esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.11 Radio Access Technology (RAT)  
The design of the radio access technique had to be reconsidered in light of the various needs for 5G networks. Due to the growing demand for gigabit speeds, the underlying radio access technology ought to be capable of transmitting data at higher rates. Given the finite and expensive nature of the spectrum, spectral efficiency is a crucial component of radio access technology that will enable gigabit speeds.  
Certain 5G services and applications, such as tactile Internet, holography, and mixed worlds, require millisecond latency. This constrains the radio access mechanism's latency perspective, allowing for the lower latency required at higher layers to be realized. Devices may not necessarily be connected to the BS in other applications such as IoT. These devices' power restrictions prevent them from being fully synchronized with the BS. As a result, the multiple access technique used should be able to handle slack or, better yet, no synchronization. Interference is a risk in multi-access strategies, as it is in other wireless systems.  
Finally, the radio access technique should be power efficient to avoid exhausting the power of low-power devices. The fifth generation cellular network is expected to provide exceptional data speeds when compared with high-speed wired lines. Because the future network system will be heterogeneous, small cells will be absorbed into macro-cells. As a result, the cells will be fragmented into disparate radio access networks that will be linked to nearby RATs, such as Wi-Fi and other licensed networks [35; 109].
## 3 Results and Discussion
### Pore Pressure  
Figure 7 illustrates the development of pore pressure in soil samples subjected to various stress paths. The pore pressure development of silt and silty sand was similar. Pore pressure rapidly increased and then decreased in the stress path of Groups A and C. At first, the soil sample showed shear contraction and then exhibited shear dilation. For Group B's lateral unloading stress path, pore pressure had no obvious growth but continuously decreased. In the unloading path, as unloading intensity increased, the final pore water pressure decreased, and silty sand had a greater decline. The final pore pressure of silty sand decreased with increasing unloading intensity in the unloading-reloading path. The final pore pressure of silt decreased slightly, but peak pore pressure was significantly lower than that under a conventional triaxial path.  
Figure 6: Normalized deviator stress–unloading intensity relationship of three soils under different stress paths.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Soils**} & \multicolumn{8}{c}{**Stress Path**} \\ \cline{3-8}  & & **A\(-\)0** & **B\(-\)1** & **B\(-\)2** & **B\(-\)3** & **C\(-\)1** & **C\(-\)2** & **C\(-\)3** \\ \hline \multirow{2}{*}{Cohesive pressure \(c^{\prime}\) (kPa)} & Silt & 8.66 & 1.16 & 1.1 & 0.98 & 8.55 & 8.12 & 7.83 \\  & Silty sand & 6.84 & 0.24 & 0.23 & 0.19 & 6.77 & 6.55 & 6.38 \\  & Mucky silty clay & 1.24 & 0.5 & 0.44 & 0.36 & 1.07 & 0.62 & 0.59 \\ \hline \multirow{2}{*}{Friction angle \(\varphi^{\prime}\) (\(c^{\prime}\))} & Silt & 36.35 & 28.71 & 28.56 & 26.69 & 35.5 & 35.3 & 35.26 \\  & Silty sand & 37.36 & 29.82 & 29.01 & 28.94 & 36.05 & 35.7 & 35.63 \\  & Mucky silty clay & 4.5 & 1.81 & 1.59 & 1.52 & 4.39 & 4.35 & 3.83 \\ \hline \multirow{2}{*}{Shear strength \(\tau\) (kPa)} & Silt & 119.05 & 83.32 & 82.75 & 76.39 & 115.54 & 114.32 & 113.87 \\  & Silty sand & 121.36 & 86.22 & 83.41 & 83.13 & 115.95 & 114.34 & 113.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Shear strength indicators of soil under different unloading intensity levels.  
