esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Sum-Channel Radiating Element for a Patch-Monopole Monopulse Feed
## 3 Results
### Radiation Parameters  
The total sum-channel directivity of the proposed design at the center frequency of 2.21 GHz is simulated to be 8.9 dBi. Indoor laboratory measurements in a semi-anechoic space confirmed the simulation results within 0.4 dB of the difference in both radiation-pattern cross-sections (cuts), even with the nonideal balun. The radiation-pattern cuts of the sum channel are shown in Figure 7 and those of the difference channel are shown in Figure 8. The horizontal cut plane (mark \(H\)) is parallel to the line between the patch feed points, and the vertical cut plane (mark \(V\)) is perpendicular to them.  
The patch has a bandpass frequency characteristic. The axial ratio is 0.1 dB at the center frequency, where the circular polarization is optimal, and the 3 dB bandwidth of the axial ratio is 32 MHz. The total directivity and the LHCP gain frequency characteristics are shown in Figure 9. As explained in Section 2, the radiation efficiency seems to be in the 80% to 90% range, resulting in a center-frequency LHCP realized gain value of around 8 dBi. The peak aperture illumination efficiency is calculated to be 75% at an \(\frac{\mathrm{F}}{\mathrm{D}}\) ratio of 0.4. The difference radiation pattern, shown in Figure 8, exhibits a strong dip, suitable for tracking operation. A quick test of the monopulse operation of the feed with a transmitting circularly-polarized antenna and a vector voltmeter at the receiving side showed that the feed is working as expected.  
The rotational symmetry of the sum-channel pattern at the reflector's edge is close to 0 dB according to the simulation results. The measurement results agree well, considering a semi-anechoic-only measurement space. The difference in both points of a cut intersecting the reflector's edge is 0.4 dB for both cuts. The values between both cuts differ by 1.3 dB, and, on average, the measurements correspond well to the simulation results at the reflector's edge. Backside radiation results are not accurate due to suboptimal measurement space and the effect of the mounting structure.  
Figure 6: Crosstalk frequency characteristic measurement between the channels.  
Figure 8: Radiation-pattern simulation and measurement results of the difference channel.  
Figure 7: Radiation-pattern simulation and measurement results of the sum channel.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 2 Figures of Merit for Photodetectors  
A photodetector is a device capable of converting light signals into electrical signals. In order to assess the performance of a photodetector, several key parameters will be introduced in this section.
## 4 Experimental Validation
### Results and Discussion  
Since the true values of the coordinates of the target points to be measured are unknown, the standard deviation of the distance among the target points is used to provide a preliminary evaluation of the accuracy of the measurement control network. By verifying the repeatability precision of the laser tracker within a range of 36 m, the distance standard deviation can be 0.01 mm. According to the principle of slight error, its accuracy can be used to evaluate the distance measurement accuracy of the method proposed in this article. In addition, due to the large amount of data, partial measurement results of target points on the rocket shed test track and distance benchmark measurements by the tracker are listed in Table 2.  
The distance benchmark values compared to the adjusted distances of the target points are shown in Figure 15. We can see that the maximum absolute distance deviation of the target point measured by our method is 0.98 mm, ranging from \(-0.85\) mm to 0.98 mm. The average absolute distance deviation is 0.10 mm, and the standard deviation of distance deviation is 0.20 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name of** & \multicolumn{3}{c}{**Adjustment Value (mm)**} & \multicolumn{3}{c}{**Benchmark Value (mm)**} \\
**Target Points** & **X** & **Y** & **Z** & **D** & **X** & **Y** & **Z** & **D** \\ \hline T418-5 & 301,016.547 & 20,846.382 & \(-154.961\) & & & & & & \\ T416-5 & 271,071.847 & 18,853.947 & \(-217.947\) & & & & & & \\ T414-5 & 241,130.805 & 16,861.628 & \(-276.460\) & & & & & & \\ T412-5 & 211,194.338 & 14,869.440 & \(-338.217\) & & & & & & \\ T410-5 & 181,269.500 & 12,880.078 & \(-399.741\) & & & & & & \\ T408-5 & 151,330.618 & 10,888.560 & \(-464.993\) & & & & & & \\ T406-5 & 121,396.550 & 8893.324 & \(-532.379\) & & & & & & \\ T404-5 & 91,468.443 & 6901.757 & \(-591.386\) & & & & & & \\ T404-3 & 85,497.167 & 6507.226 & \(-613.216\) & & & & & & \\ T402-3 & 55,553.523 & 4516.372 & \(-676.373\) & & & & & & \\ T398-2 & \(-7328.702\) & 329.941 & \(-800.793\) & & & & & & \\ T396-2 & \(-37,248.397\) & \(-1657.261\) & \(-856.687\) & & & & & & \\ T394-3 & \(-64,192.461\) & \(-347.315\) & \(-914.768\) & & & & & & \\ T392-3 & \(-94,123.554\) & \(-5439.251\) & \(-983.907\) & & & & & & \\ T392-5 & \(-88,140.887\) & \(-5043.801\) & \(-963.617\) & & & & & & \\ T390-5 & \(-118,065.474\) & \(-7030.229\) & \(-1020.486\) & & & & & & \\ T388-4 & \(-151,000.444\) & \(-9223.686\) & \(-1092.545\) & & & & & & \\ T386-4 & \(-180,922.572\) & \(-11,211.152\) & \(-1151.979\) & & & & & & \\ T382-3 & \(-243,787.618\) & \(-15,394.827\) & \(-1269.284\) & & & & & & \\ T380-3 & \(-273,711.745\) & \(-17,388.306\) & \(-1319.127\) & & & & & & \\ T380-1 & \(-279,692.691\) & \(-17,785.630\) & \(-1326.242\) & & & & & & \\ T378-1 & \(-309,656.296\) & \(-19,778.164\) & \(-1379.070\) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinates of partial target points after length–angle mixed intersection adjustment.
