esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Loops in GCS-Routing
#### 3.4.2 Loop-Free GCS-Routing Proof
**Theorem 1**: _There is one shortest path from \(s\) to \(v\) for any vertex \(v\in V(G_{s}^{t})\) at any time instant \(t\in\) any interval \([T_{i}+\delta(T_{i},T_{i+1}),T_{i+1}]\), \(i\geq 1\)._  
Suppose there are two simple paths from \(s\) to vertex \(v\): \(p_{1}\) and \(p_{2}\). The former can be decomposed into \(s\rightsquigarrow v_{i}\rightsquigarrow v_{j}\rightsquigarrow v_{l}\rightsquigarrow v\), and the latter into \(s\rightsquigarrow v_{i}\rightsquigarrow v_{k}\rightsquigarrow v_{l}\rightsquigarrow v\) (see Figure 8). Then, the predecessor vertex of vertex \(v_{l}\) on \(p_{1}\) from \(s\) to \(v\) is \(v_{j}\), and the vertex on \(p_{2}\) is \(v_{k}\), which implies \(v_{j}=v_{k}\) since GCS-routing generates a shortest paths tree rooted at \(s\). This contradicts the assumption. Consequently, GCS-routing generates a shortest paths tree for each vertex \(s\) that can be used to deliver packets generated by \(s\), and it maintains the tree even if topology changes occur, except for the short period when all routing messages are delivered to all drones.
# Cable-Driven Exoskeleton for Ankle Rehabilitation in Children with Cerebral Palsy
## 2 Exoskeleton Development and Control Methodology
### Discover2 Walk Overview
#### 2.1.2 Ankle Control Module  
The ankle module allows the control of 2 DOFs, both translational in the longitudinal and vertical directions of gait (Cartesian axes \(x\) and \(z\)). In this section, we will briefly describe the operation of the previous system and the reason to be replaced by the new module to be described later.  
The module uses 3 Dual Shaft Motor-D6374 150 KV brushless motors (ODrive Robotics, Sunnyvale, CA, USA) per ankle, 6 motors in total. Each of the motors has an associated AMT102-V encoder (CUI Devices, USA), which, as in the pelvis module, allows the length of the cables to be controlled. Each motor acts on a Kevlar cable, which in turn connects to commercial foot straps (SYL Fitness, La Mesa, CA USA).
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 3 Methods
### Loss Function  
The loss function in our method is multistage completion loss L\({}_{\text{com}}\). Because we set the pyramid level N = 3 in our network, the multistage completion loss L\({}_{\text{com}}\) was composed of three parts (d\({}_{\text{CD1}}\), d\({}_{\text{CD2}}\), d\({}_{\text{CD3}}\)):  
\[\text{L}_{\text{com}}=\text{d}_{\text{CD1}}\big{(}\text{F}_{1}^{\text{out}}, \text{F}_{\text{gt}}\big{)}+\alpha\times\text{d}_{\text{CD2}}\Big{(}\text{F} _{2}^{\text{out}},\text{F}_{\text{gt}}^{\prime}\Big{)}+2\alpha\times\text{d} _{\text{CD3}}\Big{(}\text{F}_{3}^{\text{out}},\text{F}_{\text{gt}}^{\prime \prime}\Big{)} \tag{6}\]  
where \(\alpha\) is the hyperparameter, and d\({}_{\text{CD}}\) is the Chamfer Distance proposed by [PERSON] [26]. The mean Chamfer Distance can measure the average nearest squared distance between prediction point cloud F\({}^{\text{out}}\) and ground truth Fgt, which is calculated with:  
\[\text{d}_{\text{CD}}(\text{F}^{\text{out}},\text{F}_{\text{gt}})=\frac{1}{ \text{F}^{\text{out}}}{\sum}_{\alpha\in\text{F}^{\text{out}}}\min_{y\in\text{ F}_{\text{gt}}}\|x-y\|_{2}^{2}+\frac{1}{\text{F}_{\text{gt}}}{\sum}_{y\in\text{F}_{ \text{gt}}}\min_{x\in\text{F}^{\text{out}}}\|y-x\|_{2}^{2} \tag{7}\]  
In detail, d\({}_{\text{CD1}}\) calculates the squared distance between predicted detailed points F\({}_{1}^{\text{out}}\) and the ground truth of the missing region Fgt. Items d\({}_{\text{CD2}}\) and d\({}_{\text{CD3}}\) calculate the squared distance among secondary central points F\({}_{2}^{\text{out}}\), predicted primary central points, F\({}_{3}^{\text{out}}\) and ground truth F\({}_{\text{gt}}^{\prime}\), F\({}_{\text{gt}}^{\prime\prime}\).  