Pore pressure development takes on some distinct characteristics in mucky silty clay. Pore pressure is always positive under different stress paths. Under the unloading path, the pore pressure of silty clay initially increased and then slightly decreased. However, pore pressure continued to rise in the unloading-reloading path. As the final pore pressure of mucky silty clay slightly decreased with the increase in unloading intensity, growth rate significantly slowed down when unloading intensity was relatively high.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 6 GNSS Meta-Signal RTK Performance Results
### Nominal Conditions: Good Satellite Geometry Scenario  
The RTK performance results for the good GDOP scenario are summarized in Figure 10. The first thing to point out is the huge performance gain provided by a correct exploitation of the phase information, which was clear from the gap between CRB\({}_{real}\) and CRB\({}_{real/integer}\). For instance, considering the reference Galileo E5 signal and a SNR\({}_{\text{out}}=25\) dB, CRB\({}_{real}=18\) cm, and CRB\({}_{real/integer}=2\) mm. Several interesting conclusions can be extracted from these results:  
* The convergence to the RTK CRB\({}_{real/integer}\) was driven by the phase MLE threshold region, that is for every meta-signal, the RMSE\({}_{mixed}\) (i.e., fixed solution considering all the estimates and not only the correct fix ones) started to deviate from the corresponding CRB at the same point as the phase estimate deviated from the phase CRB, which in turn was driven by the time-delay MLE behavior. Recall that such slow convergence (or intermediate threshold behavior) was directly related to the false locks due to large secondary peaks. Therefore, a first conclusion was that subcarriers that induced large secondary ACF peaks strongly impacted the achievable RTK performance.
* The second interesting point was that when the RMSE\({}_{mixed}\) started to deviate from the CRB, it rapidly joined thefloat solution behavior (i.e., RMSE\({}_{real}\)). Therefore, it was fundamental to characterize the threshold region correctly. Below such SNR\({}_{\text{out}}\), there was no reason to try to fix the phase ambiguities, and therefore, a straight WLS could be used instead. This supported the statement that RTK typically does not work in harsh propagation conditions, at least for some of the signals.
* The impact of the two previous points can also be seen on the fixing success rate, which is shown in Figure 11. In the results presented in Figure 10, only when the success rate was equal to 100%, the RMSE\({}_{mixed}\) was on CRB\({}_{real/integer}\). When the noise increased and the fixing probability decreased, the RMSE\({}_{mixed}\) rapidly deviated from the optimal.  
Overall, because the goal was to maximize the operation region correctly exploiting the phase information, the best performance was provided by the E5 signal, for which the threshold was SNR\({}_{\text{out}}=16\) dB. Therefore, we could conclude that in the nominal RTK case, there was no sense in exploiting GNSS meta-signals (i.e., except E5). In addition, among the meta-signals, E5 + E6 and B2 + B 3 provided a clear advantage w.r.t. the other signal combinations.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 3 Proposed Classifier  
This section details the system model, the Gabor filter network, and the optimization of Gabor parameters and weights. First, the mathematical model of the proposed signal flow from the satellite to the ground station with and without the IRS is developed. This type of channel modeling for modulation classification purposes has yet to be reported in the literature. After channel modeling, the received signal is fed to the classifier structure; this research uses the Gabor filter network to extract distinct features. After successfully extracting the Gabor features, these features are optimized using the genetic algorithm (GA) and its hybrid local search variants, i.e., GA-Fminsearch, GA-Pattern Search, GA-Fminunc,and GA-Fmincon. This section discusses the communication model, the GFN, and the optimization of GFN parameters in detail.
## 2 Theoretical Background and Hypotheses Development
### Attitude Toward Using Technology (ATUIT)  
This construct has been linked to the intention of use in several studies [44; 55; 56]. In particular, attitude toward using technology is defined as \"an individual's overall affective reaction to using a system\" [43]. In the research context, attitude toward using technology refers to the predisposition of teachers to use IoT in their teaching activities. They were proposed as hypotheses:  
**Hypothesis 13 (H13):**: _Attitude toward using technology has a significant effect on behavioral intention to use IoT._  
**Hypothesis 14 (H14):**: _Gender is a factor that has a significant effect on attitude toward using technology of IoT._  
**Hypothesis 15 (H15):** _Age is a factor that has a significant effect on attitude toward using technology of IoT._
## 3 Results
### Relationship Between Phenolic Profile and Bioactivities of Extracts  
To assess the contribution of each quantified phenolic compound to the bioactivity of the extracts, a multivariate statistical analysis was performed. The burdock root extracts were categorized into groups based on similarities in extraction yield, TPC, antioxidant and anticholinergic capacities, and the phenolic composition identified by HPLC-DAD. Hierarchical Cluster Analysis (HCA) (Figure 3A) revealed the formation of two distinct clusters, where one cluster grouped the SPME extract (green type), while the SPLE and OLE extracts were classified into a second cluster (blue type).  
