esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The tilt angle is directly correlated to the shift in the COG of the manipulator. A higher tilt angle equates to a more unstable system when attached to a drone. As seen in the results, as well as Figures 15 and 16, the CoG compensation system greatly reduced the tilt angle from a starting value of \(-\)14.4\({}^{\circ}\) to an average steady-state value of 1.28\({}^{\circ}\) when stabilized. As demonstrated in motion scenario II, the end-effector horizontal position was increased. This sent the tilt angle from \(-\)12.5\({}^{\circ}\) to \(-\)29.4\({}^{\circ}\) without compensation but remained close to \(-\)0.5\({}^{\circ}\) when using the CoG compensation system, which corresponds to a 98.3% improvement based on the tilt angle. The reverse motion of scenario II, which is scenario III, also showed an improvement of 91.08% in the steady-state tilt angle. This validates the effectiveness of the proposed method at enhancing the stabilization of an  
Figure 19: System tilt angle for motion from coordinates (0.28, \(-\)0.02) to (0.14, 0.15).  
Figure 20: System tilt angle for (**a**) compensated, (**b**) uncompensated systems at coordinates (0.14, 0.15).  
aerial manipulator undergoing different arm motions during operation with an average of 94.69% improvement, as expected. From the accelerometer data sheet, the sensitivity scale factor tolerance of the sensor is \(\pm\)3%. Since the sensor is only used to measure and verify the system's response, a very small error only influences the analysis of collected data. The MG995 TowerPro servo motors used in this system to actuate the manipulator links and the end effector rotate from 0\({}^{\circ}\) to 180\({}^{\circ}\). These servos are accurate to 1\({}^{\circ}\). Although this value is small, the error may be influenced by using multiple servos. Two servos are used on the links that affect the position of the manipulator. In turn, the error in the final position value that could be caused by the servos is doubled. The PID controller is used to account for any deviations and errors caused by the DC motor and the encoder. However, there is a small steady state error visible in the results as a result of assuming the belt as being completely rigid in the used setup and the used sensors. Even with the presence of these errors, the main test of stability lies within the tilt angle of the system. The tilt angle is directly correlated to the shift in the CoG; a higher tilt angle equates to a more unstable system when attached to a drone. As seen in the results, the CoG compensation system greatly reduced the tilt angle as expected, effectively stabilizing the manipulator's CoG.
# Galileo Receiver Performance Analysis with New I/NAV Improvements Live Data +
## 3 Results
### Secondary Synchronization Pattern Results  
Figure 4a,b show the results in the Open Sky environment. Looking at the 95 percentile value of the time needed for time convergence in Figure 4a, we see an improvement of 5 s (35.7%) using SSP, while Figure 4b shows the histogram where the distribution of the values is represented.  
Figure 4c,d show the results in an Urban environment. Looking at the 95 percentile value of the time needed to time convergence in Figure 4c, we see an improvement of 4.2 s (23.1%) using SSP, while Figure 4d shows the histogram where the distribution of the values is represented.  
Figure 3: Comparison of legacy I/NAV to RS (I/NAV 17–20) and RedCED (I/NAV 16) Data Demodulation: (**a**) Open Sky CDF of Data Demodulation and (**b**) Urban CDF of Data Demodulation.
## 4 Results and Discussion: Finite Element Validation
### FEA Evaluation Original Designs (Skewless Machines)
In turn, for the six-pole machine it was observed that the relevant torque ripple harmonic component with \(v=24\) was slightly reduced (to 87% of the original value), and that other relevant harmonics of order \(v=6\) and \(v=36\) were reduced to 77% and 14% of their original magnitude, respectively. This agrees with the estimated reduction factor proposed in Equation (10), since \(k_{\text{rs,6}}=0.7\), \(k_{\text{rs,24}}=1\) and \(k_{\text{rs,36}}=0\). In addition, the trend of other less relevant harmonic components of both machines matches closely with the estimations of Equation (10). These findings are summarized in Table 3.  
