esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Comparison of Detection Performance with Other Models  
To evaluate detection effectiveness, YOLOv11-OAM was compared with five representative models: Faster R-CNN, SSD, RetinaNet, YOLOv8, and YOLOv9. All models were trained and tested on the six-class UAV fruit tree dataset and assessed using precision, recall, F1-score, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]. The results are presented in Figures 11-13 and Table 7.  
As shown in Figure 11, YOLOv11-OAM achieved the highest overall accuracy, with [EMAIL_ADDRESS] of 74.08% and an F1-score of 88.5%, outperforming all baseline detectors. In contrast, Faster R-CNN performed the worst, achieving only 71.19% precision and 78.38% [EMAIL_ADDRESS], underscoring its limited suitability for dense orchard detection tasks.  
Figure 12 illustrates training dynamics. YOLOv11-OAM converged rapidly with the lowest validation loss. In comparison, YOLOv8 and YOLOv9 displayed oscillations during training, SSD and RetinaNet converged more slowly with higher losses, and Faster R-CNN exhibited unstable convergence.  
Table 7 further reports quantitative comparisons. YOLOv11-OAM ranked highest across all metrics (precision = 87.41%, recall = 87.87%, F1-score = 0.885, [EMAIL_ADDRESS] = 93.15%, [EMAIL_ADDRESS] = 74.08%). YOLOv8 and YOLOv9 followed, SSD and RetinaNet showed moderate performance, while Faster R-CNN consistently lagged behind.  
The superior performance of YOLOv11-OAM can be attributed to the integration of ODConv, Detect_ASFF, and MPDIoU. These modules collectively strengthen spatial feature representation, refine localization, and maintain computational efficiency, enabling the model to achieve both high accuracy and real-time applicability in UAV-based orchard monitoring.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Traffic Shaping  
Besides the time-aware scheduler, TSN offers additional prioritization mechanisms in form of traffic shapers. They are used to manage the bandwidth of a stream to comply with a predefined traffic profile by pacing the output of packets (e.g., in a media stream). Three different TSN sub-standards compile the standardization activities regarding traffic shapers:  
* IEEE 802.1 Qav credit-based shaper ensures provision of maximum required bandwidth for media streams without a noticeable interruption of best-effort traffic. Time-sensitive packets are evenly distributed over time by means of a leaky bucket credit-based fair queuing.
* IEEE 802.1 Qch cyclic queuing and forwarding collects packets according to their traffic class in one cycle and forwards them to the next hop in the subsequent cycle.
* IEEE 802.1 Qcr asynchronous traffic shaping is the only one that operates asynchronously, aiming to provide deterministic and relatively low transmission delay for general time-sensitive flows according to an urgency-based scheduler [47].  
As for the potential integration of TSN-based traffic shapers into the IEEE 802.11 ecosystem, note that the credit-based traffic shaping mechanism (i.e., IEEE 802.1 Qav) could be straightforwardly adapted to the IEEE 802.11 ax scheduling mode provided that the latter was able to ensure the required periodic TXOPs to achieve a given bandwidth [25].  
Figure 4: Multi-AP resource coordination based on Co-OFDMA; MT: Multi-AP trigger, BT: Basic trigger, BE: Best-effort frame, TS: Time-sensitive frame, (B)ACK: (Block) acknowledgment, and \(t_{\text{TS}}\): Time-sensitive frame latency. Note that this example is a proposal from the authors to illustrate how multi-AP coordination may work, and so there is no direct correspondence with any working document from the IEEE 802.11 be Task Group.  
Multi-link operation in IEEE 802.11 be can also benefit from traffic shapers to efficiently allocate several packets of a stream into the available network interfaces at a given time. For instance, the synchronous mode allows for a group of aggregated packets to be split between the different active links, thus reducing the total transmission time of aggregated packets. In this regard, careful shaping considering each link's availability could help reduce the overall delay of the network.
