esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### Further Study
#### 4.4.2 Parameter Sensitivity  
This experiment explores the impact of hyper-parameter settings on C-LDA performance, including the dimensionality of the LDA projection space (referred to as LDA dimensionality) and the number of clusters in AHC, on the results of C-LDA. The results are illustrated in Figure 3.  
Firstly, we set the number of clusters at 800 (the true number of speakers in CNC1.dev is 797) and examined the impact of the LDA dimensionality. From Figure 3, it is evident that the optimal LDA dimension for C-LDA is 192, which matches the dimension of the original embedding vector. This strongly supports our hypothesis that the embedding vectors, especially those trained with AAM softmax, fully encapsulate speaker information even for cross-domain data. Therefore, no dimensions should be discarded, and simple rotation and scaling are sufficient to make it suitable for cosine scoring.  
Secondly, we fixed the LDA dimension at 192 and examined the impact of the number of clusters in the agglomerative hierarchical clustering. As shown in Figure 3, C-LDA is highly robust against the setting of the number of clusters, with negligible impact on performance. Even when the value is set to 5 times the actual number of speakers (e.g., 4000), the EER only increases by 0.47%. Furthermore, we note that overestimating the number of clusters has a more tolerable impact than underestimating it. In summary, all observations demonstrate that C-LDA is not sensitive to the setting of the clustering algorithm, which is particularly important for its practical usage.  
For comparison, we also plot the C-PLDA results in Figure 3. It is evident that C-PLDA is more impacted by the setting of the number of clusters. When the number is set to be the number of true speakers, C-PLDA achieves better performance than C-LDA; however, if the number is set far from the correct value, C-PLDA shows worse performance than C-LDA. This is because with an incorrect setting, the between-class covariance is poorly estimated, leading to suboptimal performance. Since C-LDA is irrelevant to the between-class covariance, it is less sensitive to this setting.  
Figure 3: The impact of (**a**) the dimensionality of LDA projection space and (**b**) the number of clusters in AHC on the results of C-LDA and C-PLDA.
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
Namely, in studies investigating the biological effects of microbe exposure to small air ions, it is important to distinguish the effects caused by the small air ions from those caused by the efficient but unsafe administration of ozone. It is not possible to claim with certainty what exactly caused the observed effect unless the conducted experiments were ozone-free. Therefore, this review will mainly focus on the studies using a CFI. Some of the important results where there was ozone generation to some extent will be briefly mentioned in order to perform objective comparisons and draw reliable conclusions. Considering the risk posed by airborne microbes, notably underscored by the recent SARS-CoV-2 pandemic, there exists an understandable trend towards investigating technologies designed to mitigate pathogen-laden aerosols. Among these approaches are those utilizing ions to influence the viability of microorganisms or outright eliminate them from the air circulation. The bipolar ionizer generates both positive and negative ions, initiating the formation of hydroxyl radicals. These radicals can attach to hydrogen groups on the microorganism's membrane, causing its structural integrity to break down and resulting in microbial death [7]. This technology competes favorably with ultraviolet light sterilization in terms of microbial eradication capacity, albeit with lower production and upkeep expenses [8]. However, due to its effectiveness, its application is limited to small spaces [9]. As regards unipolar ions, there have been reports of carbon-fiber ionizers (CFIs) producing unipolar ions (with remarkably low generated concentrations of O\({}_{3}\)) in sufficiently high air ion concentrations [5; 6; 10; 11; 12]. However, the effectiveness of numerous emerging technologies has not been sufficiently investigated yet, with testing often conducted on a small scale and not reflective of real-world applications. Partial or not sufficiently detailed data reporting, when assessing and comparing air treatment technologies, has made the comparison of results across different studies or types of technology quite challenging, as noted in the work by [PERSON] and colleagues [13]. Therein, a consistent assessment of several air purification technologies has been conducted. The literature appears to abound in research concerning the impact of ions on microorganisms. However, this review gives a more specific perspective, delving into the role of CFIs in microbe elimination and excluding the studies presenting mixed effects due to air ionization and the simultaneous action of other anti-microbial means. We examine the existing publications, with a focus on the type of microorganism studied, the efficacy of specific ions, the outcomes of ionization, and the generated ozone levels. The aim of this review is to provide a current viewpoint on the antimicrobial effectiveness of CFIs and comprehensively address any limitations encountered.
