esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Photogrammetric and Lidar Applications of UAV
## 6 Discussion
### Discussion on Applications of UAVs
#### 6.2.2 Environmental Activities
The main task was to extract the number of plants found in corn and citrus fields from RGB images. Images were taken from the platform carried by the UAV. The results obtained were in the form of 6224 plants per image. The article [118] describes the digital counting of sunflower plants from aerial images acquired by a UAV using low-height flights. According to the results, it is possible to estimate the number of plants in an image with an error of 10%. The research in article [119] discusses counting and sizing plants in UAV images using an R-CNN mask that detects and segments individual plants. There, three potato fields, two lettuce fields in the UK, and one field in Australia were monitored. The flight was conducted at 50 m height with DJI S900 and DJI Inspire UAV with a Panasonic GH4 sensor and a DJI Zenmuse X4S sensor, respectively. The SfM processing method was used. The aim of the research [120] was to count corn plants using images from UAVs flying at a height of 30 m AGL. The images were obtained by RGB sensors Canon PowerShot S100_5.2, Sequoia_4.9, and DJI FC6310_8.8 carried by 3 DR SOLO and a DJI Phantom 4. This research was carried out on a plot of land in the Colegio de Postgraduados, Campus Montecillo. The UAV remote sensing application is a recommended method in the field of precision agriculture precisely because of its ability to detect individual plants and plantation rows with high accuracy.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 7 Utterance-Level Prediction Experiments
### Task 1: Therapist Behaviour Prediction  
We first investigate therapist behaviour prediction. Given a therapist utterance, the task is to predict its (Main) Behaviour. As shown in Table 15, the BERT variants  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reflection**} & \multicolumn{2}{c}{**(Main) Behaviour**} & \multicolumn{2}{c}{**Talk Type**} \\  & **Question** & **Input** & **Other** & **Change Talk** & **Neutral Talk** & **Sustain Talk** \\ \hline
34\% & 36\% & 16\% & 14\% & 29\% & 57\% & 14\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Distribution of (Main) Behaviour and Talk Type after de-duplication. Overall, (Main) Behaviour has 3796 unique examples and Talk Type has 3685.  
score the highest, with macro F1s at 0.72, followed by CNN at 0.6 and Random Forest at approximately 0.5. Compared to the random baselines (Prior and Uniform) with macro F1s below 0.25, the trained models, especially the BERT variants, have clearly learned contextualised semantics. No substantial difference exists between the results of BERT w/o Adapters and BERT w/ Adapters. The effects of augmentation are minor and universally negative for the BERT variants and CNN.  
The order of per-class F1 by the best-performing models (BERT variants) is generally Input \(\approx\) Other \(<\) Reflection \(<\) Question, which largely correlates with the order of proportions of those labels in the task (Table 14). The performance gap between different classes is not reduced in the Augmented Balanced scenario, which shows that this issue cannot be resolved by simple paraphrasing-based class-wise data augmentation. Interestingly, Question shows better (e.g., \(\Delta=0.09\)\(F1\) for BERT w/ Adapters) performance than Reflection despite having similar amounts of examples, which may be because (1) Question utterances generally have syntactic cues such as question marks and are therefore easier to classify, and (2) Question has higher IAAs than Reflection (Table 9) and is thus less noisy.  
By inspecting the confusion matrix (Figure 12) of BERT w/ Adapters in the Original Unbalanced setting, we observe that Input and Other utterances are most frequently misclassified into Reflection. Since Input and Other are less than half in size compared to Reflection (Table 14), this imbalance may have contributed to the instances of misclassification. On the other hand, Reflection and Question are similarly sized but Question contributes less to the misclassifications of Input and Other, which may again be linked to its syntactic cues and less noisy labels, as mentioned before.
