esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Ultrastructural Distortion  
An ultrastructural analysis of barley roots that were treated with (-) AuNP showed that the peripheral cells of the root cap cells produced a thicker network layer on the wall surface compared to the control roots. This layer was composed, among others, of pectins, which was confirmed by many studies [48; 49; 50]. The retention of NP in this layer is because the root surface is negatively charged, and therefore the (-) AuNP were repelled and were located only within this external network of pectin. In the nuclage layer, several functional groups (-OH and -CO) were detected that might be involved in the interaction with negatively charged ions [51].  
From the studies presented here, it appears that the cell ultrastructure was also influenced by NP, which was evidenced by the increased area of the periplasmic space, a distortion of the organelles, including the breakdown of the tonoplast and the presence of electron-dense spots within the cytoplasm. Studies on _Vicia faba_ roots that were treated with TiO\({}_{2}\) NP showed that such treatment resulted in the presence of an electron-dense cytoplasm and numerous not well recognisable organelles and weakly electron-lucent bodies of about 0.3-0.5 mm diameter [52]. In the roots of _Hordeum sativum_, there were also changes in the ultrastructure of the root cells [53]. In _Pisum sativum_, the roots that were treated with TiO\({}_{2}\) NP, there were cell ultrastructure changes such as plasmolysis, mitochondria with swollen cristae and crystals as well as a cytoplasm with the features of degeneration [54]. Alterations in the root cell ultrastructure were also detected in _Oryza sativa_ plants that were treated with ZnO NP [55]. In these plants, besides the ruptured cell wall and changed ultrastructure of organelles, an increased level of starch was detected. Disruption of the root cell ultrastructure was also described for tobacco plants that were treated with iron oxide NP (Fe\({}_{3}\)O\({}_{4}\) NP) [56]. Changes in the ultrastructure of the root cells of _Brassica napus_ after treatment with ZnO NP were described, and when the observed differences were comparedto the control plants, they concerned the degree of cytoplasm density, the appearance of the periplasmic space and changes in the ultrastructure of the plastids and mitochondria [57]. In the case of barley that was treated with Cu NP, it was shown that NP accumulated in the intercellular spaces and altered the histology of the roots [12]. The presented research also showed that at the ultrastructural level, NP significantly affected the root cells. The detected changes for barley roots that were treated with (-) AuNP were similar to those that were described for other crop plants. The ultrastructural changes affected the proper functioning of the cells, especially disturbances in the structure of the mitochondria and plastids and the disruption of the continuity of the tonoplast, which indicated that cells might decay under the influence of NP.
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 3 Dynamic Analysis of the AUH  
Steady linear motion, fast turning, and small turning radius are important requirements for high maneuverability. Generally speaking, for underwater vehicles, a typical six-degree-of-freedom system with over-actuation is a prerequisite for achieving high maneuverability. For example, the hovering type AUV 'cyclops' has 8 thrusters in order to achieve high maneuverability [17]. However, too many propulsion components will compromise the good hydrodynamic characteristics of the disc shape and increase the loss of energy, as well. Therefore, based on the dynamic analysis in this section, we will attempt to achieve high maneuverability of the AUH with as few possible propulsion components as possible.
## 4 Analysis and Simulation Verification
### Optimization of One Parameter
The ranking of the control parameters for VSCs based on their impact on system A stability is as follows: \(k_{\text{ip1}}>k_{\text{ip3}}>k_{\text{ip2}}>k_{\text{p1}\text{ip2}}>k_{\text{ p1}\text{ip3}}>k_{\text{p1}\text{p1}}\), which is consistent with the theoretical analysis results. The above simulation results verify the correctness of the proposed method in this paper. This method can intuitively and quantitatively analyze the impact of parameters on system stability, and optimize the stability-weakest parameter to effectively improve system stability.