## 2 Improved Virtual Impedance Control Principle
It can be observed that the DC voltage starts to oscillate, the active power starts to fluctuate, and the system loses stability. The power-controlled converter exhibits a constant power load (CPL) when the active power is transmitted from the DC-voltage controlled converter to the power-controlled converter. The incremental input resistance characteristic caused by CPL affects the stability of the VSC-HVDC system [24]. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Parameters**} & **Values** \\ \hline \multirow{7}{*}{Converter and AC system} & System capacity \(Sn\)/MW & 500 \\  & Line voltage of grid \(u_{g}\)/kV & 110 \\  & DC voltage \(V_{dc}\)/kV & 250 \\  & Grid internal resistance \(R_{m}\)/\(\Omega\) & 0.2 \\  & Grid frequency \(\omega_{0}\)/Hz & 50 \\  & Grid internal inductance \(L_{m}\)/H & \(1\times 10^{-3}\) \\  & Filter reactor inductance \(L_{c}\)/H & \(4.5\times 10^{-2}\) \\  & Filter reactor resistance \(R_{c}\)/\(\Omega\) & 0.2 \\  & DC side capacitance \(C_{dd}\)/\(\mu f\) & 300 \\ \hline \multirow{3}{*}{DC cable} & DC cable resistance \(R_{d}\)/\(\Omega\)/km & \(1.39\times 10^{-2}\) \\  & DC cable inductance \(L_{d}\)/H/km & \(1.59\times 10^{-4}\) \\  & DC cable capacitance \(C_{d}\)/F/km & \(2.31\times 10^{-7}\) \\ \hline \multirow{3}{*}{Controller} & DC voltage outer loop \(k_{ppd}k_{intc}\) & 15/100 \\  & Current inner loop \(k_{ppd}k_{ic}\) & 0.5/0.1 \\ \cline{1-1}  & Active power outer loop \(k_{pp}k_{ip}\) & 1/10 \\ \cline{1-1}  & Phase locked loop \(k_{pPLL}\)/\(k_{pPLL}\) & 10/100 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3: Control structure of the converters. A virtual impedance control strategy is introduced in the DC-voltage-controlled converter to mitigate the DC-side oscillation caused by the negative incremental input resistance characteristic of the power-controlled converter. The expression of the virtual impedance control is  
\[v_{dcerf}\ =\ v_{dcn}-(R_{eq}+sL_{eq})i_{dc} \tag{1}\]  
where \(v_{dcerf}\) is the DC voltage reference value, \(v_{dcn}\) is the no-load DC voltage of the converter, and \(R_{eq}\) and \(L_{eq}\) are the set values for the virtual impedance. Due to the addition of current feedback, under loaded conditions, there will be a fixed steady-state error between the DC voltage reference value and the measured value, which almost equals \(R_{eq}*i_{dc}\). Thus, the steady-state error increases with an increase in the transmission power (in both power flow directions). In order to eliminate the steady-state error caused by virtual impedance, this paper modifies the voltage control loop, as shown by the blue dotted line frame in Figure 5.  
\[i_{dcn}\ =\ \frac{k_{i}}{s}(v_{dcn}-v_{dc}) \tag{2}\]  
\[v_{dcerf}\ =\ v_{dcn}-(R_{eq}+sL_{eq})(i_{dc}-i_{dcn}) \tag{3}\]  
Figure 4: DC-link voltage and the active power of the VSC–HVDC system. Figure 5: Proposed control structure of the converters.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 4 Existing Studies on the Performance of Solar Control Films
### Comfort Assessment of Glazing Systems with Solar Control Films
Even though the low-E film slightly reduced thermal discomfort when installed in the southeast-oriented glazing, the PPD values often surpass the limit value of 10%. The reduction in working hours with thermal discomfort is insufficient for the building to fully meet the existing thermal comfort requirements, so the low-E film should be complemented with other supplementary strategies.