## 4 Results and Discussion
### The Impact of SLADS on Traffic Efficiency and CO\({}_{2}\) Emissions
#### 4.1.3 The Impact of SLADS on CO\({}_{2}\) Emissions  
Under the real distribution of Chinese driving style and the scenarios of the expressway and the secondary motorway, the impact of SLADS on CO\({}_{2}\) emissions was analyzed by changing the ICV penetration rate from 0 to 100%. As shown in Figure 3a, on the expressway, SLADS does not have apparent influence on the average CO\({}_{2}\) emissions per vehicle at the first service level, and it even increases them by 19.11% at the third service level since the traffic flow is in free condition at this service level and the increase of ICV penetration rate will improve the average speed of the traffic flow. As the volume input is equal to the saturated traffic service level, the 20% oversaturated service level, and the 40% oversaturated service level, SLADS can significantly reduce the average CO\({}_{2}\) emissions per vehicle by 10.05%, 22.94% and 28.73%, respectively, when the ICV penetration rate reaches 80%, 100%, and 100% separately. This is because the increment of ICV penetration rate  
Figure 2: The impacts of SLADS on the road capacity on the expressway (**a**) and the secondary motorway (**b**).  
could make each vehicle in the traffic flow move smoothly so that the working conditions of acceleration and deceleration would decrease.  
On the secondary motorway, as depicted in Figure 3b, a quite different result from the expressway scenarios was obtained. At the first service level, SLADS has no significant impact on the average CO2 emissions per vehicles, but slightly increases it. It should be noted that there is a trough of CO2 emissions in the red circle in Figure 3b, which is probably caused by the difference in vehicle input distribution in the simulation algorithm and should be ignored. When the volume input is not lower than the third service level, as the ICV penetration rate becomes increasingly large, SLADS increases the CO2 emissions per vehicle, which is even more significant when the volume input becomes larger. As the volume input is at the saturated traffic service level, and the 20% and 40% oversaturated service levels, SLADS can significantly reduce the CO2 emissions per vehicle at most by 10.05%, 22.94% and 28.73%, respectively. Although the increase in ICV penetration rate can improve the traffic efficiency, the working conditions of acceleration and deceleration within the traffic flow cannot be improved because the secondary road has only one lane in one direction so that vehicles cannot change lanes and overtake other vehicles.  
Scenario Analysis: The Impact of SLADS under Different Driving Styles on Traffic Efficiency and CO2 Emissions
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 3 Results and Discussion
### Maximum Crack Width Measurement
#### 3.2.3 Performance in Outdoor Scenarios  
To evaluate the method's suitability for use in outdoor scenarios, images for crack E to crack H were captured outside on a sunny day, as shown in Figure 7. The yellow bounding boxes in the images show how the images were cropped to focus on the critical regions that need measuring. Cropping or resizing an image does not affect the accuracy of the laser measurement method if the distance from the camera to the measurement surface is known. The proposed method in this study only requires knowledge of the camera-to-surface distance and uses image processing techniques to calculate other necessary parameters. This makes it usable with any camera without the need to know its specifications, unlike other methods [6; 19] that require additional information such as focal length and the number of pixels. It can be seen from Table 3 that the proposed method achieves results of low absolute error and low mean relative error even for outdoor scenarios. The MAE for cracks measured indoors was 0.13 mm, while the MAE for cracks measured outdoors was 0.39 mm. Maximum crack widths measured on images captured outdoors had higher MAE mainly because the images captured outdoors were captured from long distances away from the surface being measured. The absolute error increases as the distance away from the measuring plane increases, as shown in Figure 6b.  
Figure 6: (**a**) Effect of distance to measuring plane on the relative error; (**b**) Effect of distance to measuring plane on the absolute error.  
Figure 7: Application of the laser method in outdoor scenarios.  
The conversion factor developed using the laser beam is effective in converting the results of the maximum crack width from pixels to millimeters. We achieved this by attaching a laser to the image acquisition devices. [PERSON] et al. [36] measured crack width in millimeters by using planer markers which needed to be physically attached to the surface being measured. Good results with a 1-2% relative error were achieved. However, the attachment of markers to structures with low accessibility poses a safety hazard. The proposed method does not require any physical attachment of markers onto the surface of the structure being measured and thus promoting safety. The laser is easily attachable to any image acquisition device; attaching it to an unmanned aerial vehicle will promote safer inspections of structures.