As an overall result of the discrete two-step skewing, the peak-to-peak value of the torque ripple was reduced. Table 4 summarizes the torque ripple as a percentage of the mean torque for the 2D and 3D simulation results, respectively. The torque ripple reduction is greater than 70% for the four-pole machine and up to 55% for the six-pole design, and an expected slight reduction of the average torque was also obtained. In this specific case, it can be observed that the 3D evaluation shows worse results than the 2D assessment, since in the 3D the ripple reduction is lower, and the mean torque reduction is increased, with respect to the 2D simulations. This can be ascribed to the fact that 2D simulations on ANSYS consider each slice of the machine as independent machines, when discrete skewing is applied, and the interface between slices is not taken into account, resulting in an idealization of the problem. On the other hand, the 3D simulation evaluates the rotor as a whole unit comprised of physically rotated slices, hence considering effects within the adjacent slices interface. This is further addressed in Section 4.4.
# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 4 Discovery and Recommendation Mechanism Based on Social Link
### RRDTL  
If the user's request conditions are too restrictive or the request statements are vague, \(L_{1}\) will be empty, then, CSDRM will recommend a candidate service set by performing RRDTL algorithm.  
```
Input:\(u_{m}\)'s Social Link \(SL_{m}\), user \(u_{m}\)'s request \(r\) Output: candidate service list \(L_{1}\)
1.if\(L_{1}=\phi\)do
2.for each \(u_{n}\in\)\(TL_{m}\)do// query the user collection in the current user's trust link
3.for each \(ws_{i}\in\)\(PL_{n}\)do// query the service collection in each user's preference link
4. calculate \(R_{i}=RSim_{i}\)\({}^{*}\)\((1+RD_{i})\);// calculate the reliability of each service
5.endfor
6. add \(ws_{i}\) which has the maximum \(R_{i}\) to \(L_{1}\);// select the most reliable service
7.endfor
8. rank services in \(L_{1}\) by \(R_{i}\) and remain Top-\(k\) services
9.return\(L_{1}\)
10.endif
```  
**Algorithm**RRDTL
Benchmarks in various fields, including energy, meteorology, and hydrology, have demonstrated that transformer-based models can achieve 10-30% lower forecasting errors compared to BiLSTM ensembles [27, 28]. Recently, these advancements have been applied to groundwater modeling, showing promising results in improving prediction accuracy and understanding aquifer dynamics [2]. Leveraging these advancements in deep learning models, we integrate small baseline subset (SBAS)-InSAR, multisource geoinformation, and station hydrographs in a unified attention-based framework with three objectives: (i) quantify historical groundwater depletion in the plain extending to Persepolis and Naqsh-e Rustam; (ii) benchmark various transformer-based models (e.g., Transformer, Informer, and ConvTransformer) against traditional models like LSTM, BiLSTM, and CNN-LSTM; and (iii) elucidate the drivers of aquifer stress through attention attribution. Our approach, detailed below, provides heritage managers with an actionable, transferable tool for proactive groundwater statewandship while enabling us to identify the most effective models for groundwater level prediction. The structure of this paper is as follows: Section 2 elaborately introduces the study area and its geological and hydrogeological characteristics. Section 3 describes the material, methodology, and proposed workflow used in this study. Section 4 presents the results and discussion, including InSAR analysis, deep learning model performance, temporal groundwater table variations, the validation process, and framework performance. Finally, Section 5 presents the conclusions.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 3 Enabling Technologies
### Network Function Virtualization (NFV)  
We have discussed the SDN architecture and presented a detailed review of its functionalities. Due to its limitations, it lacks the ability to effectively operate the network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SDN Controllers** & **Throughput (millions/sec)** & **Latency \(\upmu\)s** & **Architecture** \\ \hline Floodlight & 0.2 & 25 & Centralized \\ \hline Onix & 2.2 & 15 & Centralized \\ \hline ONOS & 1.2 & 50 & Distributed \\ \hline OpenDaylight (ODL) & 0.1 & 10 & Centralized \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of SDN controllers.  