## 5 Analysis of Computational Complexity
### Key Observations  
* **DAWPC-MAC** exhibits the lowest computational complexity, growing as \(O(n^{1.2})\).
* **TDTSPC-MAC** has the highest complexity, growing as \(O(n^{2})\).
* **DC-MAC** and **UW-ALOHA-QM** show intermediate complexity, growing as \(O(n^{1.8})\) and \(O(n^{1.9})\), respectively.
* **At 100 nodes**, DAWPC-MAC's complexity is **79** (arbitrary units), while TDTSPC-MAC, DC-MAC, and UW-ALOHA-QM have complexities of **2000**, **950**, and **1900**, respectively.
* **Normalized complexity at 100 nodes is **1** for DAWPC-MAC, compared to **25** for TDTSPC-MAC, **12** for DC-MAC, and **24** for UW-ALOHA-QM.
# Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging
## 3 Results
### Quantitative MR Image Analysis  
Based on qualitative investigation, quantitative analysis was performed with data acquired from 54 samples. Figure 8 shows MSI and MSI ratio variations of three cherry tomato cultivars with six different maturity stages. The ratio was calculated based on the MSI of the oil reference and the MSI of the locule region. The MSI of the oil reference changes throughout all stages without any trends due to optimization of imaging parameters, including field uniformity and receiver gain, and hence, it is used to standardize the signal intensity to yield signal variations from changes in tomato maturity only. The MSI of the three main parts of samples changes significantly throughout all maturity stages but does not show any typical trends. The MSI value of background noise from air surrounding the sample is even smaller than the placenta region. Four ratios based on the MSI of the oil reference and two ratios based on the MSI of the locule region were analyzed, as shown in Figure 8. The ratio of the MSI from the pericarp region based on the locule region shows an increasing trend with different maturity stages for all three cultivars (labeled PL in color purple on the right-side plots in Figure 8). This result indicates that cherry tomato maturity stage can be quantified nondestructively.  
Figure 7: Representative MR images of three cherry tomato cultivars at different maturity stages. Row images represent cultivars, top row (Tiara), middle row (Tiara TY), and bottom row (Unicorn). Column images represent different maturity stages: green, breaker, turning, pink, light red, and red, from left to right, respectively.  
Figure 8: Mean signal intensity (left column) and mean signal intensity ratio (right column) variations of three cherry tomato cultivars with 6 different maturity stages (with standard error bar, 90% prediction interval). Rows represent cultivars, top row (Tiara), middle row (Tiara_TY), and bottom row (Unicorn). OO: ratio of oil reference against oil reference, PO: ratio of pericarp against oil reference, LO: ratio of locoule against oil reference, TO: ratio of placenta against oil reference, PL: ratio of pericarp against locale, and TL: ratio of placenta against locale.
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification
### Bandwidth Part  
A notion of BWP is introduced in 5G NR to support the variable channel bandwidth and SCS of different AVs. Each BWP consists of one or more consecutive sets of continuous physical resource blocks within the CBW with its own numerology. Each AV can be configured with a maximum of 4 BWP for downlink and uplink; however, at a given point of time, only one BWP is active for downlink and one for uplink. The AVs are informed about the active BWP using a radio resource control message. AVs are not expected to transmit or receive control messages or data traffic outside of their active BWP. The BWP concept enables AVs to operate in a narrow or wide bandwidth depending on their demand, with several numerologies associated with their BWP. Because different BWPs overlap in the same CBW, BWPs with several different numerologies can coexist in one CBW [11].
