esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
As shown in Figure 9, there is some improvement in the calibration after excluding the divergent error parameters, and there is a significant improvement in its offset error, gain error, and first-order memory effect. According to the frequency domain analysis of the TIADC system in Section 2.3, the noise generated at 0 MHz, 197 MHz, 250 MHz, 303 MHz, 447 MHz, and 500 MHz by order 0 and 1 of Volterra series is effectively suppressed, and the noise generated by the error of order 3 of the Volterra series is also slightly improved. The SFDR becomes 76.88 dB, the SNR becomes 74.45 dB, and the ENOB becomes 12.06 bits. After the offline LS method to obtain the error parameters, the online RLS calibration was performed with the error parameters after excluding the divergence series kernel as the recursive initial values to ensure that the recursion reaches stability. The spectrum is plotted as shown in Figure 9. It can be seen from the graph that the errors caused by various mismatches have improved significantly. At this point the SFDR improves to 100.51 dB, the SNR improves to 94.79 dB, and the ENOB improves to 15.42 bits. After the sinusoidal fitting calibration carried out by the joint calibration algorithm proposed in this paper, to further verify the practicability of the proposed algorithm, the recursive value after stabilization was used as the error parameter to reconstruct the error to calibrate the actual signal. A random analog signal was input into the system, and the final time-domain output is partly shown in Figure 10 and the error bit comparison before and after calibration is shown in Figure 11. As is shown in the figure, compared to the  
Figure 8: Spectrum of the TIADC model with memory nonlinearity mismatch. Figure 9: Spectrum of the TIADC model calibrated by offline LS estimation. output before calibration, the output digital signal after the joint calibration better fits the analog input. The comparison of calibration of different stages is shown in Table 1.  
In order to verify the practicability of the proposed calibration method, analog signals of different frequencies were input into the calibration system in the experiment. Since the SNR of signals shows a linear relationship with ENOB, only the ENOB and the SFDR are plotted, and the final calibration effects of input signals with different frequencies are shown in Figure 5:0 Theoretically, an ADC can sample signals at frequencies less than half of the highest sampling rate according to the Nyquist sampling criterion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Calibration Mode** & **SFDR (dB)** & **SNR (dB)** & **ENOB (Bit)** \\ \hline Before calibration & 64.29 & 62.35 & 10.06 \\ After LS calibration & 76.88 & 74.45 & 12.06 \\ After joint calibration & 100.51 & 94.79 & 15.42 \\ \hline
**Final Increment** & 36.22 & 32.44 & 5.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Final calibration effect comparison of the 16-bit ADC. Figure 11: Error bit comparison before and after calibration. Figure 10: Time-domain output of the calibration system.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding
### Parameterized Mutual Information
#### 3.1.1 Estimation of KL Divergence  
KL divergence measures the difference between two probability distributions. Let \(P_{1}(x)\), \(P_{2}(x)\) be two discrete probability distributions defined on the same finite or countable space. Assume that \(P_{1}\) is absolutely continuous with respect to \(P_{2}\), i.e., for every event \(x\), \(P_{1}(x)=0\) whenever \(P_{2}(x)=0\). The KL divergence between \(P_{1}\) and \(P_{2}\) is defined as  
\[D_{\text{KL}}(P_{1}\|P_{2})=\mathbb{E}_{P_{1}}\bigg{[}\log\frac{P_{1}(X)}{P_{2 }(X)}\bigg{]}. \tag{12}\]  
Evaluating KL divergence based on its definition requires knowledge of both probability distributions \(P_{1}\) and \(P_{2}\), which is usually not available in the learning scenarios. A widely-used variational method leverages deep learning to optimize a discriminator network \(T_{\theta}(x)\) to approximate KL divergence. The discriminator network \(T_{\theta}(x)\) is trained with respect to the objective  
\[T_{\theta^{*}}=\arg\max_{T_{\theta}}\big{\{}\mathbb{E}_{P_{1}}\big{[}\log \sigma(T_{\theta}(X))\big{]}+\mathbb{E}_{P_{2}}\big{[}\log(1-\sigma(T_{\theta }(X)))\big{]}\big{\}}, \tag{13}\]  
where  
\[\sigma(x)=\frac{e^{x}}{1+e^{x}}. \tag{14}\]  
Once the discriminator network is optimized, the KL divergence can be estimated as  
\[D_{\text{KL}_{\#^{*}}}(P_{1}\|P_{2})=\mathbb{E}_{P_{1}}[T_{\theta^{*}}(X)]. \tag{15}\]  
This approach provides an efficient estimation of KL divergence and has been widely adopted in deep learning and information theory research [32; 33].
