for such a small message size.  
We could not totally explain the poor quality of links when the packet payload was only 2 bytes, which might be even due to some operating system bug. However, we can confirm that this phenomenon manifested in all the different settings we tested, and it disappeared for applicative payloads larger than 10 bytes.