## 5 Conclusions  
In conclusion, our results demonstrate that nitrate responsive gene, _OsNLP2_, plays key role in regulating ABA content by NLP2-NR module-dependent NO production and by activating the expression of _OsABA80x1_ gene during seed germination under salt stress (Figure 9). Seed germination was significantly reduced by salt stress by promoting ABA biosynthesis and ROS accumulation. The expression of OsNLP2 was induced by salt stress during seed germination, which thereby promote the expression of both _OsABA80x1_ and _OsNR1/2_, resulting in an enhanced ABA catabolism and accumulation of NO production. The elevated NR-dependent NO will then mitigate the inhibition effect of salinity by further promoting ABA catabolism.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/plants11060795/s1](https://www.mdpi.com/article/10.3390/plants11060795/s1)]([https://www.mdpi.com/article/10.3390/plants11060795/s1](https://www.mdpi.com/article/10.3390/plants11060795/s1)), Figure S1 Mutation patterns of mutants used in this study. a, Sequencing results for mutation position of triple mutants, H5 and H10; b-d, Sequencing results for mutation position of _aba8 ox1_ and _aba8 ox2_; Figure S2 Phenotypes of WT and triple mutant at other developmental stages. a, tilling stage; b, early stage of grain filling; c, mature stage; Figure S3 Seed germination of ABA catabolism gene mutants under treatment of water and 5 uM GA. a, _aba8 ox1_; b, _aba8 ox2_. The data are the means of three independent replications (\(n=50\)) \(\pm\) SD; Figure S4 Application of SNP on _aba8 ox1_ and _aba8 ox2_ during seed germination. a and b, Seed germination of _ab8 ox1_ and _aba8 ox2_ under water and 100 uM SNP treatments; c and d, Seed germination of _aba8 ox1_ and _aba8 ox2_ under treatments of NaCl and NaCl plus SNP. The data are the means of three independent replications (\(n=50\)) \(\pm\) SD; Table S1 Primer sequences for qRT-PCR and of luciferase assay in vivo used in this study.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] designed and conceived the research; [PERSON] and [PERSON]. ([PERSON]) performed all the research; [PERSON] and [PERSON] performed the germination experiments; [PERSON], [PERSON] and [PERSON] conduct the enzyme assays and transient activation assay; [PERSON] wrote the manuscript, [PERSON] ([PERSON]), [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research and Development Program of Hunan Province (2018 NK1010), the National Natural Science Foundation of China (31971924, 32171927), the Natural Science Foundation of Hunan Province (2021J)30349), Science and Technology Plan of Changsha City (kq2004034), Scientific Research Project of Education Department of Hunan Province (19A245), and the Hong Kong Research Grant Council (AoE/M-05/12, AoE/M-403/16, GRF12103219, 12103220, 14160516).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### RF-Based Detection
#### 2.1.1 Traditional RF-Based Detection
Power spectral entropy (PSE) [32] is a signal feature that describes the relationship between power spectrum and entropy rate, and can effectively differentiate between different signals. Existing literature indicates that under the same acquisition environment, UAV signals exhibit significantly higher PSE values compared to non-UAV signals. With respect to PSE, an accuracy of 83.85% has been achieved. As shown in Table 3, the accuracy of FD, AIB, and SIB is higher than SFS, WEE, and PSE. However, the former ones (FD, AIB, and SIB) can only accurately identify known UAV types, and the accuracy of identification is low for UAVs that are unknown. The latter ones (SFS, WEE, and PSE) identify UAVs based on the physical characteristics of their flight, and maintains a high accuracy rate for UAVs that are not visible yet. Simultaneously utilizing multiple fingerprint features can enhance the accuracy of UAV identification.
## 4 Design of RT-CCCS  
The traffic model plays an important role in analyzing the performance of resource scheduling schemes. The arrival, or departure, of users in a network is usually modeled as a birth-death process, as shown in Figure 4. \(\lambda_{k}\) is the arrival rate going from state \(k\) to state \(k+1\) (a 'birth'), \(\mu_{k}\) is the departure rate of going back to state \(k-1\) (a 'death'). The birth-death process is a special case of a continuous-time Markov process, where the states represent the current number of active users and transitions are between neighboring states. The 'birth' is the transition towards increasing the number of active users by 1, and the \"death\" is the transition towards decreasing the number of active users by 1. This is a typical M/M/1/A process, where the arrival of users follows the Poisson distribution, and the service time follows a negative exponential distribution. \(A\) is the corresponding maximum number of users in the system. Although the negative exponential distribution of service time is usually assumed for voice calls, it can also roughly represent the time for SUs to transmit data in CR-based SA. The latter is the case considered here, and the assumptions are later verified via simulation results, showing a good match.  