Figure 3: ONF SDN network slicing architecture [15].  
slices and its respective resources [18]. In contrast, the NFV architecture helps to manages the resources more effectively [19]. NFV enables the virtualization attributes in order to remodel the network functions into virtual network functions (VNF), which can be combined with each other for different network services. NFV uses end nodes for the execution of virtualization to implement the network functions (e.g., VPN, content delivery network (CDN), and firewalls), and can be allocated dynamically. As compared to SDN, by using NFV, infrastructure resources can be effectively allocated to VNFs. It will allow the Internet service providers (ISPs) to instantiate new services according to the customer demands without changing the infrastructure hardware. Further, it will also cut short the operational expenditure (OPEX) and capital expenditure (CAPEX). It will provide flexibility and becomes easy to manage, if an ISP needs to update a function then configuration only be required at ISP end, also results in decreasing the implementation time. NFV provides quality of service (QoS) and serve the low latency requirements by separating the software entities from hardware. Decoupling offers flexibility since both can execute their functions separately and dynamically which allows ISPs to install more services using the same infrastructure resources. Conventional models of virtual machines (VMs) concepts do not meet the performance and high-availability requirements of the cloud environment [20].
## 3 Material Characterization Results  
### 3.1. Paint Characterization  
The paints were characterized regarding their consistency [23], specific gravity [24], and volume solids [25] prior to the application, and Table 2 presents the results.  
The consistency results in Table 2 show that paints B, E, F, and G were in accordance with the limits required, while paints A, C, and D were out of the range. Although they are considered inadequate for use based on the specification ABNT NBR 13699 [32], the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Limits *} & \multicolumn{6}{c}{**Paint**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**Test Site 1**} & \multicolumn{2}{c}{**Test Site 2**} & \multicolumn{3}{c}{**Test Site 3**} \\ \cline{3-8}  & & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline Consistency (KU) & 80 \(\leq\) KU \(\leq\) 95 & 97 & 89 & 96 & 101 & 92 & 89 & 90 \\ Specific gravity (g/cm\({}^{3}\)) & \(\geq\)1.59 & 1.74 & 1.70 & 1.75 & 1.71 & 1.70 & 1.70 & 1.70 \\ Volume solids (\%) & \(\geq\)62.0 & 63.8 & 64.2 & 62.0 & 60.5 & 66.0 & 65.0 & 65.0 \\ \hline \hline \end{tabular}
* Limits according to ABNT NBR 13699 [32].  
\end{table}
Table 2: Basic characterization of paints.  
Figure 4: Experimental matrix for test sites 1, 2, and 3.  
materials were used for research purposes and applied at the test sites to identify the impact of such characteristics on the paint performance. Regarding the specific gravity, all the paints were in accordance with the required value, which indicates that the paints presented adequate balance and formulation. Considering the volume solids, the parameter evaluates the percentage of the paint's volume without the volatile fraction, therefore representing the dried paint's thickness as a percentage of the wet thickness.
## 3 Results and Discussion
### Fourier Transform Infrared (IR) Absorption Spectroscopy  
FT-IR spectra are useful for evaluating the attributes of functional groups in foods, herbal medicines, or other products, and assessing the quality of the products. Therefore, FT-IR measurements of glucose, allose, and raffinose in Samples A, B, and C were conducted using their freeze-dried samples to comprehensively analyze the components contained in each rice wine (Figure 3). The following peaks were observed from the measurement of sugars alone: glucose: 3394, 3303, 1148, 1111, 1078, 1050, 1023, 997, 916, and 839 cm\({}^{-1}\); allose: 3489, 3379, 3338, 1168, 1123, 1094, 1081, 1031, 947, 896, and 885 cm\({}^{-1}\); and raffinose: 3943, 3294, 3220, 1148, 1094, 1075, 1049, 1031, 999, 966, 938, 875, 861, and 833 cm\({}^{-1}\), respectively. The peaks derived from the -OH group in Samples A, B, and C were identified at 3375 cm\({}^{-1}\), 3367 cm\({}^{-1}\), and 3376 cm\({}^{-1}\), respectively. The peaks derived from water in Samples A, B, and C were observed at 1634 cm\({}^{-1}\), 1626 cm\({}^{-1}\), and 1635 cm\({}^{-1}\), respectively. The peaks associated with the C-O-C of sugar for Samples A, B, and C were observed at 1048 cm\({}^{-1}\), 1047 cm\({}^{-1}\), and 1046 cm\({}^{-1}\), respectively. However, because the differences among the samples were difficult to distinguish visually, principal component analysis was performed using the IR spectra of Samples A, B, and C.  
