esa-sceva/satcom-chunk-collection huggingface dataset:
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 4 Experiments
### Dataset  
The DTU dataset [30], as shown in Figure 8, is an indoor small target multi-view stereo dataset proposed in 2016. The dataset was created by capturing several views of an object utilizing an industrial robot arm with adjustable brightness lights. Each view captured was comprehensively controlled to obtain camera intrinsic and extrinsic parameters. The dataset comprises 124 diverse scenes. Each scene includes 49 views, with each view containing seven distinct levels of brightness information. Therefore, there exists a total of 343 images per scene, all possessing an image resolution of \(1600\times 1200\). To enhance network training efficiency during the training phase, the dataset resolution was pruned to \(640\times 512\). Ultimately, the training set is comprised of 79 scenes consisting of a total of 27,097 images, alongside corresponding depth ground truth data. The validation set consists of 18 scenes incorporating 6174 images and corollary depth ground truth data, whilst the test set employs 22 scenes providing 7546 images in total complemented with corresponding depth ground truth information. In order to attain ground truth normal information, which will be utilized as supervised information for the normal estimation module, the k nearest neighbor points in the space based on the depth ground truth information were calculated, resulting in the derivation of 33,271 normal vector maps.  
The Tanks and Temples dataset [31], as shown in Figure 9 contains photos of buildings such as tanks and temples, as well as vehicles, which can be used for 3D reconstruction. Compared to the DTU dataset, it faces more challenges due to factors such as changes in lighting and dynamic targets in outdoor scenes, requiring algorithms to have higher robustness and accuracy. This dataset also includes the trajectory of the appearance camera and the true ground coordinates of buildings, which can be used to evaluate the reconstruction accuracy of algorithms.
## 4 Results of Probabilistic Stability and Performance Assessment  
This section evaluates the impact of uncertainties in grid SCR and operating point conditions and different PLL bandwidths on the system stability and performance through the proposed probabilistic methodology. It is worth remarking that although many research works considered these uncertainties, this section tried to present a new probabilistic point of view and widen the available knowledge. The proposed methodology measured the system stability and performance, not only by the absolute values of the stability indices (based on the deterministic assessment) but also in terms of their statistical properties (based on the proposed probabilistic assessment). Therefore, the probabilistic evaluation presented in this paper reflected the system behavior better under uncertainties than the conventional one. Moreover, it could provide the likelihood of a specific condition and related the system stability analysis to the risk and reliability assessment.
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 3 Underwater Radiation Noise Prediction Method for Cabin Structures
### Simulation Calculation and Analysis  
The information processing flow diagram of the underwater radiation noise prediction method for cabin structures in this paper is shown in Figure 7:  
Based on the numerical calculation software COMSOL 6.0, the research object described in Section 2.1 was numerically modeled, the relative amplitude and phase of the sound and force excitations were changed, and a total of 500 numerical condition tests were carried out. It is necessary to ensure that the radiation noise power of two excitation sources is the same when checking the prediction accuracy of radiation noise. Based on the research results in Section 2.1, a 1 N point force source and a 1.25 Pa point sound source were selected to work together. Based on the research results in Section 2.2, 15 \(\times\) 6 = 90 vibration monitoring points and 9 \(\times\) 5 = 45 acoustic monitoring points were uniformly  
Figure 7: Information processing flowchart for noise prediction.  
arranged inside the cabin, and 15 \(\times\) 9 = 135 acoustic response points were selected outside the cabin.  
3.1 Modeling Accuracy Analysis of the \"Acoustic/Vibration-Acoustic\" Transmission Network inside and outside the Cabin Structure  
Figure 8 shows the forecast results of the mean square sound pressure of noise on the outer cabin surface. The prediction accuracy of functional modeling based on synchronous vibration and acoustic monitoring is higher than that based on vibration monitoring, and the prediction error of the mean square sound pressure of noise outside the cabin is less than 0.5 dB in the studied frequency band. The error of the prediction accuracy of the functional modeling based on vibration monitoring increases obviously at some specific frequency points in the middle-frequency band. Figure 9 shows the radiated noise on the exterior surface of the cabin caused by different excitation forms, and the total radiated noise curve in the figure is the true value curve of radiated noise in Figure 8a. Combined with Figures 8b and 9, it can be seen that if the radiation noise caused by the two excitation forms is in the opposite phase at the characteristic frequency, the transfer function modeling based on vibration monitoring is prone to producing large errors, which is generally consistent with the research conclusions in Section 2.1. Therefore, only monitoring vibration information under hybrid excitation will miss the radiation noise information generated by the sound source.  
