esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Challenges and Limitations
### Low RF-to-DC Conversion Efficiency  
One of the primary challenges facing RFEH systems is the inherently low RF-to-DC conversion efficiency, especially at low input power levels. While modern rectenna designs have achieved efficiencies exceeding 80% under optimal conditions, this level of performance typically only occurs at relatively high input powers or specific frequency bands. Under ambient RF conditions where power levels are often below \(-20\) dBm, the efficiency drops significantly, often falling below 30% [107]. Recent studies have emphasized the importance of optimizing impedance matching, rectifier design, and power management circuits to mitigate these losses, yet achieving consistently high efficiency across diverse and unpredictable RF environments remains a major hurdle [10]. This inefficiency is primarily caused by the poor sensitivity of conventional Schottky diodes at ultra-low input powers and their increased threshold voltage, which limits the rectification of weak signals. Furthermore, the non-linear behaviour of rectifying diodes under low power inputs complicates circuit design, requiring innovative materials and circuit topologies to sustain performance [14; 34]. To address these issues, researchers are exploring the use of zero-threshold MOSFET-based rectifiers, differential-drive rectifier topologies, and advanced multi-stage voltage multipliers to maintain effective rectification, even under sub-microwatt input power levels. Additionally, the integration of wideband impedance matching networks and self-tuning rectifiers using varactors or MEMS-based components has shown promise in dynamically adapting to fluctuating RF power levels, thereby enhancing conversion efficiency across real-world environments.
## 1 Introduction
### Contribution of the Paper  
We propose a computationally efficient intelligent spectrum sensing system in this paper. We incorporate a DFT-based multibeam beamformer to directionally sense RF signals. In order to reduce the computational complexity of the multibeam beamformer, we employ approximate discrete Fourier transform (ADFT) instead of DFT. The digital processing back-end of the proposed multibeam beamformer is implemented on a field-programmable gate array (FPGA). The digital processing back-end connected to an antenna array consisting of 32 patch antennas operating at 5.7 GHz confirms the real-time spectrum sensing. Furthermore, the experimental setup demonstrates the application of ADFT-based multibeam beamforming in spectrum sensing, offering a promising approach for real-time, low-complexity, and efficient directional spectrum perception.
## 3 Results
### 4 HR Did Not Increase Prolactin and ER\(\alpha\) Expression in the Pituitary Gland  
To further explore the xenoestrogen effect of 4 HR, we tested 4 HR's effect in an estrogen-deficient ovariectomized rat model. Daily application of 1 ug/kg of 17-\(\beta\)-estradiol in the ovariectomized rats increased prolactin, ER\(\alpha\), and ER\(\beta\) expression levels in the pituitary gland as expected (Figure 4a). Blood prolactin levels were also significantly increased compared to the solvent only group (\(p<0.05\), Figure 4b). However, daily application of 128 mg/kg for 4 HR in the ovariectomized rats showed low levels of prolactin, ER\(\alpha\), and ER\(\beta\) expression in the pituitary gland (Figure 4c). Accordingly, blood prolactin levels were similar to those of the solvent only group and significantly lower than the 17-\(\beta\)-estradiol group (\(p<0.05\), Figure 4b). These results further confirmed that 4 HR had no xenoestrogen effects in an ovariectomized rat model.  
Figure 3: The expression of extracellular signal-regulated kinase-1/-2 (ERK1/2) and phosphorylated p-ERK1/2 (p-ERK1/2). **(a)** The application of BPA (1 to 100 \(\upmu\)M) to MCF-7 cells increased the expression of p-ERK1/2. **(b)** The application of 4 HR (1 to 100 \(\upmu\)M) to MCF-7 cells showed no change of p-ERK1/2 expression. **(c)** Relative protein expression of ERK1/2 to \(\beta\)-actin. The application of BPA slightly increased relative expression level of ERK1/2 compared to that of 4 HR. However, the difference between groups seemed to be not significant. **(d)** Relative protein expression of p-ERK1/2 to \(\beta\)-actin. The application of BPA increased relative expression level of p-ERK1/2 compared to that of 4 HR. In the case of 4 HR, p-ERK1/2 was barely detected. Full length blots were available in Supplementary Figures S3 and S4.