## 4 Experimental Results
### Validation Using Direct Leveling Based Subsidence Observations
A scatter plot of the prediction error versus the distance from the ground point to the starting point of the corresponding observation line is shown in Figure 14. Table 4 shows the mean error, the error STD and the RMSE of the proposed method-based subsidence predictions. It can be seen from Figure 14 and Table 4 that there exists a significantly positive mean  
Figure 12: Mining area and the location of ground observation lines. Figure 13: Fourth-order direct leveling-based subsidence observations for the observation line of A, B and C.  
error, indicating that the direct leveling-based subspaces are greater than the predicted ones. This is probably because the ground subsidence induced a decline of the water table, and thus produced an additional subsidence in the ground [36]. The average of the absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum of the absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation. Basically, the results show a good agreement between the proposed method-based subsidence predictions and the direct leveling-based subsidence observations.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 4 Simulation Results  
### Simulation Scenario  
This section discusses the ranging performance of proposed time delay estimation algorithm in NLOS channel model. In order to evaluate the performance of this method accurately, we adopted the 5G channel model developed by 3 GPP TR38.901 [34] for simulation experiments. Two of the six types of UMi (urban micro) and UMa (urban macro) specified in the selection standard of experimental scenes, in which UMi scene means the base stations are mounted below rooftop levels of surrounding buildings and Uma scene means the base stations are mounted above rooftop levels of surrounding buildings. These two scenarios are used as the baseline for 5G positioning performance evaluation in 3 GPP [35]and can fully represent the channels in urban canyon. The basic parameters for both scenarios are shown in Table 1. All simulations in this paper assume that TX and RX are rigorously time-synchronized so that errors due to time synchronization are not added when validating delay estimation errors. If the simulations do not assume that Tx and Rx are rigorously time-synchronized, then it is impossible to know whether the time delay error is caused by NLOS or by time synchronization. In practice, the errors due to time synchronization between the vehicle and the base station can be ignored by RTT or TDOA methods.  
In the simulation, the base station layout of cellular network is adopted. In both scenarios, seven base stations were arranged, and 60 UE were distributed randomly within the coverage area of seven base stations. The distribution of base station and UE is shown in Figures 4 and 5.  
In this paper the parameters of the simulation system are shown in Table 2, subcarrier spacing, transmission power, noise coefficient used the parameters specified in the TR38.855 Settings. The carrier frequency is 3.5 GHz due to the spectrum near the 3.5 GHz is commonly adopted by telecommunications operators, and 20 MHz, 50 MHz, 100 MHz are adopted as bandwidth. Moreover, 1, 2, and 3 times of cancellations are used for comparison. Finally, NC-MUSIC is compared with the conventional method.  
Figure 5: The distribution of base station and UE in UMa scenario.  
Figure 6: Channel model generation process in TR38.901.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 5 Discussion
### Limitations of Current Research  
Although there have been great advances in multimodal interaction technology for remote education, there are still several limitations in existing studies, as follows:  
* Diversity and Representativeness of Data Existing multimodal learning datasets are mostly acquired in controlled experimental environments, while there is no adequately large-scale data available from authentic classroom settings. This limitation restricts the external validity and generalizability of research findings. [PERSON] et al. [64] emphasized the importance of capturing diverse datasets in real-world educational settings to advance the relevance of multimodal learning studies.
* Exploiting the Gap Between Experimental Settings and Practical Utilization Although randomized experiments are used to measure a wide variety of interventions to improve student learning, these interventions are often implemented in highly controlled laboratory environments that do not reflect the complexity of real educational settings. This divergence can lead to experimental results that do not translate well to the field. [PERSON] et al. [78] emphasized the need to validate multimodal interaction systems in real educational scenarios to ensure their real-world effectiveness and scalability.