## 4 Discussion
### Performance of Atmospheric Correction Processors  
Among the final 28 top-performing models, only two were developed using Level-1 (i.e., without atmospheric correction) imagery, underscoring the vital importance of atmospheric correction as a preprocessing step, as expected from previous studies [22]. ACOLITE and LEDAPS feature prominently, with six and five top-performing models, respectively. Three out of the six ACOLITE-corrected models apply to Sentinel-2 imagery. The broad applicability of ACOLITE is also apparent in the literature [15; 17]. Despite a recent study demonstrating its superiority over ACOLITE [86], Polymer appears in only one of the eight top-performing models for Sentinel-2 scenarios. The land-oriented Sen2 Cor and iCOR are absent from any of the Sentinel-2 top-performing models, which aligns with the findings of similar comparative studies [19]. Among the five LEDAPS-corrected models, four are associated with Landsat 7 and one with Landsat 5. Regarding performance, the RMSLE for ACOLITE-corrected models ranges from 0.16 to 0.41, while for LEDAPS-corrected models, RMSLE ranges from 0.20 to 0.49. It is worth mentioning that SeaDAS was out of the scope of this study; however, comparative studies report mixed findings regarding its performance compared to ACOLITE for the OLI and MSI sensors, with some suggesting underperformance [23] and others indicating overperformance [17; 26].  
More surprisingly, even though more sophisticated atmospheric correction processors specific to Sentinel-2 are available, the simpler DOS1 seems to be preferred for correcting Sentinel-2 data. Only for M\({}_{\text{S2-HH}}\) is the more advanced Polymer selected. This aligns with previous work [18], where DOS1 outperformed four other atmospheric correction processors for Sentinel-2, including ACOLITE (DSF and EXP), Sen2 Cor, and ATCOR. Similarly, Landsat 8 models often perform better with simpler atmospheric correction processors, such as QUAC for M\({}_{\text{LS-OM}}\), or even with uncorrected Level-1 as in the case of M\({}_{\text{LS-WLO}}\). Nonetheless, more specialized atmospheric correction processors, like FLAASH (three times) or C2 RCC, also rank among the top performers. Some studies even suggest a superior performance of C2 RCC over ACOLITE, particularly for turbid waters [31]. By contrast, ATCOR is only preferred in two models, one for Landsat 5 and the other for Landsat 7, while none of the top-performing models use iCOR- or Sen2 Cor-corrected data. These results are consistent with previous comparative studies [31], probably because of overcorrection of iCOR and Sen2 Cor in the blue and green ranges [23] and unsuitability for red-edge to NIR wavelengths [29].
## 2 Theory and Modeling
### Semi-Analytic MC Model  
We developed an improved semi-analytic MC simulation model for analytically estimating the probability of photon scatter back to a remote receiver \(E(\mathrm{i})\) at collision point \(i\), which is also appropriate for inhomogeneous water:  
\[\begin{array}{ll}E(\mathrm{i})&=\frac{\mathrm{P}(\theta)}{4\pi}\Delta\Omega \exp(\frac{i}{1}-c(\mathrm{i})\mathrm{d}_{i})\mathrm{w}(\mathrm{i})\\ &=\frac{\mathrm{P}(\theta)}{4\pi}\frac{A_{r}}{(H+z_{i})^{2}}\exp(\frac{i}{1}- c(\mathrm{i})\mathrm{d}_{i})\mathrm{w}(\mathrm{i})\end{array} \tag{7}\]  
where \(\mathrm{p}(\theta)\) is the spf value at angle \(\theta\); \(\Delta\Omega\) is the small solid angle of collision photon received by a remote detector; \(A_{r}\) means the detector aperture; \(H\) is lidar flight height; \(z_{i}\) is the distance from point \(i\) to sea surface, which equals the photon's depth; \(c(\mathrm{i})\) is sea water using the beam attenuation coefficient, and \(\mathrm{d}_{i}\) is the thickness of each divided layer. Because the solid angle of collision photon received by a remote detector is so small, the cosine value of the actual backscattering line is similar to the line perpendicular to the air/water interface. Therefore, each layer distance \(\mathrm{d}_{i}\) is vertical. The analytical lidar geometric model for describing the photon's scattering event is shown in Figure 3. Before using the equation, we should determine whether the photon's position is within the receiver field of view (FOV) first.  