## 5 Challenges and Future Research Direction
### Reactive and Proactive Flow Table Rule Installation  
Previous sections have discussed the potentialities of both reactive and proactive flowtable rule allocation approaches. Table 6 summarizes the challenges associated with each approach. Therefore, efficient flow rules allocation should combine both reactive and proactive to improve the flowtable usage and reduce overhead, packet delay. It is recommended that re-routing rules [7] must be completed within 25 milliseconds (ms) to meet stringent QoS requirements of real-time applications. One way is to use proactive entries allocation for delay-sensitive application. While non-delay application or best-effort traffic should benefit from the reactive approach. It will be an interesting area of research to devise a scheme considering traffic variabilities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Issues** & **Reactive** & **Proactive** \\ \hline Switch flowtable TCAM resource & More storage & Space limitation \\ \hline Frequent and dynamic & Frequent & Less \\ \hline Packet processing delay & High & Low \\ \hline Packet losses & Proportional to the usage & Proportional to the usage of flowtable. \\  & of flowtable: low & High may increase the chance of overflow, which leads to more packet losses \\ \hline TCAM update operation & Less because flows are installed on demand & Hard with a significant delay because flow is installed in advance \\ \hline Controller overhead & Higher overhead because of the frequent controller consultation & Less because already installed \\ \hline Switch overhead & Low & High \\ \hline Scalability & Scalable for a large network & Scalable for a small network \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reactive and proactive rule allocation challenges.
###### Abstract  
Generally, photovoltaic (PV) fault detection approaches can be divided into two groups: end-to-end and threshold methods. The end-to-end method typically uses a deep neural network (DNN) to learn fault patterns from labeled datasets, which directly detect whether faults occur or not. The threshold method first estimates power generation and uses thresholds to detect atypical deviations of measured values from estimated ones. The former method heavily relies on fault-labeled data and, therefore, requires the collection of abnormal event records, which is usually difficult, due to the sparseness of these events. The latter method typically uses statistical approaches, such as 3-sigma, to find thresholds, and it can be practically utilized without fault labels. However, setting a threshold with a proper confidence interval is still challenging, as PV power generation is sensitive to variations in environmental conditions, such as irradiance, ambient temperature, wind speed and humidity. In this paper, we propose a novel deep reinforcement learning (DRL)-based label-free fault detection scheme in which thresholds are dynamically assigned with suitable confidence intervals under varying environmental conditions. Various weather properties were used as input features (i.e., states) to a DRL agent, and proper thresholds were estimated in real time from the actions of the DRL agent. To this end, a reward function was designed for learning proper thresholds without fault labels under different weather conditions. To evaluate the performance of the proposed scheme, the PV dataset of the National Institute of Standards and Technology (NIST) was used, as it includes paired records of local weather and PV generations. The DRL-based scheme was compared with static and conventional dynamic threshold methods, based on statistical approaches. The results revealed that the proposed scheme outperformed the existing methods, providing a 5.67% higher \(F_{1}\)-score in the NIST dataset.  
label-free fault detection; photovoltaic systems; deep reinforcement learning; dynamic threshold +
Footnote †: journal: Applied Sciences
## 4 Methodology
### Classification Metrics  
There are four different kinds of records left over after the evaluation: true positives (TP), false positives (FP), true negatives (TN), and false negatives (TN). The metrics are then calculated as. Equations are used to calculate the categorization metrics (16-20) (43).  
1. Predictive accuracy is defined as positive values and is given in Equation (12) \[\text{PPV}=\frac{\text{TP}}{\text{TP}+\text{FP}}=1-\text{FDR}\] (12) where FDR stands for false discovery
2. Recall or sensitivity (true positive rate) \[\text{TPR}=\frac{\text{TP}}{\text{P}}=\frac{\text{TP}}{\text{TP}+\text{FN}}= 1-\text{FNR}\] (13)
3. F-measure \[F_{1}=2.\frac{\text{precision}\cdot\text{recall}}{\text{precision}\,+\, \text{recall}}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}(\text{FP}+\text{FN})}\] (14)
4. G-mean \[G-mean=\sqrt{\text{recall precision}}\] (15)
5. Accuracy \[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{P}+\text{N}}=\frac{\text{TP}+ \text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\] (16) Another way of calculating the accuracy is using this Equation (17) \[acc=\frac{number\,of\,true\,predictions}{total\,number\,prediction}\] (17)  
We use the accuracy \(acc\) for reporting the performance of our algorithms in the evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Value** \\ \hline Number of images & 1120 \\ Number of people & 224 \\ Number of male & 176 \\ Number of female & 48 \\ Resolution & 320 by 240 pixels \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of IIT Delhi Iris Database attributes.