Figure 8: Modeling results of the “vibration/acoustic–acoustic” hybrid transfer function inside and outside the cabin structure: **(a)** Spectrum curve of surface noise outside the cabin. **(b)** Prediction error of surface noise outside the cabin.  
Figure 9: Surface radiation noise caused by different excitation forms outside the cabin.
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions
### DeEPSO Algorithm
#### 3.1.1 PSO Algorithm  
The Particle Swarm Optimization (PSO) algorithm is an algorithm based on the behavior of fish or bird populations, which either seek to avoid a predator or search for food [23]. There is an initial population of particles of size (number of elements) N and dimension (number of variables) D, which is denoted as follows:  
\[\textbf{X}=\begin{bmatrix}X_{1}\\ X_{2}\\ X_{3}\\ \cdot\\ \cdot\\ X_{N}\end{bmatrix} \tag{27}\]  
\[X_{i}=\begin{bmatrix}x_{1}&x_{2}&\cdot&\cdot&x_{D}.\end{bmatrix} \tag{28}\]  
The Particle **X** moves to a new position \(\textbf{X}^{(2)}\) according to the following rule [22; 23; 24]:  
\[\textbf{X}^{(k+1)}=\textbf{X}^{(k)}+\textbf{V}^{(k)}, \tag{29}\]  
where **V** is known as the velocity of the particle and is defined as:  
\[\textbf{V}^{(k+1)}=\textbf{AV}^{(k)}+\textbf{B}(\textbf{b}_{i}-\textbf{X}^{(k )})+\textbf{C}(\textbf{b}_{G}-\textbf{X}^{(k)}). \tag{30}\]  
The expressions (29) and (30) can be rewritten in a vector manner as follows for each element of the population [24]:  
\[X_{i}^{(k+1)}=X_{i}^{(k)}+V_{i}^{(k+1)} \tag{31}\]  
\[V_{i}^{(k+1)}=wV_{i}^{(k)}+c_{1}r_{1}(P_{best_{i}}^{(k)}-X_{i}^{(k)})+c_{2}r_{ 2}(G_{best_{j}}^{(k)}-X_{i}^{(k)}). \tag{32}\]  
In the expression (32), \(w\) represents a scalar value that descends from \(w_{max}=0.9\) to \(w_{min}=0.4\) as the number of iterations of the algorithm progresses [24], \(c_{1}\) and \(c_{2}\) are constants inherent to the method and \(r_{1}\) and \(r_{2}\) are random values uniformly distributed among 0 and 1.  
The operation of the PSO algorithm consists of making a search for new optimal values by each particle using the expressions (31) and (32), where a new position of particle \(i\), which is \(X_{i}^{(k+1)}\), is generated from its old position \(X_{i}^{(k)}\), taking into account the best historical position of each particle \(P_{best_{i}}^{(k)}\). That is, the value of \(X_{i}\) that has given the best value of the objective function and the best historical position of the total set of particles \(G_{best_{j}}^{(k)}\). This is illustrated in Figure 1.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
###### Abstract  
An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets, including Indian Pines, Pavia Centre, and Pavia University.  
noise reduction; nonlinear optimization; low-rank modeling; hyperspectral imaging; signal-to-noise ratio improvement +
Footnote †: journal: Applied Sciences
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 4 The Goals and Challenges of Middleware Architectures for WSNs
### Quality of Service (QoS)  
It is important for the wireless networks to support QoS as it pertains to the accuracy of data, coverage and tolerance. The quality of service is important on the application level as well as on the network level. The QoS considers the resource constraints in new and adaptive WSN designs.  