# Study on Two-Phase Mixing Inside Flow Focusing/Blurring Nozzle by Gray Distribution Analysis Method
###### Abstract  
The application prospect of a flow focusing/blurring nozzle is broad but research on gas-liquid flow inside the nozzle is not comprehensive. The gas-liquid mixing inside the nozzle is difficult to study by visualization experiment, so this paper proposes to study the gas-liquid flow or mixing inside the nozzle by the gray scale level distribution of the experimental images. The results show that the increase of air flow rate is beneficial to two-phase mixing inside the nozzle, while the influence of water flow rate, tube hole distance (the distance between inner tube and nozzle outlet) and orifice diameter increase is opposite. The influence of air inertia force on two-phase mixing is weaker than the water inertia force under different parameters, the effect of the air inertia force on two-phase mixing is similar to tube hole distance under a small flow rate, the effect of the orifice diameter on two-phase mixing is relatively weak. In addition, the analysis of the gas-liquid flow field in the mixing zone shows that the gas-liquid flow in the nozzle is stable in the flow focusing mode. In the flow blurring mode, the gas-liquid flow inside the nozzle has radial stability but axial pulsation. In the transition mode, the gas-liquid flow inside the nozzle is unstable, but the gas-liquid flow is close to the flow blurring mode.  
flow focusing/blurring nozzle; two-phase flow and mixing; experimental study; gray scale level distribution +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Quantitative Evaluation  
For quantitative evaluation of the reconstruction performance, the mean absolute error (MAE) was calculated for each training/test dataset pair by the following formula (Table 2).  
\[MAE=\frac{1}{N}\sum_{x_{i},\ y_{i}\in\mathcal{B}}\left|c_{\text{pred}}(x_{i}, y_{i})-c_{\text{true}}(x_{i},y_{i})\right|, \tag{6}\]  
where \(N\) is the number of grids in the reconstructed region, \(B\) is the set of grids in the reconstructed region, \(c_{\text{pred}}\) is the predicted sound speed by a DNN, and \(c_{\text{true}}\) is the true sound speed. The mean absolute error (MAE) was calculated for each sample. Subsequently, the mean and standard deviation of the MAE across the entire dataset were evaluated. The BP-DNN was able to predict the sound speed distribution with a small MAE for the breast phantom test data. However, the average MAE for the natural image data was about 50 times larger than that for the breast phantom test data. This suggests that the DNN, when trained on breast phantom data, struggles to generalize to more diverse patterns, such as those found in natural image data. On the other hand, the NI-DNN showed a larger error in the breast test data than the BP-DNN did. However, the MAE for the breast phantom data is 5.5 m/s, whereas typical tumours have a sound speed difference to the surrounding tissue of around 30-50 m/s. Therefore, this DNN is considered to have the required sound speed accuracy for tumour evaluation. Together with the subjective visual evaluation, the NI-DNN is considered to have a high generalization performance that is applicable to breast data as well.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 1 Introduction
The predictive performance of six commonly used machine learning models, including two deep learning models (LSTM and BiLSTM), was evaluated using MAE and other evaluation metrics as accuracy measures. The results indicate that the LSTM model performed the best. Consequently, this paper used the LSTM model to predict the maximum magnitude in the study area for the upcoming year. The experimental results demonstrate that the maximum magnitude prediction method based on the LSTM model has solid predictive capabilities and broad application prospects. The rest of the paper is organized as follows: the data and preprocessing procedure are described in Section 2, the methodology is presented in Section 3, the results are given in Section 4 and discussed in Section 5, and finally, the conclusions are given in Section 6.
## 4 Intravalley Relaxation and Fermi Degeneracy Formation  
It is well known that the fermions in solids occupy the band states to make the total energy the lowest. As a result, the states below the Fermi energy are completely occupied, but those above the Fermi energy are unoccupied at zero temperature. This is a well-established phenomenon called Fermi degeneracy. Most theories in solid state physics assume the presence of the Fermi degeneracy trivially as their starting point [16]. However, it is not clear how the fermions go to the Fermi degeneracy and how long it takes, since the real system never starts from the ground state. Thus, it is meaningful to examine what happens if a macroscopic number of electrons are photo-injected into the conduction band at once.  
