esa-sceva/satcom-chunk-collection huggingface dataset:
# A Context-Aware Conversational Agent in the Rehabilitation Domain
## 8 Testing Settings and Usage Scenarios
### Preliminary User-Centred Evaluation  
The objective of the preliminary user-centred evaluation is to obtain early feedback from end-users of the system to fuel the development before the actual deployment in real environments. While the system currently is not considered fully operational, a compromised pre-alpha version with specific functionalities is available for testing and evaluation in controlled laboratory settings. The operational modules only provide limited options, which restricts significantly the potential outcome; however, the provided feedback is enough to influence the platform's direction.  
As described also in Section 3.2, the modules have been integrated in the REA framework; however, they are not yet part of the pilots. Instead, internal IT and end-users were invited to test the current implementation through guided conversations with the system in accordance with Good Clinical Practice (GCP). The process took about 15 minutes per participant (6 participants in total) that filled in a five-point scale questionnaire (1--completely agree, 5--completely disagree) with assistance of personnel. The questionnaire was compiled and underwent two review processes within the consortium. Sample questions are depicted in Table 2.  
Due to the limited number of participants, the results may not be considered as representative and must be interpreted carefully. However, the questionnaires revealed the following critical aspects:  
* The response of the system is too often \"Can you repeat the question?\" The topic detection task depends solely on the results coming from speech and language analysis, which are used by the underlying reasoner to classify utterance contexts in the topic hierarchy. The current implementation is not able to handle missing information and uncertainty. Therefore, the absence of a term from the input hampers the detection of the correct topic.
* It takes too long to provide a response. The average response time of the framework was 2.4 s that, based on users' feedback, needs to be further improved. Although ontological reasoning imposes some inherent scalability issues, we plan to investigate more scalable reasoning schemes, such as to reduce, if possible, the expressivity of the models.  
On the positive side, users highlighted the fact that the framework addresses in principle the information requests of the users and that no contradictions are returned, taking into account the profile information that the system has been initialized with.
## 3 Results and Discussion
### Influence of Electromechanical Coupling
The results showed that the critical values of the electromechanical couplings for foldover reduction were \(\alpha_{2 CR}=12\) N/A and \(\alpha_{1 CR}=4\) N/A. It should be noted that the foldover effect is more sensitive to the \(\alpha_{1}\) parameter, which is in agreement with the resonance curves in Figure 11.  
Figure 12b shows the two-parameter continuation of the foldover effect occurring in the main resonance. The continuation began at the point where \(\alpha_{1}=\alpha_{2}=30\) N/A. For this case, both electromechanical couplings affect the foldover region in the same way. Interestingly, for some frequencies, the foldover regions overlap. This means that both couplings can be used to control the resonance curve bend. Figure 11: Influence of \(\alpha_{1}\) on the resonance curves of a bottom magnet (**a**), top magnet (**b**), and induced current (**c**), for \(\alpha_{2}=30\) N/A and \(A=0.6\)\(g\). The results of the numerical simulations indicate that the electromechanical coupling parameters \(\alpha_{1}\) and \(\alpha_{2}\) have a significant impact on the harvester dynamics and the recovered current. In addition to this, the electromechanical coupling parameters can be used for multistability control.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 2 Preliminaries
### Ensemble Learning  
Ensemble learning is growing in popularity due to its variability and efficiency [8, 9]. Ensemble learning is composed of multiple weaker models that are independently trained; then, the collective prediction will be more accurate. The ensemble approach can simultaneously consist of several learners applying unsupervised algorithms, supervised algorithms, and so forth, in distinct ways, e.g., sequence, parallel, and mixture. Figure 1 shows a simple instance of ensemble learning with the parallel type.  