\[{x_{i}}^{2}+{y_{i}}^{2}\leq A_{z} \tag{8}\] \[A_{z}=\Omega_{FOV}(H+z_{i})^{2}\]  
where \(\Omega_{FOV}\) is the receiver FOV solid angle; \(A_{z}\) is the area at depth \(z_{i}\) seen by the receiver; \(x_{i},y_{i}\), and \(z_{i}\) are the photon's position in Cartesian coordinates.  
At each photon scattering event, a photon is scattered into a new trajectory and the scattering angle \(\theta\) is determined by the LUT method described in the previous section. Meanwhile, the photon's weight \(\mathrm{w}(\mathrm{i})\) is multiplied by the single scattering albedo \(\mathrm{w}(\mathrm{i})\). If a photon's weight drops under the preset threshold of \(10^{-4}\), a roulette procedure is triggered and employed. In this study, divided layer vertical resolution can reach up to 0.11 m.  
Figure 2: A cdf of Fournier-Forand phase function as tabulated using Equation (4).
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Comparison
#### 5.3.3 Comparison on Virtex 6 Platform  
When compared to the architecture presented in [16], our proposed non-pipelined design operates at a higher clock frequency and delivers a better throughput/area ratio, albeit using slightly more hardware resources. This indicates that our design achieves improved computational efficiency without compromising on performance. On the other hand, our proposed 2-stage pipelined architecture consumes fewer resources, operates at a higher clock frequency, and achieves an improved throughput/area ratio. This demonstrates the optimization achieved by our design in terms of resource utilization and performance. Moreover, compared to the approach proposed in [25], our proposed non-pipelined design achieves a higher clock frequency and a better throughput/area ratio while utilizing significantly fewer hardware resources. This indicates that our design optimizes the utilization of available resources, resulting in improved performance. Conversely, our proposed 2-stage pipelined architecture exhibits a higher clock frequency but consumes more resources. However, this increased resource usage is justified by the improved throughput/area ratio, highlighting the efficiency of the design.  
In comparison to the non-pipelined architecture proposed in [29], the proposed architecture demonstrates significant optimizations. The proposed approach achieves a reduction of 11.2% in hardware resource utilization while operating at 35.37% lower frequency. Despite the lower frequency, the architecture achieves a notable improvement of 1.25 times higher throughput/area. These results indicate that the proposed hybrid algorithm offers superior resource efficiency and computational performance compared to the approach presented in [29]. Additionally, when comparing the proposed architecture to the two-stage pipelined architecture introduced in [29], further improvements are observed. The proposed approach reduces hardware resource utilization by 7.3% and achieves a modest increase of 1.3% in operating frequency. Furthermore, the architecture demonstrates a significant enhancement in computational efficiency with a 1.37 times higher throughput/area. These findings underscore the advantages of the proposed architecture in terms of resource utilization and overall performance compared to the two-stage pipelined architecture presented in [29].