# Evaluation of the Nasolabbial Angle in Orthodontic Diagnosis: A Systematic Review
## 4 Discussion
### NLA in Treatment of Class II Malocclusion  
Regarding the variations in the NLA as recorded in studies concerning class II malocclusions, there were no statistically significant differences when patients were treated with functional orthodontic, orthopedic devices or with brackets. [PERSON] et al. reported a correlation between changes in the NLA and the posterior occlusal plane in patients with class II malocclusion [18]. [PERSON] et al. recorded a greater increase in the NLA in patients treated with Herbst (118.64\({}^{\circ}\)) compared to patients treated with removable appliances for mandibular advancement (114.78\({}^{\circ}\)) [7]. [PERSON] et al. did not find significant variations in NLA values in their study comparing the effects of two different devices (Distal Jet and Jones Jig, respectively) used for the treatment of class II malocclusion, with a control group of untreated patients [8].  
The results for two articles evaluating the variations of the NLA obtained after treatment with extraoral tractions were contradictory. [PERSON] et al. did not record statistically significant changes in NLA values after the \"short-term\" (as defined by the authors) treatment of 930 patients with extraoral tractions. The effects achieved on the NLA were associated with the degree of protrusion of the maxilla before receiving orthodontic treatment and there was no relationship with the modification of the longitudinal growth recorded in these patients. Moreover, there was no statistically significant difference between the three types of extraoral tractions studied, which were, respectively, high traction, cervical traction, and combined traction [19]. However, [PERSON] et al. did record differences that were statistically significant in their study of 44 subjects comparing patients with class II malocclusions treated with extraoral cervical traction to patients treated with C-palatal plane on orthodontic mini screws [20]. Although the results obtained by these two studies were contradictory, it should be noted that 930 patients were included in the study by [PERSON] et al., a much larger sample of orthodontic patients with class II malocclusion than the 44 patients treated by [PERSON] et al. Furthermore, it should be considered that the first study was about growing patients, while the second one dealt with adult patients.
## 2 Materials and Methods
### Model Architecture  
The proposed model, SDKU-Net, is built upon the U-Net architecture, which features an encoder-decoder structure with skip connections. This design enables the model to effectively capture both global context and local details. As illustrated in Figure 2, each block within the encoder and decoder comprises three convolutional layers with kernel sizes \(\mathrm{k}-1\), \(\mathrm{k}\), and \(\mathrm{k}+1\), along with Batch Normalization (BN) applied after each convolutional layer, followed by activation functions (ReLU). This configuration enhances training stability and efficiency by normalizing the inputs to each layer. Additionally, the incorporation of residual connections facilitates gradient flow throughout the network.  
One of the key innovations of SDKU-Net is the dynamic adjustment of kernel sizes in the convolutional layers. The kernel size is adaptively modified based on the validation IoU metric during training. This mechanism allows the model to concentrate on capturing finer details when performance stagnates or declines, thereby improving feature extraction capabilities without overfitting to specific patterns. In this approach, \(k\) represents the filter size in all convolutional operations across different resolutions. Each block applies three convolutions with kernel sizes \(k-1\), \(k\), and \(k+1\), ensuring multi-scale feature extraction. The value of \(k\) is dynamically adjusted between 2 and 6 based specifically on validation IoU performance. This mechanism prevents data leakage and maintains the integrity of the model's learning process.  
An additional novelty is the use of an Average Pooling layer instead of a Max Pooling layer to reduce the spatial dimensions of the feature maps. While Max Pooling retains only the maximum value in each pooling region, Average Pooling considers all values within the region. This characteristic helps preserve spatial information and contributes to better feature representation, which is particularly beneficial in tasks requiring nuanced detail recognition. Although this proposal could be adapted to work with UNET++, we selected UNET due to its lower computational cost and higher interpretability. These characteristics enhance the adaptability of our approach across a wider range of hardware configurations, ensuring feasibility in resource-constrained environments.  