Figure 4: The birth-death process in queueing modeling.  
In this section, the proposed resource scheduling structure is described and analyzed in detail. In this paper, we will use the following notation:  
* Arrival rate of PUs on the _i_th CC, in users per second
* Service rate of PUs on the _i_th CC, in users per second
* Arrival rate of SUs, in users per second
* Service rate of SUs, in users per second
* The throughput of the _i_th CC in Mbps, assuming the CC is fully loaded
* Payload size for each SU packet transmission, in Mbits
* The load of SUs which has been transmitted on the _i_th CC
* Mathematical expectation operation
* Variance operation
## 5 Numerical Results  
In this section, we present some selected numerical results to illustrate the effect of the proposed approach. The common parameters, used in the simulation, are shown in Table 1. There are three hidden layers for each actor network, each of which has 16 neurons. As for the critic network, there is only one hidden layer with 128 neurons. To simplify the process, we assume that there are three sensors in the simulation.  
Figure 3 presents the minimum value for the maximum decoding outage probability of all sensors as a function of the first-hop channel parameter \(\alpha_{SR}\), where \(\alpha_{SR}\) varied from 0.3 to 24. As expected, the minimum value for the maximum decoding outage probability of all sensors from the exhaustive search is very close to that from the DRL approach. The complexity of the proposed DRL approach is mainly from the offline training, which needs around 90,000 iterations. As for the online operation, the well-trained deep network only needs to output the action, without involving any iterations, where the complexity is very low. As such, we can show that our proposed approach has arrived at near-optimal performance.  
Figure 4 presents the statistical transmit power levels of the well-trained actor networks as a function of the first-hop channel parameter \(\alpha_{SR}\). We can see that most power is allocated to transfer signal to the first sensor, and the transmit power of the third sensor's signal has the minimum level. In addition, we can observe that the transmit power level of the first sensor's signal decreased as \(\alpha_{SR}\) increased, while the transmit power levels for other two sensors' signals have a contrary trend. Note that the sum of all transmit power levels is always equal to \(P_{\max}\) with varying \(\alpha_{SR}\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notations** & **Meaning** & **Values** \\ \hline \(\sigma^{2}\) & Average noise power & \(10^{-5}\) Watt \\ B & Channel bandwidth & 200 KHz \\ \(P_{\max}\) & The peak power & 1 Watt \\ \(v\) & Exploration variance & 14 \\ \(\beta\) & Updation factor & 0.99 \\ \(\gamma_{\text{th}}\) & SINR threshold & 0.2 \\ \(P_{secr}\) & security outage probability & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the simulation.  
Figure 5 presents the average reward over 100 consecutive steps during the training process of the optimal scheme for different learning rate settings. As expected, we can see that the training reward firstly increases with on-going training and then converges to a stable value. We can also observe that the learning rate of 0.0000007 leads to a considerably higher convergent reward value than those of 0.000007 and 0.00007. As such, it is of great importance to set an appropriate learning rate to achieve good training performance. Note that the fluctuation of the reward function is caused by random exploration, which will approach zero after a certain amount of training iterations.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.2 Integration Issue  
Secondly, in terms of integrating the QKD network with conventional telecommunication networks and also optical routing purposes [68; 69], a technique such as wavelength division multiplexing (WDM) can be used in fiber-based QKD networks. Furthermore, the QKD integration scheme with conventional telecommunication data using a single fiber can considerably reduce the cost while able to increase the robustness of QKD applications. [PERSON] in 1997 [70] first introduced the scheme by concurrently transmitting a combination of QKD and conventional data. Later on, numerous QKD experiments are conducted to verify the feasibility in terms of QKD network integration with conventional telecommunication networks [71; 72; 73; 74]. Moreover, an integration between 3.6 Tbsp. optical communication data and QKD over 66 km backbone fiber network has been conducted in 2018 [75].