In metal [17; 18; 19; 20], it has been reported that the photo-excited system shows an extremely rapid relaxation, since the ratio of the photo-excited electrons is low, meaning the unexcited electrons work as a heat reservoir for the photo-excited ones.  
Being different from the above metallic cases, what happens in the gapped system such as a semiconductor? The intravalley relaxation in a direct gap semiconductor is a good target for this problem. In this case, the electrons relax toward the conduction band minimum (CBM) or \(\Gamma\) valley bottom, and finally, the Fermi degeneracy would be formed at the CBM.  
While this intravalley relaxation has been studied intensively so far [21; 22; 23; 24; 25; 26], its microscopic mechanism is still under considerable debate. The 2 PPES measurement for this process has been performed by [PERSON] [7], by observing the time evolution of the energy distribution function (EDF). The crucial aspects of this experiment are as follows: The whole distribution of photo-excited carriers shows an extremely rapid collective relaxation in the initial first picosecond (ps) from the photo-excitation. In this early stage, the electrons take a non-equilibrium distribution, which is far from the Fermi-Dirac one. After 1 ps from the photo-excitation, the relaxation speed keeps slowing down with a slight change of the EDF. This slow relaxation is inversely proportional to time.
## 4 Evaluation
### AI Model Evaluation Result  
To evaluate the VideoPose3D model, the mean per joint position error (MPJPE), Procrustes-aligned Mean Per Joint Position Error (P-MPJPE), Normalized mean Per Joint Position Error (N-MPJPE), and Mean Per Joint Velocity Error (MPJVE) were measured. To calculate the MPJPE, the average Euclidean distance between the estimated joint location and the location of the Ground Truth (GT) joint was measured.  
[PERSON] et al. [1] used the MPJPE as a measure to evaluate the algorithm for estimating the 3D pose of a dog. [PERSON] et al. [27] adopted the MPJPE as a measure to evaluate their proposed multi-animal 3D pose dataset. Accordingly, the MPJPE was selected for the evaluation of the VideoPose3D model used in this study.  
[PERSON] et al. [24] stated that 3D poses estimated by Procrustes transformation should be compared with the GT to calculate the P-MPJPE. The Procrustes transformation eliminates translation, rotation, and scaling. To calculate the N-MPJPE, the estimated 3D pose is compared with the GT in consideration of only size. Finally, the single-dimensional derivative obtained from the estimated 3D pose sequences is applied as the MPJVE, which indicates the dynamic quality of the smoothness of the 3D pose sequences. Table 2 presents reconstruction errors calculated by applying the VideoPose3D model to the test dataset. The VideoPose3D model used in this study had an MPJPE of 75.9 mm.  
Table 3 presents the results of calculating the loss and accuracy of the DD-Net model. Training Loss and Training Accuracy refer to the loss and accuracy values, respectively, calculated from the training dataset. Validation Loss and Validation Accuracy refer to the loss and accuracy values, respectively, calculated based on the validation dataset. Google Developers reported that the loss value indicates a poor prediction performance of an AI model. If the AI model performs an accurate prediction, the loss value is 0 [28]. When the DD-Net model was applied to the validation dataset, the loss and accuracy values were calculated to be 0.7443 and 81.82%, respectively. The change in accuracy according to epochs can be seen in Figure 11, and the change in loss according to epochs can be seen in Figure 12. It is evident that the accuracy gradually increases as the epoch increases, while the loss gradually decreases as the epoch increases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Value [mm]** \\ \hline MPJPE & 75.9 \\ P-MPJPE & 45.6 \\ N-MPJPE & 70.9 \\ MPJVE & 4.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reconstruction Errors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Value** \\ \hline Training Loss & 0.0344 \\ Training Accuracy & 1.0000 \\ Validation Loss & 0.7443 \\ Validation Accuracy & 0.8182 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loss and Accuracy after 2500 epochs.
# LSH Models in Federated Recommendation
## 4 Comparative Analysis of Experiment
### NCF Federated Recommendation with LSH  
The experiment was conducted on federated recommendation using locality sensitive hashing (LSH) with different dimensions of hash vectors. Three models (GMF, MLP, and NeuMF) were compared across various vector dimension levels to evaluate their federated recommendation performance.  