## 3 System Control Structure
### Control Strategy of the Energy Storage Converter  
The energy storage converter (ESC) can realize the primary frequency regulation function through absorbing and releasing energy. Besides, when the short-circuit fault occurs, the ESC can also absorb the excessive power to maintain the DC-link voltage within a reasonable range, avoiding the DC-link overvoltage. In Figure 3, the topology of the ESC is a bidirectional buck/boost converter. In the control loop of the ESC, the difference between the rated DC-link voltage, \(\mathrm{u_{dcn}}\), and the feedback value, \(\mathrm{u_{dc}}\), passes through a droop controller with a droop coefficient, \(\mathrm{K_{d}}\), and its output is the current reference value of the ESC. Since the DC-link voltage of the wind turbine can reflect the dynamics of the grid frequency, the droop control can enable the ESC to automatically realize the primary frequency regulation function. Moreover, when the power level of the battery energy is too high or too low, the SOC control adjusts the setting value of the DC-link voltage, so as to move the droop characteristic curve up or down, which can realize the forced discharge or charge of the battery.  
Since the droop control is the differential adjustment, the DC-link voltage cannot be controlled at the rated value, \(\mathrm{u_{dcn}}\). When the short-circuit fault occurs in the grid, i.e., the flag is 1, in order to avoid the DC-link overvoltage, the switch SE is closed. Therefore, the integrating regulator is put into the control loop of the ESC, which can maintain the DC-link voltage within the reasonable range. After the short-circuit fault is cleared, i.e., the flag is 0, the switch SE is closed and the integrating regulator is cut out from the control loop, which means the ESC returns to the normal operation state.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 2 Materials and Methods
### Incremental Naive Bayes Model  
#### 2.2.1 Incremental Learning Theory  
Incremental learning refers to the process of absorbing new knowledge while maintaining or enhancing pre-existing knowledge as continuous information streams are processed [49]. Unlike traditional machine learning algorithms, which primarily utilize offline batch learning, incremental learning does not require retraining the model with the addition of new samples. This feature allows incremental learning to predict slope stability continuously and efficiently. Moreover, incremental learning models can identify historical slope cases and demonstrate outstanding performance in handling legacy and novel tasks. Therefore, incremental learning methods are well suited for managing streaming and large datasets. However, as the number of cases in the database grows over time, unwieldy datasets may be formed that are cumbersome to manage with batch learning. Thus, incremental learning methods emerged as the optimal solutions for managing ever-evolving slope databases.
## 4 Design and Method
### System Implement  
Due to available energy constrained, it is a common practice to deploy multiple sinks for collecting packets from all sources in realistic application, which means there are at least two advantages compared with just one sink. Firstly, in the multiple sinks scenario, since packets generated by sensors are only needed to be forwarded to any one sink, the degree of routing path from source node to sink will be shorted significantly. Secondly, a limited number of sensors are distributed around the sink and it often represents a bottleneck for data transmission because all packets from sources have to be forwarded to these sensors before reaching a destination, while it will relieve this pressure for data forwarding with more than one sink. These two strong points will save energy and improve data generation rate.  
Figures 10 and 11 show the routing paths established using different communication models when 20 sensors and 2 sinks deployed in experiment scenario with a 200 m \(\times\) 240 m square area, which contains 6 source nodes, a sensing signal and other sensors undertaking forwarding tasks. Figure 10 presents the routing paths for all sources to reach at least one sink based on maximum data collection rate of our algorithm. By considering the limited energy supplement, a source node still chooses a closer base station as its destination; however, the packets from sources may travel through all intermediate nodes and the energy consumption will be balanced to all nodes in order to improve sensor usage efficiency.  
Figure 11: The routing process using minimum energy consumption scheme.  
Figure 10: The routing process using maximum data collection rate scheme.  
From the Figure 11, we can observe that only some of the nodes participate in the routing path formation. The Figure 11 shows that the routing process is based on the minimum energy consumption [70]. A source node will create a route path to the nearest base station with smallest link distance between each of the two adjacent nodes in order to reduce energy consumption, which is recognized and widely used in traditional WSNs. When a source sends its data to another source, data aggregation occurs and total packets are reduced, which is attributed to low power expenditure. There are at least four advantages of this scheme, such as, high data delivery speed, better system stability, low data transmission delay and low energy expenditure. However, it is designed for traditional WSNs based on fixed battery with constrained energy, which must be correct in R-WSNs.