Figure 1: An example of ensemble learning with parallel type.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 5 Conclusions  
In this paper, a novel HABO-OFDM scheme was proposed for VLC to achieve the high spectrum efficiency and power efficiency. In terms of 256 QAM-16 PAM, we saw about a 16.6% enhancement in spectrum efficiency compared with the other two schemes. Compared with the conventional superimposed systems, the complexity of the HABO-OFDM receiver was significantly reduced due to the introduction of adaptive bias, which not only had no interference to the transmitted signal but also guaranteed the non-negativity of the signal. Specifically, the complexity of the HABO-OFDM receiver was reduced by \(N\log_{2}N-3N+4\) and \(2N\log_{2}N\) compared to HACO-OFDM and PHO-OFDM. Meanwhile, HABO-OFDM had a superior PAPR performance in comparison with HACO-OFDM and ABO-OFDM. There was about a 3.2 dB reduction and 1.7 dB reduction in PAPR compared with HACO-OFDM and ABO-OFDM. Additionally, HABO-OFDM also maintained a better power efficiency performance than HACO-OFDM and ABO-OFDM in a wide range of the Bit rate/Normalized bandwidth. However, in the proposed scheme, the real parts of some subcarriers were still not used. We will continue to exploit the reserved parts in future researches. Furthermore, the proposed HABO-OFDM scheme can be applied in high-speed VLC in future due to its high spectral efficiency, low receiver complexity, low PAPR, and high power efficiency.  
Conceptualization, H.H.; methodology, H.H.; software, H.H.; validation, H.H., Z.L.; formal analysis, H.H.; investigation, H.H.; resources, H.H.; data curation, H.H.; writing--original draft preparation, H.H.; writing--review and editing, Z.L.; visualization, H.H.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant 61571108; in part by the Wuxi Science and Technology Development Fund(No.H20191001, No.G20192010); in part by the Natural Science Foundation of Jiangsu Province under Grants BK20190582.  
Not applicable.  
Figure 8: \(\langle E_{b(dec)}/N_{0}\rangle_{BER}\) required for the BER target of \(10^{-3}\) versus Bit rate/Normalized bandwidth.
## 2 Materials and Methods
### Image Processing Module
#### 2.2.2 Image Processing Module Block Diagram  
The image processing module general block diagram is presented in Figure 3. The function of this module is to identify clear sky regions, vegetation regions and to generate a LoS probability map for each image acquired. The first step of the algorithm which implements the image processing module is the segmentation described in the previous sub-section. Using edges and curvature information of the image edges, the vegetation map is constructed as well. Both these maps, sky and vegetation, are then fused to produce the intermediary probability map, using the three-levels discretization, which represents the fourth step of the camera aiding algorithm presented at the end of the Introduction. The final sky probability map is obtained after a refinement that takes into account the position uncertainty of the satellites in the image. Basically, a low-pass filter is used in order to blur the probabilities around the vegetation-related edges and also for the buildings, but to a smaller degree.  
Figure 3: Block diagram of image processing module and an example of results.  
Next, the fifth step of the camera-aided algorithm is realized. The remaining GNSS data go through the Kalman filter correction step, which provides the final PVT solution.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
## 3 Numerical Results and Discussions
### A Cracked Plate with Four Holes  
As depicted in Figure 10, we consider a square plate that has a single edge crack of 6 mm and four holes with identical radii of 5 mm each. The dimensions of the plate are 100 mm on each side and 10 mm in depth. The top and bottom edges are subjected to a uniformly distributed stress of 10 MPa. The properties of the plate, which is made of the aluminum alloy 7075-T6, are modulus of elasticity, \(E=72\) GPa, Poisson's ratio, \(v=0.33\), yield strength, \(\sigma_{y}=469\) MPa, ultimate strength, \(\sigma_{u}=538\) MPa, and fracture toughness, \(K_{IC}=3288.76\) MPa \(\sqrt{mm}\).  
Figure 9: Total deformation of the last step of crack propagation (**a**) present study, (**b**) DBEM by [44].  