The negative-positive training sample ratio was set to 4:1 with a K value equal to 10. Each model uses a hidden vector layer of length 8, while the MLP component includes a hidden layer of dimension 10. Each round of the survey had five participants. The specific structure mainly follows the framework of experiment 3.6, where the noise addition mechanism is replaced by the hash vector, and the results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{\(\varepsilon\) = 10} & \multicolumn{3}{c}{\(\varepsilon\) = 20} & \multicolumn{3}{c}{\(\varepsilon\) = 40} & \multicolumn{3}{c}{\(\varepsilon\) = 60} \\ \cline{2-11}  & **HR** & **NDCG** & **F1** & **HR** & **NDCG** & **F1** & **HR** & **NDCG** & **F1** & **HR** & **NDCG** & **F1** \\ \hline GMF & 0.0028 & 0.0011 & 0.0014 & 0.2228 & 0.1213 & 0.1506 & 0.3787 & 0.2511 & 0.2879 & 0.4401 & 0.2411 & 0.3221 \\ \hline MLP & 0.4389 & 0.2511 & 0.3459 & 0.4489 & 0.2677 & 0.3726 & 0.4458 & 0.2611 & 0.3785 & 0.4489 & 0.2622 & 0.3797 \\ \hline NeuMF & 0.3020 & 0.2009 & 0.2459 & 0.33777 & 0.2255 & 0.2785 & 0.4626 & 0.2418 & 0.3465 & 0.4837 & 0.2590 & 0.3680 \\ \hline \hline \end{tabular}
\end{table}
Table 5: NCF federated recommendation with DP.  
The experimental results indicate that reducing the dimensionality of hash vectors leads to decreases in both HR and NDCG, resulting in degraded model performance and reduced recommendation accuracy. However, it may also contribute to reducing overfitting and improving computational efficiency. In practical applications, the choice of hash vector dimensionality needs to be balanced according to specific circumstances to achieve the optimal recommendation effectiveness.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 3 Challenges Brought by Crowd Density  
In high-density crowd scenes, crowd behavior recognition faces a series of challenges, which stem from the high complexity brought by the dense distribution of the crowd. Specifically, the frequent occlusion between individuals and the motion blur induced by rapid movement together constitute various obstacles in the recognition and analysis process. These factors are intertwined, greatly increasing the difficulty of accurately detecting, continuously tracking and deeply understanding crowd behaviors, and posing a severe test for realizing crowd density distribution, pedestrian trajectories, and abnormal behavior recognition [14].
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 7 Results
### Memory Task  
For the _Memory_ task, the correctly played keys and the mistakes are recorded. Firstly, the success rate of correctly repeating a sequence of notes is shown in Table 2 for all participants. This measure represents the fraction of correctly played sequences of all sequences played, including every retry of a sequence until they succeeded. In general, the non-clinical test users made fewer mistakes across the board than the clinical patients. Moreover, there are significant differences in success rates between the different patients. Patient 2 played the most erroneous sequences, while Patient 4 had a success rate approaching that of the non-clinical test users. Test user 4 did not make any mistakes.  
Figure 15: The median of the time it took to hit the required note during the _Scales_ task for patients and test users. Quartiles Q1 and Q3 are indicated by the horizontal lines. Patient 1 required more time to hit the final key at the extreme end of their neglected side, whereas, for all patients, the time to hit the next key reduced gradually as they moved along the scale. The test users indicated a significantly faster response time compared to the clinical users.  
Figure 16 shows the distributions of the notes in the instruction sequences and the played sequences for patients and test users. The size of the marker indicates the relative frequency of that note in both distributions. For all participants, most keys were played correctly, which can be seen by the large values on the diagonals of the graphs. However, the patients made significantly more mistakes than the test users. In particular, Patient 2 made many mistakes on individual keystrokes. Remarkably, Patient 2 confused almost each key for every other key at least once. A higher concentration can be seen towards the neglected side. This indicates that keys in the instruction sequence on the neglected side were often confused for another key. The data of Patient 3 show a similar pattern, where some keys in the far neglected side are more confused for other keys. The mistakes made by the other users are situated more closely to the instructed keys. In other words, the mistakes by the other users meant that they played a key only a few keys apart from the instructed key. The test users made minimal mistakes, which resulted in a sparse plot.