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 3 Experiments
### Discussion  
Experiments show that the proposed balanced loss has superiority in the training of surface defect segmentation models, especially in comparison with other point-wise loss functions CE, WCE and focal loss. The ablation experiment (Table 4) shows that dynamical class weighting and the truncated cross-entropy loss have significant contributions to the performance. In addition, the balanced loss exhibits its advantage on tiny defects, for example the spot and pit in Table 5. Region-based loss functions (Dice loss, inverse weighting loss and Lovasz loss) also show competitive performance. Unifying the merits of region-based loss functions and the balancing mechanisms of the balanced loss is an intriguing future research direction.  
As for the segmentation models, networks with skip connections between the encoder (feature extractor) and the decoder (up-sampling modules), including U-Net and DeepLabV3, have better performance with the balanced loss. The recognition of surface defects is mainly dependent on a limited region of features at the center of the receptive field, which is very different from natural scene objects that have important context clues. Considering this fact, networks that link the output and low-level feature maps have a shortcut to efficiently exploit local features.
## 6 Conclusions and Future Steps  
### Conclusions  
In this paper, we have presented a unified system for AR-integrated drone use, encompassing gesture control, tracking, and camera feed visualization in context with the user's environment. We have outlined the overall system architecture as well as individual components, described the mechanics of the vital tasks necessary for its function and conducted both objective and subjective evaluation experiments. Different aspects of the proposed solution were evaluated, with the results described in Section 5, including gesture selection and usability; drone responsiveness in terms of time lag; drone tracking accuracy and the efficacy of compensation; and video transmission quality.  
Although the presented implementation focused on specific hardware (the HoloLens and DJI drones), the underlying logic and architecture are modular and not tied to the current hardware choices. As mentioned in the Section 4.2, the same gesture control methodology has been implemented and tested successfully with alternative hardware, including a regular webcam or smartphone camera. Hence, integration with different drone models or AR hardware is possible and mostly a matter of implementation.  
The presented system has largely achieved its objectives, and future plans include both refinements and wider validation. In particular, gesture control has proved both intuitive and accurate, providing the same level of control as a traditional handheld remote.  
Figure 13: IMU measurement compensation. (**Left**): MSE of the error without compensation (solid lines) and with (dashed lines) for different pitch values. Note that the vertical axis is in logarithmic scale. (**Right**): Comparison of average MSE values with and without compensation, for different pitch values. Compensation has a drastic impact on slower speeds, where the error is greatest.  
However objective evaluation with real drones (i.e., not a simulator) has not yet been completed and is scheduled for the near future. This should be in comparison with the handheld remote controller, with pilots being asked to place the drone in a specified position. Measurements could include accuracy (distance from the specified position) and time (to achieve that position). In addition, more complex drone behaviors can be tied to either specific gestures or virtual buttons in future implementations; such behaviors can include flying in a circular or other pattern or returning to land either at the take-off location or near the user.