The predicted crack growth trajectory (Figure 11a) was in agreement with a variety of studies that used other ways of modeling, such as the fast multipole method (FMM) with the boundary element method (BEM) [46] (Figure 11b), the boundary cracklet method [47] (Figure 11c), and improved smoothed particle hydrodynamics [48] (Figure 11d). As seen in Figure 11d, there was a little divergence in the crack trajectory that was observed by [PERSON] et al. [48] when compared to the path that was predicted by the developed program and the two crack propagation paths that were addressed by [46; 47].  
Figure 11 shows that, at first, the crack moves straight forward until it nears the first upper hole. At that point, the crack starts moving toward the hole. Subsequently, the crack is diverted in a different direction due to the fact that the first upper hole is not located in sufficient proximity to draw the crack into it. The crack eventually keeps moving forward in a straight line until it comes near enough to the second upper hole to sink into that hole. Figures 12 and 13 show that the estimated values of the mixed-mode SIFs, \(K_{I}\) and \(K_{II}\), have great agreement with the expected values achieved using the Ansys program [6]. The developed program results were nearly identical to the Ansys results, with a 0.4-5% deviation. Figures 12 and 13 illustrate very clearly how the effect of the first upper hole on the crack growth path was represented in the curvature that was seen after 20 mm and how it disappeared after passing from the influence of this hole. This effect can be seen in the curvature that was seen after 20 mm in both figures.  
Figure 11: Crack propagation paths: (**a**) present study, (**b**) [PERSON] et al. FMM BEM [46], (**c**) [PERSON] et al. [47], and (**d**) [PERSON] et al. [48].  
Figure 10: Geometrical dimensions (**left**) and its initial adaptive mesh (**right**).
## 2 Test Apparatus  
The apparatus that was used for the tensile tests is schematically shown in Figure 1; it is comprised of two parts: a direct tension testing system and an image capture system. As is illustrated in Figure 2, the tension testing system adopts an '8' shaped split mold, which is specially designed to reduce the central cross-sectional area of the soil specimen in order that the tensile fracture can occur at its middle part. Similar shapes of mold have been applied in [33; 34] and they have been shown to enable the tensile failure to take place at the center neck section. During the test, the mold with the prepared soil specimen was placed upright on the platform where one half of the mold was clamped to the fixed end of the platform through an S-type load cell while the other half was connected to a movable frame that was driven by an electric motor to pull the specimen forward in the horizontal direction with a given displacement rate (ranging from 0.01 mm/min to 10 mm/min). A linear sliding roller was fabricated and placed between the mold and the base plate of the platform in order to minimize the mechanical friction. The S-type load cell that was screwed into the immovable half of the mold was employed in order to measure the tensile force and it had a range of 0-500 N with a 0.02% full scale (FS) accuracy. The tensile displacement was recorded by a LVDT (with a capacity of 20 mm and accuracy of 0.1%FS) that was attached to the movable half of the mold, as is indicated on Figure 2. Prior to the test, the connections between all of these components were carefully examined and adjusted so as to ensure their tightness. Therefore, it is reasonable to envisage that the specimen deformed synchronously with the loading and measuring devices.  
Figure 1: The apparatus developed for tensile tests.  
The image capture system involved a high-resolution (16.1 megapixel) camera that was placed right in front of the tested specimen and photographic luminaire equipment that was used to create even and diffused light in order to enhance the quality of the photography. The tensile deformation and failure process of the soil specimen was automatically recorded by the camera which was set to capture images at certain intervals. These images were then used for PIV analysis through the use of an open software named Geo-PIV so as to obtain the information regarding the tensile strain and displacement within the region of interest (ROI) on the specimen's surface.