Figure 5: Comparison between (**a**) FPN and (**b**) the proposed BiPPAN decoder.
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
## 3 Method
### Solution Method for Unconstrained Normal Vectors
Let \(\mathbf{A}_{i}=\mathbf{U}_{i}\mathbf{\Sigma}_{i}\mathbf{V}_{i}^{\mathrm{T}}\) be the singular value decomposition \((\mathbf{U}_{i}\mathbf{U}_{i}^{\mathrm{T}}=\mathbf{V}_{i}\mathbf{V}_{i}^{ \mathrm{T}}=\mathbf{I}\) and \(\mathbf{\Sigma}_{i}=diag(\alpha_{i},\beta_{i},0),\alpha_{i}\geq\beta_{i})\). Then, \(\mathbf{R}_{i}=\mathbf{U}_{i}\mathbf{V}_{i}^{\mathrm{T}}\). If \(det(\mathbf{U}_{i})det(\mathbf{V}_{i})=-1\), it is essential to invert the sign of the third column of \(\mathbf{U}_{i}\) (or \(\mathbf{V}_{i}\)) before computing \(\mathbf{R}_{i}\). This inversion ensures that the resulting rotation matrix has a determinant of 1, making it a valid rotation matrix.
# Competing Failure Modeling for Systems under Classified Random Shocks and Degradation
## 6 Conclusions and Summary  
We develop a reliability model for systems under competing risk modes of soft failure and hard failure. The presented model classifies the random shocks into three types according to their sizes. Besides system reliability, competing failure probabilities are also studied. To make the results more accurate and logical, the modified values of system reliability and competing failure probabilities are obtained.  
The hard failures are modeled by the general extreme shock model, while the soft failure is composed of pure degradation and instantaneous degradation caused by extreme shock. The degradation increments caused by random shocks are classified into three types according to their size in this paper, which is more logical. Besides the system reliability, competing failure probability for specific risk modes is derived in this paper. Competing failure probability indicates the failure probability for a certain failure mode (soft failure or hard failure) when the other failure mode is normal. Competing failure probability is significant to find out which risk mode is more likely to occur during the working process. For a system under competing risk modes, it is impossible for both risk modes to occur together. The accurate probabilities need to elide this situation instead  
Figure 6: Probability with different D\({}_{1}\). **(A)**: MSRP; **(B)**: MCFP 1 (Probability of hard failure while there is no soft failure); **(C)**: MCFP 2 (Probability of soft failure while there is no hard failure).  
Figure 7: Probability with different D\({}_{2}\). **(A)**: MSRP; **(B)**: MCFP 1 (Probability of hard failure while there is no soft failure); **(C)**: MCFP 2 (Probability of soft failure while there is no hard failure).  
of calculating by distribution expression directly. The accurate probabilities include the modified system reliabilities and modified competing failure probabilities for both risk modes. The numerical case indicates the presented model is more reasonable in dealing with the practice problems. Sensitivities of typical parameters with changing values are analyzed.  
For further research, the maintenance based on the presented model can be studied. The exact values of thresholds (like \(D_{0}\), \(D_{1}\), and \(D_{2}\)) need to be set according to a practice experiment or engineering case.  
Conceptualization, J.L.; methodology, J.L.; validation, K.Z. and H.P.; formal analysis, K.Z.; writing--original draft preparation, J.L.; writing--review and editing, J.L., K.Z. and H.P. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (Grant No. 52202507).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methods
### Investigation of Missing MODIS FRP Due to Sampling Limitations
#### 3.2.2 Examination of Fire Detection Capability  
The difference between fire the detection capabilities of the MODIS sensor and VIIRS I-band was examined by comparing their minimum, mean, and maximum FRP per fire pixel across swath. Specifically, the extracted contemporaneous fire detections were first grouped by every 1\({}^{\circ}\) VZA. Then, the minimum and maximum FRP estimates were calculated for each group as the FRP values at 0.5% and 99.5% percentiles of all the corresponding pixel-level FRP to avoid potential extreme FRP values.
## 3 Materials and Methods
### Methodology  
#### 3.2.1 Mapping Unit Selection  
A suitable mapping unit is critical for LSM. Five typical mapping units [48] include: (1) grid unit, (2) slope unit (SU), (3) regional unit, (4) homogeneous unit, and (5) subwatershed unit. Among these five units, grid and slope units are frequently used for LSM due to their simplicity. Though the grid unit is simple and easy to operate, it breaks down the slope integrity and reduces the relationship between landslides and slopes. By contrast to the grid unit, the slope unit boundary is more consistent with the ridge and valley lines, and better reflects the topographic features. Therefore, this study selects SU as the basic mapping unit and then divides the study area into 1351 Sus; see Figure 7.  