## 4 Simulation Results and Performance Analysis  
### Experimental Setup  
The proposed method was simulated in TETCOS NetSim(tm) V10.2 using SUMO for generating realistic vehicular traffic traces. A highway with four lanes in each direction for 5 km length was considered under simulation. The vehicle speeds varied from 7 m/s to 27 m/s. If the total number of vehicles moving in the same direction in all four lanes on the road per kilometer was less than or equal to 15, then it was considered a sparse traffic scenario, if it was between 16 and 30, then it was considered an average traffic scenario, and if it was between 31 and 50 (and above) it was considered a dense traffic scenario. The performance of the protocol was measured in all three scenarios. The vehicle's movement was predicted using the Modified Krauss Mobility Model which was implemented in SUMO with some modifications to the original Krauss model. Matlab(r) was interfaced with TETCOS NetSim(tm) for implementing the clustering process. The performance of the proposed algorithm was evaluated in terms of the cluster stability, cluster density, cluster head density, cluster state distribution throughput, packet delivery ratio, end-to-end delay, packet collisions ratio, and packet loss ratio. The number of messages transmitted for cluster formation and maintenance was also extensively studied.
# A Robust Wind Turbine Component Health Status Indicator
## 3 Results
### Results Site 2
As shown in this paper, the best failure index performance has been detected for the gearbox case. In Figure 14, a summary of the observed results for this site is presented through a box and whisker plot, aiming to show the performance of the calculated index. This allows for a visual comparison of the range of \(FI\) values and the monitored SCADA variables for healthy gearbox cases (in a lighter tone) and faulty gearbox cases (in a darker tone). As shown in this result for site 2, the performance of the fault index for a different site is confirmed. Figure 14 shows that the median values of the \(FI\) signal (horizontal lines in each box) range between 20% and 40% in healthy cases, while in faulty cases they range between 60% and 80%. No significant variations are observed in the median wind speed measurements, which for all eight cases are in a mid-operational range between 6 and 8 m/s. This finding is coherent with the existing literature, where the failures associated with the gearbox component showed a high number in the presence of wind speeds that were steadily in the low or high range. Alternations in wind speed did not seem to play a role in the gearbox failures (see Tables 5 and 8 in [58]). Similarly, generator speed, rotor speed, and power also do not vary significantly between the cases analyzed and observed no relationship with component health. As for the oil input pressure, the oil mechanical pump pressure and the oil electrical pump pressure, there seems to be a slight decrease in the values compared to the healthy cases. However, although it is more accentuated in the case of the oil input pressure, this does not occur in the two failure cases studied. This issue could be due to some unknown operating mode or any type of failure mode. This should be studied further with the maintenance team. In any case, \(FI\) allows simultaneously evaluating the considered variables. The relationship of these predictor variables makes the failure index a good indicator of the component's health. Figure 13: Site 2, Time evolution Failure Index (gearbox). (**a**) Site 2, Faulty case 2 (gearbox). (**b**) Site 2, Healthy case 2 (gearbox).
## 4 Emollients and Their Role in the Treatment of Psoriasis  
### Lipid Emollients  
Emollients are substances and preparations for topical application in the form of a gel, cream, lotion, or ointment. The most important goal of using emollients is to restore the disrupted epidermal barrier and maintain a normal structure with an appropriate composition for as long as possible [45]. Emollients should not contain allergens and irritants. The application of a formulation with such a composition to damaged skin can cause additional breach of the skin barrier, which can manifest as inflammation, swelling, or irritation [46].  
In patients with mild psoriasis, the use of topical emollients is the mainstay of therapy [46]. Regular use of emollients maintains the tightness of the stratum corneum and  
Figure 3: Biologic drugs—interleukin 23 inhibitors [34; 37; 38].  