## 4 Simulation Results
This simulation result shows that the LEO satellite makes tradeoffs according to the requirements of different UEs in the process of resource allocation. Figure 3: Average UE rate under different covertness parameter \(\varepsilon\). Figure 2: Total rate of CUEs under different bandwidth \(B\) and the number of beams \(N_{0}\). The communication rate of each UE and the beam scheduling decision at each time slot \(t\) are shown in Table 2 and Figure 5. This simulation result is generated based on a specific set of conditions with \(N_{0}=3\) and \(D=10\) Mbps. It can be seen that in order to improve the total communication rate of CUEs, the LEO satellite illuminates beam area 4 and beam area 5 at all time slots. Meanwhile, in order to guarantee the communication rate of NUEs, the LEO satellite should illuminate other beam areas at certain time slots. Taking NUE 1 as an example, its rate at the first two time slots is very low; thus, at the third time slot, the LEO satellite illuminates beam area 1, which meets its communication rate requirement. In Figure 6, we compare the impact of our proposed resource allocation scheme with two other schemes on the total rate of CUEs. We first utilize the beam round robin scheme, where the beam areas illuminated by the LEO satellite at each time slot are fixed. Specifically, the illuminated areas at the five time slots are 123, 234, 345, 451, and 512, respectively, with \(N_{0}=3\). For instance, when \(t=1\), \"123\" represents the case that area 1, area 2, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(t=1\) & \(t=2\) & \(t=3\) & \(t=4\) & \(t=5\) \\ \hline \(01011\) & \(00111\) & \(10011\) & \(01011\) & \(10011\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The beam scheduling decision at each time slot. For instance, when \(t=1\), “\(01011\)” represents the case that beam area 2, beam area 4, and beam area 5 are illuminated. Figure 4: Average UE rate under different rate thresholds \(D\) for NUEs, with \(N_{0}=1\). Figure 5: UE rate at different time slots, with \(N_{0}=3\), \(D=10\) Mbps. area 3 are illuminated. As shown in the figure, this round robin scheme performs worse than our proposed scheme because it does not optimize time resource allocation. Next, we utilize a heuristic algorithm as the comparison, which is similar to the greedy algorithm for solving MILP problems. In the comparison algorithm, it optimizes the LEO satellite scheduling decision at each time slot by selecting the decision with the highest CUE rate. If the rate constraint (14) and the covertness constraint (18) are not satisfied, the current decision is discarded, and the next highest CUE rate decision is selected until an optimal solution is found. It can be seen that the performance of comparison algorithm is lower than the TMDP method of our scheme. This is because the comparison algorithm optimizes decisions at each time slot independently, without considering the overall time scheduling problem. This simulation result demonstrates that our proposed multi-beam LEO satellite multi-dimensional resource allocation strategy is more effective.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Device to Device Communication
#### 3.3.2 Device Discovery
Different device discovery algorithms have been used in recent research to resolve the coverage and capacity issues in 5G networks, as summarised in Table 8.  
The future challenges that need to be addressed in device discovery are:  
* Initial device discovery signal: In D2D communication, initially the device will send a device discovery signal to detect the neighbouring devices. Any malicious user can also send the discovery signal to identify trusted users and create some security issues. Therefore, to avoid security issues due to malicious users, the initial signal generation parameters such as signal data size and control data have to be secured;
* Multi cell device discovery: The proposed research till now has focused on single cell device discovery models. If the user is at the cell edge or dynamically changing his location from one cell region to another cell region, then multi-cell device discovery isrequired. Therefore, detection of multi-cell devices is one of the future challenges in D2D communication systems;
* Non Repudiation and Traceability: Non repudiation prevents D2D users from being denied transmission and reception of messages. Traceability is mandatory to detect the source of false messages. This is also one of the future challenges to providing a secured data transfer;
* Availability and Efficiency: In D2D communication systems, the availability of a device largely depends on the degree of cooperation of devices. Efficiency is the ability of a communication system to implement and operate economically. Therefore, achieving higher efficiency in device discovery is one of the future challenges in D2D communications.