Figure 1: Methodological framework of the SDKU-Net model.
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 5 Results  
The results first show the tracking performance for select cases, then the PVT performance. Finally, the full PVT results are tabled for a detailed comparison.  
\begin{table}
\begin{tabular}{l l c c} \hline
**Metric** & **Unit** & **Min Value** & **Max Value** \\ \hline Signal generator output power \(P_{t}\) & dBm & \(-70\) & \(0\) \\ \(I/N_{0}\) & dBHz & \(51.54\) & \(121.54\) \\ INR for a \(10\,\text{MHz}\) bandwidth receiver & dB & \(-18.46\) & \(51.54\) \\ INR for a \(50\,\text{MHz}\) bandwidth receiver & dB & \(-25.45\) & \(44.55\) \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of interference power metrics.  
Figure 6: Estimated \(I/N_{0}\) from the \(4\,\text{MHz}\) bandwidth filtered noise interference scenario.
## 3 Proposed Framework
### Foam Effects Based on Projective-Space
#### 3.1.2 Foam Particle Generation  
In this section, we extract 2D candidate regions where foam can be generated using the projection map described above (see Equation (6)).  
\[\mathbf{C}=\Big{\{}(i,j,k)|d>\gamma,z\in\mathbf{Z},d\in\mathbf{D},(i,j)\in \mathbb{R}^{N_{x}\times N_{y}}\ \Big{\}}, \tag{6}\]  
where \(\gamma\) is the threshold used to find the fast flow region, and in this paper, it is set to 0.0001. \(\mathbf{Z}\) and \(\mathbf{D}\) are the depth and acceleration maps obtained through projection. If the \(\gamma\) value is reduced, foam is generated even in the slow flow region, and the user can easily control the amount of foam generated by using this value. The 'Marching Squares' algorithm [38] was  
Figure 4: Acceleration map projected onto screen-space (red: fast acceleration, blue: slow acceleration, inset image: simulation view) (**a**) Frame 115. (**b**) Frame 243.  
used to triangulate the extracted 2D foam area, and as a result, the candidate group of foam particles consists of 2D triangles. These triangles are transformed into 3D space through the inverse transformation of Equations (1) and (2). The coordinates of the transformed triangle are calculated with Equation (7).  
\[\begin{bmatrix}x\\ y\\ z\\ 1\end{bmatrix}=\mathbf{Q}\begin{bmatrix}(-1+2x_{p}/W)w\\ (-1+2y_{p}/H)w\\ z_{p}\\ w\end{bmatrix}, \tag{7}\]  
where  
\[w=\frac{1-q_{4,3}z_{p}}{q_{4,1}(-1+2x_{p}/W)+q_{4,2}(-1+2y_{p}/H)+q_{4,4}}, \tag{8}\]  
where \(q_{i,j}\) is an element of the inverse projection matrix \(\mathbf{Q}\), and as mentioned earlier, perspective division does not apply to \(z_{p}\)[38]. The number of foam particles is determined based on the Weber number. (It is recommended to read the paper of [PERSON] et al. [26; 27] for more details.)  
The next step is to calculate the wave pattern of the foam using the refined projection map. First, we calculate flow-based curvature in the projected space using the method proposed by [PERSON] et al. [36] (see Equation (9)).  
\[\frac{\partial z}{\partial t}=H^{*}, \tag{9}\]  
Here, \(t\) is the time-step, and \(H^{*}\) is the mean curvature of the depth map calculated in the 2D projection space. The depth map used in this process is \(\mathbf{Z}\). Since the method of [PERSON] et al. [36] contains a noise pattern, it is necessary to repeatedly calculate the curvature in order to weaken it. Since this method erases the change pattern of the original curvature, in this paper, the wave pattern of the foam is calculated by modifying Equation (6) as Equation (10).  