Providing most efficient and suitable nodes to the client who is in need of the resources has been a major problem in cloud computing. The ability of the system to efficiently locate and provide the needed resources to the clients is vital. Recently, some researchers [24; 25] have tried to increase and optimize the QoS by using computing environments such as Cloud/Grid systems that comprise of several trusted nodes to manage local resources individually. A trust model is associated with each node that accurately evaluates the trustworthiness of its communicating clients [24]. The time-consuming and inefficient process of exploring the whole node space is avoided by allowing each node to efficient allocating resources by finding suitable collaborations. The authors showed the employment of a decentralized approach using Hypertrust where the nodes are organized in an overlay network given the criteria by the client. The Hypertrust gives the client an efficient way of searching for available resources while empowering the nodes to use their respective trust models to limit the search. The unique node called Task Allocator (TA) allows clients to delegate the selection processes of the task as well as improving the overall QoS.  
Another approach, called the partnership based approach [25], is introduced to maximize the QoS by improving and optimizing the global QoS for the large-scale federated resources [25]. This approach combines the trust models for software agents to support the federated computing nodes. The intelligent agents support the model computational nodes which can manage the Friendship and a Group of Membership (FGM). The Friendship and Group Formation (FGF) algorithms used in this approach enable the federated nodes to select their FGM that can increase and improve the global QoS. The authors in [25] showed metrics that allow most suitable resources in such Grid/Cloud systems. Potential collaborations and competition between resources providers for clients' needs are explored by the federation of computing.
## 2 RASMR for Big Data Analysis under GLM
### RASMR with the Delta Ratio Split  
As we will demonstrate in Section 4.2, RASMR significantly improves upon SMR in estimating model parameters. However, it may not perform sufficiently well when approximating likelihood with non-Gaussian covariates \(\mathbf{x}_{i}\) or predictors \(\mathbf{X}_{i}\) for model selection purposes. To investigate this, we start with the mean representative (MR) of the predictors in block \(l_{k}\), \(\hat{\mathbf{X}}_{k}=n_{k}^{-1}\sum_{i\in l_{k}}\mathbf{X}_{i}\), which is a natural choice for the block center. Then, the predictor radius of \(l_{k}\) can be defined as \(\Delta_{k}=\max\limits_{i\in l_{k}}\|\mathbf{X}_{i}-\hat{\mathbf{X}}_{k}\|\), where \(\|\cdot\|\) is the Euclidean norm. We call the relative distance of \(\hat{\mathbf{X}}_{k}\) from \(\hat{\mathbf{X}}_{k}\), the _delta ratio_, defined as \(\delta_{k}=\big{\|}\hat{\mathbf{X}}_{k}-\hat{\mathbf{X}}_{k}\big{\|}/\Delta_{k}\). By definition, \(\delta_{k}=0\) if \(n_{k}=1\). If \(\delta_{k}>1\) for some \(l_{k}\), then its predictor representative \(\hat{\mathbf{X}}_{k}\) is outside the corresponding data block, which implies that it may not be a good representative for calculating the likelihood. Compared with SMR, RASMR's delta ratiosare significantly smaller (see Figure S1 in the Supplementary Materials), which partly explains why RASMR improves SMR significantly. Nevertheless, the delta ratios of RASMR may tend to inflate when the distribution of covariates is too extreme or complicated (see Section S2 in the Supplementary Materials for more details). If \(\tilde{\delta}_{k}\) is greater than a predefined threshold \(\delta_{0}\), a fourth layer of further splitting at the mean \(\eta_{k}=n_{k}^{-1}\sum_{i\in I_{k}}\eta_{i}\) of \(\eta_{i}\)'s may be applied. Since the sample mean is sensitive to outliers, using \(\tilde{\eta}_{k}\) as the cutoff point of the split may enable us to separate the outliers from the majority of the data block, which will make the leftover members in the data blocks closer to each other. In other words, we may identify the minorities of data points in the original data block and separate them into their own data block. In practice, if the likelihood approximation is not a serious concern, setting the threshold for delta ratios to be one is a conservative choice, since it only requires the representatives to stay inside its data block, not necessarily very close to the center (see Section 4.3 for further discussion on choosing \(\delta_{0}\)). The pseudocode of the RASMR algorithm with the delta ratio split is described in Algorithm 2. ```
Data:\(\mathcal{D}=\{(\mathbf{X}_{i},y_{i}),i=1,\ldots,N\}\) with a partition of \(K\) blocks indexed by \(\{I_{1},\ldots,I_{K}\}\). Denote \(\mathcal{D}_{i}=\{(\mathbf{X}_{i},y_{i}),i\in I_{k}\}\) as the \(k\)th data block. Set the delta ratio threshold \(\delta_{0}>0\) (e.g., \(\delta_{0}=1\)). Result: Parameter estimate \(\hat{\beta}\) of a given GLM with a predetermined number \(T\) of iterations.