# Folded Heterogeneous Silicon and Lithium Niobate Mach-Zehnder Modulators with Low Drive Voltage
###### Abstract  
Optical modulators were, are, and will continue to be the underpinning devices for optical transceivers at all levels of the optical networks. Recently, heterogeneously integrated silicon and lithium niobate (Si/LN) optical modulators have demonstrated attractive overall performance in terms of optical loss, drive voltage, and modulation bandwidth. However, due to the moderate Pockels coefficient of lithium niobate, the device length of the Si/LN modulator is still relatively long for low-drive-voltage operation. Here, we report a folded Si/LN Mach-Zehnder modulator consisting of meandering optical waveguides and meandering microwave transmission lines, whose device length is approximately two-fifths of the unfolded counterpart while maintaining the overall performance. The present devices feature a low half-wave voltage of 1.24 V, support data rates up to 128 gigabits per second, and show a device length of less than 9 mm.  
integrated photonics; electro-optical modulator; thin-film lithium niobate; optical communication +
Footnote †: journal: mricomachines
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Canopy G\({}_{\rm SFC}\) and G\({}_{\rm S}\)  
The measurement of canopy ET in controlled environments makes it possible for calculating a \"big-leaf\" surface canopy conductance (G\({}_{\rm SFC}\)) with a corresponding effective VPD at the \"big-leaf\" surface (D\({}_{\rm S}\)). Surface G\({}_{\rm SFC}\) was calculated from the ratio of E\({}_{\rm can}\) to D\({}_{\rm S}\) (Equation (8)):\[\mathrm{G_{SFC}}=\mathrm{E_{can}}\times\mathrm{P_{atm}}/\mathrm{D_{S}} \tag{8}\]  
where \(\mathrm{G_{SFC}}=\mathrm{canopy}\) surface conductance, \(\mathrm{E_{can}}=\mathrm{canopy}\) transpiration measured using the gas exchange system (mmol m\({}^{-2}\) s\({}^{-1}\)), and \(\mathrm{P_{Atm}}=\mathrm{atmospheric}\) pressure. \(\mathrm{D_{Bulk}}\) was calculated using \(\mathrm{T_{Air}}\) measured at the reference location above the canopy. \(\mathrm{D_{Aero}}\) is the VPD of the air within the canopy at \(\mathrm{T_{Aero}}\). When \(\mathrm{D_{S}}=\mathrm{D_{Aero}}\) in Equation (8), each leaf surface is at the mean aerodynamic temperature and sees the same saturation deficit at its surface, which treats the canopy as a giant single leaf where the average canopy leaf temperature equals \(\mathrm{T_{Aero}}\).  
Canopy \(\mathrm{G_{SFC}}\) calculated from Equation (8) includes canopy \(\mathrm{G_{S}}\) and \(\mathrm{g_{A}}\)[10] because these conductances are additive in series. Canopy \(\mathrm{G_{S}}\) was calculated from surface \(\mathrm{G_{SFC}}\) and \(\mathrm{g_{A}}\) using Equation (9), the resistance subtraction method [7]. \(\mathrm{G_{S}}\) is metabolically controlled canopy stomatal conductance that influences land atmosphere interactions via land surface feedbacks.  
\[\mathrm{Gs}=\mathrm{G_{SFC}}\times\mathrm{g_{A}}/(\mathrm{g_{A}}-\mathrm{G_{SFC }})=((1/\mathrm{G_{SFC}})-(1/\mathrm{g_{A}}))^{-1}. \tag{9}\]
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 2 Background and Related Works  
Recently, several studies have shed light on fairness issues [21], including unfairness in TCP and extreme unfairness, also known as starvation. In [18], the authors presented experimental evidence demonstrating that achieving equal bandwidth distribution among network users is challenging. Instead, users often experience unfairness or a low share of available bandwidth, with at least one user receiving extremely low or zero bandwidth. This phenomenon, known as \"TCP Starvation\", is evident not only in delay-bounded congestion control algorithms but also in loss-based CCAs.  
CCAs are crucial in network protocols, influencing data transmission performance and fairness. Traditionally, loss-based congestion control algorithms such as NewReno, CUBIC, and Compound TCP have been widely used. These algorithms adjust the congestion window size in response to packet loss, signaling network congestion. However, their reactive nature often leads to suboptimal performance as delay increases, posing challenges to maintaining fairness among flows [25].  