increases the water-binding capacity of the stratum corneum, resulting in reduced desquamation [47]. The moisturizers in emollients also help normalize hyperproliferation, differentiation and apoptosis [48]. With the use of a moisturizer, damaged skin returns to its normal structure in a process in which lipids play a major role [49]. Skin that contains adequate amounts of lipids, ceramides, fatty acids, and cholesterol forms an airtight lipid barrier. When deficiencies in skin-building components occur, microbial ingress, water loss, and skin cracking occur, ultimately exacerbating psoriatic lesions [50]. Physiological lipids accumulate and are transported between keratinocytes, thus causing the skin barrier to seal [49]. In moderate and severe psoriasis, the use of emollients alone in therapy is insufficient. They can act as an adjunct to phototherapy or glucocorticoids treatment [46]. In 2021, [PERSON] et al. [51] conducted a study on the impact of emollients on epidermal barrier function in patients with psoriasis. The study involved patients aged 18 to 65 years. Skin changes were assessed before and after the application of two emollients: one being petrolatum and the other a water-based formulation without added lipids, containing the following ingredients: emulsifier base NEO PCL O/W, distilled water, Phenonip, and glycerol. Measurements of transepidermal water loss (TEWL) and stratum corneum hydration (SCH) were performed. The results demonstrated a decrease in TEWL following the application of petrolatum, while SCH increased after the application of both the moisturizing agent and petrolatum. Based on the findings, it can be concluded that the simultaneous use of moisturizing emollients and lipid-based agents is justified in order to achieve optimal therapeutic outcomes [51].
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.9 Comparative Study of Temperature-Aware Routing Protocols
To reduce the packet delivery delay, HPR [29] performs better as compared to others, and due to the withdrawal strategy of TARA [28], it has the highest packet delivery delay. In terms of packet delivery ratio, TARA [28] drops more packets due to longer paths while TSHR [57] reduces the packets drop by adopting the shortest paths. Finally, to reduce the temperature rise, HPR [29] results in less temperature rise as compared to others. All temperature aware routing protocols use a global address scheme except RAIN [58], which uses a randomly generated local address scheme. As compared to C-FLOOD, RAIN performs well to reduce temperature rise, power consumption, and packet delivery delay, but has slightly lower packet delivery ratio as compared to C-FLOOD. Finally, the last temperature aware routing protocol M-ATTEMP [59] has increased the packet delivery ratio and decreased the energy consumption and temperature rise as compared to multi-hop communication.
## 5 Research Directions
In general, packet dropping in visual sensor networks is a result of network congestion or bit errors. When packets are transmitted through wireless links, there is a probability that bit errors occur during the transmission. Many works expect a linear error probability over a single bit, but such consideration is unrealistic. In fact, bit errors appear in bursts and large packets are more likely to be discarded than small packets [39,40]. As smaller packets lead to additional protocol header overhead, researchers should be worried about the ideal size of packets carrying multimedia encoded data. An interesting approach is cross-layer packet size optimization, as discussed in [40]. However, most of the survey works have no clear considerations of the size of transmitted packets. When the packet size is defined, as in [38], which defines RTP packets of 1024 bytes, a clear arguing of the reasons for such choice is missing. As energy constraints should remain a major concern for the upcoming years, multimedia-based cross-layer design in visual sensor networks has to save energy while achieving the desired optimization, or at least not incur in additional energy consumption. There are many energy-unaware works in the literature proposing multimedia-based cross-layer optimization [73-75]. Future research could further enhance their contributions adding energy saving mechanisms, achieving feasible solutions for visual sensor networks. New challenges in transmission rate adjustment, energy preservation, congestion control, error recovery, multipath transmission and in-network compression should still arise, requiring additional research in multimedia-based cross-layer optimization. Some of these challenges may be more stringent in specific monitoring applications, concerning for example multi-tier architectures [76,77] or heterogeneous sources as described in [64]. Other issues as source nodes mobility and coverage preservation also request from the academic community new research efforts, directly impacting the employed coding technique and the adopted cross-layer solution. Finally, priority-oriented scheduling based on appropriate computer vision techniques, such as foreground distinguishing, should also be considered in future work [78,79].