## 2 Self-Adaptive Differential Evolution Algorithm with Ensemble of Mutation Strategies
### The Basic Differential Evolution
#### 2.1.1 Initialization  
In the basic \(DE\) algorithm, the initial target population has \(NP\) individuals with a \(D\)-dimensional real-parameter vectors. Each vector is obtained randomly and uniformly within the search space restricted by the given minimum and maximum bounds: \(\left[x_{ij}^{min},x_{ij}^{max}\right]\). Thus, the initialization of \(j\)-th component of \(i\)-th vector can be defined as:  
\[x_{ij}^{0}=x_{ij}^{min}+\left(x_{ij}^{max}-x_{ij}^{min}\right)\times r, \tag{1}\]  
where \(x_{ij}^{0}\) is the \(i\)-th target population at generation \(g=0\); and \(r\) is a uniform random number in the range \([0,1]\).
## 2 Methods
### Projection of Side Points and Elevation Correction
Considering the points of the innermost coastline as the standard, the vertical line of the x-axis passes through the x coordinate of each point and intersects the outermost coastline; this forms a straight line equation from the front and rear points of the outermost coastline and at the intersection, bringing in x coordinates to calculate the y coordinate of the intersection, mean value of y coordinates of points of the innermost coastline and the intersection, y coordinates of the middle point, and mean value of elevation. In this way, the three-dimensional coordinates of the intermediate points can be calculated, and the set of all these intermediate points constitutes the point cloud dataset of the optimal coastline, as shown in Figure 5. After calculating the point set of the optimal coastline, the point set of the coastline under the cliff is the point set of hollowed-out places under the cliff in this case. Therefore, the complete coastline of the coastal zone can be obtained by fusing the point cloud of this coastline with the point cloud of the coastline on both sides of the cliff. The fused point cloud dataset of the coastline is \(\text{P}_{\text{line}}\), and the point cloud completion method is used to interpolate. The completed point cloud dataset is \(\text{P}^{\prime}_{\text{line}}\). Combined with the average high-tide surface elevation \(\text{H}_{\text{MHWS}}\) determined by the hydrological observation data of local area, the point cloud of coastline in a strict sense is acquired.
# Electrical Coupling of Monolithic 3D Inverters (M3 INVs): MOSFET and Junctionless FET
## 3 DC/AC Characteristics
\(\Delta V_{thr}\), \(\Delta V_{gm}\), and \(\Delta V_{Cngng}\) of M3 INV-JL has larger than those of M3 INV-MOS as \(T_{ILD}\) decreases, and the average variations of \(\Delta V_{th}\) and \(\Delta V_{gm}\) in M3 INV-JL are smaller than those in M3 INV-MOS as \(L_{G}\) increases, but the variation of \(\Delta V_{Cngng}\) does not depend on \(L_{G}\) at both M3 INV-MOS and M3 INV-JL. As the channel length decreases, PN\({}^{+}\) junction in M3 DINV-MOS makes the effective channel length decrease, resulting in short-channel effects (SCEs), but the junctionless in M3 DINV-JL can make the SCEs decrease [18; 19; 20]. Because JLFET is more immune than MOSFET with PN junction in terms of SCE, the average variations of \(\Delta V_{th}\) and \(\Delta V_{gm}\) in M3 INV-JL are smaller than those in M3 INV-MOS as \(L_{G}\) decreases. In the case of both M3 INV-MOS and M3 INV-JL, these results are dependent on the bottom-gate voltage variation (\(\Delta V_{Pgs}\)) are similar to the classical capacitive coupling ratio \(\gamma\) (=\(\Delta V_{th}/\Delta V_{Pgs}\)) of asymmetric double-gate (DG) ultra-thin body silicon on insulator (UTB-SOI) MOSFET [21] because both structures of the top N-type transistors in M3 INV-MOS and M3 INV-JL are similar to one of asymmetric DG UTB-SOI MOSFET. As \(T_{ILD}\) decreases, \(\gamma\) increases [14; 21], as follows. \[\gamma=\frac{T_{ox}+\frac{\epsilon_{xx}}{\epsilon_{Si}}X_{bar}}{T_{ILD}+\frac {\epsilon_{xx}}{\epsilon_{Si}}(T_{Si}-X_{bar})}, \tag{1}\]  