Figure 6: Hydrological-related causative factors. (**a**) Distance to river; (**b**) TWI; (**c**) Rainfall.
## 4 Conclusions  
For removing FD&C Red 40 dye with the CS-TiO\({}_{2}\)-GLA beads, ANOVA analysis showed a good fit for a 95% confidence level. The optimal operating conditions had values of 55.23 mg L\({}^{-1}\) for the initial dye concentration, 1.73 for the initial pH, and 279.77 mg for the TiO\({}_{2}\) dose, treatment for which the maximum removal was obtained, demonstrating the usefulness of experimental designs involving surface response methodologies as valuable tools to optimize the independent variables and analyze the effect on the response variable.  
The kinetics evaluation for the two systems showed an adequate fit of the intraparticle diffusion model, evidenced by the correlation coefficient R\({}^{2}\) > 0.9661. The parameters \(\alpha\) (related to the initial adsorption rate) and \(\beta\) (related to the activation energy of the chemisorption process) obtained from the Elovich model highlighted the decrease in the activation energy \(\beta\) in the initial adsorption process upon addition of GO to the beads; thus chemisorption interactions predominated in the adsorption mechanism. Similarly, the time required to remove FD&C Red 40 dye decreases when introducing the GO to the beads of CS-TiO\({}_{2}\)-GLA. Finally, the obtained removal results for CS-GO-TiO\({}_{2}\)-GLA beads suggest using these beads as possible candidates for removing neutral and cationic pollutants in water bodies with pH values reaching multiple levels of alkalinity and acidity since the GO improved the removal efficiency of the CS-TiO\({}_{2}\)-GLA beads (run 6 of the CCD) independently of the pH.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11209439/s1](https://www.mdpi.com/article/10.3390/app11209439/s1)]([https://www.mdpi.com/article/10.3390/app11209439/s1](https://www.mdpi.com/article/10.3390/app11209439/s1)), Figure S1. Calibration curve for FD&C Red 40 dye measured at 500 nm. Table S1. Effect of contact time for the adsorption of FD&C Red 40 dye on the various adsorbents.  
Conceptualization, C.D.G.-T.; Data curation, M.A.S., J.G., W.V. and V.A.A.; Formal analysis, M.A.S., V.A.A. and C.D.G.-T.; Investigation, M.A.S., W.V. and C.D.G.-T.; Methodology, M.A.S., J.G., W.V. and C.D.G.-T.; Project administration, C.D.G.-T.; Resources, C.D.G.-T.; Supervision, W.V.; Validation, V.A.A.; Writing--original draft, M.A.S. and J.G.; Writing--review & editing, M.A.S., J.G., V.A.A. and C.D.G.-T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by la Vicerrectoria de Investigaciones of the Universidad del Atlantico (Project number CB92-TGI2018).  
Not applicable.
# Preparation of Hybrid Molybdenum Disulfide/Single Wall Carbon Nanotube-n-Type Silicon Solar Cells
## 3 Results
### Optimisation of the Electrode Transparency  
Previous work to find an optimal thickness for a SWCNT film determined that a film transmittance of \(\sim\)70% gave an optimal efficiency, although thicker films displayed fairly stable efficiencies. The device efficiency was found to taper significantly with thinner films. This was due to a decrease in the number of intimate SWCNT/Si contacts for charge separation and an exponential increase in sheet resistance as the film conductivity became dominated by the limited pathways in very thin films [15]. To determine the best transparency for the systems using MoS\({}_{2}\), cells were made maintaining the volume ratio between the SWCNT and MoS\({}_{2}\) dispersions and increasing the total volume used thereby increasing the film thickness. The summary of the results are shown in Figure 3 while the detailed parameters are provided in Table S2 from the representative JV curves given in Figure S4.  
Figure 3: (**a**) Electrode properties for films of different thicknesses; (**b**) Efficiencies of solar cells with different thickness films.  