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 1 Introduction
The authors of [37] trained a CNN to estimate the direction of arrival of interference with scanty snapshots and, as a result, to have an undistorted beamformer response to the wideband signal of interest. The CNN helped solve the synthesis task by mapping the stepped-impedance planar filter geometry and its frequency response using top-view images of the planar filter [38]. Datasets of the parameters and characteristics of microwave devices for ANN training are usually quite small due to the complex and time-consuming procedure of data collection using commercial software packages like CST Studio Suite(r), HFSS(r), or Sonnet(r) [39]. CST Studio Suite(r) works on basis of the finite-difference time domain (FDTD) method, HFSS(r) uses the finite element method (FEM), and Sonnet(r) uses the method of moment (MoM). All of these methods belong to a family of techniques which use a differential form of Maxwell's equations. The authors consider such datasets reliable, since they are obtained using traditional full-wave methods. For example, only 64 training and 49 testing samples were collected using HFSS(r) software for mapping the geometry and corresponding electromagnetic parameters of the antenna using cross-sectional binary images [40]. An ANN's training and prediction accuracy are usually evaluated with measures such as the mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) [41]. In this article, significant attention is paid to meander structures (MSs), which are more commonly applied in microwave devices for the purpose of miniaturization [42]. MSs allow miniaturization of microwave devices while maintaining the operational parameters of the device [43]. Three scientific problems are investigated in this article. Firstly, the design of one unique meander structure using traditional full-wave methods is time-consuming and takes approximately two hours. This is especially relevant when simulations need to be repeated many times until an MS with the desired characteristics is achieved. Secondly, gathering a correctly structured training dataset is complicated and has a significant impact on the accuracy of neural network training. Thirdly, selection of the type and structure of the ANN is complex and depends on the training dataset. Therefore, the dataset and ANN must be selected individually each time in order to maintain a high prediction accuracy. ANNs are rarely applied in the design and analysis of meander structures. Therefore, the applied studies on the possible usage of ANNs in order to predict preliminary characteristics, such as \(S_{11}\), \(S_{21}\), and the group delay \(t_{\text{d}}\), hold the potential to accelerate the meander structure design process by an order of magnitude while maintaining the desired prediction accuracy.
## 3 Topological Classification
### Hybrid FCS and Battery System
#### 3.1.4 Indirect Connection of Both FC and Battery  
Figure 10 depicts the connection between the FCS and the motor DC bus through a UDC, and the connection between battery packs and the motor DC bus through a BDC. This configuration helps in minimizing the adverse impact of power fluctuations on the FCS, and improves the battery's power delivered and absorbed control [49]. The connection can also regulate the bus voltage level. In addition, the voltage matching requirements for both the FCS and battery are significantly reduced, and this simplifies the control of each component's operation [50]. However, this configuration also negatively impacts the overall efficiency of the system, increasing layout space, manufacturing cost, ripple current, and the need for a larger filter capacitor [51]. To solve these issues, multi-port DC/DC converters could be utilized [52, 53]. This architecture is typically utilized for very demanding power systems. Table 1 provides a comprehensive comparison between the advantages and disadvantages of the four topologies.  
Figure 10: FC and battery indirect connection.  
Figure 9: Battery direct connection.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Hybrid Protocols
#### 3.3.1 MAC Protocol for Spectrum-Agile Wireless Networks  
The MAC protocol for spectrum-agile wireless networks (OS-MAC) [79] is a cluster-based MAC protocol for CRAHN. In OS-MAC, the author assumed the presence of a CCC where inter-channel control traffic, as well as the clustering operation take place. On the other hand, intra-cluster communication is accomplished on the Data Channel (DC) nominated by the cluster. CR users with solitary half-duplex transceivers need to switch the use of the data frequency and the CCC. To deal with the access in both the authorized and unlicensed spectrum, CR users self-organize into clusters such that every one of the individuals from the same cluster needs to communicate with each other. The cluster leader, called the delegate, is the only active node in the cluster at any given time. The other nodes keep their radio tuned to the CCC. For accessing the spectrum band, cluster members use IEEE 802.11 Distributed Coordination Function (DCF) access mode.  
The OS-MAC uses periods in time that are called Opportunistic Spectrum Periods (OSPs), which are further divided into the delegate phase, select phase and update phase. Each delegate acquires information about DCs traffic load in the update phase and transfers it at the beginning of the next OSP. Based on this information of traffic, each cluster selects a DC in the select phase. The primary node that could effectively convey a message becomes the cluster delegate, which takes place in the delegate phase. This active cluster delegate also supervises the CCC for staying alert about the spectral information of the environment. In the update phase, it sends information about traffic on its cluster to other delegates, while nodes inside its cluster keep on accessing the DC. By exchanging this information with the neighboring cluster, the delegate may change the spectrum used in the cluster.