Figure 3: **(A)** Dendogram obtained by HCA using the Ward method and shorted by the height of TPC, antioxidant capacity (DPPH and ORAC methods), anticholinergic capacity (AChE and BChE assays), and the quantification of phenolic compounds by HPLC-DAD-IT-MS from OLE, SPLE, and SPME burdock root extracts. **(B)** Score plot resulting from a PCA of the different extracts correlated with TPC, antioxidant and anticholinergic values, and the amounts of each phenolic compound determined by HPLC-DAD-IT-MS. **(C)** Loading plot obtained from a PCA. Numbers correspond to the phenolic compound identified in Table 4.  
Furthermore, principal component analysis (PCA) was employed to identify the most influential variables, as determined by the analytical techniques applied in this study. The first principal component accounted for 83.57% of the total variance in the data. PCA grouped the extracts similarly to the HCA results (Figure 3B), with each group demonstrating a comparable bioactive phenolic profile. For example, group 1 (green type), corresponding to the SPME extract, was positioned in the quadrant associated with the highest TPC values in the PCA loading plot (overlapping Figure 3B,C).  
Moreover, the SPME extract exhibited the highest antioxidant capacity, as determined by both the DPPH and ORAC assays, as well as the greatest anticholinergic activity, assessed through the AChE and BChE assays. This extract was positioned in the opposite quadrant of the PCA loading plot, relative to the bioactivities, reflecting the inverse relationship between IC\({}_{50}\) values (with lower IC\({}_{50}\) indicating higher activity). In contrast, the OLE and SPLE extracts displayed the lowest bioactivity, as they were located further from the bioactivity axes in the PCA loading plot.  
The analysis of the phenolic compounds regarding bioactivity indicated that dicafeoyldimaloylquinic acid (compound **10**), dicaffeoylmaloylquinic acid 2 (compound **9**), and 1,5-dicaffeoylquinic acid (compound **8**) contributed most significantly to the high bioactivity of the SPME extract. These compounds were closest to the bioactivity metrics within the PCA loading plot. While the OLE extract contained the highest amount of 4-O-caffeoylquinic acid, this compound did not demonstrate higher bioactivity compared to the other phenolic compounds found in higher concentrations in the SPME extract.
## 2 System Design
### Power Consumption  
Power consumption is the most challenging aspect of the UWSN. Ultra-low power consumption will allow the sensor node to operate for an extended period (in excess of the shelf life of the battery)  
Figure 3: **(a)** Rear view of the sensor node; **(b)** Front view of the sensor node.  
of time without the need for battery replacement. Various parts of the UWSN can affect the power consumption. The environment in which the sensor nodes are being deployed also imposes restrictions on the design (_i.e._, lack of maintainability, lack of access to common power supplies, _etc._).  
Structural conditions of pipes change, in most cases very slowly so there is no need for a high sampling frequency; therefore these nodes will spend most of their time in sleep mode. This makes the sleep power consumption of the components very important [24]. Figure 4 shows a general schematic of the sensor node routine.  
During the sleep period the MCU of the sensor node cuts power to all components in order to minimise power consumption. Unnecessary internal functions of the MCU are also disabled during the sleep mode. The time the nodes spend in sleep mode is managed by a watchdog timer module in the MCU. This timer allows the MCU to keep time while it is in sleep mode and wakes up after a programmable interval. Figure 5 illustrates the power consumption profile of the sensor node.  
Figure 4: Schematic of the sensor node routine.  
Figure 5: Power consumption profile of the sensor node during one cycle at 2.5 V.  
The hardware and software design of the nodes allow it to operate at only 2.2 \(\mu\)W for one transmission per 6 h. This enables the node to have a theoretical lifetime of more than 100 years on the equivalent energy stored within two AA size lithium iron batteries. This indicates that the operating lifetime of the sensor nodes is limited by the lifetime of the batteries and not by its power consumption.