where \(X_{bar}\) means the distance between the front SiO\({}_{2}\)/Si interface and barycenter location of charge in the silicon channel [22] of the top N-type transistors in both M3 INV-MOS and M3 INV-JL. Because the barycenter of the top N-type transistors in M3 INV-MOS and M3 INV-JL are located close to the front SiO\({}_{2}\)/Si interface and the center in silicon-channel, respectively, \(\gamma\) of the top N-type transistors in M3 INV-JL is higher than one of M3 INV-MOS, as shown in Figure 4. The threshold conditions of M3 INV-MOS and M3 INV-JL define strongly-inverted and fully-depleted in all the channel, respectively. In the threshold regime of M3 INV-MOS and M3 INV-JL, the back SiO\({}_{2}\)/Si interfaces on both top N-type transistors are depleted without any accumulation [21] and inversion [20], respectively. Figure 3: Current-voltage characteristics of the top N-type junctionless field-effect transistor (JLFET). (**a**) \(I_{nls}\)-\(V_{ngs}\) characteristics at \(V_{Pgs}\) = 0 (solid lines and filed circles) and 1 V (dotted lines and empty squares) in the case of IL = 10 nm and (**b**) \(I_{nls}\)-\(V_{ngs}\) characteristics at \(V_{Pgs}\) = 0 (solid lines and filled circles) and 1 V (dotted lines and empty squares) in the case of \(T_{ILD}\) = 100 nm. The lines and symbols denote \(L_{G}\) = 20 nm and 50 nm, respectively. The channel width \(W\) = 0.2 \(\upmu\)m.  
It is noted that dependence of back-gate voltage for full-depletion in all the channel of M3 INV-JL is larger than one for strong inversion of M3 INV-MOS.
# A Review of Sound Field Control
## 4 Active Noise Control
### Controller Algorithm
Nonlinearity is introduced due to the nonlinear effects of the secondary path, secondary source, and primary path [60]. It limits adaptive algorithm performance. Methods such as the Volterra filter [79], optimization algorithm, and neural network [80] can predict and eliminate nonlinear terms. Theoretical analysis can provide a better understanding of the algorithm and provide a guide to its practical applications. Through theoretical analysis, we can improve the algorithm in a goal-oriented and effective way. In recent years, the re-analysis of some classic LMS-based algorithms remove the assumption of slow convergence and is suitable for arbitrary input distributions. Such analysis provided us a more accurate prediction of the transient behavior and steady-state performance [81; 82; 83; 84; 85].
## 4 Conclusions  
A dual-gridded field emission architecture based on CNT cold cathode is investigated here to solve the non-uniform electric field distribution at the cathode surface, which is the major cause of  
Figure 7: The presentation of the particle-in-cell (PIC) results after beam-wave interaction stable with operation voltage 21 kV: (**a**) signal power amplitude at output port, (**b**) output signal spectrum showing mode purity, and (**c**) electron bunch phenomenon.  
-cathode arcing. High-emission current density electron beams with a transparency of 100% have been achieved using the proposed unique topology. Furthermore, a CNT cold cathode electronic optical system based on a dual-gridded field emission structure has been designed with a high-compression ratio of 39. The high-quality electron beam current density, correspondingly, was increased by a substantial amount, satisfying the 0.22 THz BWO operational design. Finally, the 0.22 THz disk-loaded waveguide BWO with a 32 W output power at an operating voltage of 21 kV was obtained by exploiting the high-performance CNT-based electronic optical system.  