## 3 Results and Discussion
### Flame Appearance  
Figure 4 shows the photographs of methane diffusion flames under different pressures. The width of flame is defined as the maximum radial width of flame envelope. As the pressure decreases, the flame width increases, providing an overall shrinking appearance to the flame as noted previously [9, 11, 21, 24, 25, 26]. On the one hand, the reduced pressure leads to an increase in the mass diffusion coefficient of combustible gases, which makes it more easily diffusible and results in an increase in the combustion radius. On the other hand, the yellow carbon black, due to its higher density than gaseous products, cannot diffuse as easily as gaseous products. Therefore, soot particles are lifted upward, mainly due to the buoyancy force created by the hot gas, which leads to a stubby flame under sub-atmospheric pressure [23, 27]. However, the height of maximum radial width of flame envelope increases with decreasing pressure. This is because the combustion reaction is delayed under sub-atmospheric pressure. As the pressure drops from 97.75 kPa to 61.66 kPa, the flame brightness darkens under fuel-rich combustion. It can also be observed that the rise jet velocity and reduced burning rate result in an increase in the blue region height of the flame under sub-atmospheric pressure. Figure 5 shows variations of flame length with pressure and \(\alpha\). It is found that the flame length under fuel-lean and -rich combustion shows a reverse trend with pressure. Under fuel-lean combustion, the \(\alpha\) of flame region is larger than 1, the air/methane mixing at the flame region increases and the buoyancy effect decreases with decreasing pressure, which leads to a decrease in the flame length under sub-atmospheric pressure. However, under fuel-rich combustion, more unburned components are drawn into the flame area, deepening the lack of oxygen in the flame. A large amount of smoke generates to escape the flame, resulting in a decrease in flame length. The behavior we observe in the flame height variation with pressure conflicts with previous studies in an open environment [9, 11, 12].  
Figure 4: Photographs of methane diffusion flames under different pressures. (Fuel-lean combustion: \(\alpha=1.3\) and \(1.2\); fuel-rich combustion: \(\alpha=0.9\)).
## 2 System Model and Evaluated Precoding Schemes
### Fully Digital Precoding Schemes  
In fully digital implementations, where \(P=N_{t}\), precoding is commonly done in the digital baseband, thus allowing a very flexible design of the precoding matrix or vector. Many fully-digital precoding designs for MU-MIMO systems can be found in the literature [22]. Although the optimum precoders, in terms of achieving capacity, require non-linear implementation, in this work, we will focus on linear precoding, which is known to provide a good tradeoff between performance and complexity. In particular, we selected the well-known Maximum Ratio Transmission (MRT) linear precoder, which is designed to maximize the SNR at the intended user and, thus, it only requires the knowledge of the channel vector between UE \(k\) and its serving BS in FB \(b\), denoted by \(\mathbf{h}_{k,b}\in\mathbb{C}^{1\times N_{t}}\). The MRT precoder in FB \(b\) for UE \(k\), \(\mathbf{w}_{BB_{k,b}}\), is computed as:  
\[\mathbf{w}_{BB_{k,b}}=\frac{\mathbf{h}_{k,b}^{H}}{||\mathbf{h}_{k,b}||}, \tag{1}\]where the superscript \({}^{H}\) stands for conjugate-transpose operation. The main reason for the selection of this digital precoder is that, when the number of antennas is very high, its performance is very close to the performance of other precoding schemes that require higher computational complexity, such as zero-forcing (ZF) or regularized ZF [23].  
We focus on digital MU transmission, that is, a set of as many users as BS antennas can be served simultaneously at each subframe by multiplexing them in the spatial domain. Furthermore, we will investigate the effect of multiplexing different sets of users in different FBs of each subframe. To this end, two variants of digital precoding have been implemented and simulated, denoted by digital precoding non frequency selective (DP NFS) and digital precoding frequency selective (DP FS). In DP NFS, all FBs are allocated to a single set of UEs per subframe, while, in DP FS, the allocation is optimized per RB, thereby allowing different sets of UEs to be scheduled per subframe in different FBs.