## 5 Discussion  
Mature, functional and affordable UAS are a comparatively new aerospace technology. For a time it was considered likely that an unbounded future of air vehicle design and innovation would emerge. The various government procurement processes have had the effect of rationalizing the field. The purchase price and operating cost of fielded systems to date has not encouraged an explosion of diversity. In general the cost model associated with aerial capabilities did not change with the advent of capable UAS [49].  
Although government selected air frames may be difficult to supplant, opportunities do exist to improve navigation systems, aerodynamic models, control systems and avionics. The less stringent certification processes and absence of a human on-board do allow the scientific community to modify systems that are already in service [50], but this too is changing.  
Smaller UAS research is the area where industry might be reluctant to invest substantial resources due to limited opportunities for profit. Micro UAS are also the area where the science community can safely develop and operate their own research capabilities. Large laboratories have formed around micro UAS and Group 1 UAS [51]. Diversity of procured and fielded systems is most likely to occur in this scale due to low past investment, low cost and low risk. Unlike the larger UAS, the micro UAS domain will continue to be strongly influenced by scientific research.  
The question of scalability of UAS for missions becomes significant when micro UAS are considered. It appears that physical constraints create limits that can not be overcome. For example for high altitude ISR missions, satellite communications requires large steerable antennas; large apertures are needed for sensors; active sensors demand significant electrical power. In many large UAS, electrical power for payload is more at issue than mechanical power for manoeuvre. A deeper consideration would ask whether these missions are simply the result of the current paradigm. All of the physical constraints listed above can be overcome by distribution of sensors, emitters and receivers under a different operational concept. Outside the domain of information and data there are challenges that simply reflect the physics underpinning aerospace: a conventionally powered micro UAS will not have long range at high speed.
# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 2 Materials and Methods
### Algorithm Logic  
In this algorithm, there are pre-defined fixed positions, namely the observation position, the (0,0) position, and the unloading position. For the variable positions, only the X, Y, and Z values are updated, while the rotational values (RX, RY, and RZ) remain constant throughout the process.  
The logic for updating the X and Y coordinates is based on calculating the distance in meters from the reference point (0,0) using a function called pixel_to_metres. This function works by converting pixel displacement into real-world distances in meters, as described in detail in the pixel-to-metres conversion algorithm. The inputs for this function include the camera's resolution, field of view (FOV), and the distance between the camera and the observed object (Z-axis). By using these parameters, the function computes how many meters each pixel represents, thus allowing accurate calculation of the X and Y coordinates in meters based on the pixel displacement from the reference point.  
The Z-coordinate has predefined positions, as shown in Figure 7, and does not rely on pixel calculations. Instead, Z is set based on specific operational requirements, such as the observation or unloading positions. This approach ensures that the X and Y positions are dynamically adjusted based on real-time camera input, while the Z position remains consistent according to predefined parameters.  
The image above depicts the configuration of a robotic arm designed for the z-axis manipulation of tomatoes. Figure 7 illustrates three key positions: Figure 7a shows the movement initiation position, set at 260 mm from the reference point. Figure 7b depicts the approach position, located 180 mm from the reference point, in which the arm prepares to engage with the fruit. Finally, Figure 7c displays the position in which the arm grasps the fruit, at 160 mm. This configuration is essential for accurately positioning and manipulating the tomatoes based on the predefined positions and the variables calculated by the algorithm.
# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 2 System Overview  
Figure 2a shows the system setup for the automated removal of cumulus cells. A two-axis X-Y stage (HLD117, Prior, travel range: 120 mm \(\times\) 72 mm) equipped with a motorized focusing module (FM-100, Suzhou Boundless Medical Technology Co., Ltd., Suzhou, China, resolution: 0.1 mm/r) was used to position COCs in the field of view (FOV) within the focal plane. A micropipette (WG-DD-125, WEGO, Singapore, inner diameter: 125 \(\upmu\)m) was mounted on a four-axis micromanipulator (MX7600, Siskiyou, Grants Pass, OR, USA, resolution: 0.1 \(\upmu\)m) for manipulating COCs. A motorized oil pump (CellTram 4r Oil, Eppendorf, resolution: 1 \(\upmu\)L/r) was connected to the micropipette to control the pressure at the micropipette tip for aspirating and dispensing COCs. A camera (acA1300-60 gm, Basler, Ahrensburg, Germany, frame rate: 60 frames/s) was connected to the microscope captures visual feedback via bright-field imaging. Visual feedback of the micropipette and COCs was used for position control and to assess both oocyte's maturity and the completeness of cumulus cell removal.  
Figure 2: _Cont_.  
Female C57 mice (8-12 weeks old, Nanjing Aibei Biotechnology Co., Ltd., Nanjing, China) were injected with PMSG and HCG hormones, and each ampulla of the oviduct was incised to release COCs after injection. The COCs were then enzymatically treated with hyaluronidase and transferred to a droplet of M2 medium for cumulus cell removal. During the automated cumulus cell removal process, the X-Y stage was controlled to position the target COC in the FOV, and the focal plane was adjusted to the middle plane of the target COC using the motorized focusing module. To avoid manipulating immature oocytes, the maturity of the oocyte was evaluated by an EPSANet50 network before removing the cumulus cells from the COC. Then, the micropipette tip was automatically positioned 140 \(\upmu\)m to the left of the COC within the focal plane. The COC was repeatedly aspirated into the micropipette, positioned at the target position, and then dispensed outside the micropipette by an adaptive controller. After COC dispensing, the remaining cumulus cells around the oocyte were detected by an improved Yolov5s network to quantitatively assess the completeness of cumulus cell removal. The oocyte was then transferred to a droplet of KSOM medium for subsequent ICSI and PGT procedures. The workflow of the robotic system is described in Figure 2b.
## 4 Case Study
### Deformation Safety Monitoring of Important Monitoring Points of Dam  
According to the clustering results of dam deformation zoning, the monitoring points PL11-4, PL11-5, PL13-4, PL13-5, PL16-4, and PL16-5 in the deformation sensitive area in the middle of the dam are selected as key monitoring points. The key monitoring parts of dam deformation are modeled and analyzed, in which the water pressure component is taken as the fourth power, and the temperature component is replaced by two groups of harmonic factors [40], so as to obtain the monitoring model of representative measuring points. The fitting value, predicted value, and the residual value of deformation at monitoring points are shown in Figure 12. And the calculation accuracy of monitoring points at key monitoring positions is shown in Table 5.  
Figure 12: Measurement points fitting and residual diagram.  
It can be seen from Figure 10 and Table 4 that the multi-correlation coefficient of the deformation fitting values of important monitoring points is between 0.96 and 1.0, and the root mean squared error is between 0 and 3, indicating high fitting accuracy. The mean relative error of the predicted value of deformation is less than 5%, which shows that the model is reasonable, the accuracy can meet the requirements of engineering practice, and can be used to monitor the deformation of the dam.
## 2 Materials and Methods
### Dynamic Light Scattering and Zeta Potential  
The diameter, polydispersity index and zeta potential were measured using a Malvern Zetasizer S90 (Westborough, MA, USA). Samples were diluted in deionized water for this measurement without a stabilizer.