Figure 3: FT-IR spectra of (**X**) (a) glucose, (b) allose, (c) raffinose, and (**Y**) (d) A, (e) B, (f) C.  
Figure 2: \({}^{1}\)H-NMR (D\({}_{2}\)O) spectra of Samples A, B, and C. (a) A, (b) B, (c) C.
# Metallic, 3D-Printed, K-Band-Stepped, Double-Ridged Square Horn Antennas
## 4 Conclusions  
We demonstrated the design and fabrication of metallic, 3D-printed, K-band-stepped, double-ridged square horn antennas in this paper. The antenna was printed by SLM technology using aluminum alloy and copper. Compared with the popular dielectric, 3D-printed corrugated horns, the metallic counterparts have process simplicity. The measured and simulated antenna performance agreed well. Future work lies in the integration of the antenna with other microwave passive devices into a radio frontend.  
This work was supported in part by the National Research Foundation National Additive Manufacturing Innovation Cluster under grant '3D printed 60 GHz horn antennas and arrays for next-generation 5G applications' of Singapore, in part by National Natural Science Foundation of China under the Grant No. 61401296, in part by the National Natural Science Foundation of China under grant 61701326 'Research on millimeter-wave multi-functional complex structure waveguide devices based on metallic 3D printing technology', and in part by 'the Fundamental Research Funds for the Central Universities' of China. The authors thank [PERSON], [PERSON], [PERSON], [PERSON] for the valuable discussions.  
[PERSON] is responsible for the antenna design, data processing and manuscript preparation. [PERSON] is responsible for the manuscript preparation. [PERSON] is responsible for antenna measurement. [PERSON] is responsible for antenna design.  
The authors declare no conflict of interest.
## 3 Methodology  
In conventional tuberculosis (TB) detection using chest X-ray images, the workflow is typically divided into two main stages: lung segmentation and TB manifestation prediction. Based on an extensive review of prior research, DLabv3+X has been identified as an effective technique for segmentation tasks, while an ensemble of Deep Convolutional Neural Networks (DCNNs), which integrates multiple neural network models, demonstrates superior predictive performance compared to individual models. Therefore, our algorithm used a cascade of DLabv3+X lung segmentation and an ensemble of DCNNs for TB diagnosis and localization. It has three main branches: lung segmentation, prediction of TB, and visualization and localization of TB lesions. A DLabv3+X-based segmentation branch was used as the basis for normalization and extraction of lung regions from the background. The DCNN ensemble branch aimed to predict the presence of TB and make a diagnosis decision. The visualization branch showed the location of possible diseased areas. A schematic diagram of our cascade algorithm is in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Non-TB** & **TB** & **File Type** & **Image Resolution (Pixels)** \\ \hline MC & 80 & 58 & PNG & 4892 \(\times\) 4020 or 4020 \(\times\) 4892 \\ SZ & 326 & 336 & PNG & 948–3001 \(\times\) 1130–3001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brief description of the datasets.  
Figure 2: Schematic diagram of our cascade deep learning model for TB detection and manifestation localization.
## 2 Hybrid System and Techno-Economic Analysis Method
### The Design of Hybrid System Components
#### 2.2.3 Electrolyzer  
An electrolyzer is an electrochemical device used to split water into hydrogen and oxygen through the electrolysis process. The power required for the electrolysis process comes from the excess power of renewable energy sources. The quantity of released gases depends on electrolyzer capacity and the amount of current passed through electrolyzer's cells [66]. The alkaline electrolyzer is selected to undertake this process due to its wide utilization and low cost among other electrolyzer technology [67].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PV Tracking System** & **\$/kWh** \\ \hline Horizontal single axis tracker (HSAT) & 880 \\ Vertical single axis tracker (VSAT) & 265 \\ Dual axis tracker (DAT) & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The economic cost of different PV tracking techniques [14].  