* Assessment of Long-Term Learning Outcomes Much of the existing research focuses on short-term experimental studies, and few studies have conducted rigorous longitudinal investigations covering the sustained effect of multimodal interaction technology. Future studies should include long-term follow-up investigations of its impact on the ongoing cognitive and behavioral development of learners.  
Table 3 compares the limitations of existing studies.  
Most studies were conducted in labs or controlled environments. Only a few were conducted in real classrooms or remote learning settings. Many studies used speech or gesture as the main interaction methods. Some used sensors like EEG or eye tracking. These systems often focused on college students. Fewer studies looked at younger learners or people with special needs.  
Many systems did not give real-time feedback. Some had small sample sizes. Others used short-term tests. Most studies did not include long-term data or results from large user groups.  
Because most research was carried out in ideal conditions, the findings might not match those from real-world classrooms. The systems might work differently when students are at home or using their own devices. Some studies did not talk about cost or technical limitations.
## 5 Conclusions  
Major challenges in scaling up MFCs are to reduce the time it takes to build an efficient electroactive biofilm on an anode and to better understand and control the biological system. Usually, once the inoculation of the system has started, a waiting period of at least three weeks is required prior to achieving a stable and active biofilm. Since biofilm development is based on the microbial community composition of the inoculum source, this is an important criterion to a successful active biofilm. Therefore, in this work, a real-time monitoring approach of the planktonic phase was proposed as a tool for predicting, in advance, when and how the system would perform. Interestingly, the phylogenetic classification of the two main \(\delta\)-Proteobacteria players was _Geobacter lovleyi_ SZ and _Desulfuronomas acterxigens_; both have been previously reported to be highly abundant in contaminated soils capable of utilizing PCE and TCE (Trichloroethylene) as metabolic electron acceptors. A specific 16S rRNA gene PCR primer set that was designed for detecting the acetate-oxidizing, PCE-decholrinating _Desulfuronomas_ group was tested on the anodic biofilm and planktonic samples in this study. Surprisingly, a positive PCR product was associated with efficient-MFCs samples. Nevertheless, an MFC that was considered non-efficient at early phases of operation and had no PCR product could yield a positive amplification at later stages, inferring that different _Desulfuronomas_ strains gradually acclimate the anodic biofilms. Finally, a novel electroactive species, closely related to _Desulfuronomas_, was successfully enriched from a working anodic biofilm. Direct application of the enrichment to an MFC anode surface showed a rapid increase in current density relative to MFCs with clean anode surfaces.  
under temperature gradient conditions, Figure S2: Electrochemical performance of the six closed-circuit MFC reactors in each experiment, Figure S3: Composition of the enrichment consortium, used for the direct application experiment based on 16S-amplicon sequencing, Figure S4: Steady-state current density obtained for each experiment set, Table S1: Components added to 0.5 L DDW with agar for preparation of petri plates.  
Conceptualization, K.Y.-G., U.G. and L.R.; Formal analysis, K.Y.-G.; Investigation, K.Y.-G.; Methodology, K.Y.-G. and L.R.; Resources, J.R.; Supervision, J.R. and U.G.; Writing--original draft, K.Y.-G.; Writing--review and editing, L.R. and U.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Israel's Ministry of Environmental Protection (MOEP), grant number 132-4-2.  
The authors are grateful for the Tel-Aviv University Smaller-Winnikow Fellowship. In addition, we would like to personally thank [PERSON] for helping with the MFCs setup.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract
Different drought characteristics, e.g., drought duration and drought intensity, and the drought initiating season affect the response of vegetation to drought [1; 31; 32]. [PERSON] et al. (2012) [33] studied the effects of long-term drought on the structure and functional composition of tropical forest ecosystems. [PERSON] et al. (2018) [34] considered time and space characteristics of drought events such as drought duration, intensity, severity, and affected areas, and assessed the impact of drought on vegetation. Furthermore, [PERSON] et al. (2019) [35] found that under long-term drought, the function of different tropical forest ecosystems changes with the precipitation gradient. However, few studies have been conducted that quantify multiple drought gradients and initiating dry seasons and analyze trend changes in TF-GPP affected by multiple drought events. In addition, different ecological types, regional drought events, and GPP responses to drought under extreme drought vary [36; 37; 38; 39]. Therefore, it is necessary to study the influence of drought on TF-GPP. Based on the Xishuangbanna Tropical Rainforest Flux Observatory, this study simulated the dynamic response process of tropical forest ecosystem productivity under gradient scenarios with multiple drought characteristics (initiating dry season, duration, intensity) to achieve the aim of this research, that is, to reveal TF-GPP's response process to various drought features and analyze its sensitivity. The results of this study provide theoretical support for GPP changes in tropical forest ecosystems and their response to extreme events under future climate change scenarios and provide technical support for the realization of \"carbon neutrality\" and \"nature-based solutions\".