In recent years, delay-bounding congestion control algorithms have garnered attention for their ability to control network traffic delay. Unlike their loss-based counterparts, delay-bounding algorithms prioritize delay as the primary congestion indicator. This approach is particularly advantageous for real-time and interactive applications requiring low latency. Notable delay-bounding algorithms include Vegas, FAST, BBR, PCC, and Copa, each with distinct design principles and mechanisms for delay control.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Application Layer  
Although CS was developed as a sensing/compression paradigm, it can be extended to other domains such as signal detection and sparse problem optimization. Thus, for IoT platform scenario, the application layer relies on the data routed from the local processing layer (edge computing platform) to provide both object-based and event-based data analytics, such as data aggregation, classification, active detection, signal identification, smart cities application, cloud status monitoring and even CS-based recovery.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 5 Discussion
### Antenna Requirements for Soil Moisture Retrievals Using Polarimetric GNSS-R  
To summarize the findings in Section 4.4, combined RHCP-LHCP measurements provide better performance for soil moisture retrievals. Moreover, the use of VOD as a proxy to estimate \(Q_{LR}\) provides the combination of RHCP and LHCP signals with the best soil moisture estimation, where only H-V antennas can provide similar errors at \(\theta=40^{\circ}\). However, as it is discussed in [51; 53], the reflectivity at RHCP at incidences angles lower than \(55^{\circ}\) is 15-25 dB lower than the LHCP reflectivity. At \(\theta=40^{\circ}\), this difference is \(\sim\)10 dB for dry soils, and more than 15 dB for 0.2 cm\({}^{3}\)/cm\({}^{3}\) or higher SM, masking the RHCPsignal below the receivers' noise floor. In such cases, the H/V component shows similar performances as the L/R component for soil moisture retrieval.  
As shown in Figure 8, at lower angles (30\({}^{\circ}\) and smaller), the RHCP signal suffers attenuations up to 25 dB larger than the LHCP signals for high soil moisture values. For current antenna configurations of four or six patches (e.g., CYGNSS [65] or FSSCat [9], providing \(\sim\)12 dB gain and \(\sim\)40\({}^{\circ}\) half-power beam-width), such large attenuations may cause the GNSS-R signal to be well below the receiver's noise floor. As an example, current LHCP observations over land show SNRs between 0 and 15 dB (avg. \(\sim\)5 dB and std. \(\sim\)5 dB [66]).  
At higher angles (50\({}^{\circ}\) and higher), where both RHCP and LHCP are on the same range, the vegetation attenuation due to the high incidence angle (e.g., Equation (1)) can be large enough to destroy both L and R components of the signal.  
Hence, following the model presented in Figure 8, not many reflections collected by an RHCP antenna would be above the receiver's noise floor. One way to mitigate this issue is by using H and V polarized antennas, with higher SNRs than the RHCP, and computing the Stokes parameters of the received signal as shown in this manuscript and further detailed in [18]. The estimated synthetic LHCP/RHCP ratio would be as good as the 50 SNR, meaning that no RHCP component could be retrieved if the total H and V SNR are too low. Even though, one could still perform polarimetric retrievals using H and V components even at low SNRs, and use the four polarization (H/V and synthetic L/R) when the S\({}_{0}\) SNR is high enough.
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
## 5 Conclusions  
A triple-band stacked patch antenna, operating at DSRC, 5G, and 6G frequency bands, was designed, simulated, and fabricated. The simulation results show that the \(-10\) dB impedance bandwidth is 0.11 GHz (5.85-5.96 GHz) for the lowest DSRC frequency band, 1.17 GHz (27.62-28.79 GHz) for the middle 5G frequency band, and 14.81 GHz (131.91-137.56 GHz, 140.53-146.87 GHz, and 153.06-155.88 GHz) for the highest 6G frequency band. At their corresponding frequency bands, the simulated antenna realized peak gain at the boresight is 6.87 dBi, 12.3 dBi, and 19.8 dBi. These simulated peak gains at DSRC and 5G frequency bands agree well with the measured results. Therefore, the proposed antenna applies to DSRC, 5G, and 6G antennas for future telematics applications.