\[\mathbf{C}^{*}=\Big{\{}(i,j,k)|d>\gamma\,\cap\,f>\beta,f\in\mathbf{F}_{s}(i,j) \in\mathbb{R}^{N_{x}\times N_{y}}\,\Big{\}}, \tag{10}\]  
where \(\mathbf{F}\) is the curvature of the depth map, and \(\beta\) is a threshold value for finding a high curvature region, which is set to 0.1 in this paper. For advection of foam particles, the grid velocity field and water particles calculated through FLIP were used. In this process, [PERSON] and [PERSON]'s approach [26; 27] was used as it is. (It is recommended to read the paper for a more detailed explanation.)
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Problem Formulation  
The network model of a vehicular ad hoc network can be represented by an undirected graph \(G=\{V,E\}\), where \(V\) is the set of vehicles and \(E\) is the set of communication links connecting them. A communication link is established between two vehicles when each one is in the communication range of the other. Each vehicle has an IEEE 802.11p-compliant radio transceiver, through which it can communicate with other vehicles and GPS to obtain its location coordinates. The definition of neighbour relation noted by \((u,v)\) is: vehicle \(u\) and \(v\) are one-hop neighbours if \(\exists\ l\in E\) connecting directly \(u\) and \(v\). \(N(u)\) is the set of all one-hop neighbours of vehicle \(u\); \(N(u)=\{\forall\ v\in V,\exists\ (u,v)\in E\}\). Each link between two vehicles \(u\) and \(v\) is associated with a link weight vector \(w(u,v)=(w_{0},w_{1},\ldots,w_{n})\) in which \(w_{i}\) is an individual weight component for a given factor, that is, a single metric such as \(MOS_{routing}\), link lifetime, and distance.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 5 Conclusions  
In this paper, a quantum multi-access channel model based on symmetric coherent states is proposed, and an expression for the average inter-user interference is given. Based on this, the application of quantum detection in a multi-access channel with coherent-state signals is studied, and the channel capacities of MED and USD are given. The results show that MED is an optimal detection method that can reach the maximum channel capacity. The ideal USD measurement can discriminate between signals from different users with no errors, although its channel capacity is theoretically lower than that of MED, so USD is a better multi-user detection method than the MED measurement in scenarios where information error correction is considered.  
Without loss of generality, we studied a two-user channel and examined its maximum capacity and inter-user detection error probability. Theoretical analysis and numerical results show that the maximum channel capacity of USD is smaller than that of MED and that there exists a region of channel capacity that is reachable by MED but not by USD. This verifies that USD sacrifices a portion of the channel capacity to achieve error-free discrimination. In addition, under the influence of thermal noise, the inter-user detection error probability of USD peaks at an average signal photon number of about 1.0 to 1.5 and decreases with increasing signal power. However, after the average photon number exceeds five, the USD can overcome the effect of noise by increasing the signal-to-noise ratio, showing the advantage in error correction. Additionally, we investigated the BER-SNR curves of the USD method compared to NOMA.  
We have given theoretical results for general multi-user detection. In future work, we will focus on numerical simulations for the case of large-scale user numbers, as well as the study of large-scale user numbers in the noise case.  
Conceptualization, W.Y.; methodology, W.Y.; writing--original draft preparation, W.Y., F.C., Z.X., Y.Z. and C.Z.; writing--review and editing, W.Y., F.C., Z.X., Y.Z., A.X.L. and C.Z.; supervision, W.Y. and C.Z.; funding acquisition, W.Y., A.X.L. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant numbers 61501247, 62071240, 61703212, 61872082 and 61472184; the Six Talent Peaks Project of Jiangsu Province, grant number 2015-XXRJ-013; the Natural Science Foundation of Jiangsu Province, grant number BK20171458; the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China, grant number 6 KJB520030; the Guangdong Leading Talent Program, grant number 2016 LJ06D658; the Guangdong Innovation Team Program, grant number 2018 KCXTD030; and the PAPD and CICAEET funds.  
Not applicable.  
The authors declare no conflict of interest.