###### Abstract  
Multi-_h_ continuous phase modulation (CPM), with extremely high spectral efficiency, involves the plague of high demodulation complexity with a large number of matched filters and a complex trellis. In this paper, an efficient all-digital demodulator for multi-_h_ continuous phase modulation (CPM) is proposed based on a low-complexity decision-directed synchronization algorithm. Based on the maximum-likelihood estimation of the carrier phase and timing errors, we propose a reduced-complexity timing error detector with linear phase approximation (LPA) to the phase of the multi-_h_ CPM. Compared with the traditional synchronization methods, it avoids derivative matched filtering and reduces about 2/3 of matched filters. The estimated accuracy and bit error rate (BER) performance of the LPA-based synchronization algorithm have no loss, as shown by the numerical simulation. Its stability is verified by the derived S-curve. Then, the receivers with the LPA-based synchronization for the three kinds of promising multi-_h_ CPM are implemented on a Xilinx Kintex-7 FPGA platform. The experimental results show that the onboard tested BER of the proposed design has an ignorable loss in the numerical simulation. The implementation overhead on FPGA is significantly reduced by about 27% slices, 64% DSPs, and 70% block RAMs compared with the conventional method.  
multi-_h_ CPM; linear phase approximation (LPA); synchronization; FPGA implementation
Directly obtaining the envelope signal through the Hilbert transform results in overshoot. Overshoot makes it difficult to distinguish between some pixels inside the real source and the pixels in the artifact regions, resulting in the suppression of both. This is determined by the properties of the Hilbert transformation. When the signal contains an abrupt change, the Hilbert transform produces the Gibbs phenomenon. Specifically, the Hilbert transform involves convolution of the signal using a kernel function of \(\frac{1}{\pi t}\), which is singular and particularly sensitive to abrupt points, resulting in overshoot. The simplest and most effective way to solve this problem is the smooth preprocessing of the original signal. We chose to use the window function to smooth the original signal. This paper builds upon the recovery of the complete signal, and a window function is multipliedbefore taking the envelope to weaken the abrupt changes in the N-shaped signal, thereby suppressing the overshoot in the envelope signal obtained via the Hilbert transform. The simulation of a simple circular source model and an irregularly shaped source model has verified that this method can effectively avoid distortions caused by overshooting when directly using envelope signals for imaging.
## 5 Experimental Results
### Expanded Scenarios for Validation
#### 5.3.3 Scenario 3: Dynamic User Movement  
* **User Movement**: Simulated movement of the user across multiple locations: Start in **Tunisia City**\(\rightarrow\) Move to **Ariana**\(\rightarrow\) End in **Nabeul**.
* Validation Focus:
* The adaptability of mobile agents to update recommendations in real time as the user changes location. Demonstrates the system's ability to process dynamic inputs and provide relevant suggestions.
## 1 Introduction
### Clustering for IoT in 5G Scenarios  
Currently, the 3 GPP organisations, mobile operators and academics are trying to realise 5G by the end of 2020. 5G, which is an enabler for Machine-to-Machine (M2M) type communication, has features such as 1 to 10 Gbps speed, 1 millisecond latency, 100% coverage and reliability. It aims to provide support for good Quality of user Experience (QoE) for a large range of applications and usages. Cellular involved backhaul is becoming a popular approach adopted by many systems in order to provide full connectivity. A novel two-stage re-clustering algorithm has been introduced in [16] to reduce high load on cells in hotspot areas and in turn to improve user satisfaction. Smart coordinating multiple access points can improve the overall spectral efficiency.  
In such a 3 GPP involved IoT backhaul, we indicate that clustering techniques are still necessary and beneficial for the following reasons:  
1. **Energy efficiency:** In IoT systems, many sensors/devices are still deployed requiring a lifetime measuring in terms of years. 5G aims to provide M2M communication, allowing devices to have up to 10 years' battery life. Therefore, energy efficiency still is a challenging problem in IoT systems regarding of Quality of Service (QoS) [17]. In addition, from the perspective of green computing, with 50 billion connected devices, if every single device could reduce 1% energy consumption, it can significantly reduce the total electricity bill.
2. **Distributed processing:** In the last 10 years, big data has been a really hot topic and people generally expect measures in the data. However, not all the data is useful. Treasure hunting in massive amounts of meaningless data can be costly. Furthermore, data transmission through the network and data maintaining on the server are also expensive. It is essential to filter out worthless or redundant data from the source, rather than transmitting it back to the core network. This task can be assigned to CHs.
3. **Management hierarchy:** As we have discussed, the major difference between WSNs and IoT is the diversities--from the devices themselves to the OTT applications. Besides, with such a large number of devices, an extensible and dynamic hierarchical structure can achieve effective and efficient management.