Figure 2: Schematic diagram of major four PV tracking techniques [56].  
The search space sizes of electrolyzer is considered in range 0-1400 kW, Homer seeks of optimal size in this range. The economic specification is addressed 1500 $/kW capital cost, 1000 $/kW replacement cost, and 20 $/year O and M cost. The life cycle 15 year is considered, and 85% operation efficiency [34].
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 3 Discussion
This strongly indicates that IPP, not detected in stem tissues, is the limiting factor under the conditions of our study. While the quantification of upstream isoprenoids in plant tissues is challenging and infrequently reported, metabolomics information holds promise to further reveal new insights, guiding future strategies in natural rubber bioengineering. In summary, downregulation of squalene synthase in guayule successfully increased natural rubber production, but not to the extent of squalene reduction on a carbon count basis. Isoprenoid pathways producing NR and squalene substrates were perturbed in quite unpredictable ways. Importantly, analysis of MVA and MEP pathway intermediates has provided the first evidence of cellular crosstalk between these essential metabolic pathways in guayule.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 6 Conclusions  
The paper describes four new mechanisms that allow synchronization in acoustic steganography systems. All of these methods have been tested against transparency, robustness, and data rate.  
The presented research results regarding the objective and subjective assessment of the quality of signals in relation to the developed methods of synchronization confirm the initial assumption that the use of hidden synchronization of acoustic signals will not significantly deteriorate the quality of the signal being the information carrier.  
The presented research results on steganographic transmission in real telecommunications channels allow us to conclude that the use of hidden synchronization of acoustic signals increases the efficiency of steganographic data transmission in a telecommunications channel with signal degrading factors.  
Machine learning algorithms can help increase the effectiveness of acoustic synchronization mechanisms. These algorithms build a mathematical model from sample data, called the training set. Machine learning that may prove helpful in the synchronization recovery process include the following methods: Decision Tree Learning for acquiring knowledge based on examples with numerous variants, Bayesian Learning as a probabilistic inference and Instance-based Learning method for modelling the synchronization procedure based on previous sample solutions. There are known methods of synchronization recovery for Forward Error Correction enabled channel [74] and the solution of the problem of network time synchronization [75] with the use of machine learning. Further work on the synchronization in acoustic steganographic channels should also cover the implementation of machine learning algorithms.  
Conceptualization, [PERSON] and Z.P.; methodology, [PERSON] and Z.P.; software, J.W.; validation, Z.P.; formal analysis, [PERSON] and Z.P.; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, J.W.; writing--review and editing, [PERSON] and Z.P.; visualization, [PERSON]; supervision, Z.P.; project administration, Z.P.; funding acquisition: Z.P. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Centre for Research and Development, grant number CYBERSECIDENT/381319/II/NCBR/2018 on \"The federal cyberspace threat detection and response system\" (acronym DET-RES) as part of the second competition of the CyberSecIdent Research and Development Program--Cybersecurity and e-Identity.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to project restrictions.  
The authors would like to express their great appreciation to the Sensors journal editors and anonymous reviewers for their valuable suggestions, which have improved the manuscript quality.  
The authors declare no conflict of interest.
## 2 Experimental Section
### Verifying the Antenna Range  
To verify the quality and robustness of the wireless connection of the presented solution, we monitored the Received Signal Strength Indicator (RSSI) metric of the uplink channel, i.e., of the packages from the sensor nodes to the cell tower, over the course of a month while the nodes were embedded on the rebar in the concrete columns. During this time, the concrete was poured, the concrete dried, and the structure was demolded and moved. This highly variable environment for the node displays the robustness of the communication link. The chip for the communication used is Quectel BG95-M6, using the Proximus network in Ghent, Belgium.