Our studies also suggest that the size of the proposed dual-gridded architecture can be changed from the \(\upmu\)m to the mm scale, and the emission current will be augmented accordingly. Our findings suggest that the spatial architecture, and therefore the emission current, can be tailored for the development of high-power millimetre-wave VEDs. In addition, the dual-gridded architecture can be modified asymmetrically to generate high-aspect-ratio, sheet-beam VEDs. The high-compression-ratio, dual-gridded CNT electron gun, as a new structure towards new VED paradigms, shows much potential not only in traditional backward wave oscillators but also in traveling wave tubes, extended-interaction klystrons, and extended-interaction oscillators. In the future, further experiments will be conducted to verify the present simulation results. This study provides a positive outlook towards the realization of CNT-based, cold cathode, terahertz, vacuum electronic radiation sources.  
Conceptualization, X.Y., Q.C., and Y.Y.; Data curation, X.Y., Q.C., M.T.C., Y.Z., L.M., and Y.Y.; Formal analysis, X.Y., Q.C., Y.Z., and Y.Y.; Investigation, X.Y. and Q.C.; Methodology, X.Y., Q.C., and M.T.C.; Project administration, X.Y., L.M., and Y.Y.; Resources, X.Y., M.T.C., and Y.Z.; Software, X.Y. and Q.C.; Supervision, X.Y. and Y.Y.; Validation, X.Y.; Visualization, X.Y. and Y.Y.; Writing--original draft, X.Y. and Q.C.; Writing--review & editing, M.T.C., Y.Z., L.M., and Y.Y.  
This research was funded by the National Natural Science Foundation of China, grant number 61771096; Fundamental Research Funds for the Central Universities, grant number ZYGX2016059; and National Basic Research Program of China, grant number 2013 CB933603.  
The authors declare no conflict of interest.
## 4 Proposed Method: Variable Embedding Locations Exploration  
As mentioned in the previous section, the intDCT-based reversible audio watermarking method is advantageous over other tampering detection methods; however, this method has a drawback in that it is easily detected by spectral analysis.  
To conceal the embedding boundary and enhance the robustness against attacks by spectral analysis, we tried to explore variable embedding locations based on DCT coefficients expansion-based digital watermarking. The basic idea is to avoid embedding data into the constant location of the DCT coefficients. However, our previous work [5] indicated that embedding data into random locations may cause distortion to make the original data sound noisy. Thus, distortion should be controlled for the imperceptibility of the watermark.
## 2 Results and Discussion
### Genetic Characterisation  
The amplification of the rbcL (Ribulose-1,5-Bisphosphate Carboxylase) gene in the two samples tested showed a PCR product of \(\pm\)500 bp. Samples D1 (_A.P_ var. _pyrethrum_) and D4 (_A.P_ var. _depressus_) are well amplified. Blast analysis using the NCBI genebank revealed that the D1 and D4 sequences were 99% similar to the _Anacyclus pyrethrum_ (L.) sequence in the genebank. The two sequences were submitted to the GenBank adapted reference database under accession numbers MZ900911 and MZ900912 and were identified as _Anacyclus pyrethrum_ var. _pyrethrum_ (L.) Link and _Anacyclus pyrethrum_ var. _depressus_ (Ball) [PERSON], respectively. Phylogenetic analysis of our sequences was carried out by comparing them to GenBank references using the Maximum Neighbour Join (MNJ) method and the tree was evaluated by bootstrap analysis based on 1000 replicates. Both sequences were classified in a single clade with _Anacyclus pyrethrum_ (L.) Link (Figure 3). These results indicate that there is genetic diversity between the two sequences or varieties analysed, with a similarity percentage of 91%.  
The similarity between some varieties could be explained by the presence of several physiological and morphological criteria in common, as well as by the history, origin, and ancestry of these varieties. However, related varieties are classified together [124]. Several studies have been carried out to analyse the genetic diversity of the genus _Anacyclus_[125; 126; 127; 128], the results of which confirm the relationships distinguished by the genetic analysis between the different species and varieties of the genus. The present study adds to the published data set information on the genetic diversity of the two varieties _A.P_ var. _pyrethrum_ and _A.P_ var. _depressus_. However, a full molecular study is needed to provide stronger evidence to elevate _A.P_ var. _pyrethrum_ and _A.P_ var. _depressus_ to subspecies status.