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 3 LightR: An \(N\times N\) Scalable and Fault-Tolerant WRONoC Topology
### Logic Scheme of HashR  
As shown in Figure 7, the HashR consists of eight PSEs configured on four wavelengths. For each communication from a master to a slave, the HashR reserves two independent signal paths, i.e., the signal paths are constructed with different routing resources. For example, Figure 7 shows two signal paths reserved for the communication from \(m_{1}\) to \(s_{4}\). Specifically, two signals on \(\lambda_{1}\) and \(\lambda_{2}\), represented by red and sky-blue lines, are coupled to the upper-left MRRs and reach \(s_{4}\). When an MRR is defective along a signal path, the master can still communicate with the slave using another signal path. For example, the top-left MRR, highlighted by a black dashed square in Figure 8a, is defective and resonant with \(\lambda_{3}\). The signals on \(\lambda_{1}\) and \(\lambda_{3}\) from \(m_{1}\) suffer the s-a-0 and the s-a-1 fault, respectively. Nevertheless, \(m_{1}\) can still communicate with \(s_{2}\) and \(s_{4}\) using the signals on \(\lambda_{4}\) and \(\lambda_{2}\), respectively, as shown in Figure 8b.  
The matrix in Figure 7 shows the wavelengths used by all communications. For example, \(m_{1}\) communicates with \(s_{2}\) using wavelengths \(\lambda_{3}\) and \(\lambda_{4}\). The signals from \(m_{1}\) follow the waveguide connected to \(s_{3}\) until it is coupled to the bottom-left MRRs as shown in Figure 7. If a master is directly connected to a slave by a waveguide, the wavelengths of the signal can be any wavelength except for the resonant wavelengths of all the MRRs along the path. For example, \(m_{1}\) communicates with \(s_{3}\) on wavelengths \(\lambda_{5}\) and \(\lambda_{6}\).  
The HashR supports 24 signal paths for the 12 communications among four nodes using only eight MRRs, which is the least possible MRR usage. Specifically, among the 24 signal paths, the HashR directly connects two nodes with waveguides and supports eight signal paths that do not rely on MRRs, such as the paths reserved for the signals on \(\lambda_{5}\) and \(\lambda_{6}\) from \(m_{1}\) to \(s_{3}\); moreover, for the 16 signal paths that rely on MRRs, the HashR uses only eight MRRs to form eight PSEs, each of which routes two signals, and thus reduces the MRR usage by half compared to the CSEs with two identical MRRs.
## 2 Clothing Components in Wearable Textile Antennas
### Discussion on Research Data of Wearable Textile Antennas Characteristics and Performance  
As can be seen from Tables 1-4, a considerable number of wearable textile antennas that use clothing components have been developed to enable wireless body-area applications. Various wireless standards such as WLAN, Wi-Fi, ISM, Bluetooth, UWB, WiGig, and X-band can support wearable applications. The proposed button antenna designs with monopole-like behavior present satisfactory radiation characteristics similar to a monopole antenna. Further, the button antennas with patch-like behavior can mostly achieve high radiation efficiencies. Adequate resonance and radiation performance are, also, provided by the snap-on antenna designs. It should be noted that the dimensions of the buttons and snap-on buttons WTA are within or lower than the expected conventional size (or equivalently electrical size) with a maximum length of 6 cm. The Velcro WTAs present sufficient performance properties with slightly increased dimensions compared with the button patch-like designs at similar operation frequencies (maximum dimension of 10 cm). The zip antennas present good monopole-like characteristics with a compact (5.7 cm) or increased length (38.5 cm) in the case of the handbag zipper. The impedance bandwidths obtained by the CC textile antennas can cover from narrow bands such as 90 MHz to increased frequency bands such as 300 MHz and 1000 MHz for increased data rates and 4-12 GHz for UWB and mm-Wave high data rates. All recorded bandwidths reveal the considerable use for 5G mm-wave and 5G sub-6 GHz communications. The achieved far-field gain values are compatible with the antenna type and the respective way of radiation. Still, it is observed that the gain is decreased compared to conventional (without CC) WTAs. This may occur due to inherent losses of the CCs (e.g., shape and materials). Generally, it has been shown that various antenna performance requirements can be covered by a CC WTA.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 3 Design of Multi-Robotic Cell Synchronization Algorithm
### Advanced Synchronization Algorithm  
If the master manipulator \(M_{I}\) (or slave manipulator \(MS_{i}\)) activity change has a global character (endpoint movement speed increasing/decreasing), then it is possible to indicate this change using variable \(D\). This variable represents the percentage proportion deviations among operation durations of master and slave manipulators, where \(D_{i,j}\in D=\left\{\begin{array}{ccc}D_{2,1}&\ldots&D_{2,m}\\ \ldots&\ldots&\ldots\\ D_{n,1}&\ldots&D_{n,m}\end{array}\right\}\) is calculated based on Equation (2).  