Figure 2: (**a**) AFM image of typical MoS2 flake; image size is 855 nm \(\times\) 855 nm with a z-range of 24 nm. (**b**) Wide view SEM image of MoS2 flakes in the dispersion. (**c**) AFM of SWCNT/MoS2 hybrid made by mixing 600 \(\upmu\)L of MoS2 dispersion with 300 \(\upmu\)L SWCNT dispersion; image size is 2.0 \(\upmu\)m \(\times\) 2.0 \(\upmu\)m with a z-range of 220 nm. (**d**) SEM image of SWCNT/MoS2 hybrid made by mixing 600 \(\upmu\)L of MoS2 dispersion with 300 \(\upmu\)L SWCNT dispersion. (**e**) AFM of SWCNT/MoS2 layered system made by first depositing a film made from a 600 \(\upmu\)L of MoS2 dispersion followed by a film made using 300 \(\upmu\)L SWCNT dispersion; image size is 6.0 \(\upmu\)m \(\times\) 6.0 \(\upmu\)m with a z-range of 800 nm. (**f**) SEM image of SWCNT/MoS2 layered system made by first depositing a film made from a 600 \(\upmu\)L of MoS2 dispersion followed by a film made using 300 \(\upmu\)L SWCNT dispersion.  
As expected, the transmission decreases as the amount of material used to create the film is increased. The same trends observed for SWCNTs films are found for the SWCNT/MoS2 hybrid films. For the films with approximately the same transmission (300 uL of SWCNT and 300 uL of SWCNT + 400 uL of MoS2), the addition of the MoS2 does improve the solar cell performance. Determining the best ratio of the dispersion will improve this performance further. The relatively poor performance of thinnest films can be traced to the low fill factor. This arises due to the lack of connectedness of the conducting layer, which will lead to a higher level of recombination. The decrease in performance of cells with thicker layers is due to lower short circuit currents as a result of the lower transmittance of the films. The darker layers mean less light makes it through to the silicon substrate and hence fewer excitons are created.
## 4 Methodology  
In the proposed framework, a BoF model is introduced for pre-processing to achieve noise robustness and distinctiveness in the feature accumulation process of indoor positioning based on RSS fingerprinting. The proposed BoF approach employs RSS fingerprint training data for vocabulary generation based on clustering. The vocabulary here is used to create the feature vectors employed both in training and testing of the classification model. Figure 2 shows how the proposed approach is different from conventional fingerprinting algorithms. RSS fingerprints consist of the coordinates of the RP and the raw RSS values corresponding to each AP. The BoF framework considers the geographical coordinates as labels but the RSS from each AP as raw features.  
\[X=\begin{bmatrix}RP_{1}\\ \vdots\\ RP_{N}\end{bmatrix} \tag{6}\]  
\[Y=\begin{bmatrix}(\psi_{1,1},..,\psi_{1,M})\\ \vdots\\ (\psi_{N,1},..,\psi_{N,M})\end{bmatrix} \tag{7}\]  
where \(X\) in Equation (6) represents the location coordinates of RPs, while \(\psi_{i,j}\) in Equation (7) denotes the RSS value of the \(i\)th AP at \(j\)th RP. The BoF generates clusters by employing the raw feature set \(Y\) and records the cluster centers as vocabulary features.  
\[\mathcal{L}=\sum_{\
u=1}^{k}\sum_{i=1}^{n}a_{<i,\
u>}\Big{\{}\|\psi_{i,j}-\mu_ {i}^{\
u}\|^{2}\Big{\}}_{i=1}^{M}\quad\text{where }a_{<i,\
u>}=\begin{cases}1,&\text{if }\psi_{i,j} \text{ belong to }\upsilon\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
where \(\mathcal{L}\) in Equation (8) denotes the cost function, and \(\mu_{i}^{\
u}\) is the \(v\)th cluster center of the \(i\)th AP RSS. The variable \(\upmu\) denotes the cluster mean value, which updates with each iteration. The coefficient \(a\) in Equation (8) defines the class of each \(\psi\) vector associated with each grid point. The variable \(\upsilon\) denotes the cluster class label in the group of k classes. The k-mean clustering minimizes \(\mathcal{L}\) with respect to \(a\) and \(\upmu\), as given bellow:  
1. Initialize \(\mu_{i}^{1}\) to \(\mu_{i}^{k}\) arbitrarily;
2. Choose the optimal \(a\) for a fixed \(\upmu\);
3. Choose the optimal \(\upmu\) for a fixed \(a\);  
Figure 2: Flowchart of conventional fingerprinting-based IPS and proposed BoF-assisted approach.
# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 3 Methodology  
This study presents a new approach for the classification of paranasal sinus diseases, focusing mainly on maxillary sinus pathologies. Our approach leverages the capability of deep learning models, particularly generative adversarial networks (GANs) along with convolutional neural networks (CNNs), to successfully analyze medical imaging data collected from CT scans. We follow a two-stage approach whereby a lightweight GAN is used to generate synthetic data that closely resemble true sinus pathologies for data augmentation. Afterwards, the augmented dataset is applied for training and testing ResNet-50 and ResNeXt-50 models, where random search is utilized for hyperparameter tuning purposes. The utilized performance metrics are accuracy, precision, recall, and the F1-score, and the area under the ROC curve is used to determine the discriminative ability. The details of the framework are represented in Figure 1.  