\[D_{i,j}=(1-(T_{i,j}/T_{1,j}))\times 100 \tag{2}\]  
The variable \(D\) is calculated in each step of the algorithm, but is only usable from the second production cycle. The first production cycle has diverse values of deviations \(D_{i,j}\), from first step of second cycle, \(D_{i,j}\) within defined tolerance represent synchronized state. Every change of \(D_{i,j}\) values exceeding the limit can be considered as a desynchronization indicator.  
If two (or another specified number \(d\)) differences \(D_{i,j\text{-}(d-1)}\)... \(D_{i,j}\) of consecutive operations \(O_{j\text{-}(d-1)}\)... \(O_{j}\) are sufficiently similar based on the similarity threshold \(D_{ratio\text{-}limit}\) (Equation (3))  
\[(|(1-(D_{i,j\text{-}i}/D_{i,j}))|\times 100<D_{ratio\text{-}limit})\wedge \ldots\wedge(|(1-(D_{i,j\text{-}(d-1)}/D_{i,j\text{-}(d-2)}))|\times 100<D_{ratio\text{-}limit}) \tag{3}\]  
and this difference exceeds the given threshold of change \(D_{limit}\) (Equation (4)),  
\[|D_{i,j}|>D_{limit} \tag{4}\]  
Figure 2: Basic algorithm for synchronization of robotic cell operations.  
then advanced synchronization coefficient \(K_{i}\) (Equation (5)) is applied to all other operation execution speeds \(V_{i,j}\), except the actual operation (which is already adjusted in this step) and previous \(d-1\) values (which were adjusted in previous \(d-1\) steps).  
\[K_{i}=1-\left(\left(\frac{1}{d}\sum_{z=0}^{d-1}D_{i,j-z}\right)/100\right) \tag{5}\]  
Both algorithms were designed for (theoretically) endless repetition of production cycle. The above-mentioned previous values of the current \(D_{i,j}\) in the initial steps of every cycle are based on an endless loop of variable \(j\). For example, in the case of 10 operations in one production cycle, after the initialization cycle, the previous two elements \(V_{2,j}\) (endpoint speed of slave manipulator \(M_{2}\)) to element \(V_{2,1}\) are \(V_{2,10}\) and \(V_{2,g}\).  
The synchronization algorithm with a feedforward reaction to the general change of the master manipulator \(M_{1}\) (or slave manipulators \(MS_{i}\)) endpoint movement speed is identical for every slave manipulator \(MS_{i}\) and is depicted in Figure 3.
## 3 Control System Architecture
### Control of the Solar PV Array  
To monitor the maximum power point in a solar PV array, the P&O technique is used. The duty ratio corresponding to the power output is adjusted in response to disturbances introduced into the system. The control mechanism continuously monitors and comparesthe PV array's power output to its preceding value. If the power increases with an increment in perturbation, the perturbation is increased further. Conversely, if the power decreases, the previous perturbation value is restored. This iterative procedure determines the optimal duty ratio for the SEPIC, ensuring the solar PV array operates at its maximum power point.  
While a smaller step size slows the tracking process, it improves accuracy and facilitates a smoother motor startup. However, the P&O technique has the drawback of exhibiting oscillations near the optimally stable operating region. To address this issue, the perturbation size is restricted to a maximum of 0.05 p.u. In this way, the tracking process is fast enough, and the amplitude of these oscillations is effectively reduced.