The reliability of the deep learning models employed in this study is highlighted by several key factors. Firstly, rigorous pre-processing techniques were applied to the medical imaging data, ensuring high-quality input for the models. Secondly, detailed descriptions of the model architectures and hyperparameters were provided, enhancing transparency and reproducibility. Additionally, the training process was meticulously conducted, with optimization algorithms, learning rate schedules, and convergence criteria carefully selected to facilitate robust learning. Moreover, a comprehensive validation strategy, such as cross-validation, was employed to assess the models' performance stability. The utilization of GANs further enhances reliability by facilitating data augmentation and generating synthetic data, thereby diversifying the training dataset and potentially improving model generalization. The validation of synthetic data produced by GANs involves assessing their fidelity to real data through metrics like structural similarity indices or perceptual similarity scores. This validation process ensures that the synthetic data accurately represent the characteristics of real medical images, enhancing the robustness and trustworthiness of the deep learning models employed in medical imaging tasks.  
Figure 1: Proposed framework.
## 5 Path-Planning Training
### Rewarding Formulation
The value \(H_{1}\) in Equation (8) is the threshold value for the difference in \(x\) coordinates that the agent could make in each sample navigation part. The smaller difference in \(x\) coordinates of the navigation produces the path that is more parallel to the sides. In our model, with \(N=200\), \(H_{1}\) is given a value of 0.4. In addition, our model will put a negative reward on the agent whenever its \(x\) coordinate is less than 0 as in Equation (9), meaning the agent is at the left side of the road. Regarding Equation (10), as suggested in other studies [14; 38], the agent would stay approximately 0.5 meters from the walls to avoid possible accidents. In our model, the navigation path has a width of 10 meters; therefore, the value \(H_{2}\) is set to 4.5 so that, when the agent's position has an x coordinate higher than 4.5 or less than \(-4.5\), it would receive a negative reward. Lastly, with respect to collision avoidance, the agent needs to keep a certain distance from the obstacle. The highest risk would seemingly be at the center of the obstacle, and the risk gradually decreased with longer distance. However, once the agent has reached a certain distance with the obstacle, any further than this would be unnecessary. For example, if the pedestrian in real life would like to avoid stepping on a puddle, as long as the navigation path does not conflict with the puddle, it does not matter if the path needs to be much further away from it. Because of this reason, we formulate our rewarding for the collision avoidance behavior as follows:  
\[\mathcal{R}_{6}=\sum_{i=0}^{N}\begin{cases}\dfrac{\delta(s_{i},obs)}{{R_{obs}} ^{2}}\,r^{2}\,&\text{if }\delta(s_{i},obs)\leq 0,\\ 0.01\,r^{2}\,&\text{if }\delta(s_{i},obs)>0,\end{cases
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 5 Discussion of Results
### DAo.con, Daon, and DAo  
A clear pattern emerges when comparing the performance of DA and DAo.con under varying orientations. In north-facing fenestrations, DAo.con frequently yields higher values than Daylight Autonomy (DA), while in south-facing orientations, DAo.con generally falls below DA, except near the window. This trend aligns with the metric's underlying assumptions and formulation: the diffuse illuminance provided by overcast skies is advantageous in north-facing spaces, enhancing daylight availability, whereas in south-facing rooms, overcast conditions reduce direct solar gains, increasing the need for electric lighting.  
These results, consistent with the observations in the review section, suggest the potential value of a contextual adjustment to the DAo.con algorithm in north-facing spaces. Such a modification could help improve the accuracy of electric lighting predictions by avoiding the overestimation of daylight availability under predominantly diffuse conditions.  
As a hybrid metric combining DAo and DAcon, DAo.con consistently produces intermediate values between them across all models. While DAo and DAcon have seen limited application individually, their integration in DAo.con offers promising utility for Dynamic Lighting Control Systems--particularly if further refined to account for orientation-specific daylight characteristics.  
The key aspects to highlight from this section are as follows:  
* DAo.con typically exceeds DA in north-facing fenestrations but tends to be lower with south-facing orientations, especially away from the window.
* To better serve as a dynamic metric for sensorless lighting control, DAo.con requires contextual adjustments in north-facing rooms to avoid overestimating daylight availability.